elasticsearch/docs/reference/docs/update-by-query.asciidoc

[[docs-update-by-query]]
== Update By Query API

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
<<picking-up-a-new-property,pick up a new property>> or some other online
mapping change. Here is the API:

[source,js]
--------------------------------------------------
POST twitter/_update_by_query?conflicts=proceed
--------------------------------------------------
// CONSOLE
// TEST[setup:big_twitter]

That will return something like this:

[source,js]
--------------------------------------------------
{
  "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" : [ ]
}
--------------------------------------------------
// TESTRESPONSE[s/"took" : 147/"took" : "$body.took"/]

`_update_by_query` gets a snapshot of the index when it starts and indexes what
it finds using `internal` versioning. That means that 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.

NOTE: 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 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:

[source,js]
--------------------------------------------------
POST twitter/_update_by_query?conflicts=proceed
--------------------------------------------------
// CONSOLE
// TEST[setup:twitter]

You can also limit `_update_by_query` using the
<<query-dsl,Query DSL>>. This will update all documents from the
`twitter` index for the user `kimchy`:

[source,js]
--------------------------------------------------
POST twitter/_update_by_query?conflicts=proceed
{
  "query": { <1>
    "term": {
      "user": "kimchy"
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[setup:twitter]

<1> The query must be passed as a value to the `query` key, in the same
way as the <<search-search,Search API>>. You can also use the `q`
parameter in the same way as the search api.

So far we've only been updating documents without changing their source. That
is genuinely useful for things like
<<picking-up-a-new-property,picking up new properties>> but it's only half the
fun. `_update_by_query` <<modules-scripting-using,supports scripts>> to update
the document. This will increment the `likes` field on all of kimchy's tweets:

[source,js]
--------------------------------------------------
POST twitter/_update_by_query
{
  "script": {
    "source": "ctx._source.likes++",
    "lang": "painless"
  },
  "query": {
    "term": {
      "user": "kimchy"
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[setup:twitter]

Just as in <<docs-update,Update API>> you can set `ctx.op` to change the
operation that is executed:


`noop`::

Set `ctx.op = "noop"` if your script decides that it doesn't have to make any
changes. That will cause `_update_by_query` to omit that document from its updates.
 This no operation will be reported in the `noop` counter in the
<<docs-update-by-query-response-body, response body>>.

`delete`::

Set `ctx.op = "delete"` if your script decides that the document must be
 deleted. The deletion will be reported in the `deleted` counter in the
<<docs-update-by-query-response-body, response body>>.

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:

[source,js]
--------------------------------------------------
POST twitter,blog/_update_by_query
--------------------------------------------------
// CONSOLE
// TEST[s/^/PUT twitter\nPUT blog\n/]

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

[source,js]
--------------------------------------------------
POST twitter/_update_by_query?routing=1
--------------------------------------------------
// CONSOLE
// TEST[setup:twitter]

By default `_update_by_query` uses scroll batches of 1000. You can change the
batch size with the `scroll_size` URL parameter:

[source,js]
--------------------------------------------------
POST twitter/_update_by_query?scroll_size=100
--------------------------------------------------
// CONSOLE
// TEST[setup:twitter]

`_update_by_query` can also use the <<ingest>> feature by
specifying a `pipeline` like this:

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

[float]
=== 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 <<docs-update-by-query-task-api,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 <<index-wait-for-active-shards,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
<<docs-bulk,Bulk API>>. As `_update_by_query` uses scroll search, you can also specify
the `scroll` parameter to control how long it keeps the "search context" alive,
eg `?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 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`:

[source,txt]
--------------------------------------------------
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`.

[float]
[[docs-update-by-query-response-body]]
=== Response body

//////////////////////////
[source,js]
--------------------------------------------------
POST /twitter/_update_by_query?conflicts=proceed
--------------------------------------------------
// CONSOLE
// TEST[setup:twitter]

//////////////////////////

The JSON response looks like this:

[source,js]
--------------------------------------------------
{
  "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" : [ ]
}
--------------------------------------------------
// TESTRESPONSE[s/"took" : 147/"took" : "$body.took"/]

`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 <<docs-update-by-query-task-api, 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 and 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.



[float]
[[docs-update-by-query-task-api]]
=== Works with the Task API

You can fetch the status of all running update-by-query requests with the
<<tasks,Task API>>:

[source,js]
--------------------------------------------------
GET _tasks?detailed=true&actions=*byquery
--------------------------------------------------
// CONSOLE
// TEST[skip:No tasks to retrieve]

The responses looks like:

[source,js]
--------------------------------------------------
{
  "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" : {    <1>
            "total" : 6154,
            "updated" : 3500,
            "created" : 0,
            "deleted" : 0,
            "batches" : 4,
            "version_conflicts" : 0,
            "noops" : 0,
            "retries": {
              "bulk": 0,
              "search": 0
            },
            "throttled_millis": 0
          },
          "description" : ""
        }
      }
    }
  }
}
--------------------------------------------------
// TESTRESPONSE

<1> this object contains the actual status. It is just like the response json
with the important addition of the `total` field. `total` is the total number
of operations that the reindex expects to perform. You can estimate the
progress by adding the `updated`, `created`, and `deleted` fields. The request
will finish when their sum is equal to the `total` field.

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

[source,js]
--------------------------------------------------
GET /_tasks/r1A2WoRbTwKZ516z6NEs5A:36619
--------------------------------------------------
// CONSOLE
// TEST[catch:missing]

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 them 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.


[float]
[[docs-update-by-query-cancel-task-api]]
=== Works with the Cancel Task API

Any Update By Query can be canceled using the <<tasks,Task Cancel API>>:

[source,js]
--------------------------------------------------
POST _tasks/r1A2WoRbTwKZ516z6NEs5A:36619/_cancel
--------------------------------------------------
// CONSOLE

The task ID can be found using the <<tasks,tasks API>>.

Cancellation should happen quickly but might take a few seconds. The task status
API above will continue to list the task until it is wakes to cancel itself.


[float]
[[docs-update-by-query-rethrottle]]
=== Rethrottling

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

[source,js]
--------------------------------------------------
POST _update_by_query/r1A2WoRbTwKZ516z6NEs5A:36619/_rethrottle?requests_per_second=-1
--------------------------------------------------
// CONSOLE

The task ID can be found using the <<tasks, 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 on after completing the current batch. This prevents scroll
timeouts.

[float]
[[docs-update-by-query-slice]]
=== 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.

[float]
[[docs-update-by-query-manual-slice]]
==== Manual slicing
Slice an update-by-query manually by providing a slice id and total number of
slices to each request:

[source,js]
----------------------------------------------------------------
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'"
  }
}
----------------------------------------------------------------
// CONSOLE
// TEST[setup:big_twitter]

Which you can verify works with:

[source,js]
----------------------------------------------------------------
GET _refresh
POST twitter/_search?size=0&q=extra:test&filter_path=hits.total
----------------------------------------------------------------
// CONSOLE
// TEST[continued]

Which results in a sensible `total` like this one:

[source,js]
----------------------------------------------------------------
{
  "hits": {
    "total": {
        "value": 120,
        "relation": "eq"
    }
  }
}
----------------------------------------------------------------
// TESTRESPONSE

[float]
[[docs-update-by-query-automatic-slice]]
==== 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:

[source,js]
----------------------------------------------------------------
POST twitter/_update_by_query?refresh&slices=5
{
  "script": {
    "source": "ctx._source['extra'] = 'test'"
  }
}
----------------------------------------------------------------
// CONSOLE
// TEST[setup:big_twitter]

Which you also can verify works with:

[source,js]
----------------------------------------------------------------
POST twitter/_search?size=0&q=extra:test&filter_path=hits.total
----------------------------------------------------------------
// CONSOLE
// TEST[continued]

Which results in a sensible `total` like this one:

[source,js]
----------------------------------------------------------------
{
  "hits": {
    "total": {
        "value": 120,
        "relation": "eq"
    }
  }
}
----------------------------------------------------------------
// TESTRESPONSE

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:

* You can see these requests in the
<<docs-update-by-query-task-api,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 `size` 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 the using
`size` with `slices` might not result in exactly `size` documents being
`_update_by_query`ed.
* Each sub-requests gets a slightly different snapshot of the source index
though these are all taken at approximately the same time.

[float]
[[docs-update-by-query-picking-slices]]
===== 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.

[float]
[[picking-up-a-new-property]]
=== 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:

[source,js]
--------------------------------------------------
PUT test
{
  "mappings": {
    "_doc": {
      "dynamic": false,   <1>
      "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/_doc   <2>
{
  "properties": {
    "text": {"type": "text"},
    "flag": {"type": "text", "analyzer": "keyword"}
  }
}
--------------------------------------------------
// CONSOLE

<1> This means that new fields won't be indexed, just stored in `_source`.

<2> This updates the mapping to add the new `flag` field. To pick up the new
field you have to reindex all documents with it.

Searching for the data won't find anything:

[source,js]
--------------------------------------------------
POST test/_search?filter_path=hits.total
{
  "query": {
    "match": {
      "flag": "foo"
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

[source,js]
--------------------------------------------------
{
  "hits" : {
    "total": {
        "value": 0,
        "relation": "eq"
    }
  }
}
--------------------------------------------------
// TESTRESPONSE

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

[source,js]
--------------------------------------------------
POST test/_update_by_query?refresh&conflicts=proceed
POST test/_search?filter_path=hits.total
{
  "query": {
    "match": {
      "flag": "foo"
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

[source,js]
--------------------------------------------------
{
  "hits" : {
    "total": {
        "value": 1,
        "relation": "eq"
    }
  }
}
--------------------------------------------------
// TESTRESPONSE

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