Before you read this guide, we recommend you run the Elasticsearch Error Check-Up which detects issues in ES causing log errors (free tool that requires no installation).
This guide will help you check for common problems that cause the log “`search_after` cannot be used in a scroll context.” to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts: scroll, search.
Advanced users might want to skip right to the common problems section in each concept or try running the Check-Up which analyses ES to discover the cause of many errors and provides suitable actionable recommendations how to resolve them (join over 700 users who use this free tool).
Scroll in Elasticsearch
What it is:
In Elasticsearch, the concept of scroll comes into play when you have a large set of search results. Large search results are exhaustive for both the Elasticsearch cluster and the requesting client in terms of memory and processing. The scroll API enables you to take a snapshot of a large number of results from a single search request.
Examples:
To perform a scroll search, you need to add the scroll parameter to a search query and specify how long Elasticsearch should keep the search context viable.
GET mydocs-2019/_search?scroll=40s
{
"size": 5000,
"query": {
"match_all": {}
},
"sort": [
{
"_doc": {
"order": "asc"
}
}
]
}This query will return a maximum of 5000 hits and if the scroll is idle for more than 40 seconds, then it will be deleted. The response will return the first page of the results and a scroll id. You can use the scroll id to get additional documents from the scroll. You’re able to keep retrieving the documents until you have all of them.
Notes:
- Changes made to documents after the scroll will not show up in your results.
- When you are done with the scroll, you can delete it manually using the scroll id.
DELETE _search/scroll/<scroll_id>
Search in Elasticsearch
What it is:
Search refers to the searching of documents in an index or multiple indices. The simple search is just a GET request to the _search endpoint. The search query can either be provided in query string or through a request body.
Examples:
When looking for any documents in this index, if search parameters are not provided, every document is a hit and by default 10 hits will be returned.
GET my_documents/_search
A JSON object is returned in response to a search query. A 200 response code will mean the request completed successfully.
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
...
]
}
}Notes:
- Distributed search is challenging and every shard of the index needs to be searched for hits, and then those hits are combined into a single sorted list as a final result.
- There are two phases of search: the query phase and the fetch phase.
- In the query phase, the query is executed on each shard locally and top hits are returned to the coordinating node. The coordinating node merges the results and creates a global sorted list.
- In the fetch phase, the coordinating node brings the actual documents for those hit IDs and returns them to the requesting client.
- A coordinating node needs enough memory and CPU in order to handle the fetch phase.
Log Context
Log”`search_after` cannot be used in a scroll context.”classname is SearchService.java We extracted the following from Elasticsearch source code for those seeking an in-depth context :
if (source.stats() != null) {
context.groupStats(source.stats());
}
if (CollectionUtils.isEmpty(source.searchAfter()) == false) {
if (context.scrollContext() != null) {
throw new SearchException(shardTarget; "`search_after` cannot be used in a scroll context.");
}
if (context.from() > 0) {
throw new SearchException(shardTarget; "`from` parameter must be set to 0 when `search_after` is used.");
}
FieldDoc fieldDoc = SearchAfterBuilder.buildFieldDoc(context.sort(); source.searchAfter());
