- Elasticsearch Guide: other versions:
- Getting Started
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Maximum size virtual memory check
- Max file size 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
- Stopping Elasticsearch
- Upgrade Elasticsearch
- Set up X-Pack
- Breaking changes
- Breaking changes in 6.0
- Aggregations changes
- Analysis changes
- Cat API changes
- Clients changes
- Cluster changes
- Document API changes
- Indices changes
- Ingest changes
- Java API changes
- Mapping changes
- Packaging changes
- Percolator changes
- Plugins changes
- Reindex changes
- REST changes
- Scripting changes
- Search and Query DSL changes
- Settings changes
- Stats and info changes
- Breaking changes in 6.0
- X-Pack Breaking Changes
- API Conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- Stats Aggregation
- Sum Aggregation
- Top Hits Aggregation
- Value Count Aggregation
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Children Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Range Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Pipeline Aggregations
- Avg Bucket Aggregation
- Derivative Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Cumulative Sum Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Shrink Index
- Rollover Index
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Indices Stats
- Indices Segments
- Indices Recovery
- Indices Shard Stores
- Clear Cache
- Flush
- Refresh
- Force Merge
- cat APIs
- Cluster APIs
- Query DSL
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Token Filters
- Standard Token Filter
- ASCII Folding Token Filter
- Flatten Graph Token Filter
- Length Token Filter
- Lowercase Token Filter
- Uppercase Token Filter
- NGram Token Filter
- Edge NGram Token Filter
- Porter Stem Token Filter
- Shingle Token Filter
- Stop Token Filter
- Word Delimiter Token Filter
- Word Delimiter Graph Token Filter
- Stemmer Token Filter
- Stemmer Override Token Filter
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Snowball Token Filter
- Phonetic Token Filter
- Synonym Token Filter
- Synonym Graph Token Filter
- Compound Word Token Filters
- Reverse Token Filter
- Elision Token Filter
- Truncate Token Filter
- Unique Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Trim Token Filter
- Limit Token Count Token Filter
- Hunspell Token Filter
- Common Grams Token Filter
- Normalization Token Filter
- CJK Width Token Filter
- CJK Bigram Token Filter
- Delimited Payload Token Filter
- Keep Words Token Filter
- Keep Types Token Filter
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- Minhash Token Filter
- Character Filters
- Modules
- Index Modules
- Ingest Node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Fail Processor
- Foreach Processor
- Grok Processor
- Gsub Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- Dot Expander Processor
- Monitoring Elasticsearch
- X-Pack APIs
- Info API
- Explore API
- Machine Learning APIs
- Close Jobs
- Create Datafeeds
- Create Jobs
- Delete Datafeeds
- Delete Jobs
- Delete Model Snapshots
- Flush Jobs
- Get Buckets
- Get Categories
- Get Datafeeds
- Get Datafeed Statistics
- Get Influencers
- Get Jobs
- Get Job Statistics
- Get Model Snapshots
- Get Records
- Open Jobs
- Post Data to Jobs
- Preview Datafeeds
- Revert Model Snapshots
- Start Datafeeds
- Stop Datafeeds
- Update Datafeeds
- Update Jobs
- Update Model Snapshots
- Security APIs
- Watcher APIs
- Migration APIs
- Deprecation Info APIs
- Definitions
- X-Pack Commands
- How To
- Testing
- Glossary of terms
- Release Notes
- X-Pack Release Notes
WARNING: Version 6.0 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Foreach Processor
editForeach Processor
editProcesses elements in an array of unknown length.
All processors can operate on elements inside an array, but if all elements of an array need to
be processed in the same way, defining a processor for each element becomes cumbersome and tricky
because it is likely that the number of elements in an array is unknown. For this reason the foreach
processor exists. By specifying the field holding array elements and a processor that
defines what should happen to each element, array fields can easily be preprocessed.
A processor inside the foreach processor works in the array element context and puts that in the ingest metadata
under the _ingest._value
key. If the array element is a json object it holds all immediate fields of that json object.
and if the nested object is a value is _ingest._value
just holds that value. Note that if a processor prior to the
foreach
processor used _ingest._value
key then the specified value will not be available to the processor inside
the foreach
processor. The foreach
processor does restore the original value, so that value is available to processors
after the foreach
processor.
Note that any other field from the document are accessible and modifiable like with all other processors. This processor
just puts the current array element being read into _ingest._value
ingest metadata attribute, so that it may be
pre-processed.
If the foreach
processor fails to process an element inside the array, and no on_failure
processor has been specified,
then it aborts the execution and leaves the array unmodified.
Table 19. Foreach Options
Name | Required | Default | Description |
---|---|---|---|
|
yes |
- |
The array field |
|
yes |
- |
The processor to execute against each field |
Assume the following document:
{ "values" : ["foo", "bar", "baz"] }
When this foreach
processor operates on this sample document:
{ "foreach" : { "field" : "values", "processor" : { "uppercase" : { "field" : "_ingest._value" } } } }
Then the document will look like this after preprocessing:
{ "values" : ["FOO", "BAR", "BAZ"] }
Let’s take a look at another example:
{ "persons" : [ { "id" : "1", "name" : "John Doe" }, { "id" : "2", "name" : "Jane Doe" } ] }
In this case, the id
field needs to be removed,
so the following foreach
processor is used:
{ "foreach" : { "field" : "persons", "processor" : { "remove" : { "field" : "_ingest._value.id" } } } }
After preprocessing the result is:
{ "persons" : [ { "name" : "John Doe" }, { "name" : "Jane Doe" } ] }
The wrapped processor can have a on_failure
definition.
For example, the id
field may not exist on all person objects.
Instead of failing the index request, you can use an on_failure
block to send the document to the failure_index index for later inspection:
{ "foreach" : { "field" : "persons", "processor" : { "remove" : { "field" : "_value.id", "on_failure" : [ { "set" : { "field", "_index", "value", "failure_index" } } ] } } } }
In this example, if the remove
processor does fail, then
the array elements that have been processed thus far will
be updated.
Another advanced example can be found in the attachment processor documentation.
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