Scroll
editScroll
editWhile 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.
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 Keeping the search context alive), eg ?scroll=1m.
POST /twitter/_search?scroll=1m
{
"size": 100,
"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.
POST /_search/scroll { "scroll" : "1m", "scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==" }
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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.
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.
If the request specifies aggregations, only the initial search response will contain the aggregations results.
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:
GET /_search?scroll=1m
{
"sort": [
"_doc"
]
}
Keeping the search context alive
editThe 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, 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.
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 File Descriptors.
You can check how many search contexts are open with the nodes stats API:
GET /_nodes/stats/indices/search
Clear scroll API
editSearch context are automatically removed when the scroll timeout has been
exceeded. However keeping scrolls open has a cost, as discussed in the
previous section so scrolls should be explicitly
cleared as soon as the scroll is not being used anymore using the
clear-scroll API:
DELETE /_search/scroll
{
"scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ=="
}
Multiple scroll IDs can be passed as array:
DELETE /_search/scroll
{
"scroll_id" : [
"DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==",
"DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB"
]
}
All search contexts can be cleared with the _all parameter:
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:
DELETE /_search/scroll/DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB
Sliced Scroll
editFor scroll queries that return a lot of documents it is possible to split the scroll in multiple slices which can be consumed independently:
GET /twitter/_search?scroll=1m
{
"slice": {
"id": 0,
"max": 2
},
"query": {
"match" : {
"title" : "elasticsearch"
}
}
}
GET /twitter/_search?scroll=1m
{
"slice": {
"id": 1,
"max": 2
},
"query": {
"match" : {
"title" : "elasticsearch"
}
}
}
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 _uid field
with the following formula:
slice(doc) = floorMod(hashCode(doc._uid), 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.
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_valuesare 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.
GET /twitter/_search?scroll=1m
{
"slice": {
"field": "date",
"id": 0,
"max": 10
},
"query": {
"match" : {
"title" : "elasticsearch"
}
}
}
For append only time-based indices, the timestamp field can be used safely.
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.