Mutations
Available mutations in the Management API
createEnvironment
Creates an Environment and returns the newly created Environment (or an error if creating the Environment fails).
Arguments
Returns
The result of a mutation which creates or modifies an Environment.
The Environment which was created or modified.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
modifyEnvironment
Modifies an Environment with the provided fields. If any of the optional fields are omitted, those properties will be unchanged on the Environment.
Arguments
Returns
The result of a mutation which creates or modifies an Environment.
The Environment which was created or modified.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
deleteEnvironment
Deletes a Environment by ID and returns its ID if the Environment was deleted successfully.
Arguments
createDataPoolAccessPolicy
Creates a Data Pool Access Policy for the specified Data Pool.
Arguments
The Data Pool Access Policy’s unique name. If not specified, Propel will set the ID as unique name.
The Data Pool Access Policy’s description.
The Data Pool to which the Access Policy belongs.
Columns that the Access Policy makes available for querying.
If set to ["*"], all columns will be available for querying.
Row-level filters that the Access Policy applies before executing queries, in the form of SQL.
Row-level filters that the Access Policy applies before executing queries.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The Data Pool Access Policy.
The ID of the Data Pool Access Policy.
The Data Pool Access Policy’s unique name.
The Data Pool Access Policy’s description.
The Data Pool Access Policy’s Environment.
See Environment
The Data Pool Access Policy’s creation date and time in UTC.
The Data Pool Access Policy’s last modification date and time in UTC.
The Data Pool Access Policy’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool Access Policy’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
Columns that the Access Policy makes available for querying.
Row-level filters that the Access Policy applies before executing queries, in the form of SQL.
Applications that are assigned to this Data Pool Access Policy.
Arguments
modifyDataPoolAccessPolicy
Modifies a Data Pool Access Policy with the provided unique name, description, columns and rows. If any of the optional arguments are omitted, those properties will be unchanged on the Data Pool Access Policy.
Arguments
The Data Pool Access Policy’s new unique name.
The Data Pool Access Policy’s new description.
Columns that the Access Policy makes available for querying. If not provided this property will not be modified.
Row-level filters that the Access Policy applies before executing queries, in the form of SQL. If not provided this property will not be modified.
Row-level filters that the Access Policy applies before executing queries. If not provided this property will not be modified.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The Data Pool Access Policy.
The ID of the Data Pool Access Policy.
The Data Pool Access Policy’s unique name.
The Data Pool Access Policy’s description.
The Data Pool Access Policy’s Environment.
See Environment
The Data Pool Access Policy’s creation date and time in UTC.
The Data Pool Access Policy’s last modification date and time in UTC.
The Data Pool Access Policy’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool Access Policy’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
Columns that the Access Policy makes available for querying.
Row-level filters that the Access Policy applies before executing queries, in the form of SQL.
Applications that are assigned to this Data Pool Access Policy.
Arguments
deleteDataPoolAccessPolicy
Deletes a Data Pool Access Policy by ID and returns its ID if the Data Pool Access Policy was deleted successfully.
Arguments
assignDataPoolAccessPolicyToApplication
Assign a Data Pool Access Policy to an Application.
The Data Pool Access Policy will restrict the Data Pool rows and columns that the Application
can query. If the Data Pool has accessControlEnabled set to true, the Application
must have a Data Pool Access Policy assigned in order to query the Data Pool.
An Application can have at most one Data Pool Access Policy assigned for a given Data Pool. If an Application already has a Data Pool Access Policy for a given Data Pool, and you call this mutation with another Data Pool Access Policy for the same Data Pool, the Application’s Data Pool Access Policy will be replaced.
Arguments
unAssignDataPoolAccessPolicyFromApplication
Unassign a Data Pool Access Policy from an Application.
Once unassigned, whether the Application will be able to query the Data Pool is
controlled by the Data Pool’s accessControlEnabled property. If
accessControlEnabled is true, the Application will no longer be able to query the
Data Pool. If accessControlEnabled is false, the Application will be able to query
all data in the Data Pool, unrestricted.
Arguments
createApplication
Creates a new Application and returns the newly created Application (or an error message if creating the Application fails).
Required scopes:
- APPLICATION_ADMIN
mutation {
createApplication(
input: {
uniqueName: "example_sample_application"
description: "My dashboard app"
scopes: [DATA_POOL_QUERY]
propeller: P1_X_SMALL
}
) {
... on ApplicationResponse {
application {
id
uniqueName
}
}
... on FailureResponse {
error {
code
message
}
}
}
}
Arguments
The fields for creating an Application.
The Application’s unique name. If not specified, Propel will set the ID as unique name.
The Application’s description.
The Application’s Propeller. If no Propeller is provided, Propel will set the Propeller to P1_X_SMALL.
A Propeller determines your Application’s query processing power. The larger the Propeller, the faster the queries and the higher the cost. Every Propel Application (and therefore every set of API credentials) has a Propeller that determines the speed and cost of queries.
P1_X_SMALL: Max records per second: 5,000,000 records per secondP1_SMALL: Max records per second: 25,000,000 records per secondP1_MEDIUM: Max records per second: 100,000,000 records per secondP1_LARGE: Max records per second: 250,000,000 records per secondP1_X_LARGE: Max records per second: 500,000,000 records per second
The Application’s API authorization scopes. If specified, at least one scope must be provided; otherwise, all scopes will be granted to the Application by default.
The API operations an Application is authorized to perform.
ADMIN: Grant read/write access to Data Sources, Data Pools, Metrics and Policies.APPLICATION_ADMIN: Grant read/write access to Applications.DATA_POOL_QUERY: Grant read access to query Data Pools.DATA_POOL_READ: Grant read access to read Data Pools.DATA_POOL_STATS: Grant read access to fetch column statistics from Data Pools.ENVIRONMENT_ADMIN: Grant read/write access to Environments.METRIC_QUERY: Grant read access to query Metrics.METRIC_STATS: Grant read access to fetch Dimension statistics from Metrics.METRIC_READ: Grant read access to Metrics.
This does not allow querying Metrics. For that, see METRIC_QUERY.
Returns
The result of a mutation which creates or modifies an Application.
If successful, an ApplicationResponse will be returned; otherwise, a
FailureResponse will be returned.
The result of a mutation which creates or modifies an Application.
The Application which was created or modified.
See Application
modifyApplication
Modifies an Application with the provided unique name, description, Propeller, and scopes. If any of the optional arguments are omitted, those properties will be unchanged on the Application.
Arguments
The fields for modifying an Application.
The Application’s new unique name.
The Application’s new description.
The Application’s new Propeller.
A Propeller determines your Application’s query processing power. The larger the Propeller, the faster the queries and the higher the cost. Every Propel Application (and therefore every set of API credentials) has a Propeller that determines the speed and cost of queries.
P1_X_SMALL: Max records per second: 5,000,000 records per secondP1_SMALL: Max records per second: 25,000,000 records per secondP1_MEDIUM: Max records per second: 100,000,000 records per secondP1_LARGE: Max records per second: 250,000,000 records per secondP1_X_LARGE: Max records per second: 500,000,000 records per second
The Application’s new API authorization scopes.
The API operations an Application is authorized to perform.
ADMIN: Grant read/write access to Data Sources, Data Pools, Metrics and Policies.APPLICATION_ADMIN: Grant read/write access to Applications.DATA_POOL_QUERY: Grant read access to query Data Pools.DATA_POOL_READ: Grant read access to read Data Pools.DATA_POOL_STATS: Grant read access to fetch column statistics from Data Pools.ENVIRONMENT_ADMIN: Grant read/write access to Environments.METRIC_QUERY: Grant read access to query Metrics.METRIC_STATS: Grant read access to fetch Dimension statistics from Metrics.METRIC_READ: Grant read access to Metrics.
This does not allow querying Metrics. For that, see METRIC_QUERY.
Returns
The result of a mutation which creates or modifies an Application.
If successful, an ApplicationResponse will be returned; otherwise, a
FailureResponse will be returned.
The result of a mutation which creates or modifies an Application.
The Application which was created or modified.
See Application
deleteApplication
Deletes an Application by ID and returns its ID if the Application was deleted successfully.
Arguments
deleteApplicationByName
Deletes an Application by unique name and returns its ID if the Application was deleted successfully.
Arguments
createSnowflakeDataSource
Creates a new Data Source from the given Snowflake database using the specified Snowflake account, warehouse, schema, username, and role.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The fields for creating a Snowflake Data Source.
The Data Source’s unique name. If not specified, Propel will set the ID as unique name.
The Data Source’s description.
The Data Source’s connection settings.
The fields for creating a Snowflake Data Source’s connection settings.
The Snowflake account. Only include the part before the “snowflakecomputing.com” part of your Snowflake URL (make sure you are in classic console, not Snowsight). For AWS-based accounts, this looks like “znXXXXX.us-east-2.aws”. For Google Cloud-based accounts, this looks like “ffXXXXX.us-central1.gcp”.
The Snowflake database name.
The Snowflake warehouse name. It should be “PROPELLING” if you used the default name in the setup script.
The Snowflake schema.
The Snowflake username. It should be “PROPEL” if you used the default name in the setup script.
The Snowflake password.
The Snowflake role. It should be “PROPELLER” if you used the default name in the setup script.
Returns
The result of a mutation which creates or modifies a DataSource.
If successful, an DataSourceResponse will be returned; otherwise, a
FailureResponse will be returned.
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
See DataSource
modifySnowflakeDataSource
Modifies a Data Source with the provided unique name, description, and connection settings. If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The fields for modifying a Snowflake Data Source.
The Data Source’s new unique name.
The Data Source’s new description.
The Data Source’s new connection settings.
The fields for modifying a Snowflake Data Source’s connection settings.
The Snowflake account. Only include the part before the “snowflakecomputing.com” part of your Snowflake URL (make sure you are in classic console, not Snowsight). For AWS-based accounts, this looks like “znXXXXX.us-east-2.aws”. For Google Cloud-based accounts, this looks like “ffXXXXX.us-central1.gcp”. If not provided this property will not be modified.
The Snowflake database name. If not provided this property will not be modified.
The Snowflake warehouse name. It should be “PROPELLING” if you used the default name in the setup script. If not provided this property will not be modified.
The Snowflake schema. If not provided this property will not be modified.
The Snowflake username. It should be “PROPEL” if you used the default name in the setup script. If not provided this property will not be modified.
The Snowflake password. If not provided this property will not be modified.
The Snowflake role. It should be “PROPELLER” if you used the default name in the setup script. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a DataSource.
If successful, an DataSourceResponse will be returned; otherwise, a
FailureResponse will be returned.
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
See DataSource
reconnectDataSource
Attempts to reconnect a Data Source. The mutation then returns the Data Source object.
Arguments
Returns
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Account.
The Account object.
The Account’s unique identifier.
The Data Source’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
The types of Data Sources.
WEBHOOK: Indicates a Webhook Data Source.TWILIO_SEGMENT: Indicates a Twilio Segment Data Source.S3: Indicates an Amazon S3 Data Source.Redshift: Indicates a Redshift Data Source.POSTGRESQL: Indicates a PostgreSQL Data Source.KAFKA: Indicates a Kafka Data Source.Http: Indicates an Http Data Source.CLICKHOUSE: Indicates a ClickHouse Data Source.AMAZON_DYNAMODB: Indicates an Amazon DynamoDB Data Source.AMAZON_DATA_FIREHOSE: Indicates an Amazon Data Firehose Data Source.Snowflake: Indicates a Snowflake Data Source.INTERNAL: Indicates an internal Data Source.
The Data Source’s status.
The status of a Data Source.
CREATED: The Data Source has been created, but it is not connected yet.CONNECTING: Propel is attempting to connect the Data Source.CONNECTED: The Data Source is connected.BROKEN: The Data Source failed to connect.DELETING: Propel is deleting the Data Source.
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
The Data Source Check object.
Data Source Checks are executed when setting up your Data Source. They check that Propel will be able to receive data and setup Data Pools.
The exact Checks to perform vary by Data Source. For example, Snowflake-backed Data Sources will have their own specific Checks.
The name of the Data Source Check to be performed.
A description of the Data Source Check to be performed.
The status of the Data Source Check (all checks begin as NOT_STARTED before transitioning to SUCCEEDED or FAILED).
If the Data Source Check failed, this field includes a descriptive error message.
See Error
The time at which the Data Source Check was performed.
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
introspectTables
Introspects the tables in a Data Source.
Returns the tables along with when they were last cached from the Data Source.
Arguments
Returns
The table introspection object.
When setting up a Data Source, Propel may need to introspect tables in order to determine what tables and columns are available to create Data Pools from. The table introspection represents the lifecycle of this operation (whether it’s in-progress, succeeded, or failed) and the resulting tables and columns. These will be captured as table and column objects, respectively.
The Data Source the table introspection was performed for. See DataSource
The status of the table introspection.
The status of a table introspection.
NOT_STARTED: The table introspection has not started.STARTED: The table introspection has started.SUCCEEDED: The table introspection succeeded.FAILED: The table introspection failed.
The table introspection’s creation date and time in UTC.
The table introspection’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The table introspection’s last modification date and time in UTC.
The table introspection’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The number of tables introspected.
testDataSource
Tests that Propel can actually connect to the data warehouse. Updates the status.
Arguments
Returns
The result of a mutation which creates or modifies a DataSource.
If successful, an DataSourceResponse will be returned; otherwise, a
FailureResponse will be returned.
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
See DataSource
deleteDataSource
Deletes a Data Source by ID and returns its ID if the Data Source was deleted successfully.
Arguments
deleteDataSourceByName
Deletes a Data Source by unique name and returns its ID if the Data Source was deleted successfully.
Arguments
createDataPoolV2
Creates a new Data Pool.
Returns the newly created Data Pool or an error message if it fails.
mutation {
createDataPoolV2(input: {
dataSource: "DSOXXXXX"
table: "tacosoft_sales_analytics"
timestamp: { columnName: "timestamp" }
columns: [
{
columnName: "timestamp",
type: TIMESTAMP,
isNullable: true
},
{
columnName: "order_id",
type: STRING,
isNullable: true
},
{
columnName: "taco_name",
type: STRING,
isNullable: true
},
{
columnName: "toppings",
type: JSON,
isNullable: true
},
{
columnName: "quantity",
type: INT32,
isNullable: true
},
{
columnName: "taco_unit_price",
type: INT32,
isNullable: true
},
{
columnName: "taco_total_price",
type: INT32,
isNullable: true
}
]
}) {
dataPool {
id
uniqueName
tableSettings {
orderBy
engine {
... on MergeTreeTableEngine {
type
}
... on ReplacingMergeTreeTableEngine {
type
ver
}
... on SummingMergeTreeTableEngine {
type
columns
}
... on AggregatingMergeTreeTableEngine {
type
}
... on PostgreSqlTableEngine {
type
}
}
}
}
}
}
Arguments
The fields for creating a Data Pool.
The Data Source that will be used to create the Data Pool.
The table that the Data Pool will sync from.
The table’s primary timestamp column.
Propel uses the primary timestamp to order and partition your data in Data Pools. It’s part of what makes Propel fast for larger data sets. It will also serve as the time dimension for your Metrics.
If you do not provide a primary timestamp column, you will need to supply an alternate timestamp when querying your Data Pool or its Metrics using the TimeRangeInput.
The fields to specify the Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
The Data Pool’s unique name. If not specified, Propel will set the ID as the unique name.
The Data Pool’s description.
The list of columns.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type to use when type is set to CLICKHOUSE.
Whether the column is nullable, meaning whether it accepts a null value.
The Data Pool’s syncing settings.
The fields for modifying the Data Pool syncing.
Enables or disables access control for the Data Pool.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
Override the Data Pool’s table settings. These describe how the Data Pool’s table is created in ClickHouse, and a
default will be chosen based on the Data Pool’s timestamp and uniqueId values, if any. You can override these
defaults in order to specify a custom table engine, custom ORDER BY, etc.
A Data Pool’s table settings.
These describe how the Data Pool’s table is created in ClickHouse.
The ClickHouse table engine for the Data Pool’s table.
This field is optional. A default will be chosen based on the Data Pool’s timestamp and uniqueId values, if specified.
See TableEngineInput
The PARTITION BY clause for the Data Pool’s table.
This field is optional. A default will be chosen based on the Data Pool’s timestamp and uniqueId values, if specified.
The PRIMARY KEY clause for the Data Pool’s table.
This field is optional. A default will be chosen based on the Data Pool’s timestamp and uniqueId values, if specified.
The ORDER BY clause for the Data Pool’s table.
This field is optional. A default will be chosen based on the Data Pool’s timestamp and uniqueId values, if specified.
The TTL clause for the Data Pool’s table.
The Data Pool’s optional tenant ID column. The tenant ID column is used to control access to your data with access policies.
deprecated: Will be removed; use Data Pool Access Policies insteadThe fields to specify the Data Pool’s tenant ID column. The tenant ID column is used to control access to your data with access policies.
The name of the column that represents the tenant ID.
The Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.The fields to specify the Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
The name of the column that represents the unique ID.
Returns
The result of a mutation which creates or modifies a Data Pool.
The Data Pool which was created or modified.
The Data Pool object. Data Pools are Propel’s high-speed data store and cache
The Data Pool’s unique identifier.
The Data Pool’s unique name.
The Data Pool’s description.
The Data Pool’s Environment.
See Environment
The Data Pool’s creation date and time in UTC.
The Data Pool’s last modification date and time in UTC.
The Data Pool’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Pool’s Data Source. See DataSource
The Data Pool’s status.
See DataPoolStatus
The Data Pool’s data retention in days (not yet supported).
The name of the Data Pool’s table.
The number of records in the Data Pool.
The amount of storage in terabytes used by the Data Pool.
The Data Pool’s columns.
Arguments
The list of measures (numeric columns) in the Data Pool.
Arguments
A list of setup tasks performed on the Data Pool during its most recent setup attempt.
Settings related to Data Pool syncing.
See DataPoolSyncing
The Deletion Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The Add Column Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The UpdateDataPoolRecords Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
Whether the Data Pool has access control enabled or not.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
A paginated list of Data Pool Access Policies available on the Data Pool.
Arguments
Validates a custom expression against the Data Pool’s available columns. If the provided expression is invalid, the ValidateExpressionResult response will contain a reason explaining why.
Arguments
The Data Pool’s table settings.
See TableSettings
The Data Pool’s columns that participate in its PARTITION BY clause.
See DataPoolColumn
The Data Pool’s columns that participate in its PRIMARY KEY clause.
See DataPoolColumn
The Data Pool’s columns that participate in its ORDER BY clause.
See DataPoolColumn
The Data Pool’s tenant ID, if configured.
deprecated: Will be removed; use Data Pool Access Policies insteadSee Tenant
The Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.See UniqueId
modifyDataPool
Modifies a Data Pool with the provided unique name, description, and data retention time. If any of the optional arguments are omitted, those properties will be unchanged on the Data Pool.
Arguments
The fields for modifying a Data Pool.
The Data Pool’s new unique name.
The Data Pool’s new description.
The Data Pool’s new data retention in days.
The Data Pool’s new syncing settings.
The fields for modifying the Data Pool syncing.
The table’s primary timestamp column.
Propel uses the primary timestamp to order and partition your data in Data Pools. It’s part of what makes Propel fast for larger data sets. It will also serve as the time dimension for your Metrics.
If you do not provide a primary timestamp column, you will need to supply an alternate timestamp when querying your Data Pool or its Metrics using the TimeRangeInput.
The fields to specify the Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
Enables or disables access control for the Data Pool.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
Returns
The result of a mutation which creates or modifies a Data Pool.
If successful, an DataPoolResponse will be returned; otherwise, a
FailureResponse will be returned.
retryDataPoolSetup
Retries to set up the Data Pool identified by the given ID.
Arguments
Returns
The Data Pool object. Data Pools are Propel’s high-speed data store and cache
The Data Pool’s unique identifier.
The Data Pool’s unique name.
The Data Pool’s description.
The Data Pool’s Account.
The Account object.
The Account’s unique identifier.
The Data Pool’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Data Pool’s creation date and time in UTC.
The Data Pool’s last modification date and time in UTC.
The Data Pool’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Pool’s Data Source. See DataSource
The Data Pool’s status.
The status of a Data Pool.
CREATED: The Data Pool has been created and will be set up soon.PENDING: Propel is attempting to set up the Data Pool.LIVE: The Data Pool is set up and serving data. Check its Syncs to monitor data ingestion.SETUP_FAILED: The Data Pool setup failed. Check its Setup Tasks before re-attempting setup.CONNECTINGCONNECTEDBROKENPAUSINGPAUSEDDELETING: Propel is deleting the Data Pool and all of its associated data.
The Data Pool’s data retention in days (not yet supported).
The name of the Data Pool’s table.
The Data Pool’s primary timestamp column, if any.
A Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
The primary timestamp column’s type.
The number of records in the Data Pool.
The amount of storage in terabytes used by the Data Pool.
The Data Pool’s columns.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
The list of measures (numeric columns) in the Data Pool.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
A list of setup tasks performed on the Data Pool during its most recent setup attempt.
The Data Pool Setup Task object.
Data Pool Setup Tasks are executed when setting up your Data Pool. They ensure Propel will be able to sync records from your Data Source to your Data Pool.
The exact Setup Tasks to perform vary by Data Source. For example, Data Pools pointing to a Snowflake-backed Data Sources will have their own specific Setup Tasks.
The name of the Data Pool Setup Task to be performed.
A description of the Data Pool Setup Task to be performed.
The status of the Data Pool Setup Task (all setup tasks begin as NOT_STARTED before transitioning to SUCCEEDED or FAILED).
If the Data Pool Setup Task failed, this field includes a descriptive error message.
See Error
The time at which the Data Pool Setup Task was completed.
Settings related to Data Pool syncing.
Settings related to Data Pool syncing.
Indicates whether syncing is enabled or disabled.
The syncing interval.
Note that the syncing interval is approximate. For example, setting the syncing interval to EVERY_1_HOUR
does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to
when the Data Pool goes LIVE, and Propel will attempt to sync approximately every hour. Additionally,
if you pause or resume syncing, this too can shift the syncing interval around.
The date and time of the most recent Sync in UTC.
The list of Syncs of the Data Pool.
Arguments
The filter to apply when listing the Syncs for a Data Pool.
EMPTY: Returns only Syncs with empty records.NOT_EMPTY: Returns only Syncs that contain one or more records.ALL: Returns all Syncs, regardless of whether they contain records or not.
See SyncConnection
The Deletion Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The Add Column Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The UpdateDataPoolRecords Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
Whether the Data Pool has access control enabled or not.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
A paginated list of Data Pool Access Policies available on the Data Pool.
Arguments
Validates a custom expression against the Data Pool’s available columns. If the provided expression is invalid, the ValidateExpressionResult response will contain a reason explaining why.
Arguments
Response returned by the validateExpression query for validating expressions in Custom Metrics.
Returns whether the expression is valid or not with a reason explaining why.
True if the expression is valid, false otherwise.
The reason for why the expression is not valid in case it isn’t, null otherwise.
The Data Pool’s table settings.
A Data Pool’s table settings.
These describe how the Data Pool’s table is created in ClickHouse.
The ClickHouse table engine for the Data Pool’s table.
See TableEngine
The PARTITION BY clause for the Data Pool’s table.
The PRIMARY KEY clause for the Data Pool’s table.
The ORDER BY clause for the Data Pool’s table.
The TTL clause for the Data Pool’s table.
The Data Pool’s columns that participate in its PARTITION BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its PRIMARY KEY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its ORDER BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s tenant ID, if configured.
deprecated: Will be removed; use Data Pool Access Policies insteadThe Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.A Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
The name of the column that represents the unique ID.
retryDataPoolSetupByName
Retries to set up the Data Pool identified by the given unique name.
Arguments
Returns
The Data Pool object. Data Pools are Propel’s high-speed data store and cache
The Data Pool’s unique identifier.
The Data Pool’s unique name.
The Data Pool’s description.
The Data Pool’s Account.
The Account object.
The Account’s unique identifier.
The Data Pool’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Data Pool’s creation date and time in UTC.
The Data Pool’s last modification date and time in UTC.
The Data Pool’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Pool’s Data Source. See DataSource
The Data Pool’s status.
The status of a Data Pool.
CREATED: The Data Pool has been created and will be set up soon.PENDING: Propel is attempting to set up the Data Pool.LIVE: The Data Pool is set up and serving data. Check its Syncs to monitor data ingestion.SETUP_FAILED: The Data Pool setup failed. Check its Setup Tasks before re-attempting setup.CONNECTINGCONNECTEDBROKENPAUSINGPAUSEDDELETING: Propel is deleting the Data Pool and all of its associated data.
The Data Pool’s data retention in days (not yet supported).
The name of the Data Pool’s table.
The Data Pool’s primary timestamp column, if any.
A Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
The primary timestamp column’s type.
The number of records in the Data Pool.
The amount of storage in terabytes used by the Data Pool.
The Data Pool’s columns.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
The list of measures (numeric columns) in the Data Pool.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
A list of setup tasks performed on the Data Pool during its most recent setup attempt.
The Data Pool Setup Task object.
Data Pool Setup Tasks are executed when setting up your Data Pool. They ensure Propel will be able to sync records from your Data Source to your Data Pool.
The exact Setup Tasks to perform vary by Data Source. For example, Data Pools pointing to a Snowflake-backed Data Sources will have their own specific Setup Tasks.
The name of the Data Pool Setup Task to be performed.
A description of the Data Pool Setup Task to be performed.
The status of the Data Pool Setup Task (all setup tasks begin as NOT_STARTED before transitioning to SUCCEEDED or FAILED).
If the Data Pool Setup Task failed, this field includes a descriptive error message.
See Error
The time at which the Data Pool Setup Task was completed.
Settings related to Data Pool syncing.
Settings related to Data Pool syncing.
Indicates whether syncing is enabled or disabled.
The syncing interval.
Note that the syncing interval is approximate. For example, setting the syncing interval to EVERY_1_HOUR
does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to
when the Data Pool goes LIVE, and Propel will attempt to sync approximately every hour. Additionally,
if you pause or resume syncing, this too can shift the syncing interval around.
The date and time of the most recent Sync in UTC.
The list of Syncs of the Data Pool.
Arguments
The filter to apply when listing the Syncs for a Data Pool.
EMPTY: Returns only Syncs with empty records.NOT_EMPTY: Returns only Syncs that contain one or more records.ALL: Returns all Syncs, regardless of whether they contain records or not.
See SyncConnection
The Deletion Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The Add Column Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The UpdateDataPoolRecords Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
Whether the Data Pool has access control enabled or not.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
A paginated list of Data Pool Access Policies available on the Data Pool.
Arguments
Validates a custom expression against the Data Pool’s available columns. If the provided expression is invalid, the ValidateExpressionResult response will contain a reason explaining why.
Arguments
Response returned by the validateExpression query for validating expressions in Custom Metrics.
Returns whether the expression is valid or not with a reason explaining why.
True if the expression is valid, false otherwise.
The reason for why the expression is not valid in case it isn’t, null otherwise.
The Data Pool’s table settings.
A Data Pool’s table settings.
These describe how the Data Pool’s table is created in ClickHouse.
The ClickHouse table engine for the Data Pool’s table.
See TableEngine
The PARTITION BY clause for the Data Pool’s table.
The PRIMARY KEY clause for the Data Pool’s table.
The ORDER BY clause for the Data Pool’s table.
The TTL clause for the Data Pool’s table.
The Data Pool’s columns that participate in its PARTITION BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its PRIMARY KEY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its ORDER BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s tenant ID, if configured.
deprecated: Will be removed; use Data Pool Access Policies insteadThe Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.A Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
The name of the column that represents the unique ID.
inspectDataPoolSchema
Extracts the schema from the table and updates the schema object.
Arguments
Returns
The result of a mutation which creates or modifies a Data Pool.
If successful, an DataPoolResponse will be returned; otherwise, a
FailureResponse will be returned.
testDataPool
Tests that Propel has access to the Data Pool’s table in its corresponding Data Source and will be able to Sync data. Updates the status.
Arguments
Returns
The result of a mutation which creates or modifies a Data Pool.
If successful, an DataPoolResponse will be returned; otherwise, a
FailureResponse will be returned.
deleteDataPool
Deletes a Data Pool by ID and returns its ID if the Data Pool was deleted successfully.
Arguments
deleteDataPoolByName
Deletes a Data Pool by unique name and returns its ID if the Data Pool was deleted successfully.
Arguments
disableSyncing
Disables syncing of a Data Pool.
Arguments
Returns
The Data Pool object. Data Pools are Propel’s high-speed data store and cache
The Data Pool’s unique identifier.
The Data Pool’s unique name.
The Data Pool’s description.
The Data Pool’s Account.
The Account object.
The Account’s unique identifier.
The Data Pool’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Data Pool’s creation date and time in UTC.
The Data Pool’s last modification date and time in UTC.
The Data Pool’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Pool’s Data Source. See DataSource
The Data Pool’s status.
The status of a Data Pool.
CREATED: The Data Pool has been created and will be set up soon.PENDING: Propel is attempting to set up the Data Pool.LIVE: The Data Pool is set up and serving data. Check its Syncs to monitor data ingestion.SETUP_FAILED: The Data Pool setup failed. Check its Setup Tasks before re-attempting setup.CONNECTINGCONNECTEDBROKENPAUSINGPAUSEDDELETING: Propel is deleting the Data Pool and all of its associated data.
The Data Pool’s data retention in days (not yet supported).
The name of the Data Pool’s table.
The Data Pool’s primary timestamp column, if any.
A Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
The primary timestamp column’s type.
The number of records in the Data Pool.
The amount of storage in terabytes used by the Data Pool.
The Data Pool’s columns.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
The list of measures (numeric columns) in the Data Pool.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
A list of setup tasks performed on the Data Pool during its most recent setup attempt.
The Data Pool Setup Task object.
Data Pool Setup Tasks are executed when setting up your Data Pool. They ensure Propel will be able to sync records from your Data Source to your Data Pool.
The exact Setup Tasks to perform vary by Data Source. For example, Data Pools pointing to a Snowflake-backed Data Sources will have their own specific Setup Tasks.
The name of the Data Pool Setup Task to be performed.
A description of the Data Pool Setup Task to be performed.
The status of the Data Pool Setup Task (all setup tasks begin as NOT_STARTED before transitioning to SUCCEEDED or FAILED).
If the Data Pool Setup Task failed, this field includes a descriptive error message.
See Error
The time at which the Data Pool Setup Task was completed.
Settings related to Data Pool syncing.
Settings related to Data Pool syncing.
Indicates whether syncing is enabled or disabled.
The syncing interval.
Note that the syncing interval is approximate. For example, setting the syncing interval to EVERY_1_HOUR
does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to
when the Data Pool goes LIVE, and Propel will attempt to sync approximately every hour. Additionally,
if you pause or resume syncing, this too can shift the syncing interval around.
The date and time of the most recent Sync in UTC.
The list of Syncs of the Data Pool.
Arguments
The filter to apply when listing the Syncs for a Data Pool.
EMPTY: Returns only Syncs with empty records.NOT_EMPTY: Returns only Syncs that contain one or more records.ALL: Returns all Syncs, regardless of whether they contain records or not.
See SyncConnection
The Deletion Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The Add Column Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The UpdateDataPoolRecords Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
Whether the Data Pool has access control enabled or not.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
A paginated list of Data Pool Access Policies available on the Data Pool.
Arguments
Validates a custom expression against the Data Pool’s available columns. If the provided expression is invalid, the ValidateExpressionResult response will contain a reason explaining why.
Arguments
Response returned by the validateExpression query for validating expressions in Custom Metrics.
Returns whether the expression is valid or not with a reason explaining why.
True if the expression is valid, false otherwise.
The reason for why the expression is not valid in case it isn’t, null otherwise.
The Data Pool’s table settings.
A Data Pool’s table settings.
These describe how the Data Pool’s table is created in ClickHouse.
The ClickHouse table engine for the Data Pool’s table.
See TableEngine
The PARTITION BY clause for the Data Pool’s table.
The PRIMARY KEY clause for the Data Pool’s table.
The ORDER BY clause for the Data Pool’s table.
The TTL clause for the Data Pool’s table.
The Data Pool’s columns that participate in its PARTITION BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its PRIMARY KEY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its ORDER BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s tenant ID, if configured.
deprecated: Will be removed; use Data Pool Access Policies insteadThe Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.A Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
The name of the column that represents the unique ID.
enableSyncing
Re-enables syncing of a Data Pool.
Arguments
Returns
The Data Pool object. Data Pools are Propel’s high-speed data store and cache
The Data Pool’s unique identifier.
The Data Pool’s unique name.
The Data Pool’s description.
The Data Pool’s Account.
The Account object.
The Account’s unique identifier.
The Data Pool’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Data Pool’s creation date and time in UTC.
The Data Pool’s last modification date and time in UTC.
The Data Pool’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Pool’s Data Source. See DataSource
The Data Pool’s status.
The status of a Data Pool.
CREATED: The Data Pool has been created and will be set up soon.PENDING: Propel is attempting to set up the Data Pool.LIVE: The Data Pool is set up and serving data. Check its Syncs to monitor data ingestion.SETUP_FAILED: The Data Pool setup failed. Check its Setup Tasks before re-attempting setup.CONNECTINGCONNECTEDBROKENPAUSINGPAUSEDDELETING: Propel is deleting the Data Pool and all of its associated data.
The Data Pool’s data retention in days (not yet supported).
The name of the Data Pool’s table.
The Data Pool’s primary timestamp column, if any.
A Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
The primary timestamp column’s type.
The number of records in the Data Pool.
The amount of storage in terabytes used by the Data Pool.
The Data Pool’s columns.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
The list of measures (numeric columns) in the Data Pool.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
A list of setup tasks performed on the Data Pool during its most recent setup attempt.
The Data Pool Setup Task object.
Data Pool Setup Tasks are executed when setting up your Data Pool. They ensure Propel will be able to sync records from your Data Source to your Data Pool.
The exact Setup Tasks to perform vary by Data Source. For example, Data Pools pointing to a Snowflake-backed Data Sources will have their own specific Setup Tasks.
The name of the Data Pool Setup Task to be performed.
A description of the Data Pool Setup Task to be performed.
The status of the Data Pool Setup Task (all setup tasks begin as NOT_STARTED before transitioning to SUCCEEDED or FAILED).
If the Data Pool Setup Task failed, this field includes a descriptive error message.
See Error
The time at which the Data Pool Setup Task was completed.
Settings related to Data Pool syncing.
Settings related to Data Pool syncing.
Indicates whether syncing is enabled or disabled.
The syncing interval.
Note that the syncing interval is approximate. For example, setting the syncing interval to EVERY_1_HOUR
does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to
when the Data Pool goes LIVE, and Propel will attempt to sync approximately every hour. Additionally,
if you pause or resume syncing, this too can shift the syncing interval around.
The date and time of the most recent Sync in UTC.
The list of Syncs of the Data Pool.
Arguments
The filter to apply when listing the Syncs for a Data Pool.
EMPTY: Returns only Syncs with empty records.NOT_EMPTY: Returns only Syncs that contain one or more records.ALL: Returns all Syncs, regardless of whether they contain records or not.
See SyncConnection
The Deletion Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The Add Column Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The UpdateDataPoolRecords Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
Whether the Data Pool has access control enabled or not.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
A paginated list of Data Pool Access Policies available on the Data Pool.
Arguments
Validates a custom expression against the Data Pool’s available columns. If the provided expression is invalid, the ValidateExpressionResult response will contain a reason explaining why.
Arguments
Response returned by the validateExpression query for validating expressions in Custom Metrics.
Returns whether the expression is valid or not with a reason explaining why.
True if the expression is valid, false otherwise.
The reason for why the expression is not valid in case it isn’t, null otherwise.
The Data Pool’s table settings.
A Data Pool’s table settings.
These describe how the Data Pool’s table is created in ClickHouse.
The ClickHouse table engine for the Data Pool’s table.
See TableEngine
The PARTITION BY clause for the Data Pool’s table.
The PRIMARY KEY clause for the Data Pool’s table.
The ORDER BY clause for the Data Pool’s table.
The TTL clause for the Data Pool’s table.
The Data Pool’s columns that participate in its PARTITION BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its PRIMARY KEY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its ORDER BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s tenant ID, if configured.
deprecated: Will be removed; use Data Pool Access Policies insteadThe Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.A Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
The name of the column that represents the unique ID.
createCountMetric
Creates a new Count Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Count Metric.
The Data Pool that powers this Metric.
The Metric’s unique name. If not specified, Propel will set the ID as unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadcreateCountDistinctMetric
Creates a new Count Distinct Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Count Distinct Metric.
The Data Pool that powers this Metric.
The Metric’s unique name. If not specified, Propel will set the ID as unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Dimension over which the count distinct operation is going to be performed.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadcreateSumMetric
Creates a new Sum Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Sum Metric.
The Data Pool that powers this Metric.
The Metric’s unique name. If not specified, Propel will set the ID as unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The column to be summed.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadcreateAverageMetric
Creates a new Average Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Average Metric.
The Data Pool that powers this Metric.
The Metric’s unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The column to be averaged.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadcreateMinMetric
Creates a new Min Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Minimum (Min) Metric.
The Data Pool that powers this Metric.
The Metric’s unique name. If not specified, Propel will set the ID as unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadcreateMaxMetric
Creates a new Max Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Maximum (Max) Metric.
The Data Pool that powers this Metric.
The Metric’s unique name. If not specified, Propel will set the ID as unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The column to calculate the maximum from.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadcreateCustomMetric
Creates a new Custom Metric from the given Data Pool and returns the newly created Metric (or an error message if creating the Metric fails).
Arguments
The fields for creating a new Custom Metric.
The Data Pool that powers this Metric.
The Metric’s unique name. If not specified, Propel will set the ID as unique name.
The Metric’s description.
The Metric’s Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
The Metric’s Dimensions. Dimensions define the columns that will be available to filter the Metric at query time.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The expression that defines the aggregation function for this Metric.
The Metric’s Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadmodifyMetric
Modifies a Metric by ID with the provided unique name, description, and Dimensions. If any of the optional arguments are omitted, those properties will be unchanged on the Metric.
Arguments
The fields for modifying a Metric.
The ID of the Metric to modify.
The Metric’s new unique name.
The Metric’s new description.
The Metric’s new Dimensions. Used to add or remove Dimensions.
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
The Metric’s new Filters, in the form of SQL. Used to add or remove Metric Filters.
Enables or disables access control for the Metric.
The Metric’s new Filters. Used to add or remove Metric Filters.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The result of a mutation which creates or modifies a Metric.
The Metric which was created or modified.
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Environment.
See Environment
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
See Dimension
The Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
See Dimension
List the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
See MetricType
The settings for the Metric. The settings are specific to the Metric’s type.
See MetricSettings
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.See Dimension
Query the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
See CounterInput
See CounterResponse
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
See TimeSeriesInput
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
See LeaderboardInput
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteadmigrateMetric
Migrates a Metric from one Data Pool to another.
Arguments
Returns
The Metric object.
A Metric is a business indicator measured over time.
The Metric’s unique identifier.
The Metric’s unique name.
The Metric’s description.
The Metric’s Account.
The Account object.
The Account’s unique identifier.
The Metric’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Metric’s creation date and time in UTC.
The Metric’s last modification date and time in UTC.
The Metric’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Metric’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Metric’s Dimensions. These Dimensions are available to Query Filters.
The Dimension object that represents a column in a table.
The column name it represents.
The column data type.
Whether the column is nullable.
Whether the column is a unique key.
deprecated: This is Snowflake-specific, and will be removedThe statistics for the dimension values. Fetching statistics incurs query costs.
deprecated: Issue normal queries for calculating statsThe Metric’s timestamp, if any. This is the same as its Data Pool’s timestamp, if any.
The Dimension object that represents a column in a table.
The column name it represents.
The column data type.
Whether the column is nullable.
Whether the column is a unique key.
deprecated: This is Snowflake-specific, and will be removedThe statistics for the dimension values. Fetching statistics incurs query costs.
deprecated: Issue normal queries for calculating statsList the Boosters associated to the Metric.
Arguments
The Metric’s type. The different Metric types determine how the values are calculated.
The available Metric types.
COUNT: Counts the number of records that matches the Metric Filters. For time series, it will count the values for each time granularity.SUM: Sums the values of the specified column for every record that matches the Metric Filters. For time series, it will sum the values for each time granularity.COUNT_DISTINCT: Counts the number of distinct values in the specified column for every record that matches the Metric Filters. For time series, it will count the distinct values for each time granularity.AVERAGE: Averages the values of the specified column for every record that matches the Metric Filters. For time series, it will average the values for each time granularity.MIN: Selects the minimum value of the specified column for every record that matches the Metric Filters. For time series, it will select the minimum value for each time granularity.MAX: Selects the maximum value of the specified column for every record that matches the Metric Filters. For time series, it will select the maximum value for each time granularity.CUSTOM: Aggregates values based on the provided custom expression.
The settings for the Metric. The settings are specific to the Metric’s type.
A Metric’s settings, depending on its type.
The Metric’s measure. Access this from the Metric’s settings object instead.
settings object instead.The Dimension object that represents a column in a table.
The column name it represents.
The column data type.
Whether the column is nullable.
Whether the column is a unique key.
deprecated: This is Snowflake-specific, and will be removedThe statistics for the dimension values. Fetching statistics incurs query costs.
deprecated: Issue normal queries for calculating statsQuery the Metric in counter format. Returns the Metric’s value for the given time range and filters.
deprecated: Use the top-levelcounter query insteadArguments
The fields for querying a Metric in counter format.
A Metric’s counter query returns a single value over a given time range.
The Metric to query. You can query a pre-configured Metric by ID or name, or you can query an ad hoc Metric that you define inline.
See MetricInput
The time range for calculating the counter.
See TimeRangeInput
The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities.
You can set this to “America/Los_Angeles”, “Europe/Berlin”, or any other value in the IANA time zone database. Defaults to “UTC”.
The Query Filters to apply before retrieving the counter data, in the form of SQL. If no Query Filters are provided, all data is included.
The ID of the Metric to query.
Required if metricName is not specified.
metricThe name of the Metric to query.
Required if metricId is not specified.
metricThe Query Filters to apply before retrieving the counter data. If no Query Filters are provided, all data is included.
deprecated: UsefilterSql insteadSee FilterInput
Query the Metric in time series format. Returns arrays of timestamps and the Metric’s values for the given time range and filters.
deprecated: Use the top-leveltimeSeries query insteadArguments
The fields for querying a Metric in time series format.
A Metric’s time series query returns the values over a given time range aggregated by a given time granularity; day, month, or year, for example.
The Metric to Query. It can be a pre-created one or it can be inlined here.
See MetricInput
The time range for calculating the time series.
See TimeRangeInput
The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities.
You can set this to “America/Los_Angeles”, “Europe/Berlin”, or any other value in the IANA time zone database. Defaults to “UTC”.
The time granularity (hour, day, month, etc.) to aggregate the Metric values by.
The Query Filters to apply before retrieving the time series data, in the form of SQL. If no Query Filters are provided, all data is included.
Columns to group by.
The ID of the Metric to query.
Required if metricName is not specified.
metricThe name of the Metric to query.
Required if metricId is not specified.
metricThe Query Filters to apply before retrieving the time series data. If no Query Filters are provided, all data is included.
deprecated: UsefilterSql insteadSee FilterInput
The time series response object. It contains an array of time series labels and an array of Metric values for the given time range and Query Filters.
The time series labels.
The time series values.
The time series values for each group in groupBy, if specified.
Query the Metric in leaderboard format. Returns a table (array of rows) with the selected dimensions and the Metric’s corresponding values for the given time range and filters.
deprecated: Use the top-levelleaderboard query insteadArguments
The fields for querying a Metric in leaderboard format.
A Metric’s leaderboard query returns an ordered table of Dimension and Metric values over a given time range.
The Metric to query. You can query a pre-configured Metric by ID or name, or you can query an ad hoc Metric that you define inline.
See MetricInput
The time range for calculating the leaderboard.
See TimeRangeInput
The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities.
You can set this to “America/Los_Angeles”, “Europe/Berlin”, or any other value in the IANA time zone database. Defaults to “UTC”.
One or many Dimensions to group the Metric values by. Typically, Dimensions in a leaderboard are what you want to compare and rank.
See DimensionInput
The sort order of the rows. It can be ascending (ASC) or descending (DESC) order. Defaults to descending (DESC) order when not provided.
See Sort
The number of rows to be returned. It can be a number between 1 and 1,000.
The Query Filters to apply before retrieving the leaderboard data, in the form of SQL. If no Query Filters are provided, all data is included.
The ID of the Metric to query.
Required if metricName is not specified.
metricThe name of the Metric to query.
Required if metricId is not specified.
metricThe Query Filters to apply before retrieving the leaderboard data. If no Query Filters are provided, all data is included.
deprecated: UsefilterSql insteadSee FilterInput
The leaderboard response object. It contains an array of headers and a table (array of rows) with the selected Dimensions and corresponding Metric values for the given time range and Query Filters.
The table headers. It contains the Dimension and Metric names.
An ordered array of rows. Each row contains the Dimension values and the corresponding Metric value. A Dimension value can be empty. A Metric value will never be empty.
List the Policies associated to the Metric.
deprecated: Use Data Pool Access Policies insteadArguments
See PolicyConnection
Whether or not access control is enabled for the Metric.
deprecated: Use Data Pool Access Policies insteaddeleteMetric
Deletes a Metric by ID and returns its ID if the Metric was deleted successfully.
Arguments
deleteMetricByName
Deletes a Metric by unique name and returns its ID if the Metric was deleted successfully.
Arguments
createBooster
Creates a new Booster for the given Metric and returns the newly created Booster.
A Booster significantly improves the query performance for a Metric.
Arguments
The fields for creating a new Booster.
Boosters can be understood as an aggregating index. The index is formed from left to right as follows:
- The Data Pool’s Tenant ID column (if present)
- Metric Filter columns (if present)
- Query Filter Dimensions (see
dimensions) - The Data Pool’s timestamp column
The Booster’s Metric.
Dimensions to include in the Booster.
Follow these guidelines when specifying Dimensions:
- Specify Dimensions in descending order of importance for filtering and in ascending order of cardinality.
- Take into consideration hierarchical relationships as well (for example, a “country” Dimension should appear before a “state” Dimension).
The fields for creating or modifying a Dimension.
The name of the column to create the Dimension from.
Returns
The result of a mutation which creates or modifies a Booster.
The Booster which was created or modified.
Boosters allow you to optimize Metric Queries for a subset of commonly used Dimensions. A Metric can have one or many Boosters to optimize for the different Query patterns.
Boosters can be understood as an aggregating index. The index is formed from left to right as follows:
- The Data Pool’s Tenant ID column (if present)
- Metric Filter columns (if present)
- Query Filter Dimensions (see
dimensions) - The Data Pool’s timestamp column
The Booster’s unique identifier.
The Booster’s Environment.
See Environment
The Booster’s creation date and time in UTC.
The Booster’s last modification date and time in UTC.
The Booster’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Booster’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The status of the Booster (once LIVE it will be available for speeding up Metric queries).
See BoosterStatus
If the Booster fails during the optimization process, this field includes a descriptive error message.
See Error
When the Booster is OPTIMIZING, this represents its progress as a number from 0 to 1. In all other states, progress is null.
The number of records in the Booster.
The amount of storage in terabytes used by the Booster.
deleteBooster
Deletes a Booster by ID and then returns the same ID if the Booster was deleted successfully.
A Booster significantly improves the query performance for a Metric.
Arguments
createDeletionJob
Schedules a new Deletion Job on the specified Data Pool.
Arguments
The fields for creating a Deletion Job.
The Data Pool that is going to get the data deleted
The filters that will be used for deleting data, in the form of SQL. Data matching these filters will be deleted.
The list of filters that will be used for deleting data. Data matching these filters will be deleted.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The response returned by the Deletion Job.
The Deletion Job that was just created.
Deletion Job scheduled for a specific Data Pool.
The Deletion Job represents the asynchronous process of deleting data given some filters inside a Data Pool. It tracks the deletion process until it is finished, showing the progress and the outcome when it is finished.
The Deletion Job’s ID.
The Deletion Job’s creation date and time in UTC.
Who created the Deletion Job.
The Deletion Job’s last modification date and time in UTC.
Who last modified the Deletion Job.
Environment to which the Deletion Job belongs.
See Environment
The Data Pool whose records will be deleted by the Deletion Job. See DataPool
The filters that will be used for deleting data, in the form of SQL. Data matching the filters will be deleted.
The current progress of the Deletion Job, from 0.0 to 1.0.
The time at which the Deletion Job started.
The time at which the Deletion Job succeeded.
The time at which the Deletion Job failed.
createAddColumnToDataPoolJob
Schedules a new AddColumnToDataPoolJob on the specified Data Pool.
Arguments
The fields for creating an Add Column Job.
The Data Pool to which the column will be added.
Name of the new column.
Type of the new column.
The Propel data types.
BOOLEAN: True or false.STRING: A variable-length string.FLOAT: A 32-bit signed double-precision floating point number.DOUBLE: A 64-bit signed double-precision floating point number.INT8: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1.INT16: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1.INT32: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1.INT64: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1.DATE: A date without a timestamp. For example, “YYYY-MM-DD”.TIMESTAMP: A date with a timestamp. For example, “yyy-MM-dd HH:mm:ss”.JSON: A JavaScript Object Notation (JSON) document.CLICKHOUSE: A ClickHouse-specific type.
The ClickHouse type of the new column when columnType is set to CLICKHOUSE.
JSON property to which the new column corresponds.
Returns
The response returned by the Add Column Job.
The AddColumnToDataPool Job that was just created.
AddColumnToDataPoolJob scheduled for a specific Data Pool.
The Add Column Job represents the asynchronous process of adding a column, given its name and type, to a Data Pool. It tracks the process of adding a column until it is finished, showing the progress and the outcome when it is finished.
The AddColumnToDataPoolJob’s ID.
The AddColumnToDataPoolJob’s creation date and time in UTC.
Who created the AddColumnToDataPoolJob.
The AddColumnToDataPoolJob’s last modification date and time in UTC.
Who modified the AddColumnToDataPoolJob last.
Environment to which the AddColumnToDataPoolJob belongs.
See Environment
Name of the new column.
Type of the new column.
See ColumnType
The ClickHouse type of the new column when columnType is set to CLICKHOUSE.
JSON property to which the new column corresponds.
The current progress of the AddColumnToDataPool Job, from 0.0 to 1.0.
The time at which the AddColumnToDataPool Job started.
The time at which the AddColumnToDataPool Job succeeded.
The time at which the AddColumnToDataPool Job failed.
createUpdateDataPoolRecordsJob
Schedules a new UpdateDataPoolRecords Job on the specified Data Pool.
Arguments
The fields for creating an Update Data Pool Records Job.
The Data Pool that is going to get its records updated.
The filters that will be used for updating records, in the form of SQL. Records matching these filters will be updated.
Describes how the job will update the records.
The fields for creating an Update Data Pool Records Job.
{
"column": "status",
"expression": "'completed'"
}
{
"column": "counter",
"expression": "counter + 1"
}
{
"column": "full_name",
"expression": "concat(first_name, ' ', last_name)"
}
The name of the column to update.
The value to which the column will be updated. Once evaluated, it should be of the same data type as the column.
The list of filters that will be used for updating records. Records matching these filters will be updated.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The response returned by the Update Data Pool Records Job.
The UpdateDataPoolRecords Job that was just created.
UpdateDataPoolRecords Job scheduled for a specific Data Pool. The Update Data Pool Records Job represents the asynchronous process of updating records given some filters, inside a Data Pool. It tracks the process of updating records until it is finished, showing the progress and the outcome when it is finished.
The UpdateDataPoolRecords Job’s ID
The UpdateDataPoolRecords Job’s creation date and time in UTC
Who created the UpdateDataPoolRecords Job
The UpdateDataPoolRecords Job’s last modification date and time in UTC
Who last modified the UpdateDataPoolRecords Job
Environment to which the UpdateDataPoolRecords Job belongs
See Environment
The Data Pool whose records will be updated by the UpdateDataPoolRecords Job See DataPool
The filters that will be used for updating data, in the form of SQL. Data matching the filters will be updated.
Describes how the job will update the records.
The current progress of the UpdateDataPoolRecords Job, from 0.0 to 1.0.
The time at which the UpdateDataPoolRecords Job started.
The time at which the UpdateDataPoolRecords Job succeeded.
The time at which the UpdateDataPoolRecords Job failed.
syncDataPool
Manually trigger a Sync for a Data Pool.
Arguments
Returns
The Sync object.
This represents the process of syncing data from your Data Source (for example, a Snowflake data warehouse) to your Data Pool.
The Sync’s unique identifier.
The Sync’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Sync’s creation date and time in UTC.
The Sync’s last modification date and time in UTC.
The Sync’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Sync’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Sync’s Data Pool’s Data Source.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
The number of new, updated, and deleted records contained within the Sync, if known. This excludes filtered records.
The (compressed) size of the Sync, in bytes, if known.
The status of the Sync (all Syncs begin as SYNCING before transitioning to SUCCEEDED or FAILED).
The status of a Sync.
SYNCING: Propel is actively syncing records contained within the Sync.SUCCEEDED: The Sync succeeded. Propel successfully synced all records contained within the Sync.FAILED: The Sync failed. Propel failed to sync some or all records contained within the Sync.DELETING: Propel is deleting the Sync.
The time at which the Sync started.
The time at which the Sync succeeded.
The time at which the Sync failed.
The number of new records contained within the Sync, if known. This excludes filtered records.
deprecated: All records are considered to be processed; seeprocessedRecords insteadThe number of updated records contained within the Sync, if known. This excludes filtered records.
deprecated: All records are considered to be processed; seeprocessedRecords insteadThe number of deleted records contained within the Sync, if known. This excludes filtered records.
deprecated: All records are considered to be processed; seeprocessedRecords insteadThe number of filtered records contained within the Sync, due to issues such as a missing timestamp Dimension, if any are known to be invalid.
deprecated: All records are considered to be processed; seeprocessedRecords insteadresyncDataPool
Manually trigger a re-Sync for a Data Pool.
Arguments
Returns
The Sync object.
This represents the process of syncing data from your Data Source (for example, a Snowflake data warehouse) to your Data Pool.
The Sync’s unique identifier.
The Sync’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Sync’s creation date and time in UTC.
The Sync’s last modification date and time in UTC.
The Sync’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Sync’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Sync’s Data Pool’s Data Source.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
The number of new, updated, and deleted records contained within the Sync, if known. This excludes filtered records.
The (compressed) size of the Sync, in bytes, if known.
The status of the Sync (all Syncs begin as SYNCING before transitioning to SUCCEEDED or FAILED).
The status of a Sync.
SYNCING: Propel is actively syncing records contained within the Sync.SUCCEEDED: The Sync succeeded. Propel successfully synced all records contained within the Sync.FAILED: The Sync failed. Propel failed to sync some or all records contained within the Sync.DELETING: Propel is deleting the Sync.
The time at which the Sync started.
The time at which the Sync succeeded.
The time at which the Sync failed.
The number of new records contained within the Sync, if known. This excludes filtered records.
deprecated: All records are considered to be processed; seeprocessedRecords insteadThe number of updated records contained within the Sync, if known. This excludes filtered records.
deprecated: All records are considered to be processed; seeprocessedRecords insteadThe number of deleted records contained within the Sync, if known. This excludes filtered records.
deprecated: All records are considered to be processed; seeprocessedRecords insteadThe number of filtered records contained within the Sync, due to issues such as a missing timestamp Dimension, if any are known to be invalid.
deprecated: All records are considered to be processed; seeprocessedRecords insteadcreateMaterializedView
Creates a new Materialized View. Returns the newly created Materialized View (or an error message if creating the Materialized View fails).
Arguments
The fields for creating a Materialized View.
The Materialized View’s unique name. If not specified, Propel will set the ID as the unique name.
The Materialized View’s description.
The SQL that the Materialized View will execute.
By default, a destination Data Pool with default settings will be created for the Materialized View; however, you can customize the destination Data Pool (or point to an existing Data Pool), by setting this field. Use this to target an existing Data Pool or the engine settings of a new Data Pool.
The fields for targeting an existing Data Pool or a new Data Pool.
If specified, the Materialized View will target an existing Data Pool. Ensure the Data Pool’s schema is compatible with your Materialized View’s SQL statement.
See DataPoolInput
If specified, the Materialized View will create and target a new Data Pool. You can further customize the new Data Pool’s engine settings.
By default, a Materialized View only applies to records added after its creation. This option allows to backfill all the data that was present before the Materialized View creation.
Whether historical data should be backfilled or not
Defines the order in which historical data is backfilled. Defaults to OLDEST_FIRST if not specified.
See PartitionOrder
Returns
The result of a mutation which creates or modifies a Materialized View.
The Materialized View which was created or modified.
The Materialized View’s unique identifier.
The Materialized View’s unique name.
The Materialized View’s description.
The Materialized View’s Environment.
See Environment
The Materialized View’s creation date and time in UTC.
The Materialized View’s last modification date and time in UTC.
The Materialized View’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Materialized View’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The SQL that the Materialized View executes.
The Materialized View’s destination (AKA “target”) Data Pool.
See DataPool
modifyMaterializedView
Modifies a Materialized View. If any of the optional arguments are omitted, those properties will be unchanged on the Materialized View.
Arguments
Returns
The result of a mutation which creates or modifies a Materialized View.
The Materialized View which was created or modified.
The Materialized View’s unique identifier.
The Materialized View’s unique name.
The Materialized View’s description.
The Materialized View’s Environment.
See Environment
The Materialized View’s creation date and time in UTC.
The Materialized View’s last modification date and time in UTC.
The Materialized View’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Materialized View’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The SQL that the Materialized View executes.
The Materialized View’s destination (AKA “target”) Data Pool.
See DataPool
deleteMaterializedView
Deletes a Materialized View and returns its ID if the Materialized View was deleted successfully.
Note that deleting a Materialized View does not delete its target Data Pool. If you want to delete its target
Data Pool, you must use the deleteDataPool mutation.
Arguments
createAmazonDataFirehoseDataSource
Creates a new Amazon Data Firehose Data Source from the given settings.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The Amazon Data Firehose Data Source’s connection settings
The Amazon Data Firehose Data Source’s connection settings.
Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
HTTP basic access authentication credentials. You must configure these same credentials to be included in the
X-Amz-Firehose-Access-Key header when Amazon Data Firehose issues requests to its custom HTTP endpoint.
Additional columns for the table in Propel.
Override the Data Pool’s table settings. These describe how the Data Pool’s table is created in ClickHouse, and a
default will be chosen based on the Data Pool’s timestamp value, if any. You can override these
defaults in order to specify a custom table engine, custom ORDER BY, etc.
The primary timestamp column, if any.
The Amazon Data Firehose Data Source’s description.
The Amazon Data Firehose Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyAmazonDataFirehoseDataSource
Modifies the Data Source by the ID or unique name provided with the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The Amazon Data Firehose Data Source’s new connection settings. If not provided this property will not be modified.
The Amazon Data Firehose Data Source’s connection settings.
HTTP basic access authentication credentials. You must configure these same credentials to be included in the
X-Amz-Firehose-Access-Key header when Amazon Data Firehose issues requests to its custom HTTP endpoint. If not provided this property will not be modified.
The Amazon Data Firehose Data Source’s new description. If not provided this property will not be modified.
The Amazon Data Firehose Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createAmazonDynamoDBDataSource
Creates a new Amazon DynamoDB Data Source from the given settings.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The Amazon DynamoDB Data Source’s connection settings
The Amazon DynamoDB Data Source’s connection settings.
Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
HTTP basic access authentication credentials. You must configure these same credentials to be included in the
X-Amz-Firehose-Access-Key header when Amazon Data Firehose transmits records from your DynamoDB table to its
custom HTTP endpoint.
Additional columns for the table in Propel.
Override the Data Pool’s table settings. These describe how the Data Pool’s table is created in ClickHouse, and a
default will be chosen based on the Data Pool’s timestamp value, if any. You can override these
defaults in order to specify a custom table engine, custom ORDER BY, etc.
The primary timestamp column, if any.
The Amazon DynamoDB Data Source’s description.
The Amazon DynamoDB Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyAmazonDynamoDBDataSource
Modifies the Data Source by the ID or unique name provided with the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The Amazon DynamoDB Data Source’s new connection settings. If not provided this property will not be modified.
The Amazon DynamoDB Data Source’s connection settings.
HTTP basic access authentication credentials. You must configure these same credentials to be included in the
X-Amz-Firehose-Access-Key header when Amazon Data Firehose transmits records from your DynamoDB table to its
custom HTTP endpoint. If not provided this property will not be modified.
The Amazon DynamoDB Data Source’s new description. If not provided this property will not be modified.
The Amazon DynamoDB Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createClickHouseDataSource
This mutation creates a new ClickHouse Data Source.
The mutation returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The ClickHouse Data Source’s connection settings
The ClickHouse Data Source connection settings.
Which database to connect to
The password for the provided user
The URL where the ClickHouse host is listening to HTTP[S] connections
The user for authenticating against the ClickHouse host
The ClickHouse Data Source’s description.
The ClickHouse Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyClickHouseDataSource
This mutation selects a Data Source by its ID or unique name and modifies it to have the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The ClickHouse Data Source’s new connection settings. If not provided this property will not be modified.
The ClickHouse Data Source connection settings.
Which database to connect to If not provided this property will not be modified.
The password for the provided user If not provided this property will not be modified.
The URL where the ClickHouse host is listening to HTTP[S] connections If not provided this property will not be modified.
The user for authenticating against the ClickHouse host If not provided this property will not be modified.
The ClickHouse Data Source’s new description. If not provided this property will not be modified.
The ClickHouse Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createHttpDataSource
Creates a new HTTP Data Source from the given settings.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The HTTP Data Source’s connection settings
The HTTP Data Source connection settings.
The HTTP Basic authentication settings for uploading new data.
If this parameter is not provided, anyone with the URL to your tables will be able to upload data. While it’s OK to test without HTTP Basic authentication, we recommend enabling it.
The HTTP Data Source’s tables.
The HTTP Data Source’s description.
The HTTP Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyHttpDataSource
This mutation selects a Data Source by its ID or unique name and modifies it to have the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The HTTP Data Source’s new connection settings. If not provided this property will not be modified.
The HTTP Data Source connection settings.
The HTTP Basic authentication settings for uploading new data.
If this parameter is not provided, anyone with the URL to your tables will be able to upload data. While it’s OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified.
Set this to false to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified.
The HTTP Data Source’s tables. If not provided this property will not be modified.
The HTTP Data Source’s new description. If not provided this property will not be modified.
The HTTP Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createKafkaDataSource
This mutation creates a new Kafka Data Source.
The mutation returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The Kafka Data Source’s connection settings
The Kafka Data Source connection settings.
The type of authentication to use. Can be SCRAM-SHA-256, SCRAM-SHA-512, PLAIN or NONE
The bootstrap server(s) to connect to
The password for the provided user
Whether the the connection to the Kafka servers is encrypted or not
The user for authenticating against the Kafka servers
The Kafka Data Source’s description.
The Kafka Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyKafkaDataSource
This mutation selects a Data Source by its ID or unique name and modifies it to have the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The Kafka Data Source’s new connection settings. If not provided this property will not be modified.
The Kafka Data Source connection settings.
The type of authentication to use. Can be SCRAM-SHA-256, SCRAM-SHA-512, PLAIN or NONE If not provided this property will not be modified.
The bootstrap server(s) to connect to If not provided this property will not be modified.
The password for the provided user If not provided this property will not be modified.
Whether the the connection to the Kafka servers is encrypted or not If not provided this property will not be modified.
The user for authenticating against the Kafka servers If not provided this property will not be modified.
The Kafka Data Source’s new description. If not provided this property will not be modified.
The Kafka Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createPostgreSqlDataSource
Creates a new PostgreSQL Data Source.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Example:
mutation {
createPostgreSqlDataSource(input: {
uniqueName: "example_postgresql_data_source"
description: "My PostgreSQL Data Source"
connectionSettings: {
database: "example_database"
schema: "public"
host: "postgresql.example.com"
port: 5432
user: "user"
password: "password"
}
}) {
dataSource {
id
uniqueName
}
}
}
Replace the placeholder values with your actual configuration.
Arguments
The PostgreSQL Data Source’s connection settings
The PostgreSQL Data Source connection settings.
Which database to connect to
The host where PostgreSQL is listening
The password for the provided user
The port where PostgreSQL is listening (usually 5432)
Which schema to use
The user for authenticating against PostgreSQL
The PostgreSQL Data Source’s description.
The PostgreSQL Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyPostgreSqlDataSource
Selects a Data Source by its ID or unique name and modifies it to have the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The PostgreSQL Data Source’s new connection settings. If not provided this property will not be modified.
The PostgreSQL Data Source connection settings.
Which database to connect to If not provided this property will not be modified.
The host where PostgreSQL is listening If not provided this property will not be modified.
The password for the provided user If not provided this property will not be modified.
The port where PostgreSQL is listening (usually 5432) If not provided this property will not be modified.
Which schema to use If not provided this property will not be modified.
The user for authenticating against PostgreSQL If not provided this property will not be modified.
The PostgreSQL Data Source’s new description. If not provided this property will not be modified.
The PostgreSQL Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createS3DataSource
Creates a new Amazon S3 Data Source pointed at the specified Amazon S3 bucket.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The Amazon S3 Data Source’s connection settings
The connection settings for an Amazon S3 Data Source. These include the Amazon S3 bucket name, the AWS access key ID, and the tables (along with their paths). We do not allow fetching the AWS secret access key after it has been set.
The AWS access key ID for an IAM user with sufficient access to the Amazon S3 bucket.
The AWS secret access key for an IAM user with sufficient access to the Amazon S3 bucket.
The name of the Amazon S3 bucket.
The Amazon S3 Data Source’s tables.
The Amazon S3 Data Source’s description.
The Amazon S3 Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyS3DataSource
This mutation selects a Data Source by its ID or unique name and modifies it to have the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The Amazon S3 Data Source’s new connection settings. If not provided this property will not be modified.
The connection settings for an Amazon S3 Data Source. These include the Amazon S3 bucket name, the AWS access key ID, and the tables (along with their paths). We do not allow fetching the AWS secret access key after it has been set.
The AWS access key ID for an IAM user with sufficient access to the Amazon S3 bucket. If not provided this property will not be modified.
The AWS secret access key for an IAM user with sufficient access to the Amazon S3 bucket. If not provided this property will not be modified.
The name of the Amazon S3 bucket. If not provided this property will not be modified.
The Amazon S3 Data Source’s tables. If not provided this property will not be modified.
The Amazon S3 Data Source’s new description. If not provided this property will not be modified.
The Amazon S3 Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createTwilioSegmentDataSource
Creates a new Twilio Segment Data Source from the given settings.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The Twilio Segment Data Source’s connection settings
The Twilio Segment Data Source connection settings.
Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
The HTTP basic authentication settings for the Twilio Segment Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it’s OK to test without HTTP Basic authentication, we recommend enabling it.
The additional columns for the table in Propel.
Override the Data Pool’s table settings. These describe how the Data Pool’s table is created in ClickHouse, and a
default will be chosen based on the Data Pool’s timestamp and uniqueId values, if any. You can override these
defaults in order to specify a custom table engine, custom ORDER BY, etc.
The primary timestamp column, if any.
The Twilio Segment Data Source’s description.
The Twilio Segment Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyTwilioSegmentDataSource
Modifies the Data Source by the ID or unique name provided with the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The Twilio Segment Data Source’s new connection settings. If not provided this property will not be modified.
The Twilio Segment Data Source connection settings.
The HTTP basic authentication settings for the Twilio Segment Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it’s OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified.
Set this to false to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified.
The Twilio Segment Data Source’s new description. If not provided this property will not be modified.
The Twilio Segment Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
createWebhookDataSource
Creates a new Webhook Data Source from the given settings.
Returns the newly created Data Source (or an error message if creating the Data Source fails).
Arguments
The Webhook Data Source’s connection settings
The Webhook Data Source connection settings.
Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
The HTTP basic authentication settings for the Webhook Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it’s OK to test without HTTP Basic authentication, we recommend enabling it.
The additional columns for the table in Propel.
Override the Data Pool’s table settings. These describe how the Data Pool’s table is created in ClickHouse, and a
default will be chosen based on the Data Pool’s timestamp and uniqueId values, if any. You can override these
defaults in order to specify a custom table engine, custom ORDER BY, etc.
The tenant ID column, if any.
The primary timestamp column, if any.
The unique ID column, if any. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated.
The Webhook Data Source’s description.
The Webhook Data Source’s unique name. If not specified, Propel will set the ID as unique name.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
modifyWebhookDataSource
Modifies the Data Source by the ID or unique name provided with the given unique name, description, and connection settings.
If any of the optional arguments are omitted, those properties will be unchanged on the Data Source.
Arguments
The Webhook Data Source’s new connection settings. If not provided this property will not be modified.
The Webhook Data Source connection settings.
The HTTP basic authentication settings for the Webhook Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it’s OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified.
Set this to false to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified.
The Webhook Data Source’s new description. If not provided this property will not be modified.
The Webhook Data Source’s new unique name. If not provided this property will not be modified.
Returns
The result of a mutation which creates or modifies a Data Source.
The Data Source which was created or modified.
The Data Source object.
A Data Source is a connection to your data warehouse. It has the necessary connection details for Propel to access Snowflake or any other supported Data Source.
The Data Source’s unique identifier.
The Data Source’s unique name.
The Data Source’s description.
The Data Source’s Environment.
See Environment
The Data Source’s creation date and time in UTC.
The Data Source’s last modification date and time in UTC.
The Data Source’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Source’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Source’s type.
See DataSourceType
The Data Source’s status.
See DataSourceStatus
The Data Source’s connection settings.
The tables contained within the Data Source, according to the most recent table introspection.
Arguments
See TableConnection
A list of table introspections performed for the Data Source. You can see how tables and columns changed over time by paging through this list.
Arguments
A list of checks performed on the Data Source during its most recent connection attempt.
See DataSourceCheck
If you list Data Pools via the dataPools field on a Data Source, you will get Data Pools for the Data Source.
The dataPools field uses cursor-based pagination typical of GraphQL APIs. You can use the pairs of parameters first and after or last and before to page forward or backward through the results, respectively.
For forward pagination, the first parameter defines the number of results to return, and the after parameter defines the cursor to continue from. You should pass the cursor for the last result of the current page to after.
For backward pagination, the last parameter defines the number of results to return, and the before parameter defines the cursor to continue from. You should pass the cursor for the first result of the current page to before.
Arguments
reconnectDataPool
Deprecated. Use retryDataPoolSetup instead.
retryDataPoolSetup insteadArguments
Returns
The Data Pool object. Data Pools are Propel’s high-speed data store and cache
The Data Pool’s unique identifier.
The Data Pool’s unique name.
The Data Pool’s description.
The Data Pool’s Account.
The Account object.
The Account’s unique identifier.
The Data Pool’s Environment.
The Environments object.
Environments are independent and isolated Propel workspaces for development, staging (testing), and production workloads. Environments are hosted in a specific region, initially in us-east-2 only.
The Environment’s unique identifier.
The Environment’s unique name.
The Environment’s description.
The Environment’s creation date and time in UTC.
The Environment’s last modification date and time in UTC.
The Environment’s creator. It can be either a User ID, an Environment ID, or “system” if it was created by Propel.
The Environment’s last modifier. It can be either a User ID, an Environment ID, or “system” if it was modified by Propel.
The Data Pool’s creation date and time in UTC.
The Data Pool’s last modification date and time in UTC.
The Data Pool’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Data Pool’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The Data Pool’s Data Source. See DataSource
The Data Pool’s status.
The status of a Data Pool.
CREATED: The Data Pool has been created and will be set up soon.PENDING: Propel is attempting to set up the Data Pool.LIVE: The Data Pool is set up and serving data. Check its Syncs to monitor data ingestion.SETUP_FAILED: The Data Pool setup failed. Check its Setup Tasks before re-attempting setup.CONNECTINGCONNECTEDBROKENPAUSINGPAUSEDDELETING: Propel is deleting the Data Pool and all of its associated data.
The Data Pool’s data retention in days (not yet supported).
The name of the Data Pool’s table.
The Data Pool’s primary timestamp column, if any.
A Data Pool’s primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics.
The name of the column that represents the primary timestamp.
The primary timestamp column’s type.
The number of records in the Data Pool.
The amount of storage in terabytes used by the Data Pool.
The Data Pool’s columns.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
The list of measures (numeric columns) in the Data Pool.
Arguments
The Data Pool column connection object.
Learn more about pagination in GraphQL.
The Data Pool column connection’s edges.
The Data Pool column connection’s nodes.
See DataPoolColumn
A list of setup tasks performed on the Data Pool during its most recent setup attempt.
The Data Pool Setup Task object.
Data Pool Setup Tasks are executed when setting up your Data Pool. They ensure Propel will be able to sync records from your Data Source to your Data Pool.
The exact Setup Tasks to perform vary by Data Source. For example, Data Pools pointing to a Snowflake-backed Data Sources will have their own specific Setup Tasks.
The name of the Data Pool Setup Task to be performed.
A description of the Data Pool Setup Task to be performed.
The status of the Data Pool Setup Task (all setup tasks begin as NOT_STARTED before transitioning to SUCCEEDED or FAILED).
If the Data Pool Setup Task failed, this field includes a descriptive error message.
See Error
The time at which the Data Pool Setup Task was completed.
Settings related to Data Pool syncing.
Settings related to Data Pool syncing.
Indicates whether syncing is enabled or disabled.
The syncing interval.
Note that the syncing interval is approximate. For example, setting the syncing interval to EVERY_1_HOUR
does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to
when the Data Pool goes LIVE, and Propel will attempt to sync approximately every hour. Additionally,
if you pause or resume syncing, this too can shift the syncing interval around.
The date and time of the most recent Sync in UTC.
The list of Syncs of the Data Pool.
Arguments
The filter to apply when listing the Syncs for a Data Pool.
EMPTY: Returns only Syncs with empty records.NOT_EMPTY: Returns only Syncs that contain one or more records.ALL: Returns all Syncs, regardless of whether they contain records or not.
See SyncConnection
The Deletion Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The Add Column Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
The UpdateDataPoolRecords Jobs that were historically issued to this Data Pool, sorted by creation time, in descending order.
Arguments
Whether the Data Pool has access control enabled or not.
If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics.
A paginated list of Data Pool Access Policies available on the Data Pool.
Arguments
Validates a custom expression against the Data Pool’s available columns. If the provided expression is invalid, the ValidateExpressionResult response will contain a reason explaining why.
Arguments
Response returned by the validateExpression query for validating expressions in Custom Metrics.
Returns whether the expression is valid or not with a reason explaining why.
True if the expression is valid, false otherwise.
The reason for why the expression is not valid in case it isn’t, null otherwise.
The Data Pool’s table settings.
A Data Pool’s table settings.
These describe how the Data Pool’s table is created in ClickHouse.
The ClickHouse table engine for the Data Pool’s table.
See TableEngine
The PARTITION BY clause for the Data Pool’s table.
The PRIMARY KEY clause for the Data Pool’s table.
The ORDER BY clause for the Data Pool’s table.
The TTL clause for the Data Pool’s table.
The Data Pool’s columns that participate in its PARTITION BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its PRIMARY KEY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s columns that participate in its ORDER BY clause.
The name of the Data Source column that this Data Pool column derives from.
The Data Pool column’s type. This may differ from the corresponding Data Source column’s type.
See ColumnType
The ClickHouse type. This is the exact representation of the type in ClickHouse.
Whether the column is nullable, meaning whether it accepts a null value.
The name of the Data Source column that this Data Pool column derives from.
deprecated: Start usingcolumnName insteadThe Data Pool’s tenant ID, if configured.
deprecated: Will be removed; use Data Pool Access Policies insteadThe Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
deprecated: Will be removed; use table settings to define the primary key.A Data Pool’s unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool.
The name of the column that represents the unique ID.
createPolicy
Creates a new Policy granting an Application access to a Metric’s data.
deprecated: Use Data Pool Access Policies insteadArguments
The fields for creating a Policy.
The Metric to which the Policy will be applied.
The type of Policy to create.
The types of Policies that can be applied to a Metric.
ALL_ACCESS: Grants access to all Metric data.TENANT_ACCESS: Grants access to a specified tenant’s Metric data.
The Application that will be granted access to the Metric.
Returns
The result of a mutation which creates or modifies a Policy.
The Policy which was created or modified.
The Policy type. It governs an Application’s access to a Metric’s data.
The Policy’s unique identifier.
The Policy’s Environment.
See Environment
The Policy’s creation date and time in UTC.
The Policy’s last modification date and time in UTC.
The Policy’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Policy’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The type of Policy.
See PolicyType
The Application that is granted access. See Application
modifyPolicy
Modifies an existing Policy. You can modify the Application’s level of access to the Metric’s data.
deprecated: Use Data Pool Access Policies insteadArguments
Returns
The result of a mutation which creates or modifies a Policy.
The Policy which was created or modified.
The Policy type. It governs an Application’s access to a Metric’s data.
The Policy’s unique identifier.
The Policy’s Environment.
See Environment
The Policy’s creation date and time in UTC.
The Policy’s last modification date and time in UTC.
The Policy’s creator. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
The Policy’s last modifier. It can be either a User ID, an Application ID, or “system” if it was modified by Propel.
The type of Policy.
See PolicyType
The Application that is granted access. See Application
deletePolicy
Deletes a Policy. The associated Application will no longer have access to the Metric’s data.
deprecated: Use Data Pool Access Policies insteadArguments
requestDelete
Schedules a new Deletion Job on the specified Data Pool.
deprecated: This has been renamed tocreateDeletionJobArguments
The fields for creating a Deletion Job.
The Data Pool that is going to get the data deleted
The filters that will be used for deleting data, in the form of SQL. Data matching these filters will be deleted.
The list of filters that will be used for deleting data. Data matching these filters will be deleted.
deprecated: UsefilterSql insteadThe fields of a filter.
You can construct more complex filters using and and or. For example, to construct a filter equivalent to
(value > 0 AND value <= 100) OR status = "confirmed"
you could write
{
"column": "value",
"operator": "GREATER_THAN",
"value": "0",
"and": [{
"column": "value",
"operator": "LESS_THAN_OR_EQUAL_TO",
"value": "0"
}],
"or": [{
"column": "status",
"operator": "EQUALS",
"value": "confirmed"
}]
}
Note that and takes precedence over or.
The name of the column to filter on.
The operation to perform when comparing the column and filter values.
See FilterOperator
The value to compare the column to.
Additional filters to AND with this one. AND takes precedence over OR.
Additional filters to OR with this one. AND takes precedence over OR.
Returns
The response returned by the Deletion Job.
The Deletion Job that was just created.
Deletion Job scheduled for a specific Data Pool.
The Deletion Job represents the asynchronous process of deleting data given some filters inside a Data Pool. It tracks the deletion process until it is finished, showing the progress and the outcome when it is finished.
The Deletion Job’s ID.
The Deletion Job’s creation date and time in UTC.
Who created the Deletion Job.
The Deletion Job’s last modification date and time in UTC.
Who last modified the Deletion Job.
Environment to which the Deletion Job belongs.
See Environment
The Data Pool whose records will be deleted by the Deletion Job. See DataPool
The filters that will be used for deleting data, in the form of SQL. Data matching the filters will be deleted.
The current progress of the Deletion Job, from 0.0 to 1.0.
The time at which the Deletion Job started.
The time at which the Deletion Job succeeded.
The time at which the Deletion Job failed.
- createEnvironment
- modifyEnvironment
- deleteEnvironment
- createDataPoolAccessPolicy
- modifyDataPoolAccessPolicy
- deleteDataPoolAccessPolicy
- assignDataPoolAccessPolicyToApplication
- unAssignDataPoolAccessPolicyFromApplication
- createApplication
- modifyApplication
- deleteApplication
- deleteApplicationByName
- createSnowflakeDataSource
- modifySnowflakeDataSource
- reconnectDataSource
- introspectTables
- testDataSource
- deleteDataSource
- deleteDataSourceByName
- createDataPoolV2
- modifyDataPool
- retryDataPoolSetup
- retryDataPoolSetupByName
- inspectDataPoolSchema
- testDataPool
- deleteDataPool
- deleteDataPoolByName
- disableSyncing
- enableSyncing
- createCountMetric
- createCountDistinctMetric
- createSumMetric
- createAverageMetric
- createMinMetric
- createMaxMetric
- createCustomMetric
- modifyMetric
- migrateMetric
- deleteMetric
- deleteMetricByName
- createBooster
- deleteBooster
- createDeletionJob
- createAddColumnToDataPoolJob
- createUpdateDataPoolRecordsJob
- syncDataPool
- resyncDataPool
- createMaterializedView
- modifyMaterializedView
- deleteMaterializedView
- createAmazonDataFirehoseDataSource
- modifyAmazonDataFirehoseDataSource
- createAmazonDynamoDBDataSource
- modifyAmazonDynamoDBDataSource
- createClickHouseDataSource
- modifyClickHouseDataSource
- createHttpDataSource
- modifyHttpDataSource
- createKafkaDataSource
- modifyKafkaDataSource
- createPostgreSqlDataSource
- modifyPostgreSqlDataSource
- createS3DataSource
- modifyS3DataSource
- createTwilioSegmentDataSource
- modifyTwilioSegmentDataSource
- createWebhookDataSource
- modifyWebhookDataSource