本篇文章为你整理了Paginate search results（）的详细内容，包含有 Paginate search results，希望能帮助你了解 Paginate search results。
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.
Elasticsearch uses Lucene s internal doc IDs as tie-breakers. These internal doc
IDs can be completely different across replicas of the same data. When paging
search hits, you might occasionally see that documents with the same sort values
are not ordered consistently.
Search afteredit
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. The first step is to run an initial request. The following
example sorts the results by two fields (date and tie_breaker_id):
GET twitter/_search
"query": {
"match": {
"title": "elasticsearch"
"sort": [
{"date": "asc"},
{"tie_breaker_id": "asc"}
}
The search response includes an array of sort values for each hit:
{
"took" : 17,
"timed_out" : false,
"_shards" : ...,
"hits" : {
"total" : ...,
"max_score" : null,
"hits" : [
"_index" : "twitter",
"_id" : "654322",
"_score" : null,
"_source" : ...,
"sort" : [
1463538855,
"654322"
"_index" : "twitter",
"_id" : "654323",
"_score" : null,
"_source" : ...,
"sort" : [
1463538857,
"654323"
}
To retrieve the next page of results, repeat the request, take the sort values from the
last hit, and insert those into the search_after array:
GET twitter/_search
"query": {
"match": {
"title": "elasticsearch"
"search_after": [1463538857, "654323"],
"sort": [
{"date": "asc"},
{"tie_breaker_id": "asc"}
}
Repeat this process by updating the search_after array every time you retrieve a
new page of results. 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
{
"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.
All PIT search requests add an implicit sort tiebreaker field called _shard_doc,
which can also be provided explicitly.
If you cannot use a PIT, we recommend that you include a tiebreaker field
in your sort. This tiebreaker field should contain a unique value for each document.
If you don t include a tiebreaker field, your paged results could miss or duplicate hits.
Search after requests have optimizations that make them faster when the sort
order is _shard_doc and total hits are not tracked. If you want to iterate over all documents regardless of the
order, this is the most efficient option.
If the sort field is a date in some target data streams or indices
but a date_nanos field in other targets, use the numeric_type parameter
to convert the values to a single resolution and the format parameter to specify a
date format for the sort field. Otherwise, Elasticsearch won t interpret
the search after parameter correctly in each request.
"pit": {
"id": "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==",
"keep_alive": "1m"
"sort": [
{"@timestamp": {"order": "asc", "format": "strict_date_optional_time_nanos", "numeric_type" : "date_nanos" }}
}
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 automatically 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": {"order": "asc", "format": "strict_date_optional_time_nanos"}},
{"_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" : [
"2021-05-20T05:30:04.832Z",
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": {"order": "asc", "format": "strict_date_optional_time_nanos"}}
"search_after": [
"2021-05-20T05:30:04.832Z",
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=="
}
We no longer recommend using the scroll API for deep pagination. If
you need to preserve the index state while paging through more than 10,000 hits,
use the search_after parameter with a point in time (PIT).
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.
Some of the officially supported clients provide helpers to assist with
scrolled searches and reindexing:
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 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.
response = client.scroll(
body: {
scroll: 1m,
scroll_id: DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==
puts response
"scroll": "1m",
"scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ=="
}`)),
es.Scroll.WithPretty(),
fmt.Println(res, err)
POST /_search/scroll
"scroll" : "1m",
"scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ=="
}
GET or POST can be used and the URL should not include the index
name this is specified in the original search request instead.
The scroll parameter tells Elasticsearch to keep the search context open
for another 1m.
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:
Keeping the search context aliveedit
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.
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.
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
nodes stats API:
$params = [
metric = indices,
index_metric = search,
$response = $client- nodes()- stats($params);
resp = client.nodes.stats(metric="indices", index_metric="search")
print(resp)
es.Nodes.Stats.WithMetric([]string{"indices"}...),
es.Nodes.Stats.WithIndexMetric([]string{"search"}...),
fmt.Println(res, err)
Clear scrolledit
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:
response = client.clear_scroll(
body: {
scroll_id: DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==
puts response
es.ClearScroll.WithBody(strings.NewReader(`{
"scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ=="
}`)),
fmt.Println(res, err)
DELETE /_search/scroll
"scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ=="
}
DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,
DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB
puts response
"DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==",
"DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB"
}`)),
fmt.Println(res, err)
DELETE /_search/scroll
"scroll_id" : [
"DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==",
"DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB"
}
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:
$params = [
scroll_id = DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB,
$response = $client- clearScroll($params);
"DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==",
"DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB",
print(resp)
response = client.clear_scroll(
scroll_id: DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB
puts response
res, err := es.ClearScroll(
es.ClearScroll.WithScrollID("DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==", "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB"),
fmt.Println(res, err)
const response = await client.clearScroll({
scroll_id: DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB
console.log(response)
DELETE /_search/scroll/DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAD4WYm9laVYtZndUQlNsdDcwakFMNjU1QQ==,DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAABFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAAAxZrUllkUVlCa1NqNmRMaUhiQlZkMWFBAAAAAAAAAAIWa1JZZFFZQmtTajZkTGlIYkJWZDFhQQAAAAAAAAAFFmtSWWRRWUJrU2o2ZExpSGJCVmQxYUEAAAAAAAAABBZrUllkUVlCa1NqNmRMaUhiQlZkMWFB
Sliced scrolledit
When paging through a large number of documents, it can be helpful to split the search into multiple slices
to consume them 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 first on the
shards, then locally on each shard using the _id field. The local splitting follows the formula
slice(doc) = floorMod(hashCode(doc._id), max)).
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.
The point-in-time API supports a more efficient partitioning strategy and
does not suffer from this problem. When possible, it s recommended to use a point-in-time search
with slicing instead of a scroll.
Another way to avoid this high cost is to use the doc_values of another field to do the slicing.
The field must have the following properties:
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.
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