Paginate search results

By default, searches return the top 10 matching hits. To page through a larger set of results, you can use the search API's from and size parameters. The from parameter defines the number of hits to skip, defaulting to 0. The size parameter is the maximum number of hits to return. Together, these two parameters define a page of results.

GET /_search
{
  "from": 5,
  "size": 20,
  "query": {
    "match": {
      "user.id": "kimchy"
    }
  }
}

Avoid using from and size to page too deeply or request too many results at once. Search requests usually span multiple shards. Each shard must load its requested hits and the hits for any previous pages into memory. For deep pages or large sets of results, these operations can significantly increase memory and CPU usage, resulting in degraded performance or node failures.

By default, you cannot use from and size to page through more than 10,000 hits. This limit is a safeguard set by the index.max_result_window index setting. If you need to page through more than 10,000 hits, use the search_after parameter instead.

You can use the search_after parameter to retrieve the next page of hits using a set of sort values from the previous page.

Using search_after requires multiple search requests with the same query and sort values. If a refresh occurs between these requests, the order of your results may change, causing inconsistent results across pages. To prevent this, you can create a point in time (PIT) to preserve the current index state over your searches.

POST /my-index-000001/_pit?keep_alive=1m

The API returns a PIT ID.

{
  "id": "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA=="
}

To get the first page of results, submit a search request with a sort argument. If using a PIT, specify the PIT ID in the pit.id parameter and omit the target data stream or index from the request path.

GET /_search
{
  "size": 10000,
  "query": {
    "match" : {
      "user.id" : "elkbee"
    }
  },
  "pit": {
	    "id":  "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==", 
	    "keep_alive": "1m"
  },
  "sort": [ 
    {"@timestamp": "asc"}
  ]
}

The search response includes an array of sort values for each hit. If you used a PIT, a tiebreaker is included as the last sort values for each hit. This tiebreaker called _shard_doc is added automically on every search requests that use a PIT. The _shard_doc value is the combination of the shard index within the PIT and the Lucene’s internal doc ID, it is unique per document and constant within a PIT. You can also add the tiebreaker explicitly in the search request to customize the order:

GET /_search
{
  "size": 10000,
  "query": {
    "match" : {
      "user.id" : "elkbee"
    }
  },
  "pit": {
	    "id":  "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==", 
	    "keep_alive": "1m"
  },
  "sort": [ 
    {"@timestamp": "asc"},
    {"_shard_doc": "desc"}
  ]
}

{
  "pit_id" : "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==", 
  "took" : 17,
  "timed_out" : false,
  "_shards" : ...,
  "hits" : {
    "total" : ...,
    "max_score" : null,
    "hits" : [
      ...
      {
        "_index" : "my-index-000001",
        "_id" : "FaslK3QBySSL_rrj9zM5",
        "_score" : null,
        "_source" : ...,
        "sort" : [                                
          4098435132000,
          4294967298                              
        ]
      }
    ]
  }
}

To get the next page of results, rerun the previous search using the last hit’s sort values (including the tiebreaker) as the search_after argument. If using a PIT, use the latest PIT ID in the pit.id parameter. The search’s query and sort arguments must remain unchanged. If provided, the from argument must be 0 (default) or -1.

GET /_search
{
  "size": 10000,
  "query": {
    "match" : {
      "user.id" : "elkbee"
    }
  },
  "pit": {
	    "id":  "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==", 
	    "keep_alive": "1m"
  },
  "sort": [
    {"@timestamp": "asc"}
  ],
  "search_after": [                                
    4098435132000,
    4294967298
  ],
  "track_total_hits": false                        
}

You can repeat this process to get additional pages of results. If using a PIT, you can extend the PIT’s retention period using the keep_alive parameter of each search request.

When you’re finished, you should delete your PIT.

DELETE /_pit
{
    "id" : "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA=="
}

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.

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 /my-index-000001/_search?scroll=1m
{
  "size": 100,
  "query": {
    "match": {
      "message": "foo"
    }
  }
}

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==" 
}

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.

GET /_search?scroll=1m
{
  "sort": [
    "_doc"
  ]
}

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 Elasticsearch 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.

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.

You can check how many search contexts are open with the nodes stats API:

GET /_nodes/stats/indices/search

Search 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

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

GET /my-index-000001/_search?scroll=1m
{
  "slice": {
    "id": 0,                      
    "max": 2                      
  },
  "query": {
    "match": {
      "message": "foo"
    }
  }
}
GET /my-index-000001/_search?scroll=1m
{
  "slice": {
    "id": 1,
    "max": 2
  },
  "query": {
    "match": {
      "message": "foo"
    }
  }
}

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.

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:

GET /my-index-000001/_search?scroll=1m
{
  "slice": {
    "field": "@timestamp",
    "id": 0,
    "max": 10
  },
  "query": {
    "match": {
      "message": "foo"
    }
  }
}

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