- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.10
- Set up Elasticsearch
- Installing Elasticsearch
- Run Elasticsearch locally
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Miscellaneous cluster settings
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- Bootstrap Checks
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- Starting Elasticsearch
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- Text analysis
- Overview
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- Built-in analyzer reference
- Tokenizer reference
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- Apostrophe
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- Append
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- Search your data
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- Filters
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- Histogram
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- EQL
- SQL
- Overview
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- Aggregate Functions
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- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
- Concepts
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Data tiers
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
- Manually configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- User profiles
- Realms
- Realm chains
- Security domains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- JWT authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Looking up users without authentication
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Role restriction
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Watcher
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- How to
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
- Total number of shards per node has been reached
- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
- Fix other role nodes out of disk
- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Multiple deployments writing to the same snapshot repository
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat component templates
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
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- cat plugins
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- cat segments
- cat shards
- cat snapshots
- cat task management
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- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
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- Pending cluster tasks
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- Create or update desired nodes
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- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
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- EQL APIs
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- Alias exists
- Aliases
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- Exists
- Field usage stats
- Flush
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- Get mapping
- Import dangling index
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- List dangling indices
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- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
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- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
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- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Ingest APIs
- Info API
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- Add events to calendar
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- Flush jobs
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- Get buckets
- Get calendars
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- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
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- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
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- Machine learning trained model APIs
- Clear trained model deployment cache
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- Start trained model deployment
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- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
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- Rollup APIs
- Script APIs
- Search APIs
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- Searchable snapshots APIs
- Security APIs
- Authenticate
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- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
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- Get service account credentials
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- Get user privileges
- Get users
- Grant API keys
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- Invalidate API key
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- Activate user profile
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- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Update By Query API
editUpdate By Query API
editUpdates 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.
response = client.update_by_query( index: 'my-index-000001', conflicts: 'proceed' ) puts response
POST my-index-000001/_update_by_query?conflicts=proceed
Request
editPOST /<target>/_update_by_query
Prerequisites
edit-
If the Elasticsearch security features are enabled, you must have the following index privileges for the target data stream, index, or alias:
-
read
-
index
orwrite
-
Description
editYou 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
. Note that if you opt to count
version conflicts the operation could attempt to update more documents from the source than
max_docs
until it has successfully updated max_docs
documents, or it has gone through every document
in the source query.
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
editSpecifying 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
editIf 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}
.
Waiting for active shards
editwait_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
editTo 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
editUpdate 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 manyslices
hurts performance. Settingslices
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
edit-
<target>
-
(Optional, string) Comma-separated list of data streams, indices, and aliases to
search. Supports wildcards (
*
). To search all data streams or indices, omit this parameter or use*
or_all
.
Query parameters
edit-
allow_no_indices
-
(Optional, Boolean) If
false
, the request returns an error if any wildcard expression, index alias, or_all
value targets only missing or closed indices. This behavior applies even if the request targets other open indices. For example, a request targetingfoo*,bar*
returns an error if an index starts withfoo
but no index starts withbar
.Defaults to
true
. -
analyzer
-
(Optional, string) Analyzer to use for the query string.
This parameter can only be used when the
q
query string parameter is specified. -
analyze_wildcard
-
(Optional, Boolean) If
true
, wildcard and prefix queries are analyzed. Defaults tofalse
.This parameter can only be used when the
q
query string parameter is specified. -
conflicts
-
(Optional, string) What to do if update by query hits version conflicts:
abort
orproceed
. Defaults toabort
. -
default_operator
-
(Optional, string) The default operator for query string query: AND or OR. Defaults to
OR
.This parameter can only be used when the
q
query string parameter is specified. -
df
-
(Optional, string) Field to use as default where no field prefix is given in the query string.
This parameter can only be used when the
q
query string parameter is specified. -
expand_wildcards
-
(Optional, string) Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as
open,hidden
. Valid values are:-
all
- Match any data stream or index, including hidden ones.
-
open
- Match open, non-hidden indices. Also matches any non-hidden data stream.
-
closed
- Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
-
hidden
-
Match hidden data streams and hidden indices. Must be combined with
open
,closed
, or both. -
none
- Wildcard patterns are not accepted.
Defaults to
open
. -
-
ignore_unavailable
-
(Optional, Boolean) If
false
, the request returns an error if it targets a missing or closed index. Defaults tofalse
. -
lenient
-
(Optional, Boolean) If
true
, format-based query failures (such as providing text to a numeric field) in the query string will be ignored. Defaults tofalse
.This parameter can only be used when the
q
query string parameter is specified. -
max_docs
-
(Optional, integer) Maximum number of documents to process. Defaults to all
documents. When set to a value less then or equal to
scroll_size
then a scroll will not be used to retrieve the results for the operation. -
pipeline
-
(Optional, string) ID of the pipeline to use to preprocess incoming documents. If the index has a
default ingest pipeline specified, then setting the value to
_none
disables the default ingest pipeline for this request. If a final pipeline is configured it will always run, regardless of the value of this parameter. -
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, Boolean)
If
true
, Elasticsearch refreshes affected shards to make the operation visible to search. Defaults tofalse
. -
requests_per_second
-
(Optional, integer) The throttle for this request in sub-requests per second.
Defaults to
-1
(no throttle). -
routing
- (Optional, string) Custom value used to route operations to a specific 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 1000.
-
search_type
-
(Optional, string) The type of the search operation. Available options:
-
query_then_fetch
-
dfs_query_then_fetch
-
-
search_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 <field>:<direction> pairs.
-
stats
-
(Optional, string) Specific
tag
of the request for logging and statistical purposes. -
terminate_after
-
(Optional, integer) Maximum number of documents to collect for each shard. If a query reaches this limit, Elasticsearch terminates the query early. Elasticsearch collects documents before sorting.
Use with caution. Elasticsearch applies this parameter to each shard handling the request. When possible, let Elasticsearch perform early termination automatically. Avoid specifying this parameter for requests that target data streams with backing indices across multiple data tiers.
-
timeout
-
(Optional, time units) Period each update request waits for the following operations:
- Dynamic mapping updates
- Waiting for active shards
Defaults to
1m
(one minute). This guarantees Elasticsearch waits for at least the timeout before failing. The actual wait time could be longer, particularly when multiple waits occur. -
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
edit-
query
- (Optional, query object) Specifies the documents to update using the Query DSL.
Response body
edit-
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 forctx.op
. -
retries
-
The number of retries attempted by update by query.
bulk
is the number of bulk actions retried, andsearch
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 torequests_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
editThe 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:
response = client.update_by_query( index: 'my-index-000001', conflicts: 'proceed', body: { query: { term: { "user.id": 'kimchy' } } } ) puts response
POST my-index-000001/_update_by_query?conflicts=proceed { "query": { "term": { "user.id": "kimchy" } } }
The query must be passed as a value to the |
Update documents in multiple data streams or indices:
response = client.update_by_query( index: 'my-index-000001,my-index-000002' ) puts response
POST my-index-000001,my-index-000002/_update_by_query
Limit the update by query operation to shards that a particular routing value:
response = client.update_by_query( index: 'my-index-000001', routing: 1 ) puts response
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:
response = client.update_by_query( index: 'my-index-000001', scroll_size: 100 ) puts response
POST my-index-000001/_update_by_query?scroll_size=100
Update a document using a unique attribute:
response = client.update_by_query( index: 'my-index-000001', body: { query: { term: { "user.id": 'kimchy' } }, max_docs: 1 } ) puts response
POST my-index-000001/_update_by_query { "query": { "term": { "user.id": "kimchy" } }, "max_docs": 1 }
Update the document source
editUpdate 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
:
response = client.update_by_query( index: 'my-index-000001', body: { script: { source: 'ctx._source.count++', lang: 'painless' }, query: { term: { "user.id": 'kimchy' } } } ) puts response
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:
|
Set |
|
Set |
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
editUpdate by query can use the Ingest pipelines feature by specifying a pipeline
:
response = client.ingest.put_pipeline( id: 'set-foo', body: { description: 'sets foo', processors: [ { set: { field: 'foo', value: 'bar' } } ] } ) puts response response = client.update_by_query( index: 'my-index-000001', pipeline: 'set-foo' ) puts response
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
editYou can fetch the status of all running update by query requests with the Task API:
$response = $client->tasks()->list();
resp = client.tasks.list(detailed="true", actions="*byquery") print(resp)
response = client.tasks.list( detailed: true, actions: '*byquery' ) puts response
res, err := es.Tasks.List( es.Tasks.List.WithActions("*byquery"), es.Tasks.List.WithDetailed(true), ) fmt.Println(res, err)
const response = await client.tasks.list({ detailed: 'true', actions: '*byquery' }) console.log(response)
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" : "" } } } } }
This object contains the actual status. It is just like the response JSON
with the important addition of the |
With the task id you can look up the task directly. The following example
retrieves information about task r1A2WoRbTwKZ516z6NEs5A:36619
:
$params = [ 'task_id' => 'r1A2WoRbTwKZ516z6NEs5A:36619', ]; $response = $client->tasks()->get($params);
resp = client.tasks.get(task_id="r1A2WoRbTwKZ516z6NEs5A:36619") print(resp)
response = client.tasks.get( task_id: 'r1A2WoRbTwKZ516z6NEs5A:36619' ) puts response
res, err := es.Tasks.Get( "r1A2WoRbTwKZ516z6NEs5A:36619", ) fmt.Println(res, err)
const response = await client.tasks.get({ task_id: 'r1A2WoRbTwKZ516z6NEs5A:36619' }) console.log(response)
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
editAny update by query can be cancelled using the Task Cancel API:
$params = [ 'task_id' => 'r1A2WoRbTwKZ516z6NEs5A:36619', ]; $response = $client->tasks()->cancel($params);
resp = client.tasks.cancel(task_id="r1A2WoRbTwKZ516z6NEs5A:36619") print(resp)
response = client.tasks.cancel( task_id: 'r1A2WoRbTwKZ516z6NEs5A:36619' ) puts response
res, err := es.Tasks.Cancel( es.Tasks.Cancel.WithTaskID("r1A2WoRbTwKZ516z6NEs5A:36619"), ) fmt.Println(res, err)
const response = await client.tasks.cancel({ task_id: 'r1A2WoRbTwKZ516z6NEs5A:36619' }) console.log(response)
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
editThe value of requests_per_second
can be changed on a running update by query
using the _rethrottle
API:
$params = [ 'task_id' => 'r1A2WoRbTwKZ516z6NEs5A:36619', ]; $response = $client->updateByQueryRethrottle($params);
resp = client.update_by_query_rethrottle( task_id="r1A2WoRbTwKZ516z6NEs5A:36619", requests_per_second="-1", ) print(resp)
response = client.update_by_query_rethrottle( task_id: 'r1A2WoRbTwKZ516z6NEs5A:36619', requests_per_second: -1 ) puts response
res, err := es.UpdateByQueryRethrottle( "r1A2WoRbTwKZ516z6NEs5A:36619", esapi.IntPtr(-1), ) fmt.Println(res, err)
const response = await client.updateByQueryRethrottle({ task_id: 'r1A2WoRbTwKZ516z6NEs5A:36619', requests_per_second: '-1' }) console.log(response)
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
editSlice an update by query manually by providing a slice id and total number of slices to each request:
response = client.update_by_query( index: 'my-index-000001', body: { slice: { id: 0, max: 2 }, script: { source: "ctx._source['extra'] = 'test'" } } ) puts response response = client.update_by_query( index: 'my-index-000001', body: { slice: { id: 1, max: 2 }, script: { source: "ctx._source['extra'] = 'test'" } } ) puts response
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:
response = client.indices.refresh puts response response = client.search( index: 'my-index-000001', size: 0, q: 'extra:test', filter_path: 'hits.total' ) puts response
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
editYou 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:
response = client.update_by_query( index: 'my-index-000001', refresh: true, slices: 5, body: { script: { source: "ctx._source['extra'] = 'test'" } } ) puts response
POST my-index-000001/_update_by_query?refresh&slices=5 { "script": { "source": "ctx._source['extra'] = 'test'" } }
Which you also can verify works with:
response = client.search( index: 'my-index-000001', size: 0, q: 'extra:test', filter_path: 'hits.total' ) puts response
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
andmax_docs
on a request withslices
are distributed proportionally to each sub-request. Combine that with the point above about distribution being uneven and you should conclude that usingmax_docs
withslices
might not result in exactlymax_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
editSay 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:
$params = [ 'index' => 'test', 'body' => [ 'mappings' => [ 'dynamic' => false, 'properties' => [ 'text' => [ 'type' => 'text', ], ], ], ], ]; $response = $client->indices()->create($params); $params = [ 'index' => 'test', 'body' => [ 'text' => 'words words', 'flag' => 'bar', ], ]; $response = $client->index($params); $params = [ 'index' => 'test', 'body' => [ 'text' => 'words words', 'flag' => 'foo', ], ]; $response = $client->index($params); $params = [ 'index' => 'test', 'body' => [ 'properties' => [ 'text' => [ 'type' => 'text', ], 'flag' => [ 'type' => 'text', 'analyzer' => 'keyword', ], ], ], ]; $response = $client->indices()->putMapping($params);
resp = client.indices.create( index="test", body={ "mappings": { "dynamic": False, "properties": {"text": {"type": "text"}}, } }, ) print(resp) resp = client.index( index="test", refresh=True, body={"text": "words words", "flag": "bar"}, ) print(resp) resp = client.index( index="test", refresh=True, body={"text": "words words", "flag": "foo"}, ) print(resp) resp = client.indices.put_mapping( index="test", body={ "properties": { "text": {"type": "text"}, "flag": {"type": "text", "analyzer": "keyword"}, } }, ) print(resp)
response = client.indices.create( index: 'test', body: { mappings: { dynamic: false, properties: { text: { type: 'text' } } } } ) puts response response = client.index( index: 'test', refresh: true, body: { text: 'words words', flag: 'bar' } ) puts response response = client.index( index: 'test', refresh: true, body: { text: 'words words', flag: 'foo' } ) puts response response = client.indices.put_mapping( index: 'test', body: { properties: { text: { type: 'text' }, flag: { type: 'text', analyzer: 'keyword' } } } ) puts response
{ res, err := es.Indices.Create( "test", es.Indices.Create.WithBody(strings.NewReader(`{ "mappings": { "dynamic": false, "properties": { "text": { "type": "text" } } } }`)), ) fmt.Println(res, err) } { res, err := es.Index( "test", strings.NewReader(`{ "text": "words words", "flag": "bar" }`), es.Index.WithRefresh("true"), es.Index.WithPretty(), ) fmt.Println(res, err) } { res, err := es.Index( "test", strings.NewReader(`{ "text": "words words", "flag": "foo" }`), es.Index.WithRefresh("true"), es.Index.WithPretty(), ) fmt.Println(res, err) } { res, err := es.Indices.PutMapping( []string{"test"}, strings.NewReader(`{ "properties": { "text": { "type": "text" }, "flag": { "type": "text", "analyzer": "keyword" } } }`), ) fmt.Println(res, err) }
const response0 = await client.indices.create({ index: 'test', body: { mappings: { dynamic: false, properties: { text: { type: 'text' } } } } }) console.log(response0) const response1 = await client.index({ index: 'test', refresh: true, body: { text: 'words words', flag: 'bar' } }) console.log(response1) const response2 = await client.index({ index: 'test', refresh: true, body: { text: 'words words', flag: 'foo' } }) console.log(response2) const response3 = await client.indices.putMapping({ index: 'test', body: { properties: { text: { type: 'text' }, flag: { type: 'text', analyzer: 'keyword' } } } }) console.log(response3)
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"} } }
This means that new fields won’t be indexed, just stored in |
|
This updates the mapping to add the new |
Searching for the data won’t find anything:
$params = [ 'index' => 'test', 'body' => [ 'query' => [ 'match' => [ 'flag' => 'foo', ], ], ], ]; $response = $client->search($params);
resp = client.search( index="test", filter_path="hits.total", body={"query": {"match": {"flag": "foo"}}}, ) print(resp)
response = client.search( index: 'test', filter_path: 'hits.total', body: { query: { match: { flag: 'foo' } } } ) puts response
res, err := es.Search( es.Search.WithIndex("test"), es.Search.WithBody(strings.NewReader(`{ "query": { "match": { "flag": "foo" } } }`)), es.Search.WithFilterPath("hits.total"), es.Search.WithPretty(), ) fmt.Println(res, err)
const response = await client.search({ index: 'test', filter_path: 'hits.total', body: { query: { match: { flag: 'foo' } } } }) console.log(response)
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:
$params = [ 'index' => 'test', ]; $response = $client->updateByQuery($params); $params = [ 'index' => 'test', 'body' => [ 'query' => [ 'match' => [ 'flag' => 'foo', ], ], ], ]; $response = $client->search($params);
resp = client.update_by_query( index="test", refresh=True, conflicts="proceed", ) print(resp) resp = client.search( index="test", filter_path="hits.total", body={"query": {"match": {"flag": "foo"}}}, ) print(resp)
response = client.update_by_query( index: 'test', refresh: true, conflicts: 'proceed' ) puts response response = client.search( index: 'test', filter_path: 'hits.total', body: { query: { match: { flag: 'foo' } } } ) puts response
{ res, err := es.UpdateByQuery( []string{"test"}, es.UpdateByQuery.WithConflicts("proceed"), es.UpdateByQuery.WithRefresh(true), ) fmt.Println(res, err) } { res, err := es.Search( es.Search.WithIndex("test"), es.Search.WithBody(strings.NewReader(`{ "query": { "match": { "flag": "foo" } } }`)), es.Search.WithFilterPath("hits.total"), es.Search.WithPretty(), ) fmt.Println(res, err) }
const response0 = await client.updateByQuery({ index: 'test', refresh: true, conflicts: 'proceed' }) console.log(response0) const response1 = await client.search({ index: 'test', filter_path: 'hits.total', body: { query: { match: { flag: 'foo' } } } }) console.log(response1)
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.
On this page
- Request
- Prerequisites
- Description
- Refreshing shards
- Running update by query asynchronously
- Waiting for active shards
- Throttling update requests
- Slicing
- Path parameters
- Query parameters
- Request body
- Response body
- Examples
- Update the document source
- Update documents using an ingest pipeline
- Get the status of update by query operations
- Cancel an update by query operation
- Change throttling for a request
- Slice manually
- Use automatic slicing
- Pick up a new property