Term Vectors

https://www.elastic.co/guide/en/elasticsearch/reference/5.5/docs-termvectors.html


edit

Returns information and statistics on terms in the fields of a particular document. The document could be stored in the index or artificially provided by the user. Term vectors are realtime by default, not near realtime. This can be changed by setting realtime parameter to false.

GET /twitter/tweet/1/_termvectors

Optionally, you can specify the fields for which the information is retrieved either with a parameter in the url

GET /twitter/tweet/1/_termvectors?fields=message

or by adding the requested fields in the request body (see example below). Fields can also be specified with wildcards in similar way to the multi match query

Warning

Note that the usage of /_termvector is deprecated in 2.0, and replaced by /_termvectors.

Return valuesedit

Three types of values can be requested: term informationterm statistics and field statistics. By default, all term information and field statistics are returned for all fields but no term statistics.

Term informationedit

  • term frequency in the field (always returned)
  • term positions (positions : true)
  • start and end offsets (offsets : true)
  • term payloads (payloads : true), as base64 encoded bytes

If the requested information wasn’t stored in the index, it will be computed on the fly if possible. Additionally, term vectors could be computed for documents not even existing in the index, but instead provided by the user.

Warning

Start and end offsets assume UTF-16 encoding is being used. If you want to use these offsets in order to get the original text that produced this token, you should make sure that the string you are taking a sub-string of is also encoded using UTF-16.

Term statisticsedit

Setting term_statistics to true (default is false) will return

  • total term frequency (how often a term occurs in all documents)
  • document frequency (the number of documents containing the current term)

By default these values are not returned since term statistics can have a serious performance impact.

Field statisticsedit

Setting field_statistics to false (default is true) will omit :

  • document count (how many documents contain this field)
  • sum of document frequencies (the sum of document frequencies for all terms in this field)
  • sum of total term frequencies (the sum of total term frequencies of each term in this field)

Terms Filteringedit

With the parameter filter, the terms returned could also be filtered based on their tf-idf scores. This could be useful in order find out a good characteristic vector of a document. This feature works in a similar manner to the second phase of the More Like This Query. See example 5 for usage.

The following sub-parameters are supported:

max_num_terms

Maximum number of terms that must be returned per field. Defaults to 25.

min_term_freq

Ignore words with less than this frequency in the source doc. Defaults to 1.

max_term_freq

Ignore words with more than this frequency in the source doc. Defaults to unbounded.

min_doc_freq

Ignore terms which do not occur in at least this many docs. Defaults to 1.

max_doc_freq

Ignore words which occur in more than this many docs. Defaults to unbounded.

min_word_length

The minimum word length below which words will be ignored. Defaults to 0.

max_word_length

The maximum word length above which words will be ignored. Defaults to unbounded (0).

Behaviouredit

The term and field statistics are not accurate. Deleted documents are not taken into account. The information is only retrieved for the shard the requested document resides in. The term and field statistics are therefore only useful as relative measures whereas the absolute numbers have no meaning in this context. By default, when requesting term vectors of artificial documents, a shard to get the statistics from is randomly selected. Use routing only to hit a particular shard.

Example: Returning stored term vectorsedit

First, we create an index that stores term vectors, payloads etc. :

PUT /twitter/
{ "mappings": {
    "tweet": {
      "properties": {
        "text": {
          "type": "text",
          "term_vector": "with_positions_offsets_payloads",
          "store" : true,
          "analyzer" : "fulltext_analyzer"
         },
         "fullname": {
          "type": "text",
          "term_vector": "with_positions_offsets_payloads",
          "analyzer" : "fulltext_analyzer"
        }
      }
    }
  },
  "settings" : {
    "index" : {
      "number_of_shards" : 1,
      "number_of_replicas" : 0
    },
    "analysis": {
      "analyzer": {
        "fulltext_analyzer": {
          "type": "custom",
          "tokenizer": "whitespace",
          "filter": [
            "lowercase",
            "type_as_payload"
          ]
        }
      }
    }
  }
}

Second, we add some documents:

PUT /twitter/tweet/1
{
  "fullname" : "John Doe",
  "text" : "twitter test test test "
}

PUT /twitter/tweet/2
{
  "fullname" : "Jane Doe",
  "text" : "Another twitter test ..."
}

The following request returns all information and statistics for field text in document 1 (John Doe):

GET /twitter/tweet/1/_termvectors
{
  "fields" : ["text"],
  "offsets" : true,
  "payloads" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}

Response:

{
    "_id": "1",
    "_index": "twitter",
    "_type": "tweet",
    "_version": 1,
    "found": true,
    "took": 6,
    "term_vectors": {
        "text": {
            "field_statistics": {
                "doc_count": 2,
                "sum_doc_freq": 6,
                "sum_ttf": 8
            },
            "terms": {
                "test": {
                    "doc_freq": 2,
                    "term_freq": 3,
                    "tokens": [
                        {
                            "end_offset": 12,
                            "payload": "d29yZA==",
                            "position": 1,
                            "start_offset": 8
                        },
                        {
                            "end_offset": 17,
                            "payload": "d29yZA==",
                            "position": 2,
                            "start_offset": 13
                        },
                        {
                            "end_offset": 22,
                            "payload": "d29yZA==",
                            "position": 3,
                            "start_offset": 18
                        }
                    ],
                    "ttf": 4
                },
                "twitter": {
                    "doc_freq": 2,
                    "term_freq": 1,
                    "tokens": [
                        {
                            "end_offset": 7,
                            "payload": "d29yZA==",
                            "position": 0,
                            "start_offset": 0
                        }
                    ],
                    "ttf": 2
                }
            }
        }
    }
}

Example: Generating term vectors on the flyedit

Term vectors which are not explicitly stored in the index are automatically computed on the fly. The following request returns all information and statistics for the fields in document 1, even though the terms haven’t been explicitly stored in the index. Note that for the field text, the terms are not re-generated.

GET /twitter/tweet/1/_termvectors
{
  "fields" : ["text", "some_field_without_term_vectors"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}

Example: Artificial documentsedit

Term vectors can also be generated for artificial documents, that is for documents not present in the index. For example, the following request would return the same results as in example 1. The mapping used is determined by the index and type.

If dynamic mapping is turned on (default), the document fields not in the original mapping will be dynamically created.

GET /twitter/tweet/_termvectors
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "twitter test test test"
  }
}
Per-field analyzeredit

Additionally, a different analyzer than the one at the field may be provided by using the per_field_analyzer parameter. This is useful in order to generate term vectors in any fashion, especially when using artificial documents. When providing an analyzer for a field that already stores term vectors, the term vectors will be re-generated.

GET /twitter/tweet/_termvectors
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "twitter test test test"
  },
  "fields": ["fullname"],
  "per_field_analyzer" : {
    "fullname": "keyword"
  }
}

Response:

{
  "_index": "twitter",
  "_type": "tweet",
  "_version": 0,
  "found": true,
  "took": 6,
  "term_vectors": {
    "fullname": {
       "field_statistics": {
          "sum_doc_freq": 2,
          "doc_count": 4,
          "sum_ttf": 4
       },
       "terms": {
          "John Doe": {
             "term_freq": 1,
             "tokens": [
                {
                   "position": 0,
                   "start_offset": 0,
                   "end_offset": 8
                }
             ]
          }
       }
    }
  }
}

Example: Terms filteringedit

Finally, the terms returned could be filtered based on their tf-idf scores. In the example below we obtain the three most "interesting" keywords from the artificial document having the given "plot" field value. Notice that the keyword "Tony" or any stop words are not part of the response, as their tf-idf must be too low.

GET /imdb/movies/_termvectors
{
    "doc": {
      "plot": "When wealthy industrialist Tony Stark is forced to build an armored suit after a life-threatening incident, he ultimately decides to use its technology to fight against evil."
    },
    "term_statistics" : true,
    "field_statistics" : true,
    "positions": false,
    "offsets": false,
    "filter" : {
      "max_num_terms" : 3,
      "min_term_freq" : 1,
      "min_doc_freq" : 1
    }
}

Response:

{
   "_index": "imdb",
   "_type": "movies",
   "_version": 0,
   "found": true,
   "term_vectors": {
      "plot": {
         "field_statistics": {
            "sum_doc_freq": 3384269,
            "doc_count": 176214,
            "sum_ttf": 3753460
         },
         "terms": {
            "armored": {
               "doc_freq": 27,
               "ttf": 27,
               "term_freq": 1,
               "score": 9.74725
            },
            "industrialist": {
               "doc_freq": 88,
               "ttf": 88,
               "term_freq": 1,
               "score": 8.590818
            },
            "stark": {
               "doc_freq": 44,
               "ttf": 47,
               "term_freq": 1,
               "score": 9.272792
            }
         }
      }
   }
}


Reindex API

https://www.elastic.co/guide/en/elasticsearch/reference/5.5/docs-reindex.html


edit

Important

Reindex does not attempt to set up the destination index. It does not copy the settings of the source index. You should set up the destination index prior to running a _reindex action, including setting up mappings, shard counts, replicas, etc.

The most basic form of _reindex just copies documents from one index to another. This will copy documents from the twitter index into the new_twitter index:

POST _reindex
{
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter"
  }
}

That will return something like this:

{
  "took" : 147,
  "timed_out": false,
  "created": 120,
  "updated": 0,
  "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" : [ ]
}

Just like _update_by_query_reindex gets a snapshot of the source index but its target must be a different index so version conflicts are unlikely. The destelement can be configured like the index API to control optimistic concurrency control. Just leaving out version_type (as above) or setting it to internal will cause Elasticsearch to blindly dump documents into the target, overwriting any that happen to have the same type and id:

POST _reindex
{
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter",
    "version_type": "internal"
  }
}

Setting version_type to external will cause Elasticsearch to preserve theversion from the source, create any documents that are missing, and update any documents that have an older version in the destination index than they do in the source index:

POST _reindex
{
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter",
    "version_type": "external"
  }
}

Settings op_type to create will cause _reindex to only create missing documents in the target index. All existing documents will cause a version conflict:

POST _reindex
{
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter",
    "op_type": "create"
  }
}

By default version conflicts abort the _reindex process but you can just count them by settings "conflicts": "proceed" in the request body:

POST _reindex
{
  "conflicts": "proceed",
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter",
    "op_type": "create"
  }
}

You can limit the documents by adding a type to the source or by adding a query. This will only copy tweet's made by kimchy into new_twitter:

POST _reindex
{
  "source": {
    "index": "twitter",
    "type": "tweet",
    "query": {
      "term": {
        "user": "kimchy"
      }
    }
  },
  "dest": {
    "index": "new_twitter"
  }
}

index and type in source can both be lists, allowing you to copy from lots of sources in one request. This will copy documents from the tweet and posttypes in the twitter and blog index. It’d include the post type in the twitterindex and the tweet type in the blog index. If you want to be more specific you’ll need to use the query. It also makes no effort to handle ID collisions. The target index will remain valid but it’s not easy to predict which document will survive because the iteration order isn’t well defined.

POST _reindex
{
  "source": {
    "index": ["twitter", "blog"],
    "type": ["tweet", "post"]
  },
  "dest": {
    "index": "all_together"
  }
}

It’s also possible to limit the number of processed documents by setting size. This will only copy a single document from twitter to new_twitter:

POST _reindex
{
  "size": 1,
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter"
  }
}

If you want a particular set of documents from the twitter index you’ll need to sort. Sorting makes the scroll less efficient but in some contexts it’s worth it. If possible, prefer a more selective query to size and sort. This will copy 10000 documents from twitter into new_twitter:

POST _reindex
{
  "size": 10000,
  "source": {
    "index": "twitter",
    "sort": { "date": "desc" }
  },
  "dest": {
    "index": "new_twitter"
  }
}

The source section supports all the elements that are supported in a search request. For instance only a subset of the fields from the original documents can be reindexed using source filtering as follows:

POST _reindex
{
  "source": {
    "index": "twitter",
    "_source": ["user", "tweet"]
  },
  "dest": {
    "index": "new_twitter"
  }
}

Like _update_by_query_reindex supports a script that modifies the document. Unlike _update_by_query, the script is allowed to modify the document’s metadata. This example bumps the version of the source document:

POST _reindex
{
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter",
    "version_type": "external"
  },
  "script": {
    "inline": "if (ctx._source.foo == 'bar') {ctx._version++; ctx._source.remove('foo')}",
    "lang": "painless"
  }
}

Just as in _update_by_query, you can set ctx.op to change the operation that is executed on the destination index:

noop
Set ctx.op = "noop" if your script decides that the document doesn’t have to be indexed in the destination index. This no operation will be reported in the noopcounter in the response body.
delete
Set ctx.op = "delete" if your script decides that the document must be deleted from the destination index. The deletion will be reported in the deleted counter in the response body.

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

Think of the possibilities! Just be careful! With great power…. You can change:

  • _id
  • _type
  • _index
  • _version
  • _routing
  • _parent

Setting _version to null or clearing it from the ctx map is just like not sending the version in an indexing request. It will cause that document to be overwritten in the target index regardless of the version on the target or the version type you use in the _reindex request.

By default if _reindex sees a document with routing then the routing is preserved unless it’s changed by the script. You can set routing on the destrequest to change this:

keep
Sets the routing on the bulk request sent for each match to the routing on the match. The default.
discard
Sets the routing on the bulk request sent for each match to null.
=<some text>
Sets the routing on the bulk request sent for each match to all text after the =.

For example, you can use the following request to copy all documents from the source index with the company name cat into the dest index with routing set to cat.

POST _reindex
{
  "source": {
    "index": "source",
    "query": {
      "match": {
        "company": "cat"
      }
    }
  },
  "dest": {
    "index": "dest",
    "routing": "=cat"
  }
}

By default _reindex uses scroll batches of 1000. You can change the batch size with the size field in the source element:

POST _reindex
{
  "source": {
    "index": "source",
    "size": 100
  },
  "dest": {
    "index": "dest",
    "routing": "=cat"
  }
}

Reindex can also use the Ingest Node feature by specifying a pipeline like this:

POST _reindex
{
  "source": {
    "index": "source"
  },
  "dest": {
    "index": "dest",
    "pipeline": "some_ingest_pipeline"
  }
}

Reindex from Remoteedit

Reindex supports reindexing from a remote Elasticsearch cluster:

POST _reindex
{
  "source": {
    "remote": {
      "host": "http://otherhost:9200",
      "username": "user",
      "password": "pass"
    },
    "index": "source",
    "query": {
      "match": {
        "test": "data"
      }
    }
  },
  "dest": {
    "index": "dest"
  }
}

The host parameter must contain a scheme, host, and port (e.g.https://otherhost:9200). The username and password parameters are optional and when they are present reindex will connect to the remote Elasticsearch node using basic auth. Be sure to use https when using basic auth or the password will be sent in plain text.

Remote hosts have to be explicitly whitelisted in elasticsearch.yaml using thereindex.remote.whitelist property. It can be set to a comma delimited list of allowed remote host and port combinations (e.g. otherhost:9200, another:9200, 127.0.10.*:9200, localhost:*). Scheme is ignored by the whitelist - only host and port are used.

This feature should work with remote clusters of any version of Elasticsearch you are likely to find. This should allow you to upgrade from any version of Elasticsearch to the current version by reindexing from a cluster of the old version.

To enable queries sent to older versions of Elasticsearch the query parameter is sent directly to the remote host without validation or modification.

Note

Reindexing from remote clusters does not support manual orautomatic slicing.

Reindexing from a remote server uses an on-heap buffer that defaults to a maximum size of 100mb. If the remote index includes very large documents you’ll need to use a smaller batch size. The example below sets the batch size 10 which is very, very small.

POST _reindex
{
  "source": {
    "remote": {
      "host": "http://otherhost:9200"
    },
    "index": "source",
    "size": 10,
    "query": {
      "match": {
        "test": "data"
      }
    }
  },
  "dest": {
    "index": "dest"
  }
}

It is also possible to set the socket read timeout on the remote connection with the socket_timeout field and the connection timeout with theconnect_timeout field. Both default to thirty seconds. This example sets the socket read timeout to one minute and the connection timeout to ten seconds:

POST _reindex
{
  "source": {
    "remote": {
      "host": "http://otherhost:9200",
      "socket_timeout": "1m",
      "connect_timeout": "10s"
    },
    "index": "source",
    "query": {
      "match": {
        "test": "data"
      }
    }
  },
  "dest": {
    "index": "dest"
  }
}

URL Parametersedit

In addition to the standard parameters like pretty, the Reindex API also supports refreshwait_for_completionwait_for_active_shardstimeout, and requests_per_second.

Sending the refresh url parameter will cause all indexes to which the request wrote to be refreshed. This is different than the Index API’s refreshparameter which causes just the shard that received the new data to be refreshed.

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

wait_for_active_shards controls how many copies of a shard must be active before proceeding with the reindexing. See here for details. timeout controls how long each write request waits for unavailable shards to become available. Both work exactly how they work in the Bulk API.

requests_per_second can be set to any positive decimal number (1.461000, etc) and throttles rate at which reindex 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 reindex 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 therequests_per_second and the time spent writing. By default the batch size is1000, so if the requests_per_second is set to 500:

target_time = 1000 / 500 per second = 2 seconds
wait_time = target_time - write_time = 2 seconds - .5 seconds = 1.5 seconds

Since the batch is issued as a single _bulk request large batch sizes will cause Elasticsearch to create many requests and then wait for a while before starting the next set. This is "bursty" instead of "smooth". The default is -1.

Response bodyedit

The JSON response looks like this:

{
  "took" : 639,
  "updated": 0,
  "created": 123,
  "batches": 1,
  "version_conflicts": 2,
  "retries": {
    "bulk": 0,
    "search": 0
  }
  "throttled_millis": 0,
  "failures" : [ ]
}
took
The number of milliseconds from start to end of the whole operation.
updated
The number of documents that were successfully updated.
created
The number of documents that were successfully created.
batches
The number of scroll responses pulled back by the the reindex.
version_conflicts
The number of version conflicts that reindex hit.
retries
The number of retries attempted by reindex. 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.
failures
Array of all indexing failures. If this is non-empty then the request aborted because of those failures. See conflicts for how to prevent version conflicts from aborting the operation.

Works with the Task APIedit

You can fetch the status of all running reindex requests with the Task API:

GET _tasks?detailed=true&actions=*reindex

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/reindex",
          "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 total field. total is the total number of operations that the reindex expects to perform. You can estimate the progress by adding the updatedcreated, 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:

GET /_tasks/taskId:1

The advantage of this API is that it integrates with wait_for_completion=falseto 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 aresults or an error field. The cost of this feature is the document thatwait_for_completion=false creates at .tasks/task/${taskId}. It is up to you to delete that document.

Works with the Cancel Task APIedit

Any Reindex can be canceled using the Task Cancel API:

POST _tasks/task_id:1/_cancel

The task_id can be found using the tasks API above.

Cancelation 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.

Rethrottlingedit

The value of requests_per_second can be changed on a running reindex using the _rethrottle API:

POST _reindex/task_id:1/_rethrottle?requests_per_second=-1

The task_id can be found using the tasks API above.

Just like when setting it on the _reindex 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.

Reindex to change the name of a fieldedit

_reindex can be used to build a copy of an index with renamed fields. Say you create an index containing documents that look like this:

POST test/test/1?refresh
{
  "text": "words words",
  "flag": "foo"
}

But you don’t like the name flag and want to replace it with tag_reindexcan create the other index for you:

POST _reindex
{
  "source": {
    "index": "test"
  },
  "dest": {
    "index": "test2"
  },
  "script": {
    "inline": "ctx._source.tag = ctx._source.remove(\"flag\")"
  }
}

Now you can get the new document:

GET test2/test/1

and it’ll look like:

{
  "found": true,
  "_id": "1",
  "_index": "test2",
  "_type": "test",
  "_version": 1,
  "_source": {
    "text": "words words",
    "tag": "foo"
  }
}

Or you can search by tag or whatever you want.

Manual slicingedit

Reindex supports Sliced Scroll, allowing you to manually parallelize the process relatively easily:

POST _reindex
{
  "source": {
    "index": "twitter",
    "slice": {
      "id": 0,
      "max": 2
    }
  },
  "dest": {
    "index": "new_twitter"
  }
}
POST _reindex
{
  "source": {
    "index": "twitter",
    "slice": {
      "id": 1,
      "max": 2
    }
  },
  "dest": {
    "index": "new_twitter"
  }
}

Which you can verify works with:

GET _refresh
POST new_twitter/_search?size=0&filter_path=hits.total

Which results in a sensible total like this one:

{
  "hits": {
    "total": 120
  }
}

Automatic slicingedit

You can also let reindex automatically parallelize using Sliced Scroll to slice on _uid:

POST _reindex?slices=5&refresh
{
  "source": {
    "index": "twitter"
  },
  "dest": {
    "index": "new_twitter"
  }
}

Which you also can verify works with:

POST new_twitter/_search?size=0&filter_path=hits.total

Which results in a sensible total like this one:

{
  "hits": {
    "total": 120
  }
}

Adding slices to _reindex 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 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 `_reindex`ed.
  • Each sub-requests gets a slightly different snapshot of the source index though these are all taken at approximately the same time.

Picking the number of slicesedit

At this point we have a few recommendations around the number of slicesto use (the max parameter in the slice API if manually parallelizing):

  • Don’t use large numbers. 500 creates fairly massive CPU thrash.
  • It is more efficient from a query performance standpoint to use some multiple of the number of shards in the source index.
  • Using exactly as many shards as are in the source index is the most efficient from a query performance standpoint.
  • Indexing performance should scale linearly across available resources with the number of slices.
  • Whether indexing or query performance dominates that process depends on lots of factors like the documents being reindexed and the cluster doing the reindexing.

Reindex daily indicesedit

You can use _reindex in combination with Painless to reindex daily indices to apply a new template to the existing documents.

Assuming you have indices consisting of documents as following:

PUT metricbeat-2016.05.30/beat/1?refresh
{"system.cpu.idle.pct": 0.908}
PUT metricbeat-2016.05.31/beat/1?refresh
{"system.cpu.idle.pct": 0.105}

The new template for the metricbeat-* indices is already loaded into elasticsearch but it applies only to the newly created indices. Painless can be used to reindex the existing documents and apply the new template.

The script below extracts the date from the index name and creates a new index with -1 appended. All data from metricbeat-2016.05.31 will be reindex into metricbeat-2016.05.31-1.

POST _reindex
{
  "source": {
    "index": "metricbeat-*"
  },
  "dest": {
    "index": "metricbeat"
  },
  "script": {
    "lang": "painless",
    "inline": "ctx._index = 'metricbeat-' + (ctx._index.substring('metricbeat-'.length(), ctx._index.length())) + '-1'"
  }
}

All documents from the previous metricbeat indices now can be found in the *-1 indices.

GET metricbeat-2016.05.30-1/beat/1
GET metricbeat-2016.05.31-1/beat/1

The previous method can also be used in combination with change the name of a field to only load the existing data into the new index, but also rename fields if needed.

Extracting a random subset of an indexedit

Reindex can be used to extract a random subset of an index for testing:

POST _reindex
{
  "size": 10,
  "source": {
    "index": "twitter",
    "query": {
      "function_score" : {
        "query" : { "match_all": {} },
        "random_score" : {}
      }
    },
    "sort": "_score"    
  },
  "dest": {
    "index": "random_twitter"
  }
}

Reindex defaults to sorting by _doc so random_score won’t have any effect unless you override the sort to _score.


Bulk API

https://www.elastic.co/guide/en/elasticsearch/reference/5.5/docs-bulk.html


edit

The bulk API makes it possible to perform many index/delete operations in a single API call. This can greatly increase the indexing speed.

The REST API endpoint is /_bulk, and it expects the following newline delimited JSON (NDJSON) structure:

action_and_meta_data\n
optional_source\n
action_and_meta_data\n
optional_source\n
....
action_and_meta_data\n
optional_source\n

NOTE: the final line of data must end with a newline character \n. Each newline character may be preceded by a carriage return \r. When sending requests to this endpoint the Content-Type header should be set to application/x-ndjson.

The possible actions are indexcreatedelete and updateindex and create expect a source on the next line, and have the same semantics as the op_type parameter to the standard index API (i.e. create will fail if a document with the same index and type exists already, whereas index will add or replace a document as necessary). delete does not expect a source on the following line, and has the same semantics as the standard delete API. update expects that the partial doc, upsert and script and its options are specified on the next line.

If you’re providing text file input to curl, you must use the --data-binary flag instead of plain -d. The latter doesn’t preserve newlines. Example:

$ cat requests
{ "index" : { "_index" : "test", "_type" : "type1", "_id" : "1" } }
{ "field1" : "value1" }
$ curl -s -H "Content-Type: application/x-ndjson" -XPOST localhost:9200/_bulk --data-binary "@requests"; echo
{"took":7, "errors": false, "items":[{"index":{"_index":"test","_type":"type1","_id":"1","_version":1,"result":"created","forced_refresh":false}}]}

Because this format uses literal \n's as delimiters, please be sure that the JSON actions and sources are not pretty printed. Here is an example of a correct sequence of bulk commands:

POST _bulk
{ "index" : { "_index" : "test", "_type" : "type1", "_id" : "1" } }
{ "field1" : "value1" }
{ "delete" : { "_index" : "test", "_type" : "type1", "_id" : "2" } }
{ "create" : { "_index" : "test", "_type" : "type1", "_id" : "3" } }
{ "field1" : "value3" }
{ "update" : {"_id" : "1", "_type" : "type1", "_index" : "test"} }
{ "doc" : {"field2" : "value2"} }

The result of this bulk operation is:

{
   "took": 30,
   "errors": false,
   "items": [
      {
         "index": {
            "_index": "test",
            "_type": "type1",
            "_id": "1",
            "_version": 1,
            "result": "created",
            "_shards": {
               "total": 2,
               "successful": 1,
               "failed": 0
            },
            "created": true,
            "status": 201
         }
      },
      {
         "delete": {
            "found": false,
            "_index": "test",
            "_type": "type1",
            "_id": "2",
            "_version": 1,
            "result": "not_found",
            "_shards": {
               "total": 2,
               "successful": 1,
               "failed": 0
            },
            "status": 404
         }
      },
      {
         "create": {
            "_index": "test",
            "_type": "type1",
            "_id": "3",
            "_version": 1,
            "result": "created",
            "_shards": {
               "total": 2,
               "successful": 1,
               "failed": 0
            },
            "created": true,
            "status": 201
         }
      },
      {
         "update": {
            "_index": "test",
            "_type": "type1",
            "_id": "1",
            "_version": 2,
            "result": "updated",
            "_shards": {
                "total": 2,
                "successful": 1,
                "failed": 0
            },
            "status": 200
         }
      }
   ]
}

The endpoints are /_bulk/{index}/_bulk, and {index}/{type}/_bulk. When the index or the index/type are provided, they will be used by default on bulk items that don’t provide them explicitly.

A note on the format. The idea here is to make processing of this as fast as possible. As some of the actions will be redirected to other shards on other nodes, only action_meta_data is parsed on the receiving node side.

Client libraries using this protocol should try and strive to do something similar on the client side, and reduce buffering as much as possible.

The response to a bulk action is a large JSON structure with the individual results of each action that was performed. The failure of a single action does not affect the remaining actions.

There is no "correct" number of actions to perform in a single bulk call. You should experiment with different settings to find the optimum size for your particular workload.

If using the HTTP API, make sure that the client does not send HTTP chunks, as this will slow things down.

Versioningedit

Each bulk item can include the version value using the _version/version field. It automatically follows the behavior of the index / delete operation based on the _version mapping. It also support the version_type/_version_type (see versioning)

Routingedit

Each bulk item can include the routing value using the _routing/routing field. It automatically follows the behavior of the index / delete operation based on the _routing mapping.

Parentedit

Each bulk item can include the parent value using the _parent/parent field. It automatically follows the behavior of the index / delete operation based on the _parent / _routing mapping.

Wait For Active Shardsedit

When making bulk calls, you can set the wait_for_active_shards parameter to require a minimum number of shard copies to be active before starting to process the bulk request. See here for further details and a usage example.

Refreshedit

Control when the changes made by this request are visible to search. Seerefresh.

Updateedit

When using update action _retry_on_conflict can be used as field in the action itself (not in the extra payload line), to specify how many times an update should be retried in the case of a version conflict.

The update action payload, supports the following options: doc (partial document), upsertdoc_as_upsertscript and _source. See updatedocumentation for details on the options. Example with update actions:

POST _bulk
{ "update" : {"_id" : "1", "_type" : "type1", "_index" : "index1", "_retry_on_conflict" : 3} }
{ "doc" : {"field" : "value"} }
{ "update" : { "_id" : "0", "_type" : "type1", "_index" : "index1", "_retry_on_conflict" : 3} }
{ "script" : { "inline": "ctx._source.counter += params.param1", "lang" : "painless", "params" : {"param1" : 1}}, "upsert" : {"counter" : 1}}
{ "update" : {"_id" : "2", "_type" : "type1", "_index" : "index1", "_retry_on_conflict" : 3} }
{ "doc" : {"field" : "value"}, "doc_as_upsert" : true }
{ "update" : {"_id" : "3", "_type" : "type1", "_index" : "index1", "_source" : true} }
{ "doc" : {"field" : "value"} }
{ "update" : {"_id" : "4", "_type" : "type1", "_index" : "index1"} }
{ "doc" : {"field" : "value"}, "_source": true}

Securityedit

See URL-based access control


Delete API

https://www.elastic.co/guide/en/elasticsearch/reference/5.5/docs-delete.html


edit

The delete API allows to delete a typed JSON document from a specific index based on its id. The following example deletes the JSON document from an index called twitter, under a type called tweet, with id valued 1:

DELETE /twitter/tweet/1

The result of the above delete operation is:

{
    "_shards" : {
        "total" : 2,
        "failed" : 0,
        "successful" : 2
    },
    "found" : true,
    "_index" : "twitter",
    "_type" : "tweet",
    "_id" : "1",
    "_version" : 2,
    "result": "deleted"
}

Versioningedit

Each document indexed is versioned. When deleting a document, the versioncan be specified to make sure the relevant document we are trying to delete is actually being deleted and it has not changed in the meantime. Every write operation executed on a document, deletes included, causes its version to be incremented.

Routingedit

When indexing using the ability to control the routing, in order to delete a document, the routing value should also be provided. For example:

DELETE /twitter/tweet/1?routing=kimchy

The above will delete a tweet with id 1, but will be routed based on the user. Note, issuing a delete without the correct routing, will cause the document to not be deleted.

When the _routing mapping is set as required and no routing value is specified, the delete api will throw a RoutingMissingException and reject the request.

Parentedit

The parent parameter can be set, which will basically be the same as setting the routing parameter.

Note that deleting a parent document does not automatically delete its children. One way of deleting all child documents given a parent’s id is to use the Delete By Query API to perform a index with the automatically generated (and indexed) field _parent, which is in the format parent_type#parent_id.

When deleting a child document its parent id must be specified, otherwise the delete request will be rejected and a RoutingMissingException will be thrown instead.

Automatic index creationedit

The delete operation automatically creates an index if it has not been created before (check out the create index API for manually creating an index), and also automatically creates a dynamic type mapping for the specific type if it has not been created before (check out the put mapping API for manually creating type mapping).

Distributededit

The delete operation gets hashed into a specific shard id. It then gets redirected into the primary shard within that id group, and replicated (if needed) to shard replicas within that id group.

Wait For Active Shardsedit

When making delete requests, you can set the wait_for_active_shardsparameter to require a minimum number of shard copies to be active before starting to process the delete request. See here for further details and a usage example.

Refreshedit

Control when the changes made by this request are visible to search. See ?refresh.

Timeoutedit

The primary shard assigned to perform the delete operation might not be available when the delete operation is executed. Some reasons for this might be that the primary shard is currently recovering from a store or undergoing relocation. By default, the delete operation will wait on the primary shard to become available for up to 1 minute before failing and responding with an error. The timeout parameter can be used to explicitly specify how long it waits. Here is an example of setting it to 5 minutes:

DELETE /twitter/tweet/1?timeout=5m


Get API

The get API allows to get a typed JSON document from the index based on its id. The following example gets a JSON document from an index called twitter, under a type called tweet, with id valued 0:

GET twitter/tweet/

The result of the above get operation is:

{
    "_index" : "twitter",
    "_type" : "tweet",
    "_id" : "0",
    "_version" : 1,
    "found": true,
    "_source" : {
        "user" : "kimchy",
        "date" : "2009-11-15T14:12:12",
        "likes": 0,
        "message" : "trying out Elasticsearch"
    }
}

The above result includes the _index_type_id and _version of the document we wish to retrieve, including the actual _source of the document if it could be found (as indicated by the found field in the response).

The API also allows to check for the existence of a document using HEAD, for example:

HEAD twitter/tweet/0

Realtime

By default, the get API is realtime, and is not affected by the refresh rate of the index (when data will become visible for search). If a document has been updated but is not yet refreshed, the get API will issue a refresh call in-place to make the document visible. This will also make other documents changed since the last refresh visible. In order to disable realtime GET, one can set the realtime parameter to false.

Optional Type

The get API allows for _type to be optional. Set it to _all in order to fetch the first document matching the id across all types.

Source filtering

By default, the get operation returns the contents of the _source field unless you have used the stored_fields parameter or if the _source field is disabled. You can turn off _source retrieval by using the _source parameter:

GET twitter/tweet/0?_source=false

If you only need one or two fields from the complete _source, you can use the _source_include & _source_exclude parameters to include or filter out that parts you need. This can be especially helpful with large documents where partial retrieval can save on network overhead. Both parameters take a comma separated list of fields or wildcard expressions. Example:

GET twitter/tweet/0?_source_include=*.id&_source_exclude=entities

If you only want to specify includes, you can use a shorter notation:

GET twitter/tweet/0?_source=*.id,retweeted

Stored Fields

The get operation allows specifying a set of stored fields that will be returned by passing the stored_fields parameter. If the requested fields are not stored, they will be ignored. Consider for instance the following mapping:

PUT twitter
{
   "mappings": {
      "tweet": {
         "properties": {
            "counter": {
               "type": "integer",
               "store": false
            },
            "tags": {
               "type": "keyword",
               "store": true
            }
         }
      }
   }
}

Now we can add a document:

PUT twitter/tweet/1
{
    "counter" : 1,
    "tags" : ["red"]
}
  1. and try to retrieve it:
GET twitter/tweet/1?stored_fields=tags,counter

The result of the above get operation is:

{
   "_index": "twitter",
   "_type": "tweet",
   "_id": "1",
   "_version": 1,
   "found": true,
   "fields": {
      "tags": [
         "red"
      ]
   }
}

Field values fetched from the document it self are always returned as an array. Since the counter field is not stored the get request simply ignores it when trying to get the stored_fields.

It is also possible to retrieve metadata fields like _routing and _parent fields:

PUT twitter/tweet/2?routing=user1
{
    "counter" : 1,
    "tags" : ["white"]
}
GET twitter/tweet/2?routing=user1&stored_fields=tags,counter

The result of the above get operation is:

{
   "_index": "twitter",
   "_type": "tweet",
   "_id": "2",
   "_version": 1,
   "_routing": "user1",
   "found": true,
   "fields": {
      "tags": [
         "white"
      ]
   }
}

Also only leaf fields can be returned via the stored_field option. So object fields can’t be returned and such requests will fail.

Getting the _source directly

Use the /{index}/{type}/{id}/_source endpoint to get just the _source field of the document, without any additional content around it. For example:

GET twitter/tweet/1/_source

You can also use the same source filtering parameters to control which parts of the _source will be returned:

GET twitter/tweet/1/_source?_source_include=*.id&_source_exclude=entities'

Note, there is also a HEAD variant for the _source endpoint to efficiently test for document _source existence. An existing document will not have a _source if it is disabled in the mapping.

HEAD twitter/tweet/1/_source

Routing

When indexing using the ability to control the routing, in order to get a document, the routing value should also be provided. For example:

GET twitter/tweet/2?routing=user1

The above will get a tweet with id 2, but will be routed based on the user. Note, issuing a get without the correct routing, will cause the document not to be fetched.

Preference

Controls a preference of which shard replicas to execute the get request on. By default, the operation is randomized between the shard replicas.

The preference can be set to:

_primary
The operation will go and be executed only on the primary shards.
_local
The operation will prefer to be executed on a local allocated shard if possible.
Custom (string) value
A custom value will be used to guarantee that the same shards will be used for the same custom value. This can help with "jumping values" when hitting different shards in different refresh states. A sample value can be something like the web session id, or the user name.

Refresh

The refresh parameter can be set to true in order to refresh the relevant shard before the get operation and make it searchable. Setting it to trueshould be done after careful thought and verification that this does not cause a heavy load on the system (and slows down indexing).

Distributed

The get operation gets hashed into a specific shard id. It then gets redirected to one of the replicas within that shard id and returns the result. The replicas are the primary shard and its replicas within that shard id group. This means that the more replicas we will have, the better GET scaling we will have.

Versioning support

You can use the version parameter to retrieve the document only if its current version is equal to the specified one. This behavior is the same for all version types with the exception of version type FORCE which always retrieves the document. Note that FORCE version type is deprecated.

Internally, Elasticsearch has marked the old document as deleted and added an entirely new document. The old version of the document doesn’t disappear immediately, although you won’t be able to access it. Elasticsearch cleans up deleted documents in the background as you continue to index more data.

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