mirror of https://github.com/apache/druid.git
115 lines
6.3 KiB
Markdown
115 lines
6.3 KiB
Markdown
---
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id: delete
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title: "Data deletion"
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---
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~ Licensed to the Apache Software Foundation (ASF) under one
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## By time range, manually
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Apache Druid stores data [partitioned by time chunk](../design/architecture.md#datasources-and-segments) and supports
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deleting data for time chunks by dropping segments. This is a fast, metadata-only operation.
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Deletion by time range happens in two steps:
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1. Segments to be deleted must first be marked as ["unused"](../design/architecture.md#segment-lifecycle). This can
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happen when a segment is dropped by a [drop rule](../operations/rule-configuration.md) or when you manually mark a
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segment unused through the Coordinator API or web console. This is a soft delete: the data is not available for
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querying, but the segment files remains in deep storage, and the segment records remains in the metadata store.
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2. Once a segment is marked "unused", you can use a [`kill` task](#kill-task) to permanently delete the segment file from
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deep storage and remove its record from the metadata store. This is a hard delete: the data is unrecoverable unless
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you have a backup.
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For documentation on disabling segments using the Coordinator API, see the
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[Legacy metadata API reference](../api-reference/legacy-metadata-api.md#datasources).
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A data deletion tutorial is available at [Tutorial: Deleting data](../tutorials/tutorial-delete-data.md).
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## By time range, automatically
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Druid supports [load and drop rules](../operations/rule-configuration.md), which are used to define intervals of time
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where data should be preserved, and intervals where data should be discarded. Data that falls under a drop rule is
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marked unused, in the same manner as if you [manually mark that time range unused](#by-time-range-manually). This is a
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fast, metadata-only operation.
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Data that is dropped in this way is marked unused, but remains in deep storage. To permanently delete it, use a
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[`kill` task](#kill-task).
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## Specific records
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Druid supports deleting specific records using [reindexing](update.md#reindex) with a filter. The filter specifies which
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data remains after reindexing, so it must be the inverse of the data you want to delete. Because segments must be
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rewritten to delete data in this way, it can be a time-consuming operation.
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For example, to delete records where `userName` is `'bob'` with native batch indexing, use a
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[`transformSpec`](../ingestion/ingestion-spec.md#transformspec) with filter `{"type": "not", "field": {"type":
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"selector", "dimension": "userName", "value": "bob"}}`.
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To delete the same records using SQL, use [REPLACE](../multi-stage-query/concepts.md#replace) with `WHERE userName <> 'bob'`.
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To reindex using [native batch](../ingestion/native-batch.md), use the [`druid` input
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source](../ingestion/input-sources.md#druid-input-source). If needed,
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[`transformSpec`](../ingestion/ingestion-spec.md#transformspec) can be used to filter or modify data during the
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reindexing job. To reindex with SQL, use [`REPLACE <table> OVERWRITE`](../multi-stage-query/reference.md#replace)
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with `SELECT ... FROM <table>`. (Druid does not have `UPDATE` or `ALTER TABLE` statements.) Any SQL SELECT query can be
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used to filter, modify, or enrich the data during the reindexing job.
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Data that is deleted in this way is marked unused, but remains in deep storage. To permanently delete it, use a [`kill`
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task](#kill-task).
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## Entire table
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Deleting an entire table works the same way as [deleting part of a table by time range](#by-time-range-manually). First,
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mark all segments unused using the Coordinator API or web console. Then, optionally, delete it permanently using a
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[`kill` task](#kill-task).
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<a name="kill-task"></a>
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## Permanently (`kill` task)
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Data that has been overwritten or soft-deleted still remains as segments that have been marked unused. You can use a
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`kill` task to permanently delete this data.
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The available grammar is:
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```json
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{
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"type": "kill",
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"id": <task_id>,
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"dataSource": <task_datasource>,
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"interval" : <all_unused_segments_in_this_interval_will_die!>,
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"context": <task context>,
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"batchSize": <optional_batch size>,
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"limit": <the maximum number of segments to delete>
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}
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```
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Some of the parameters used in the task payload are further explained below:
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| Parameter | Default | Explanation |
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|-------------|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `batchSize` |100 | Maximum number of segments that are deleted in one kill batch. Some operations on the Overlord may get stuck while a `kill` task is in progress due to concurrency constraints (such as in `TaskLockbox`). Thus, a `kill` task splits the list of unused segments to be deleted into smaller batches to yield the Overlord resources intermittently to other task operations.|
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| `limit` | null - no limit | Maximum number of segments for the kill task to delete.|
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**WARNING:** The `kill` task permanently removes all information about the affected segments from the metadata store and
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deep storage. This operation cannot be undone.
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