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How transform checkpoints work

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How transform checkpoints work

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Each time a transform examines the source indices and creates or updates the destination index, it generates a checkpoint.

If your transform runs only once, there is logically only one checkpoint. If your transform runs continuously, however, it creates checkpoints as it ingests and transforms new source data.

To create a checkpoint, the continuous transform:

  1. Checks for changes to source indices.

    Using a simple periodic timer, the transform checks for changes to the source indices. This check is done based on the interval defined in the transform’s frequency property.

    If the source indices remain unchanged or if a checkpoint is already in progress then it waits for the next timer.

  2. Identifies which entities have changed.

    The transform searches to see which entities have changed since the last time it checked. The sync configuration object in the transform identifies a time field in the source indices. The transform uses the values in that field to synchronize the source and destination indices.

  3. Updates the destination index (the data frame) with the changed entities.

    The transform applies changes related to either new or changed entities to the destination index. The set of changed entities is paginated. For each page, the transform performs a composite aggregation using a terms query. After all the pages of changes have been applied, the checkpoint is complete.

This checkpoint process involves both search and indexing activity on the cluster. We have attempted to favor control over performance while developing transforms. We decided it was preferable for the transform to take longer to complete, rather than to finish quickly and take precedence in resource consumption. That being said, the cluster still requires enough resources to support both the composite aggregation search and the indexing of its results.

If the cluster experiences unsuitable performance degradation due to the transform, stop the transform and refer to Performance considerations.

Error handling

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Failures in transforms tend to be related to searching or indexing. To increase the resiliency of transforms, the cursor positions of the aggregated search and the changed entities search are tracked in memory and persisted periodically.

Checkpoint failures can be categorized as follows:

  • Temporary failures: The checkpoint is retried. If 10 consecutive failures occur, the transform has a failed status. For example, this situation might occur when there are shard failures and queries return only partial results.
  • Irrecoverable failures: The transform immediately fails. For example, this situation occurs when the source index is not found.
  • Adjustment failures: The transform retries with adjusted settings. For example, if a parent circuit breaker memory errors occur during the composite aggregation, the transform receives partial results. The aggregated search is retried with a smaller number of buckets. This retry is performed at the interval defined in the frequency property for the transform. If the search is retried to the point where it reaches a minimal number of buckets, an irrecoverable failure occurs.

If the node running the transforms fails, the transform restarts from the most recent persisted cursor position. This recovery process might repeat some of the work the transform had already done, but it ensures data consistency.

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