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

POST twitter/_update_by_query?conflicts=proceed

That will return something like this:

{
  "took" : 147,
  "timed_out": false,
  "updated": 120,
  "deleted": 0,
  "batches": 1,
  "version_conflicts": 0,
  "noops": 0,
  "retries": {
    "bulk": 0,
    "search": 0
  },
  "throttled_millis": 0,
  "requests_per_second": -1.0,
  "throttled_until_millis": 0,
  "total": 120,
  "failures" : [ ]
}

_update_by_query gets a snapshot of the index when it starts and indexes what it finds using internal versioning. That means you’ll get a version conflict if the document changes between the time when the snapshot was taken and when the index request is processed. When the versions match, the document is updated and the version number is incremented.

Since internal versioning does not support the value 0 as a valid version number, documents with version equal to zero cannot be updated using _update_by_query and will fail the request.

All update and query failures cause the _update_by_query to abort and are returned in the failures of the response. The updates that have been performed still stick. In other words, the process is not rolled back, only aborted. While the first failure causes the abort, all failures that are returned by the failing bulk request are returned in the failures element; therefore it’s possible for there to be quite a few failed entities.

If you want to simply count version conflicts, and not cause the _update_by_query to abort, you can set conflicts=proceed on the url or "conflicts": "proceed" in the request body. The first example does this because it is just trying to pick up an online mapping change, and a version conflict simply means that the conflicting document was updated between the start of the _update_by_query and the time when it attempted to update the document. This is fine because that update will have picked up the online mapping update.

Back to the API format, this will update tweets from the twitter index:

POST twitter/_update_by_query?conflicts=proceed

You can also limit _update_by_query using the Query DSL. This will update all documents from the twitter index for the user kimchy:

POST twitter/_update_by_query?conflicts=proceed
{
  "query": { 
    "term": {
      "user": "kimchy"
    }
  }
}

So far we’ve only been updating documents without changing their source. That is genuinely useful for things like picking up new properties but it’s only half the fun. _update_by_query supports scripts to update the document. This will increment the likes field on all of kimchy’s tweets:

POST twitter/_update_by_query
{
  "script": {
    "source": "ctx._source.likes++",
    "lang": "painless"
  },
  "query": {
    "term": {
      "user": "kimchy"
    }
  }
}

Just as in Update API you can set ctx.op to change the operation that is executed:

Setting ctx.op to anything else is an error. Setting any other field in ctx is an error.

Note that we stopped specifying conflicts=proceed. In this case we want a version conflict to abort the process so we can handle the failure.

This API doesn’t allow you to move the documents it touches, just modify their source. This is intentional! We’ve made no provisions for removing the document from its original location.

It’s also possible to do this whole thing on multiple indexes at once, just like the search API:

POST twitter,blog/_update_by_query

If you provide routing then the routing is copied to the scroll query, limiting the process to the shards that match that routing value:

POST twitter/_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 URL parameter:

POST twitter/_update_by_query?scroll_size=100

_update_by_query can also use the Ingest node feature by specifying a pipeline like this:

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

URL Parameters

In addition to the standard parameters like pretty, the Update By Query API also supports refresh, wait_for_completion, wait_for_active_shards, timeout, and scroll.

Sending the refresh will update all shards in the index being updated when the request completes. This is different than the Update API’s refresh parameter, which causes just the shard that received the new data to be indexed. Also unlike the Update API it does not support wait_for.

If the request contains wait_for_completion=false then Elasticsearch will perform some preflight checks, launch the request, and then return a task which can be used with Tasks APIs to cancel or get the status of the task. Elasticsearch will also create a record of this task as a document at .tasks/task/${taskId}. This is yours to keep or remove as you see fit. When you are done with it, delete it so Elasticsearch can reclaim the space it uses.

wait_for_active_shards controls how many copies of a shard must be active before proceeding with the request. See here for details. timeout controls how long each write request waits for unavailable shards to become available. Both work exactly how they work in the Bulk API. Because _update_by_query uses scroll search, you can also specify the scroll parameter to control how long it keeps the "search context" alive, e.g. ?scroll=10m. By default it’s 5 minutes.

requests_per_second can be set to any positive decimal number (1.4, 6, 1000, etc.) and throttles the rate at which _update_by_query issues batches of index operations by padding each batch with a wait time. The throttling can be disabled by setting requests_per_second to -1.

The throttling is done by waiting between batches so that scroll that _update_by_query uses internally can be given a timeout that takes into account the padding. 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 the 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 will cause Elasticsearch to create many requests and then wait for a while before starting the next set. This is "bursty" instead of "smooth". The default is -1.

Response body

The JSON response looks like this:

{
  "took" : 147,
  "timed_out": false,
  "total": 5,
  "updated": 5,
  "deleted": 0,
  "batches": 1,
  "version_conflicts": 0,
  "noops": 0,
  "retries": {
    "bulk": 0,
    "search": 0
  },
  "throttled_millis": 0,
  "requests_per_second": -1.0,
  "throttled_until_millis": 0,
  "failures" : [ ]
}

Works with the Task API

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.

Works with the Cancel Task API

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.

Rethrottling

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.

Slicing

Update by query supports Sliced Scroll to parallelize the updating process. This parallelization can improve efficiency and provide a convenient way to break the request down into smaller parts.

Manual slicing

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

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

Which you can verify works with:

GET _refresh
POST twitter/_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"
    }
  }
}

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 twitter/_update_by_query?refresh&slices=5
{
  "script": {
    "source": "ctx._source['extra'] = 'test'"
  }
}

Which you also can verify works with:

POST twitter/_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 indices, it will choose the number of slices based on the 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:

Picking the number of slices

If slicing automatically, setting slices to auto will choose a reasonable number for most indices. If you’re slicing manually or otherwise tuning automatic slicing, use these guidelines.

Query performance is most efficient when the number of slices is equal to the number of shards in the index. If that number is large, (for example, 500) choose a lower number as too many slices will hurt 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.

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.