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.
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.
Propel Applications represent the web or mobile app you are building. They provide the API credentials that allow your client- or server-side app to access the Propel API. The Application’s Propeller determines the speed and cost of your Metric Queries.
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.
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.
Propel Applications represent the web or mobile app you are building. They provide the API credentials that allow your client- or server-side app to access the Propel API. The Application’s Propeller determines the speed and cost of your Metric Queries.
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.
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.
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.
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.
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.
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.
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.
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.
Copy this value into the X-Amz-Firehose-Access-Key header when configuring your Amazon Data Firehose to
transmit records from your DynamoDB table to its custom HTTP endpoint.
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 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.
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.
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.
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.
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 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.
deprecated: Will be removed; use Table Settings to define the primary key.
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.
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 table’s creator. This corresponds to the initiator of the table Introspection. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
Once a table introspection succeeds, it creates a new table object for every table it introspected. Within each table object, it also creates a column object for every column it introspected.
The column’s creator. This corresponds to the initiator of the table introspection. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
This is the suggested Data Pool column type to use when converting this Data Source column to a Data Pool column.
Propel makes this suggestion based on the Data Source column type. If the Data Source column type is unsupported, this field returns null.
Sometimes, you know better which Data Pool column type to convert to. In these cases, you can refer
to supportedDataPoolColumnTypes for the full set of supported conversions.
This is the set of supported Data Pool column types you can use when converting this Data Source column to a Data Pool column. If the Data Source column type is unsupported, this field returns an empty array.
For example, a numeric Data Source column type could be converted to a narrower or wider numeric Data Pool column type; a string-valued Data Source column type could be mapped to a date or timestamp Data Pool column type.
Once a table introspection succeeds, it creates a new table object for every table it introspected. Within each table object, it also creates a column object for every column it introspected.
The column’s creator. This corresponds to the initiator of the table introspection. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
This is the suggested Data Pool column type to use when converting this Data Source column to a Data Pool column.
Propel makes this suggestion based on the Data Source column type. If the Data Source column type is unsupported, this field returns null.
Sometimes, you know better which Data Pool column type to convert to. In these cases, you can refer
to supportedDataPoolColumnTypes for the full set of supported conversions.
This is the set of supported Data Pool column types you can use when converting this Data Source column to a Data Pool column. If the Data Source column type is unsupported, this field returns an empty array.
For example, a numeric Data Source column type could be converted to a narrower or wider numeric Data Pool column type; a string-valued Data Source column type could be mapped to a date or timestamp Data Pool column type.
Once a table introspection succeeds, it creates a new table object for every table it introspected. Within each table object, it also creates a column object for every column it introspected.
The column’s creator. This corresponds to the initiator of the table introspection. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
This is the suggested Data Pool column type to use when converting this Data Source column to a Data Pool column.
Propel makes this suggestion based on the Data Source column type. If the Data Source column type is unsupported, this field returns null.
Sometimes, you know better which Data Pool column type to convert to. In these cases, you can refer
to supportedDataPoolColumnTypes for the full set of supported conversions.
This is the set of supported Data Pool column types you can use when converting this Data Source column to a Data Pool column. If the Data Source column type is unsupported, this field returns an empty array.
For example, a numeric Data Source column type could be converted to a narrower or wider numeric Data Pool column type; a string-valued Data Source column type could be mapped to a date or timestamp Data Pool column type.
Once a table introspection succeeds, it creates a new table object for every table it introspected. Within each table object, it also creates a column object for every column it introspected.
The column’s creator. This corresponds to the initiator of the table introspection. It can be either a User ID, an Application ID, or “system” if it was created by Propel.
This is the suggested Data Pool column type to use when converting this Data Source column to a Data Pool column.
Propel makes this suggestion based on the Data Source column type. If the Data Source column type is unsupported, this field returns null.
Sometimes, you know better which Data Pool column type to convert to. In these cases, you can refer
to supportedDataPoolColumnTypes for the full set of supported conversions.
This is the set of supported Data Pool column types you can use when converting this Data Source column to a Data Pool column. If the Data Source column type is unsupported, this field returns an empty array.
For example, a numeric Data Source column type could be converted to a narrower or wider numeric Data Pool column type; a string-valued Data Source column type could be mapped to a date or timestamp Data Pool column type.
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.
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.
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.
