- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.10
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Setting JVM options
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- HTTP
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging
- Machine learning settings
- Monitoring settings
- Node
- Network settings
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot lifecycle management settings
- Transforms settings
- Transport
- Thread pools
- Watcher settings
- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- G1GC check
- All permission check
- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Set up X-Pack
- Configuring X-Pack Java Clients
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest node
- Search your data
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Geo-distance
- Geohash grid
- Geotile grid
- Global
- Histogram
- IP range
- Missing
- Nested
- Parent
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Bucket aggregations
- EQL
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- 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
- Overview
- Concepts
- Automate rollover
- Manage Filebeat time-based indices
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Resolve lifecycle policy execution errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Monitor a cluster
- Frozen indices
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure a cluster
- Overview
- Configuring security
- User authentication
- Built-in users
- Internal users
- Token-based authentication services
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- Integrating with other authentication systems
- Enabling anonymous access
- 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
- Granting access to Stack Management features
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and index 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
- Enabling audit logging
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- 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
- Watch for cluster and index events
- Command line tools
- How To
- Glossary of terms
- REST APIs
- API conventions
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Nodes reload secure settings
- Nodes stats
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
- Enrich APIs
- Graph explore API
- Index APIs
- Add index alias
- Analyze
- Clear cache
- Clone index
- Close index
- Create index
- Delete index
- Delete index alias
- Delete component template
- Delete index template
- Delete index template (legacy)
- Flush
- Force merge
- Freeze index
- Get component template
- Get field mapping
- Get index
- Get index alias
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Index alias exists
- Index exists
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- Open index
- Put index template
- Put index template (legacy)
- Put component template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Simulate index
- Simulate template
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Resolve index
- List dangling indices
- Import dangling index
- Delete dangling index
- Index lifecycle management APIs
- Ingest APIs
- Info API
- Licensing APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Find file structure
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Revert model snapshots
- Set upgrade mode
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Create trained models
- Update data frame analytics jobs
- Delete data frame analytics jobs
- Delete trained models
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Get trained models
- Get trained models stats
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Migration APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Search APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 7.10.2
- Elasticsearch version 7.10.1
- Elasticsearch version 7.10.0
- Elasticsearch version 7.9.3
- Elasticsearch version 7.9.2
- Elasticsearch version 7.9.1
- Elasticsearch version 7.9.0
- Elasticsearch version 7.8.1
- Elasticsearch version 7.8.0
- Elasticsearch version 7.7.1
- Elasticsearch version 7.7.0
- Elasticsearch version 7.6.2
- Elasticsearch version 7.6.1
- Elasticsearch version 7.6.0
- Elasticsearch version 7.5.2
- Elasticsearch version 7.5.1
- Elasticsearch version 7.5.0
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
- Dependencies and versions
Composite aggregation
editComposite aggregation
editA multi-bucket aggregation that creates composite buckets from different sources.
Unlike the other multi-bucket
aggregations, you can use the composite
aggregation to paginate all buckets from a multi-level aggregation
efficiently. This aggregation provides a way to stream all buckets of a
specific aggregation, similar to what
scroll does for documents.
The composite buckets are built from the combinations of the values extracted/created for each document and each combination is considered as a composite bucket.
For example, consider the following document:
{ "keyword": ["foo", "bar"], "number": [23, 65, 76] }
Using keyword
and number
as source fields for the aggregation results in
the following composite buckets:
{ "keyword": "foo", "number": 23 } { "keyword": "foo", "number": 65 } { "keyword": "foo", "number": 76 } { "keyword": "bar", "number": 23 } { "keyword": "bar", "number": 65 } { "keyword": "bar", "number": 76 }
Value sources
editThe sources
parameter defines the source fields to use when building
composite buckets. The order that the sources
are defined controls the order
that the keys are returned.
You must use a unique name when defining sources
.
The sources
parameter can be any of the following types:
Terms
editThe terms
value source is equivalent to a simple terms
aggregation.
The values are extracted from a field or a script exactly like the terms
aggregation.
Example:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "product": { "terms": { "field": "product" } } } ] } } } }
Like the terms
aggregation it is also possible to use a script to create the values for the composite buckets:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "product": { "terms": { "script": { "source": "doc['product'].value", "lang": "painless" } } } } ] } } } }
Histogram
editThe histogram
value source can be applied on numeric values to build fixed size
interval over the values. The interval
parameter defines how the numeric values should be
transformed. For instance an interval
set to 5 will translate any numeric values to its closest interval,
a value of 101
would be translated to 100
which is the key for the interval between 100 and 105.
Example:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "histo": { "histogram": { "field": "price", "interval": 5 } } } ] } } } }
The values are built from a numeric field or a script that return numerical values:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "histo": { "histogram": { "interval": 5, "script": { "source": "doc['price'].value", "lang": "painless" } } } } ] } } } }
Date histogram
editThe date_histogram
is similar to the histogram
value source except that the interval
is specified by date/time expression:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } } ] } } } }
The example above creates an interval per day and translates all timestamp
values to the start of its closest intervals.
Available expressions for interval: year
, quarter
, month
, week
, day
, hour
, minute
, second
Time values can also be specified via abbreviations supported by time units parsing.
Note that fractional time values are not supported, but you can address this by shifting to another
time unit (e.g., 1.5h
could instead be specified as 90m
).
Format
Internally, a date is represented as a 64 bit number representing a timestamp in milliseconds-since-the-epoch. These timestamps are returned as the bucket keys. It is possible to return a formatted date string instead using the format specified with the format parameter:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "format": "yyyy-MM-dd" } } } ] } } } }
Supports expressive date format pattern |
Time Zone
Date-times are stored in Elasticsearch in UTC. By default, all bucketing and
rounding is also done in UTC. The time_zone
parameter can be used to indicate
that bucketing should use a different time zone.
Time zones may either be specified as an ISO 8601 UTC offset (e.g. +01:00
or
-08:00
) or as a timezone id, an identifier used in the TZ database like
America/Los_Angeles
.
Offset
Use the offset
parameter to change the start value of each bucket by the
specified positive (+
) or negative offset (-
) duration, such as 1h
for
an hour, or 1d
for a day. See Time units for more possible time
duration options.
For example, when using an interval of day
, each bucket runs from midnight
to midnight. Setting the offset
parameter to +6h
changes each bucket
to run from 6am to 6am:
PUT my-index-000001/_doc/1?refresh { "date": "2015-10-01T05:30:00Z" } PUT my-index-000001/_doc/2?refresh { "date": "2015-10-01T06:30:00Z" } GET my-index-000001/_search?size=0 { "aggs": { "my_buckets": { "composite" : { "sources" : [ { "date": { "date_histogram" : { "field": "date", "calendar_interval": "day", "offset": "+6h", "format": "iso8601" } } } ] } } } }
Instead of a single bucket starting at midnight, the above request groups the documents into buckets starting at 6am:
{ ... "aggregations": { "my_buckets": { "after_key": { "date": "2015-10-01T06:00:00.000Z" }, "buckets": [ { "key": { "date": "2015-09-30T06:00:00.000Z" }, "doc_count": 1 }, { "key": { "date": "2015-10-01T06:00:00.000Z" }, "doc_count": 1 } ] } } }
The start offset
of each bucket is calculated after time_zone
adjustments have been made.
GeoTile grid
editThe geotile_grid
value source works on geo_point
fields and groups points into buckets that represent
cells in a grid. The resulting grid can be sparse and only contains cells
that have matching data. Each cell corresponds to a
map tile as used by many online map
sites. Each cell is labeled using a "{zoom}/{x}/{y}" format, where zoom is equal
to the user-specified precision.
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "tile": { "geotile_grid": { "field": "location", "precision": 8 } } } ] } } } }
Precision
The highest-precision geotile of length 29 produces cells that cover less than 10cm by 10cm of land. This precision is uniquely suited for composite aggregations as each tile does not have to be generated and loaded in memory.
See Zoom level documentation on how precision (zoom) correlates to size on the ground. Precision for this aggregation can be between 0 and 29, inclusive.
Bounding box filtering
The geotile source can optionally be constrained to a specific geo bounding box, which reduces the range of tiles used. These bounds are useful when only a specific part of a geographical area needs high precision tiling.
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "tile": { "geotile_grid": { "field": "location", "precision": 22, "bounds": { "top_left": "52.4, 4.9", "bottom_right": "52.3, 5.0" } } } } ] } } } }
Mixing different value sources
editThe sources
parameter accepts an array of value sources.
It is possible to mix different value sources to create composite buckets.
For example:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } }, { "product": { "terms": { "field": "product" } } } ] } } } }
This will create composite buckets from the values created by two value sources, a date_histogram
and a terms
.
Each bucket is composed of two values, one for each value source defined in the aggregation.
Any type of combinations is allowed and the order in the array is preserved
in the composite buckets.
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "shop": { "terms": { "field": "shop" } } }, { "product": { "terms": { "field": "product" } } }, { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } } ] } } } }
Order
editBy default the composite buckets are sorted by their natural ordering. Values are sorted
in ascending order of their values. When multiple value sources are requested, the ordering is done per value
source, the first value of the composite bucket is compared to the first value of the other composite bucket and if they are equals the
next values in the composite bucket are used for tie-breaking. This means that the composite bucket
[foo, 100]
is considered smaller than [foobar, 0]
because foo
is considered smaller than foobar
.
It is possible to define the direction of the sort for each value source by setting order
to asc
(default value)
or desc
(descending order) directly in the value source definition.
For example:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } }, { "product": { "terms": { "field": "product", "order": "asc" } } } ] } } } }
... will sort the composite bucket in descending order when comparing values from the date_histogram
source
and in ascending order when comparing values from the terms
source.
Missing bucket
editBy default documents without a value for a given source are ignored.
It is possible to include them in the response by setting missing_bucket
to
true
(defaults to false
):
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "product_name": { "terms": { "field": "product", "missing_bucket": true } } } ] } } } }
In the example above the source product_name
will emit an explicit null
value
for documents without a value for the field product
.
The order
specified in the source dictates whether the null
values should rank
first (ascending order, asc
) or last (descending order, desc
).
Size
editThe size
parameter can be set to define how many composite buckets should be returned.
Each composite bucket is considered as a single bucket, so setting a size of 10 will return the
first 10 composite buckets created from the value sources.
The response contains the values for each composite bucket in an array containing the values extracted
from each value source. Defaults to 10
.
Pagination
editIf the number of composite buckets is too high (or unknown) to be returned in a single response
it is possible to split the retrieval in multiple requests.
Since the composite buckets are flat by nature, the requested size
is exactly the number of composite buckets
that will be returned in the response (assuming that they are at least size
composite buckets to return).
If all composite buckets should be retrieved it is preferable to use a small size (100
or 1000
for instance)
and then use the after
parameter to retrieve the next results.
For example:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "size": 2, "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } }, { "product": { "terms": { "field": "product" } } } ] } } } }
... returns:
{ ... "aggregations": { "my_buckets": { "after_key": { "date": 1494288000000, "product": "mad max" }, "buckets": [ { "key": { "date": 1494201600000, "product": "rocky" }, "doc_count": 1 }, { "key": { "date": 1494288000000, "product": "mad max" }, "doc_count": 2 } ] } } }
To get the next set of buckets, resend the same aggregation with the after
parameter set to the after_key
value returned in the response.
For example, this request uses the after_key
value provided in the previous response:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "size": 2, "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } }, { "product": { "terms": { "field": "product", "order": "asc" } } } ], "after": { "date": 1494288000000, "product": "mad max" } } } } }
The after_key
is usually the key to the last bucket returned in
the response, but that isn’t guaranteed. Always use the returned after_key
instead
of derriving it from the buckets.
Early termination
editFor optimal performance the index sort should be set on the index so that it matches parts or fully the source order in the composite aggregation. For instance the following index sort:
PUT my-index-000001 { "settings": { "index": { "sort.field": [ "username", "timestamp" ], "sort.order": [ "asc", "desc" ] } }, "mappings": { "properties": { "username": { "type": "keyword", "doc_values": true }, "timestamp": { "type": "date" } } } }
This index is sorted by |
|
… in ascending order for the
|
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "user_name": { "terms": { "field": "user_name" } } } ] } } } }
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "user_name": { "terms": { "field": "user_name" } } }, { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } } ] } } } }
|
|
|
In order to optimize the early termination it is advised to set track_total_hits
in the request
to false
. The number of total hits that match the request can be retrieved on the first request
and it would be costly to compute this number on every page:
GET /_search { "size": 0, "track_total_hits": false, "aggs": { "my_buckets": { "composite": { "sources": [ { "user_name": { "terms": { "field": "user_name" } } }, { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } } ] } } } }
Note that the order of the source is important, in the example below switching the user_name
with the timestamp
would deactivate the sort optimization since this configuration wouldn’t match the index sort specification.
If the order of sources do not matter for your use case you can follow these simple guidelines:
- Put the fields with the highest cardinality first.
- Make sure that the order of the field matches the order of the index sort.
- Put multi-valued fields last since they cannot be used for early termination.
index sort can slowdown indexing, it is very important to test index sorting
with your specific use case and dataset to ensure that it matches your requirement. If it doesn’t note that composite
aggregations will also try to early terminate on non-sorted indices if the query matches all document (match_all
query).
Sub-aggregations
editLike any multi-bucket
aggregations the composite
aggregation can hold sub-aggregations.
These sub-aggregations can be used to compute other buckets or statistics on each composite bucket created by this
parent aggregation.
For instance the following example computes the average value of a field
per composite bucket:
GET /_search { "size": 0, "aggs": { "my_buckets": { "composite": { "sources": [ { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } }, { "product": { "terms": { "field": "product" } } } ] }, "aggregations": { "the_avg": { "avg": { "field": "price" } } } } } }
... returns:
{ ... "aggregations": { "my_buckets": { "after_key": { "date": 1494201600000, "product": "rocky" }, "buckets": [ { "key": { "date": 1494460800000, "product": "apocalypse now" }, "doc_count": 1, "the_avg": { "value": 10.0 } }, { "key": { "date": 1494374400000, "product": "mad max" }, "doc_count": 1, "the_avg": { "value": 27.0 } }, { "key": { "date": 1494288000000, "product": "mad max" }, "doc_count": 2, "the_avg": { "value": 22.5 } }, { "key": { "date": 1494201600000, "product": "rocky" }, "doc_count": 1, "the_avg": { "value": 10.0 } } ] } } }
Pipeline aggregations
editThe composite agg is not currently compatible with pipeline aggregations, nor does it make sense in most cases. E.g. due to the paging nature of composite aggs, a single logical partition (one day for example) might be spread over multiple pages. Since pipeline aggregations are purely post-processing on the final list of buckets, running something like a derivative on a composite page could lead to inaccurate results as it is only taking into account a "partial" result on that page.
Pipeline aggs that are self contained to a single bucket (such as bucket_selector
) might be supported in the future.
On this page