Project: Druid (App Definition and Development / Database)
File: delete.md

id: delete title: "Data deletion" <!-- ~ Licensed to the Apache Software Foundation (ASF) under one ~ or more contributor license agreements. See the NOTICE file ~ distributed with this work for additional information ~ regarding copyright ownership. The ASF licenses this file ~ to you under the Apache License, Version 2.0 (the ~ "License"); you may not use this file except in compliance ~ with the License. You may obtain a copy of the License at ~ ~ http://www.apache.org/licenses/LICENSE-2.0 ~ ~ Unless required by applicable law or agreed to in writing, ~ software distributed under the License is distributed on an ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY ~ KIND, either express or implied. See the License for the ~ specific language governing permissions and limitations ~ under the License. --> Apache Druid stores data and supports deleting data for time chunks by dropping segments. This is a fast, metadata-only operation. Deletion by time range happens in two steps: Segments to be deleted must first be marked as . This can happen when a segment is dropped by a or when you manually mark a segment unused through the Coordinator API or web console. This is a soft delete: the data is not available for querying, but the segment files remains in deep storage, and the segment records remains in the metadata store. Once a segment is marked "unused", you can use a to permanently delete the segment file from deep storage and remove its record from the metadata store. This is a hard delete: the data is unrecoverable unless you have a backup. For documentation on disabling segments using the Coordinator API, see the . A data deletion tutorial is available at . Druid supports , which are used to define intervals of time where data should be preserved, and intervals where data should be discarded. Data that falls under a drop rule is marked unused, in the same manner as if you . This is a fast, metadata-only operation. Data that is dropped in this way is marked unused, but remains in deep storage. To permanently delete it, use a . Druid supports deleting specific records using with a filter. The filter specifies which data remains after reindexing, so it must be the inverse of the data you want to delete. Because segments must be rewritten to delete data in this way, it can be a time-consuming

For example, to delete records where `userName` is `'bob'` with native batch indexing, use a with filter `{"type": "not", "field": {"type": "selector", "dimension": "userName", "value": "bob"}}`. To delete the same records using SQL, use with `WHERE userName <> 'bob'`. To reindex using , use the [`druid` input source](../ingestion/input-sources.md#druid-input-source). If needed, can be used to filter or modify data during the reindexing job. To reindex with SQL, use with `SELECT ... FROM <table>`. (Druid does not have `UPDATE` or `ALTER TABLE` statements.) Any SQL SELECT query can be used to filter, modify, or enrich the data during the reindexing job. Data that is deleted in this way is marked unused, but remains in deep storage. To permanently delete it, use a [`kill` task](#kill-task). Deleting an entire table works the same way as . First, mark all segments unused using the Coordinator API or web console. Then, optionally, delete it permanently using a . <a name="kill-task"></a> Data that has been overwritten or soft-deleted still remains as segments that have been marked unused. You can use a `kill` task to permanently delete this data. The available grammar is: ```json { "type": "kill", "id": <task_id>, "dataSource": <task_datasource>, "interval" : <allunusedsegmentsinthisintervalwill_die!>, "versions" : <optionallistofsegmentversionstodeleteinthis_interval>, "context": <task_context>, "batchSize": <optionalbatchsize>, "limit": <optionalmaximumnumberofsegmentstodelete>, "maxUsedStatusLastUpdatedTime": <optionalmaximumtimestampwhensegmentsweremarkedasunused> } ``` Some of the parameters used in the task payload are further explained below: | Parameter | Default | Explanation | |-|--|--| | `versions` | null (all versions) | List of segment versions within the specified `interval` for the kill task to delete. The default behavior is to delete all unused segment versions in the specified `interval`.| | `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.| | `limit` | null (no limit) | Maximum number of segments for the kill task to delete.| | `maxUsedStatusLastUpdatedTime` | null (no cutoff) | Maximum timestamp used as a cutoff to include unused segments. The kill task only considers segments which lie in the specified `interval` and were marked as unused no later than this time. The default behavior is to kill all unused segments in the `interval` regardless of when they where marked as unused.| WARNING: The `kill` task permanently removes all information about the affected segments from the metadata store and deep storage. This operation cannot be undone.