[[search-request-scroll]] === Scroll 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 index into a new 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 of documents from one index to another: Perl:: See https://metacpan.org/pod/Search::Elasticsearch::Bulk[Search::Elasticsearch::Bulk] and https://metacpan.org/pod/Search::Elasticsearch::Scroll[Search::Elasticsearch::Scroll] Python:: See http://elasticsearch-py.readthedocs.org/en/master/helpers.html[elasticsearch.helpers.*] ********************************************* NOTE: The results that are returned from a scroll request reflect the state of the 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 <>), eg `?scroll=1m`. [source,js] -------------------------------------------------- curl -XGET 'localhost:9200/twitter/tweet/_search?scroll=1m' -d ' { "query": { "match" : { "title" : "elasticsearch" } } } ' -------------------------------------------------- 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,js] -------------------------------------------------- curl -XGET <1> 'localhost:9200/_search/scroll' <2> -d' { "scroll" : "1m", <3> "scroll_id" : "c2Nhbjs2OzM0NDg1ODpzRlBLc0FXNlNyNm5JWUc1" <4> } ' -------------------------------------------------- <1> `GET` or `POST` can be used. <2> The URL should not include the `index` or `type` name -- these are specified in the original `search` request instead. <3> The `scroll` parameter tells Elasticsearch to keep the search context open for another `1m`. <4> The `scroll_id` parameter 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. For backwards compatibility, `scroll_id` and `scroll` can be passed in the query string. And the `scroll_id` can be passed in the request body [source,js] -------------------------------------------------- curl -XGET 'localhost:9200/_search/scroll?scroll=1m' -d 'c2Nhbjs2OzM0NDg1ODpzRlBLc0FXNlNyNm5JWUc1' -------------------------------------------------- IMPORTANT: The initial search request and each subsequent scroll request returns a new `_scroll_id` -- only the most recent `_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,js] -------------------------------------------------- curl -XGET 'localhost:9200/_search?scroll=1m' -d ' { "sort": [ "_doc" } } ' -------------------------------------------------- [[scroll-search-context]] ==== Keeping the search context alive 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 <>) 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. Normally, the background merge process optimizes the index by merging together smaller segments to create new bigger segments, at which time the smaller segments are deleted. This process continues during scrolling, but an open search context prevents the old segments from being deleted while they are still in use. This is how Elasticsearch is able to return the results of the initial search request, regardless of subsequent changes to documents. TIP: Keeping older segments alive means that more file handles are needed. Ensure that you have configured your nodes to have ample free file handles. See <>. You can check how many search contexts are open with the <>: [source,js] --------------------------------------- curl -XGET localhost:9200/_nodes/stats/indices/search?pretty --------------------------------------- ==== Clear scroll API Search context are automatically removed when the `scroll` timeout has been exceeded. However keeping scrolls open has a cost, as discussed in the <> so scrolls should be explicitly cleared as soon as the scroll is not being used anymore using the `clear-scroll` API: [source,js] --------------------------------------- curl -XDELETE localhost:9200/_search/scroll -d ' { "scroll_id" : ["c2Nhbjs2OzM0NDg1ODpzRlBLc0FXNlNyNm5JWUc1"] }' --------------------------------------- Multiple scroll IDs can be passed as array: [source,js] --------------------------------------- curl -XDELETE localhost:9200/_search/scroll -d ' { "scroll_id" : ["c2Nhbjs2OzM0NDg1ODpzRlBLc0FXNlNyNm5JWUc1", "aGVuRmV0Y2g7NTsxOnkxaDZ"] }' --------------------------------------- All search contexts can be cleared with the `_all` parameter: [source,js] --------------------------------------- curl -XDELETE localhost:9200/_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,js] --------------------------------------- curl -XDELETE localhost:9200/_search/scroll \ -d 'c2Nhbjs2OzM0NDg1ODpzRlBLc0FXNlNyNm5JWUc1,aGVuRmV0Y2g7NTsxOnkxaDZ' ---------------------------------------