elasticsearch/docs/reference/search/request/scroll.asciidoc

[discrete]
[[scroll-search-results]]
=== Scroll search results

While a `search` request returns a single ``page'' of results, the `scroll`
API can be used to retrieve large numbers of results (or even all results)
from a single search request, in much the same way as you would use a cursor
on a traditional database.

Scrolling is not intended for real time user requests, but rather for
processing large amounts of data, e.g. in order to reindex the contents of one
data stream or index into a new data stream or index with a different
configuration.

.Client support for scrolling and reindexing
*********************************************

Some of the officially supported clients provide helpers to assist with
scrolled searches and reindexing:

Perl::

    See https://metacpan.org/pod/Search::Elasticsearch::Client::5_0::Bulk[Search::Elasticsearch::Client::5_0::Bulk]
    and https://metacpan.org/pod/Search::Elasticsearch::Client::5_0::Scroll[Search::Elasticsearch::Client::5_0::Scroll]

Python::

    See https://elasticsearch-py.readthedocs.org/en/master/helpers.html[elasticsearch.helpers.*]

JavaScript::

    See {jsclient-current}/client-helpers.html[client.helpers.*]

*********************************************

NOTE: The results that are returned from a scroll request reflect the state of
the data stream or index at the time that the initial `search` request was  made, like a
snapshot in time. Subsequent changes to documents (index, update or delete)
will only affect later search requests.

In order to use scrolling, the initial search request should specify the
`scroll` parameter in the query string, which tells Elasticsearch how long it
should keep the ``search context'' alive (see <<scroll-search-context>>), eg `?scroll=1m`.

[source,console]
--------------------------------------------------
POST /twitter/_search?scroll=1m
{
  "size": 100,
  "query": {
    "match": {
      "title": "elasticsearch"
    }
  }
}
--------------------------------------------------
// TEST[setup:twitter]

The result from the above request includes a `_scroll_id`, which should
be passed to the `scroll` API in order to retrieve the next batch of
results.

[source,console]
--------------------------------------------------
POST /_search/scroll                                                               <1>
{
  "scroll" : "1m",                                                                 <2>
  "scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==" <3>
}
--------------------------------------------------
// TEST[continued s/DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==/$body._scroll_id/]

<1> `GET` or `POST` can be used and the URL should not include the `index`
    name -- this is specified in the original `search` request instead.
<2> The `scroll` parameter tells Elasticsearch to keep the search context open
    for another `1m`.
<3> The `scroll_id` parameter

The `size` parameter allows you to configure the maximum number of hits to be 
returned with each batch of results.  Each call to the `scroll` API returns the 
next batch of results until there are no more results left to return, ie the 
`hits` array is empty.

IMPORTANT: The initial search request and each subsequent scroll request each 
return a `_scroll_id`. While the `_scroll_id` may change between requests, it doesn’t 
always change — in any case, only the most recently received `_scroll_id` should be used.

NOTE: If the request specifies aggregations, only the initial search response
will contain the aggregations results.

NOTE: Scroll requests have optimizations that make them faster when the sort
order is `_doc`. If you want to iterate over all documents regardless of the
order, this is the most efficient option:

[source,console]
--------------------------------------------------
GET /_search?scroll=1m
{
  "sort": [
    "_doc"
  ]
}
--------------------------------------------------
// TEST[setup:twitter]

[discrete]
[[scroll-search-context]]
==== Keeping the search context alive

A scroll returns all the documents which matched the search at the time of the
initial search request. It ignores any subsequent changes to these documents.
The `scroll_id` identifies a _search context_ which keeps track of everything
that {es} needs to return the correct documents. The search context is created
by the initial request and kept alive by subsequent requests.

The `scroll` parameter (passed to the `search` request and to every `scroll`
request) tells Elasticsearch how long it should keep the search context alive.
Its value (e.g. `1m`, see <<time-units>>) does not need to be long enough to
process all data -- it just needs to be long enough to process the previous
batch of results. Each `scroll` request (with the `scroll` parameter) sets a
new  expiry time. If a `scroll` request doesn't pass in the `scroll`
parameter, then the search context will be freed as part of _that_ `scroll`
request.

Normally, the background merge process optimizes the index by merging together
smaller segments to create new, bigger segments. Once the smaller segments are
no longer needed they are deleted. This process continues during scrolling, but
an open search context prevents the old segments from being deleted since they
are still in use.

TIP: Keeping older segments alive means that more disk space and file handles
are needed. Ensure that you have configured your nodes to have ample free file
handles. See <<file-descriptors>>.

Additionally, if a segment contains deleted or updated documents then the
search context must keep track of whether each document in the segment was live
at the time of the initial search request. Ensure that your nodes have
sufficient heap space if you have many open scrolls on an index that is subject
to ongoing deletes or updates.

NOTE: To prevent against issues caused by having too many scrolls open, the
user is not allowed to open scrolls past a certain limit. By default, the
maximum number of open scrolls is 500. This limit can be updated with the
`search.max_open_scroll_context` cluster setting.

You can check how many search contexts are open with the
<<cluster-nodes-stats,nodes stats API>>:

[source,console]
---------------------------------------
GET /_nodes/stats/indices/search
---------------------------------------

[discrete]
[[clear-scroll]]
==== Clear scroll

Search context are automatically removed when the `scroll` timeout has been
exceeded. However keeping scrolls open has a cost, as discussed in the
<<scroll-search-context,previous section>> so scrolls should be explicitly
cleared as soon as the scroll is not being used anymore using the
`clear-scroll` API:

[source,console]
---------------------------------------
DELETE /_search/scroll
{
  "scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ=="
}
---------------------------------------
// TEST[catch:missing]

Multiple scroll IDs can be passed as array:

[source,console]
---------------------------------------
DELETE /_search/scroll
{
  "scroll_id" : [
    "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==",
    "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB"
  ]
}
---------------------------------------
// TEST[catch:missing]

All search contexts can be cleared with the `_all` parameter:

[source,console]
---------------------------------------
DELETE /_search/scroll/_all
---------------------------------------

The `scroll_id` can also be passed as a query string parameter or in the request body.
Multiple scroll IDs can be passed as comma separated values:

[source,console]
---------------------------------------
DELETE /_search/scroll/DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB
---------------------------------------
// TEST[catch:missing]

[discrete]
[[sliced-scroll]]
==== Sliced Scroll

For scroll queries that return a lot of documents it is possible to split the scroll in multiple slices which
can be consumed independently:

[source,console]
--------------------------------------------------
GET /twitter/_search?scroll=1m
{
  "slice": {
    "id": 0,                      <1>
    "max": 2                      <2>
  },
  "query": {
    "match": {
      "title": "elasticsearch"
    }
  }
}
GET /twitter/_search?scroll=1m
{
  "slice": {
    "id": 1,
    "max": 2
  },
  "query": {
    "match": {
      "title": "elasticsearch"
    }
  }
}
--------------------------------------------------
// TEST[setup:big_twitter]

<1> The id of the slice
<2> The maximum number of slices

The result from the first request returned documents that belong to the first slice (id: 0) and the result from the
second request returned documents that belong to the second slice. Since the maximum number of slices is set to 2
 the union of the results of the two requests is equivalent to the results of a scroll query without slicing.
By default the splitting is done on the shards first and then locally on each shard using the _id field
with the following formula:
`slice(doc) = floorMod(hashCode(doc._id), max)`
For instance if the number of shards is equal to 2 and the user requested 4 slices then the slices 0 and 2 are assigned
to the first shard and the slices 1 and 3 are assigned to the second shard.

Each scroll is independent and can be processed in parallel like any scroll request.

NOTE: If the number of slices is bigger than the number of shards the slice filter is very slow on the first calls, it has a complexity of O(N) and a memory cost equals
to N bits per slice where N is the total number of documents in the shard.
After few calls the filter should be cached and subsequent calls should be faster but you should limit the number of
 sliced query you perform in parallel to avoid the memory explosion.

To avoid this cost entirely it is possible to use the `doc_values` of another field to do the slicing
but the user must ensure that the field has the following properties:

    * The field is numeric.

    * `doc_values` are enabled on that field

    * Every document should contain a single value. If a document has multiple values for the specified field, the first value is used.

    * The value for each document should be set once when the document is created and never updated. This ensures that each
slice gets deterministic results.

    * The cardinality of the field should be high. This ensures that each slice gets approximately the same amount of documents.

[source,console]
--------------------------------------------------
GET /twitter/_search?scroll=1m
{
  "slice": {
    "field": "date",
    "id": 0,
    "max": 10
  },
  "query": {
    "match": {
      "title": "elasticsearch"
    }
  }
}
--------------------------------------------------
// TEST[setup:big_twitter]

For append only time-based indices, the `timestamp` field can be used safely.

NOTE: By default the maximum number of slices allowed per scroll is limited to 1024.
You can update the `index.max_slices_per_scroll` index setting to bypass this limit.