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2 changes: 2 additions & 0 deletions explore-analyze/transforms/transform-checkpoints.md
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Expand Up @@ -19,6 +19,8 @@ To create a checkpoint, the {{ctransform}}:

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 new data is ingested with a slight delay, it might not be immediately available when the {{transform}} runs. To prevent missing documents, you can use the `delay` parameter in the `sync` configuration. This shifts the search window backward, ensuring that late-arriving data is included before a checkpoint processes it. Adjusting this value based on your data ingestion patterns can help ensure completeness.

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.
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6 changes: 6 additions & 0 deletions explore-analyze/transforms/transform-usage.md
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Expand Up @@ -27,3 +27,9 @@ You might want to consider using {{transforms}} instead of aggregations when:
* You want to create summary tables to optimize queries.

For example, if you have a high level dashboard that is accessed by a large number of users and it uses a complex aggregation over a large dataset, it may be more efficient to create a {{transform}} to cache results. Thus, each user doesn’t need to run the aggregation query.

* You need to account for late-arriving data.

In some cases, data might not be immediately available when a {{transform}} runs, leading to missing records in the destination index. This can happen due to ingestion delays, where documents take a few seconds or minutes to become searchable after being indexed. To handle this, the `delay` parameter in the {{transform}}’s sync configuration allows you to postpone processing new data. Instead of always querying the most recent records, the {{transform}} will skip a short period of time (e.g., 60 seconds) to ensure all relevant data has arrived before processing.

For example, if a {{transform}} runs every 5 minutes, it usually processes data from 5 minutes ago up to the current time. However, if you set `delay` to 60 seconds, the {{transform}} will instead process data from 6 minutes ago up to 1 minute ago, making sure that any documents that arrived late are included. By adjusting the `delay` parameter, you can improve the accuracy of transformed data while still maintaining near real-time results.
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