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Delta Lake output connector

Delta Lake is an open-source storage framework for the Lakehouse architecture. It is typically used with the Apache Spark runtime. Data in a Delta Lake is organized in tables (called Delta Tables), stored in a file system or an object stores like AWS S3, Google GCS, or Azure Blob Storage. Like other Lakehouse-native storage formats, Delta Lake is optimized for both batch and stream processing, offering a bridge between the two worlds.

The Delta Lake output connector does not yet support fault tolerance.

Delta Lake output connector configuration​

Required parameters​

  • uri - Table URI, e.g., "s3://feldera-fraud-detection-data/feature_train"
  • mode - Determines how the Delta table connector handles an existing table at the target location. Three options are available:
    • append - New updates will be appended to the existing table at the target location
    • truncate - Existing table at the specified location will get truncated. The connector truncates the table by outputing delete actions for all files in the latest snapshot of the table.
    • error_if_exists - If a table exists at the specified location, the operation will fail.

Storage parameters​

Additional configuration options are defined for specific storage backends. Refer to backend-specific documentation for details:

The small file problem and output buffer configuration​

By default a Feldera pipeline sends a batch of changes to the output transport for each batch of input updates it processes. This can result in a stream of small updates, which is normal and even preferable for output transports like Kafka; however it can cause problems for the Delta Lake format by creating a large number of small files.

The output buffer mechanism is designed to solve this problem by decoupling the rate at which the pipeline pushes changes to the output transport from the rate of input changes. It works by accumulating updates inside the pipeline for up to a user-defined period of time or until accumulating a user-defined number of updates and writing them to the Delta Table as a small number of large files.

See output buffer for details on configuring the output buffer mechanism.

Limitations​

This connector currently only appends new records to the Delta Table. Deletions output by the pipeline are discarded. The reason for this limitation is that the Delta Lake format only supports appending new data to a table in streaming mode.

Example usage​

Create a Delta Lake output connector that writes a stream of updates to a table stored in an S3 bucket, truncating any existing contents of the table.

CREATE VIEW V AS ...
WITH (
 'connectors' = '[{
    "transport": {
      "name": "delta_table_output",
      "config": {
        "uri": "s3://feldera-fraud-detection-demo/feature_train",
        "mode": "truncate",
        "aws_access_key_id": <AWS_ACCESS_KEY_ID>,
        "aws_secret_access_key": <AWS_SECRET_ACCESS_KEY>,
        "aws_region": "us-east-1"
      },
      "enable_output_buffer": true,
      "max_output_buffer_time_millis": 10000
    }
 }]'
)