Introduction
Efficient querying is crucial for maintaining high performance in Elasticsearch clusters. In this article, we will discuss advanced techniques to optimize Elasticsearch query performance, including using filters, query rewriting, and caching. If you want to learn more about Elasticsearch search, check out this guide.
1. Use Filters for Non-Scoring Queries
When you don’t need to calculate a relevance score for your query results, use filters instead of match type of queries. Filters are faster because they don’t perform scoring calculations and are generallycached for better performance. For example, use the `bool` query with `filter` clause:
GET /_search
{
"query": {
"bool": {
"filter": [
{ "term": { "status": "published" }},
{ "range": { "publish_date": { "gte": "now-1d" }}}
]
}
}
}2. Rewrite Queries for Better Performance
Some queries can be rewritten to improve performance without changing the results. For example, the `match_phrase` query can be slower than a `span_near` query with the same parameters. Replace the `match_phrase` query with a `span_near` query:
GET /_search
{
"query": {
"span_near": {
"clauses": [
{ "span_term": { "content": "quick" }},
{ "span_term": { "content": "brown" }},
{ "span_term": { "content": "fox" }}
],
"slop": 0,
"in_order": true
}
}
}3. Use the `search_after` Parameter for Pagination
When paginating through large result sets, avoid using the `from` and `size` parameters, as they can cause performance issues. Instead, use the `search_after` parameter to paginate more efficiently:
GET /_search
{
"size": 10,
"query": { "match_all": {} },
"sort": [
{ "date": "asc" },
{ "_id": "asc" }
],
"search_after": ["2022-01-01T00:00:00", "doc_id"]
}4. Leverage the Query Cache
Caching is enabled by default, so forcing cache will only make a difference if the cache is disabled at the index level, for more information follow this link: https://stackoverflow.com/a/63828533/3112848
To take advantage of this feature, use the `request_cache` parameter to force caching for specific requests:
GET /_search?request_cache=true
{
"query": {
"bool": {
"filter": [
{ "term": { "status": "published" }},
{ "range": { "publish_date": { "gte": "now-1d" }}}
]
}
}
}5. Use the `profile` API to Identify Slow Queries
The `profile` API can help you identify slow queries and understand their performance characteristics. Add the `profile` parameter to your search request to get detailed profiling information:
GET /_search
{
"profile": true,
"query": {
"match": {
"title": "elasticsearch"
}
}
}Conclusion
Optimizing Elasticsearch query performance is essential for maintaining high-performance clusters. By using filters, rewriting queries, leveraging caching, and utilizing the `profile` API, you can significantly improve the efficiency of your Elasticsearch queries.
