How transform checkpoints workedit
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. The sync property of the transform
configures checkpointing by specifying a time field.
To create a checkpoint, the continuous transform:
-
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
frequencyproperty.If the source indices remain unchanged or if a checkpoint is already in progress then it waits for the next timer.
If changes are found a checkpoint is created.
-
Identifies which entities and/or time buckets have changed.
The transform searches to see which entities or time buckets have changed between the last and the new checkpoint. The transform uses the values to synchronize the source and destination indices with fewer operations than a full re-run.
-
Updates the destination index (the data frame) with the changes.
The transform applies changes related to either new or changed entities or time buckets to the destination index. The set of changes can be paginated. The transform performs a composite aggregation similarly to the batch transform operation, however it also injects query filters based on the previous step to reduce the amount of work. After all 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.
Using the ingest timestamp for syncing the transformedit
In most cases, it is strongly recommended to use the ingest timestamp of the
source indices for syncing the transform. This is the most optimal way for
transforms to be able to identify new changes. If your data source follows the
ECS standard, you might already have an
event.ingested field. In this
case, use event.ingested as the sync.time.field property of your
transform.
If you don’t have a event.ingested field or it isn’t populated, you can set it
by using an ingest pipeline. Create an ingest pipeline either using the
ingest pipeline API (like the example below) or via Kibana
under Stack Management > Ingest Pipelines. Use a
set processor to set the field and associate it with the
value of the ingest timestamp.
PUT _ingest/pipeline/set_ingest_time
{
"description": "Set ingest timestamp.",
"processors": [
{
"set": {
"field": "event.ingested",
"value": "{{{_ingest.timestamp}}}"
}
}
]
}
After you created the ingest pipeline, apply it to the source indices of your
transform. The pipeline adds the field event.ingested to every document with
the value of the ingest timestamp. Configure the sync.time.field property
of your transform to use the field by using the
create transform API for new transforms or the
update transform API for existing transforms. The
event.ingested field is used for syncing the transform.
Refer to Add a pipeline to an indexing request and Ingest pipelines to learn more about how to use an ingest pipeline.
Change detection heuristicsedit
When the transform runs in continuous mode, it updates the documents in the destination index as new data comes in. The transform uses a set of heuristics called change detection to update the destination index with fewer operations.
In this example, the data is grouped by host names. Change detection detects
which host names have changed, for example, host A, C and G and only
updates documents with those hosts but does not update documents that store
information about host B, D, or any other host that are not changed.
Another heuristic can be applied for time buckets when a date_histogram is
used to group by time buckets. Change detection detects which time buckets have
changed and only update those.
Error handlingedit
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
frequencyproperty 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.