Update By Query API

Updates documents that match the specified query. If no query is specified, performs an update on every document in the data stream or index without modifying the source, which is useful for picking up mapping changes.

POST my-index-000001/_update_by_query?conflicts=proceed

Request

POST /<target>/_update_by_query

Description

You can specify the query criteria in the request URI or the request body using the same syntax as the Search API.

When you submit an update by query request, Elasticsearch gets a snapshot of the data stream or index when it begins processing the request and updates matching documents using internal versioning. When the versions match, the document is updated and the version number is incremented. If a document changes between the time that the snapshot is taken and the update operation is processed, it results in a version conflict and the operation fails. You can opt to count version conflicts instead of halting and returning by setting conflicts to proceed.

Documents with a version equal to 0 cannot be updated using update by query because internal versioning does not support 0 as a valid version number.

While processing an update by query request, Elasticsearch performs multiple search requests sequentially to find all of the matching documents. A bulk update request is performed for each batch of matching documents. Any query or update failures cause the update by query request to fail and the failures are shown in the response. Any update requests that completed successfully still stick, they are not rolled back.

Refreshing shards

Specifying the refresh parameter refreshes all shards once the request completes. This is different than the update API’s refresh parameter, which causes just the shard that received the request to be refreshed. Unlike the update API, it does not support wait_for.

Running update by query asynchronously

If the request contains wait_for_completion=false, Elasticsearch performs some preflight checks, launches the request, and returns a task you can use to cancel or get the status of the task. Elasticsearch creates a record of this task as a document at .tasks/task/${taskId}. When you are done with a task, you should delete the task document so Elasticsearch can reclaim the space.

Waiting for active shards

wait_for_active_shards controls how many copies of a shard must be active before proceeding with the request. See Active shards for details. timeout controls how long each write request waits for unavailable shards to become available. Both work exactly the way they work in the Bulk API. Update by query uses scrolled searches, so you can also specify the scroll parameter to control how long it keeps the search context alive, for example ?scroll=10m. The default is 5 minutes.

Throttling update requests

To control the rate at which update by query issues batches of update operations, you can set requests_per_second to any positive decimal number. This pads each batch with a wait time to throttle the rate. Set requests_per_second to -1 to disable throttling.

Throttling uses a wait time between batches so that the internal scroll requests can be given a timeout that takes the request padding into account. The padding time is the difference between the batch size divided by the requests_per_second and the time spent writing. By default the batch size is 1000, so if requests_per_second is set to 500:

target_time = 1000 / 500 per second = 2 seconds
wait_time = target_time - write_time = 2 seconds - .5 seconds = 1.5 seconds

Since the batch is issued as a single _bulk request, large batch sizes cause Elasticsearch to create many requests and wait before starting the next set. This is “bursty” instead of “smooth”.

Slicing

Update by query supports sliced scroll to parallelize the update process. This can improve efficiency and provide a convenient way to break the request down into smaller parts.

Setting slices to auto chooses a reasonable number for most data streams and indices. If you’re slicing manually or otherwise tuning automatic slicing, keep in mind that:

• Query performance is most efficient when the number of slices is equal to the number of shards in the index or backing index. If that number is large (for example, 500), choose a lower number as too many slices hurts performance. Setting slices higher than the number of shards generally does not improve efficiency and adds overhead.
• Update performance scales linearly across available resources with the number of slices.

Whether query or update performance dominates the runtime depends on the documents being reindexed and cluster resources.

Path parameters

<target>

(Optional, string) Comma-separated list of data streams, indices, and index aliases to search. Wildcard (*) expressions are supported.

To search all data streams or indices in a cluster, omit this parameter or use _all or *.

Query parameters

allow_no_indices

(Optional, boolean) If true, the request does not return an error if a wildcard expression or _all value retrieves only missing or closed indices.

This parameter also applies to index aliases that point to a missing or closed index.

Defaults to true.

analyzer

(Optional, string) Analyzer to use for the query string.

analyze_wildcard

(Optional, boolean) If true, wildcard and prefix queries are analyzed. Defaults to false.

conflicts

(Optional, string) What to do if update by query hits version conflicts: abort or proceed. Defaults to abort.

default_operator

(Optional, string) The default operator for query string query: AND or OR. Defaults to OR.

df

(Optional, string) Field to use as default where no field prefix is given in the query string.

expand_wildcards

(Optional, string) Controls what kind of indices that wildcard expressions can expand to. Multiple values are accepted when separated by a comma, as in open,hidden. Valid values are:

• all

  Expand to open and closed indices, including hidden indices.

  open

  Expand only to open indices.

  closed

  Expand only to closed indices.

  hidden

  Expansion of wildcards will include hidden indices. Must be combined with open, closed, or both.

  none

  Wildcard expressions are not accepted.

Defaults to open.

from

(Optional, integer) Starting document offset. Defaults to 0.

ignore_unavailable

(Optional, boolean) If true, missing or closed indices are not included in the response. Defaults to false.

lenient

(Optional, boolean) If true, format-based query failures (such as providing text to a numeric field) will be ignored. Defaults to false.

max_docs

(Optional, integer) Maximum number of documents to process. Defaults to all documents.

pipeline

(Optional, string) ID of the pipeline to use to preprocess incoming documents.

preference

(Optional, string) Specifies the node or shard the operation should be performed on. Random by default.

q

(Optional, string) Query in the Lucene query string syntax.

request_cache

(Optional, boolean) If true, the request cache is used for this request. Defaults to the index-level setting.

refresh

(Optional, enum) If true, Elasticsearch refreshes the affected shards to make this operation visible to search, if wait_for then wait for a refresh to make this operation visible to search, if false do nothing with refreshes. Valid values: true, false, wait_for. Default: false.

requests_per_second

(Optional, integer) The throttle for this request in sub-requests per second. Defaults to -1 (no throttle).

routing

(Optional, string) Target the specified primary shard.

scroll

(Optional, time value) Period to retain the search context for scrolling. See Scroll search results.

scroll_size

(Optional, integer) Size of the scroll request that powers the operation. Defaults to 100.

search_type

(Optional, string) The type of the search operation. Available options:

• query_then_fetch
• dfs_query_then_fetch

timeout

(Optional, time units) Explicit timeout for each search request. Defaults to no timeout.

slices

(Optional, integer) The number of slices this task should be divided into. Defaults to 1 meaning the task isn’t sliced into subtasks.

sort

(Optional, string) A comma-separated list of : pairs.

_source

(Optional, string) True or false to return the _source field or not, or a list of fields to return.

_source_excludes

(Optional, string) A comma-separated list of source fields to exclude from the response.

You can also use this parameter to exclude fields from the subset specified in _source_includes query parameter.

If the _source parameter is false, this parameter is ignored.

_source_includes

(Optional, string) A comma-separated list of source fields to include in the response.

If this parameter is specified, only these source fields are returned. You can exclude fields from this subset using the _source_excludes query parameter.

If the _source parameter is false, this parameter is ignored.

stats

(Optional, string) Specific tag of the request for logging and statistical purposes.

terminate_after

(Optional, integer) The maximum number of documents to collect for each shard, upon reaching which the query execution will terminate early.

master_timeout

(Optional, time units) Specifies the period of time to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error. Defaults to 30s.

timeout

(Optional, time units) Specifies the period of time to wait for a response. If no response is received before the timeout expires, the request fails and returns an error. Defaults to 30s.

version

(Optional, boolean) If true, returns the document version as part of a hit.

wait_for_active_shards

(Optional, string) The number of shard copies that must be active before proceeding with the operation. Set to all or any positive integer up to the total number of shards in the index (number_of_replicas+1). Default: 1, the primary shard.

See Active shards.

Request body

query

(Optional, query object) Specifies the documents to update using the Query DSL.

Response body

took

The number of milliseconds from start to end of the whole operation.

timed_out

This flag is set to true if any of the requests executed during the update by query execution has timed out.

total

The number of documents that were successfully processed.

updated

The number of documents that were successfully updated.

deleted

The number of documents that were successfully deleted.

batches

The number of scroll responses pulled back by the update by query.

version_conflicts

The number of version conflicts that the update by query hit.

noops

The number of documents that were ignored because the script used for the update by query returned a noop value for ctx.op.

retries

The number of retries attempted by update by query. bulk is the number of bulk actions retried, and search is the number of search actions retried.

throttled_millis

Number of milliseconds the request slept to conform to requests_per_second.

requests_per_second

The number of requests per second effectively executed during the update by query.

throttled_until_millis

This field should always be equal to zero in an _update_by_query response. It only has meaning when using the Task API, where it indicates the next time (in milliseconds since epoch) a throttled request will be executed again in order to conform to requests_per_second.

failures

Array of failures if there were any unrecoverable errors during the process. If this is non-empty then the request aborted because of those failures. Update by query is implemented using batches. Any failure causes the entire process to abort, but all failures in the current batch are collected into the array. You can use the conflicts option to prevent reindex from aborting on version conflicts.

Examples

The simplest usage of _update_by_query just performs an update on every document in the data stream or index without changing the source. This is useful to pick up a new property or some other online mapping change.

To update selected documents, specify a query in the request body:

POST my-index-000001/_update_by_query?conflicts=proceed
{
  "query": { 
    "term": {
      "user.id": "kimchy"
    }
  }
}

Update documents in multiple data streams or indices:

POST my-index-000001,my-index-000002/_update_by_query

Limit the update by query operation to shards that a particular routing value:

POST my-index-000001/_update_by_query?routing=1

By default update by query uses scroll batches of 1000. You can change the batch size with the scroll_size parameter:

POST my-index-000001/_update_by_query?scroll_size=100

Update the document source

Update by query supports scripts to update the document source. For example, the following request increments the count field for all documents with a user.id of kimchy in my-index-000001:

POST my-index-000001/_update_by_query
{
  "script": {
    "source": "ctx._source.count++",
    "lang": "painless"
  },
  "query": {
    "term": {
      "user.id": "kimchy"
    }
  }
}

Note that conflicts=proceed is not specified in this example. In this case, a version conflict should halt the process so you can handle the failure.

As with the Update API, you can set ctx.op to change the operation that is performed:

Update by query only supports update, noop, and delete. Setting ctx.op to anything else is an error. Setting any other field in ctx is an error. This API only enables you to modify the source of matching documents, you cannot move them.

Update documents using an ingest pipeline

Update by query can use the Ingest node feature by specifying a pipeline:

PUT _ingest/pipeline/set-foo
{
  "description" : "sets foo",
  "processors" : [ {
      "set" : {
        "field": "foo",
        "value": "bar"
      }
  } ]
}
POST my-index-000001/_update_by_query?pipeline=set-foo

Get the status of update by query operations

You can fetch the status of all running update by query requests with the Task API:

GET _tasks?detailed=true&actions=*byquery

The responses looks like:

{
  "nodes" : {
    "r1A2WoRbTwKZ516z6NEs5A" : {
      "name" : "r1A2WoR",
      "transport_address" : "127.0.0.1:9300",
      "host" : "127.0.0.1",
      "ip" : "127.0.0.1:9300",
      "attributes" : {
        "testattr" : "test",
        "portsfile" : "true"
      },
      "tasks" : {
        "r1A2WoRbTwKZ516z6NEs5A:36619" : {
          "node" : "r1A2WoRbTwKZ516z6NEs5A",
          "id" : 36619,
          "type" : "transport",
          "action" : "indices:data/write/update/byquery",
          "status" : {    
            "total" : 6154,
            "updated" : 3500,
            "created" : 0,
            "deleted" : 0,
            "batches" : 4,
            "version_conflicts" : 0,
            "noops" : 0,
            "retries": {
              "bulk": 0,
              "search": 0
            },
            "throttled_millis": 0
          },
          "description" : ""
        }
      }
    }
  }
}

With the task id you can look up the task directly. The following example retrieves information about task r1A2WoRbTwKZ516z6NEs5A:36619:

GET /_tasks/r1A2WoRbTwKZ516z6NEs5A:36619

The advantage of this API is that it integrates with wait_for_completion=false to transparently return the status of completed tasks. If the task is completed and wait_for_completion=false was set on it, then it’ll come back with a results or an error field. The cost of this feature is the document that wait_for_completion=false creates at .tasks/task/${taskId}. It is up to you to delete that document.

Cancel an update by query operation

Any update by query can be cancelled using the Task Cancel API:

POST _tasks/r1A2WoRbTwKZ516z6NEs5A:36619/_cancel

The task ID can be found using the tasks API.

Cancellation should happen quickly but might take a few seconds. The task status API above will continue to list the update by query task until this task checks that it has been cancelled and terminates itself.

Change throttling for a request

The value of requests_per_second can be changed on a running update by query using the _rethrottle API:

POST _update_by_query/r1A2WoRbTwKZ516z6NEs5A:36619/_rethrottle?requests_per_second=-1

The task ID can be found using the tasks API.

Just like when setting it on the _update_by_query API, requests_per_second can be either -1 to disable throttling or any decimal number like 1.7 or 12 to throttle to that level. Rethrottling that speeds up the query takes effect immediately, but rethrotting that slows down the query will take effect after completing the current batch. This prevents scroll timeouts.

Slice manually

Slice an update by query manually by providing a slice id and total number of slices to each request:

POST my-index-000001/_update_by_query
{
  "slice": {
    "id": 0,
    "max": 2
  },
  "script": {
    "source": "ctx._source['extra'] = 'test'"
  }
}
POST my-index-000001/_update_by_query
{
  "slice": {
    "id": 1,
    "max": 2
  },
  "script": {
    "source": "ctx._source['extra'] = 'test'"
  }
}

Which you can verify works with:

GET _refresh
POST my-index-000001/_search?size=0&q=extra:test&filter_path=hits.total

Which results in a sensible total like this one:

{
  "hits": {
    "total": {
        "value": 120,
        "relation": "eq"
    }
  }
}

Use automatic slicing

You can also let update by query automatically parallelize using Sliced scroll to slice on _id. Use slices to specify the number of slices to use:

POST my-index-000001/_update_by_query?refresh&slices=5
{
  "script": {
    "source": "ctx._source['extra'] = 'test'"
  }
}

Which you also can verify works with:

POST my-index-000001/_search?size=0&q=extra:test&filter_path=hits.total

Which results in a sensible total like this one:

{
  "hits": {
    "total": {
        "value": 120,
        "relation": "eq"
    }
  }
}

Setting slices to auto will let Elasticsearch choose the number of slices to use. This setting will use one slice per shard, up to a certain limit. If there are multiple source data streams or indices, it will choose the number of slices based on the index or backing index with the smallest number of shards.

Adding slices to _update_by_query just automates the manual process used in the section above, creating sub-requests which means it has some quirks:

• You can see these requests in the Tasks APIs. These sub-requests are “child” tasks of the task for the request with slices.
• Fetching the status of the task for the request with slices only contains the status of completed slices.
• These sub-requests are individually addressable for things like cancellation and rethrottling.
• Rethrottling the request with slices will rethrottle the unfinished sub-request proportionally.
• Canceling the request with slices will cancel each sub-request.
• Due to the nature of slices each sub-request won’t get a perfectly even portion of the documents. All documents will be addressed, but some slices may be larger than others. Expect larger slices to have a more even distribution.
• Parameters like requests_per_second and max_docs on a request with slices are distributed proportionally to each sub-request. Combine that with the point above about distribution being uneven and you should conclude that using max_docs with slices might not result in exactly max_docs documents being updated.
• Each sub-request gets a slightly different snapshot of the source data stream or index though these are all taken at approximately the same time.

Pick up a new property

Say you created an index without dynamic mapping, filled it with data, and then added a mapping value to pick up more fields from the data:

PUT test
{
  "mappings": {
    "dynamic": false,   
    "properties": {
      "text": {"type": "text"}
    }
  }
}
POST test/_doc?refresh
{
  "text": "words words",
  "flag": "bar"
}
POST test/_doc?refresh
{
  "text": "words words",
  "flag": "foo"
}
PUT test/_mapping   
{
  "properties": {
    "text": {"type": "text"},
    "flag": {"type": "text", "analyzer": "keyword"}
  }
}

Searching for the data won’t find anything:

POST test/_search?filter_path=hits.total
{
  "query": {
    "match": {
      "flag": "foo"
    }
  }
}

{
  "hits" : {
    "total": {
        "value": 0,
        "relation": "eq"
    }
  }
}

But you can issue an _update_by_query request to pick up the new mapping:

POST test/_update_by_query?refresh&conflicts=proceed
POST test/_search?filter_path=hits.total
{
  "query": {
    "match": {
      "flag": "foo"
    }
  }
}

{
  "hits" : {
    "total": {
        "value": 1,
        "relation": "eq"
    }
  }
}

You can do the exact same thing when adding a field to a multifield.