756 lines
30 KiB
Markdown
756 lines
30 KiB
Markdown
---
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layout: default
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title: Profile
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nav_order: 55
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---
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# Profile
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The Profile API provides timing information about the execution of individual components of a search request. Using the Profile API, you can debug slow requests and understand how to improve their performance. The Profile API does not measure the following:
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- Network latency
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- Time spent in the search fetch phase
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- Amount of time a request spends in queues
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- Idle time while merging shard responses on the coordinating node
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The Profile API is a resource-consuming operation that adds overhead to search operations.
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{: .warning}
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#### Example request
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To use the Profile API, include the `profile` parameter set to `true` in the search request sent to the `_search` endpoint:
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```json
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GET /testindex/_search
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{
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"profile": true,
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"query" : {
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"match" : { "title" : "wind" }
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}
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}
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```
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{% include copy-curl.html %}
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To turn on human-readable format, include the `?human=true` query parameter in the request:
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```json
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GET /testindex/_search?human=true
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{
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"profile": true,
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"query" : {
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"match" : { "title" : "wind" }
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}
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}
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```
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{% include copy-curl.html %}
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The response contains an additional `time` field with human-readable units, for example:
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```json
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"collector": [
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{
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"name": "SimpleTopScoreDocCollector",
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"reason": "search_top_hits",
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"time": "113.7micros",
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"time_in_nanos": 113711
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}
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]
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```
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The Profile API response is verbose, so if you're running the request through the `curl` command, include the `?pretty` query parameter to make the response easier to understand.
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{: .tip}
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#### Example response
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The response contains profiling information:
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<details closed markdown="block">
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<summary>
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Response
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</summary>
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{: .text-delta}
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```json
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{
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"took": 21,
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"timed_out": false,
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"_shards": {
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"total": 1,
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"successful": 1,
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"skipped": 0,
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"failed": 0
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},
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"hits": {
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"total": {
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"value": 2,
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"relation": "eq"
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},
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"max_score": 0.19363807,
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"hits": [
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{
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"_index": "testindex",
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"_id": "1",
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"_score": 0.19363807,
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"_source": {
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"title": "The wind rises"
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}
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},
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{
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"_index": "testindex",
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"_id": "2",
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"_score": 0.17225474,
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"_source": {
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"title": "Gone with the wind",
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"description": "A 1939 American epic historical film"
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}
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}
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]
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},
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"profile": {
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"shards": [
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{
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"id": "[LidyZ1HVS-u93-73Z49dQg][testindex][0]",
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"inbound_network_time_in_millis": 0,
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"outbound_network_time_in_millis": 0,
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"searches": [
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{
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"query": [
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{
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"type": "BooleanQuery",
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"description": "title:wind title:rise",
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"time_in_nanos": 2473919,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
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"set_min_competitive_score": 0,
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"next_doc": 5209,
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"match": 0,
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"next_doc_count": 2,
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"score_count": 2,
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"compute_max_score_count": 0,
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"compute_max_score": 0,
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"advance": 9209,
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"advance_count": 2,
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"score": 20751,
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"build_scorer_count": 4,
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"create_weight": 1404458,
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"shallow_advance": 0,
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"create_weight_count": 1,
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"build_scorer": 1034292
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},
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"children": [
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{
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"type": "TermQuery",
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"description": "title:wind",
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"time_in_nanos": 813581,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
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"set_min_competitive_score": 0,
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"next_doc": 3291,
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"match": 0,
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"next_doc_count": 2,
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"score_count": 2,
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"compute_max_score_count": 0,
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"compute_max_score": 0,
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"advance": 7208,
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"advance_count": 2,
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"score": 18666,
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"build_scorer_count": 6,
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"create_weight": 616375,
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"shallow_advance": 0,
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"create_weight_count": 1,
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"build_scorer": 168041
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}
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},
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{
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"type": "TermQuery",
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"description": "title:rise",
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"time_in_nanos": 191083,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
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"set_min_competitive_score": 0,
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"next_doc": 0,
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"match": 0,
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"next_doc_count": 0,
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"score_count": 0,
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"compute_max_score_count": 0,
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"compute_max_score": 0,
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"advance": 0,
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"advance_count": 0,
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"score": 0,
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"build_scorer_count": 2,
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"create_weight": 188625,
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"shallow_advance": 0,
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"create_weight_count": 1,
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"build_scorer": 2458
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}
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}
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]
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}
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],
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"rewrite_time": 192417,
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"collector": [
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{
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"name": "SimpleTopScoreDocCollector",
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"reason": "search_top_hits",
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"time_in_nanos": 77291
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}
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]
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}
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],
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"aggregations": []
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}
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]
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}
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}
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```
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</details>
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## Response fields
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The response includes the following fields.
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Field | Data type | Description
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:--- | :--- | :---
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`profile` | Object | Contains profiling information.
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`profile.shards` | Array of objects | A search request can be executed against one or more shards in the index, and a search may involve one or more indexes. Thus, the `profile.shards` array contains profiling information for each shard that was involved in the search.
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`profile.shards.id` | String | The shard ID of the shard in the `[node-ID][index-name][shard-ID]` format.
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`profile.shards.searches` | Array of objects | A search represents a query executed against the underlying Lucene index. Most search requests execute a single search against a Lucene index, but some search requests can execute more than one search. For example, including a global aggregation results in a secondary `match_all` query for the global context. The `profile.shards` array contains profiling information about each search execution.
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[`profile.shards.searches.query`](#the-query-object) | Array of objects | Profiling information about the query execution.
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`profile.shards.searches.rewrite_time` | Integer | All Lucene queries are rewritten. A query and its children may be rewritten more than once, until the query stops changing. The rewriting process involves performing optimizations, such as removing redundant clauses or replacing a query path with a more efficient one. After the rewriting process, the original query may change significantly. The `rewrite_time` field contains the cumulative total rewrite time for the query and all its children, in nanoseconds.
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[`profile.shards.searches.collector`](#the-collector-array) | Array of objects | Profiling information about the Lucene collectors that ran the search.
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[`profile.shards.aggregations`](#aggregations) | Array of objects | Profiling information about the aggregation execution.
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### The `query` object
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The `query` object contains the following fields.
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Field | Data type | Description
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:--- | :--- | :---
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`type` | String | The Lucene query type into which the search query was rewritten. Corresponds to the Lucene class name (which often has the same name in OpenSearch).
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`description` | String | Contains a Lucene explanation of the query. Helps differentiate queries with the same type.
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`time_in_nanos` | Long | The amount of time the query took to execute, in nanoseconds. In a parent query, the time is inclusive of the execution times of all the child queries.
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[`breakdown`](#the-breakdown-object) | Object | Contains timing statistics about low-level Lucene execution.
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`children` | Array of objects | If a query has subqueries (children), this field contains information about the subqueries.
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### The `breakdown` object
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The `breakdown` object represents the timing statistics about low-level Lucene execution, broken down by method. Timings are listed in wall-clock nanoseconds and are not normalized. The `breakdown` timings are inclusive of all child times. The `breakdown` object comprises the following fields. All fields contain integer values.
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Field | Description
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:--- | :---
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`create_weight` | A `Query` object in Lucene is immutable. Yet, Lucene should be able to reuse `Query` objects in multiple `IndexSearcher` objects. Thus, `Query` objects need to keep temporary state and statistics associated with the index in which the query is executed. To achieve reuse, every `Query` object generates a `Weight` object, which keeps the temporary context (state) associated with the `<IndexSearcher, Query>` tuple. The `create_weight` field contains the amount of time spent creating the `Weight` object.
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`build_scorer` | A `Scorer` iterates over matching documents and generates a score for each document. The `build_scorer` field contains the amount of time spent generating the `Scorer` object. This does not include the time spent scoring the documents. The `Scorer` initialization time depends on the optimization and complexity of a particular query. The `build_scorer` parameter also includes the amount of time associated with caching, if caching is applicable and enabled for the query.
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`next_doc` | The `next_doc` Lucene method returns the document ID of the next document that matches the query. This method is a special type of the `advance` method and is equivalent to `advance(docId() + 1)`. The `next_doc` method is more convenient for many Lucene queries. The `next_doc` field contains the amount of time required to determine the next matching document, which varies depending on the query type.
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`advance` | The `advance` method is a lower-level version of the `next_doc` method in Lucene. It also finds the next matching document but necessitates that the calling query perform additional tasks, such as identifying skips. Some queries, such as conjunctions (`must` clauses in Boolean queries), cannot use `next_doc`. For those queries, `advance` is timed.
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`match` | For some queries, document matching is performed in two steps. First, the document is matched approximately. Second, those documents that are approximately matched are examined through a more comprehensive process. For example, a phrase query first checks whether a document contains all terms in the phrase. Next, it verifies that the terms are in order (which is a more expensive process). The `match` field is non-zero only for those queries that use the two-step verification process.
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`score` | Contains the time taken for a `Scorer` to score a particular document.
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`shallow_advance` | Contains the amount of time required to execute the `advanceShallow` Lucene method.
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`compute_max_score` | Contains the amount of time required to execute the `getMaxScore` Lucene method.
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`set_min_competitive_score` | Contains the amount of time required to execute the `setMinCompetitiveScore` Lucene method.
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`<method>_count` | Contains the number of invocations of a `<method>`. For example, `advance_count` contains the number of invocations of the `advance` method. Different invocations of the same method occur because the method is called on different documents. You can determine the selectivity of a query by comparing counts in different query components.
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### The `collector` array
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The `collector` array contains information about Lucene Collectors. A Collector is responsible for coordinating document traversal and scoring and collecting matching documents. Using Collectors, individual queries can record aggregation results and execute global queries or post-query filters.
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Field | Description
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:--- | :---
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`name` | The collector name. In the [example response](#example-response), the `collector` is a single `SimpleTopScoreDocCollector`---the default scoring and sorting collector.
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`reason` | Contains a description of the collector. For possible field values, see [Collector reasons](#collector-reasons).
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`time_in_nanos` | A wall-clock time, including timing for all children.
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`children` | If a collector has subcollectors (children), this field contains information about the subcollectors.
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Collector times are calculated, combined, and normalized independently, so they are independent of query times.
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{: .note}
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#### Collector reasons
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The following table describes all available collector reasons.
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Reason | Description
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:--- | :---
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`search_sorted` | A collector that scores and sorts documents. Present in most simple searches.
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`search_count` | A collector that counts the number of matching documents but does not fetch the source. Present when `size: 0` is specified.
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`search_terminate_after_count` | A collector that searches for matching documents and terminates the search when it finds a specified number of documents. Present when the `terminate_after_count` query parameter is specified.
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`search_min_score` | A collector that returns matching documents that have a score greater than a minimum score. Present when the `min_score` parameter is specified.
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`search_multi` | A wrapper collector for other collectors. Present when search, aggregations, global aggregations, and post filters are combined in a single search.
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`search_timeout` | A collector that stops running after a specified period of time. Present when a `timeout` parameter is specified.
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`aggregation` | A collector for aggregations that is run against the specified query scope. OpenSearch uses a single `aggregation` collector to collect documents for all aggregations.
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`global_aggregation` | A collector that is run against the global query scope. Global scope is different from a specified query scope, so in order to collect the entire dataset, a `match_all` query must be run.
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## Aggregations
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To profile aggregations, send an aggregation request and provide the `profile` parameter set to `true`.
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#### Example request: Global aggregation
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```json
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GET /opensearch_dashboards_sample_data_ecommerce/_search
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{
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"profile": "true",
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"size": 0,
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"query": {
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"match": { "manufacturer": "Elitelligence" }
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},
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"aggs": {
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"all_products": {
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"global": {},
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"aggs": {
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"avg_price": { "avg": { "field": "taxful_total_price" } }
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}
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},
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"elitelligence_products": { "avg": { "field": "taxful_total_price" } }
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response: Global aggregation
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The response contains profiling information:
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<details closed markdown="block">
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<summary>
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Response
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</summary>
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{: .text-delta}
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```json
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{
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"took": 10,
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"timed_out": false,
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"_shards": {
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"total": 1,
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"successful": 1,
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"skipped": 0,
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"failed": 0
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},
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"hits": {
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"total": {
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"value": 1370,
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"relation": "eq"
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},
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"max_score": null,
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"hits": []
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},
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"aggregations": {
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"all_products": {
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"doc_count": 4675,
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"avg_price": {
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"value": 75.05542864304813
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}
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},
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"elitelligence_products": {
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"value": 68.4430200729927
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}
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},
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"profile": {
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"shards": [
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{
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"id": "[LidyZ1HVS-u93-73Z49dQg][opensearch_dashboards_sample_data_ecommerce][0]",
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"inbound_network_time_in_millis": 0,
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"outbound_network_time_in_millis": 0,
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"searches": [
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{
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"query": [
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{
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"type": "ConstantScoreQuery",
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"description": "ConstantScore(manufacturer:elitelligence)",
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"time_in_nanos": 1367487,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
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"set_min_competitive_score": 0,
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"next_doc": 634321,
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"match": 0,
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"next_doc_count": 1370,
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"score_count": 0,
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"compute_max_score_count": 0,
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"compute_max_score": 0,
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"advance": 173250,
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"advance_count": 2,
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"score": 0,
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"build_scorer_count": 4,
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"create_weight": 132458,
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"shallow_advance": 0,
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"create_weight_count": 1,
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"build_scorer": 427458
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},
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"children": [
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{
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"type": "TermQuery",
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"description": "manufacturer:elitelligence",
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"time_in_nanos": 1174794,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
|
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"set_min_competitive_score": 0,
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"next_doc": 470918,
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"match": 0,
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"next_doc_count": 1370,
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"score_count": 0,
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"compute_max_score_count": 0,
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"compute_max_score": 0,
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"advance": 172084,
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"advance_count": 2,
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"score": 0,
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"build_scorer_count": 4,
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"create_weight": 114041,
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"shallow_advance": 0,
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"create_weight_count": 1,
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"build_scorer": 417751
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}
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}
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]
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}
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],
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"rewrite_time": 42542,
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"collector": [
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{
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"name": "MultiCollector",
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"reason": "search_multi",
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"time_in_nanos": 778406,
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"children": [
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{
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"name": "EarlyTerminatingCollector",
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"reason": "search_count",
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"time_in_nanos": 70290
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},
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{
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"name": "ProfilingAggregator: [elitelligence_products]",
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"reason": "aggregation",
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"time_in_nanos": 502780
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}
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]
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}
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]
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},
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{
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"query": [
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{
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"type": "ConstantScoreQuery",
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"description": "ConstantScore(*:*)",
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"time_in_nanos": 995345,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
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"set_min_competitive_score": 0,
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"next_doc": 930803,
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"match": 0,
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"next_doc_count": 4675,
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"score_count": 0,
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"compute_max_score_count": 0,
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"compute_max_score": 0,
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"advance": 2209,
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"advance_count": 2,
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"score": 0,
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"build_scorer_count": 4,
|
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"create_weight": 23875,
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"shallow_advance": 0,
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"create_weight_count": 1,
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"build_scorer": 38458
|
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},
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"children": [
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{
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"type": "MatchAllDocsQuery",
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"description": "*:*",
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"time_in_nanos": 431375,
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"breakdown": {
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"set_min_competitive_score_count": 0,
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"match_count": 0,
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"shallow_advance_count": 0,
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"set_min_competitive_score": 0,
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"next_doc": 389875,
|
||
"match": 0,
|
||
"next_doc_count": 4675,
|
||
"score_count": 0,
|
||
"compute_max_score_count": 0,
|
||
"compute_max_score": 0,
|
||
"advance": 1167,
|
||
"advance_count": 2,
|
||
"score": 0,
|
||
"build_scorer_count": 4,
|
||
"create_weight": 9458,
|
||
"shallow_advance": 0,
|
||
"create_weight_count": 1,
|
||
"build_scorer": 30875
|
||
}
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"rewrite_time": 8792,
|
||
"collector": [
|
||
{
|
||
"name": "ProfilingAggregator: [all_products]",
|
||
"reason": "aggregation_global",
|
||
"time_in_nanos": 1310536
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"aggregations": [
|
||
{
|
||
"type": "AvgAggregator",
|
||
"description": "elitelligence_products",
|
||
"time_in_nanos": 319918,
|
||
"breakdown": {
|
||
"reduce": 0,
|
||
"post_collection_count": 1,
|
||
"build_leaf_collector": 130709,
|
||
"build_aggregation": 2709,
|
||
"build_aggregation_count": 1,
|
||
"build_leaf_collector_count": 2,
|
||
"post_collection": 584,
|
||
"initialize": 4750,
|
||
"initialize_count": 1,
|
||
"reduce_count": 0,
|
||
"collect": 181166,
|
||
"collect_count": 1370
|
||
}
|
||
},
|
||
{
|
||
"type": "GlobalAggregator",
|
||
"description": "all_products",
|
||
"time_in_nanos": 1519340,
|
||
"breakdown": {
|
||
"reduce": 0,
|
||
"post_collection_count": 1,
|
||
"build_leaf_collector": 134625,
|
||
"build_aggregation": 59291,
|
||
"build_aggregation_count": 1,
|
||
"build_leaf_collector_count": 2,
|
||
"post_collection": 5041,
|
||
"initialize": 24500,
|
||
"initialize_count": 1,
|
||
"reduce_count": 0,
|
||
"collect": 1295883,
|
||
"collect_count": 4675
|
||
},
|
||
"children": [
|
||
{
|
||
"type": "AvgAggregator",
|
||
"description": "avg_price",
|
||
"time_in_nanos": 775967,
|
||
"breakdown": {
|
||
"reduce": 0,
|
||
"post_collection_count": 1,
|
||
"build_leaf_collector": 98999,
|
||
"build_aggregation": 33083,
|
||
"build_aggregation_count": 1,
|
||
"build_leaf_collector_count": 2,
|
||
"post_collection": 2209,
|
||
"initialize": 1708,
|
||
"initialize_count": 1,
|
||
"reduce_count": 0,
|
||
"collect": 639968,
|
||
"collect_count": 4675
|
||
}
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
</details>
|
||
|
||
#### Example request: Non-global aggregation
|
||
|
||
```json
|
||
GET /opensearch_dashboards_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"avg_taxful_total_price": {
|
||
"avg": {
|
||
"field": "taxful_total_price"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
{% include copy-curl.html %}
|
||
|
||
#### Example response: Non-global aggregation
|
||
|
||
The response contains profiling information:
|
||
|
||
<details closed markdown="block">
|
||
<summary>
|
||
Response
|
||
</summary>
|
||
{: .text-delta}
|
||
|
||
```json
|
||
{
|
||
"took": 13,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 1,
|
||
"successful": 1,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 4675,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": null,
|
||
"hits": []
|
||
},
|
||
"aggregations": {
|
||
"avg_taxful_total_price": {
|
||
"value": 75.05542864304813
|
||
}
|
||
},
|
||
"profile": {
|
||
"shards": [
|
||
{
|
||
"id": "[LidyZ1HVS-u93-73Z49dQg][opensearch_dashboards_sample_data_ecommerce][0]",
|
||
"inbound_network_time_in_millis": 0,
|
||
"outbound_network_time_in_millis": 0,
|
||
"searches": [
|
||
{
|
||
"query": [
|
||
{
|
||
"type": "ConstantScoreQuery",
|
||
"description": "ConstantScore(*:*)",
|
||
"time_in_nanos": 1690820,
|
||
"breakdown": {
|
||
"set_min_competitive_score_count": 0,
|
||
"match_count": 0,
|
||
"shallow_advance_count": 0,
|
||
"set_min_competitive_score": 0,
|
||
"next_doc": 1614112,
|
||
"match": 0,
|
||
"next_doc_count": 4675,
|
||
"score_count": 0,
|
||
"compute_max_score_count": 0,
|
||
"compute_max_score": 0,
|
||
"advance": 2708,
|
||
"advance_count": 2,
|
||
"score": 0,
|
||
"build_scorer_count": 4,
|
||
"create_weight": 20250,
|
||
"shallow_advance": 0,
|
||
"create_weight_count": 1,
|
||
"build_scorer": 53750
|
||
},
|
||
"children": [
|
||
{
|
||
"type": "MatchAllDocsQuery",
|
||
"description": "*:*",
|
||
"time_in_nanos": 770902,
|
||
"breakdown": {
|
||
"set_min_competitive_score_count": 0,
|
||
"match_count": 0,
|
||
"shallow_advance_count": 0,
|
||
"set_min_competitive_score": 0,
|
||
"next_doc": 721943,
|
||
"match": 0,
|
||
"next_doc_count": 4675,
|
||
"score_count": 0,
|
||
"compute_max_score_count": 0,
|
||
"compute_max_score": 0,
|
||
"advance": 1042,
|
||
"advance_count": 2,
|
||
"score": 0,
|
||
"build_scorer_count": 4,
|
||
"create_weight": 5041,
|
||
"shallow_advance": 0,
|
||
"create_weight_count": 1,
|
||
"build_scorer": 42876
|
||
}
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"rewrite_time": 22000,
|
||
"collector": [
|
||
{
|
||
"name": "MultiCollector",
|
||
"reason": "search_multi",
|
||
"time_in_nanos": 3672676,
|
||
"children": [
|
||
{
|
||
"name": "EarlyTerminatingCollector",
|
||
"reason": "search_count",
|
||
"time_in_nanos": 78626
|
||
},
|
||
{
|
||
"name": "ProfilingAggregator: [avg_taxful_total_price]",
|
||
"reason": "aggregation",
|
||
"time_in_nanos": 2834566
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"aggregations": [
|
||
{
|
||
"type": "AvgAggregator",
|
||
"description": "avg_taxful_total_price",
|
||
"time_in_nanos": 1973702,
|
||
"breakdown": {
|
||
"reduce": 0,
|
||
"post_collection_count": 1,
|
||
"build_leaf_collector": 199292,
|
||
"build_aggregation": 13584,
|
||
"build_aggregation_count": 1,
|
||
"build_leaf_collector_count": 2,
|
||
"post_collection": 6125,
|
||
"initialize": 6916,
|
||
"initialize_count": 1,
|
||
"reduce_count": 0,
|
||
"collect": 1747785,
|
||
"collect_count": 4675
|
||
}
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
</details>
|
||
|
||
### Response fields
|
||
|
||
The `aggregations` array contains aggregation objects with the following fields.
|
||
|
||
Field | Data type | Description
|
||
:--- | :--- | :---
|
||
`type` | String | The aggregator type. In the [non-global aggregation example response](#example-response-non-global-aggregation), the aggregator type is `AvgAggregator`. [Global aggregation example response](#example-request-global-aggregation) contains a `GlobalAggregator` with an `AvgAggregator` child.
|
||
`description` | String | Contains a Lucene explanation of the aggregation. Helps differentiate aggregations with the same type.
|
||
`time_in_nanos` | Long | The amount of time taken to execute the aggregation, in nanoseconds. In a parent aggregation, the time is inclusive of the execution times of all the child aggregations.
|
||
[`breakdown`](#the-breakdown-object-1) | Object | Contains timing statistics about low-level Lucene execution.
|
||
`children` | Array of objects | If an aggregation has subaggregations (children), this field contains information about the subaggregations.
|
||
`debug` | Object | Some aggregations return a `debug` object that describes the details of the underlying execution.
|
||
|
||
### The `breakdown` object
|
||
|
||
The `breakdown` object represents the timing statistics about low-level Lucene execution, broken down by method. Each field in the `breakdown` object represents an internal Lucene method executed within the aggregation. Timings are listed in wall-clock nanoseconds and are not normalized. The `breakdown` timings are inclusive of all child times. The `breakdown` object is comprised of the following fields. All fields contain integer values.
|
||
|
||
Field | Description
|
||
:--- | :---
|
||
`initialize` | Contains the amount of time taken to execute the `preCollection()` callback method during `AggregationCollectorManager` creation.
|
||
`build_leaf_collector`| Contains the time spent running the `getLeafCollector()` method of the aggregation, which creates a new collector to collect the given context.
|
||
`collect`| Contains the time spent collecting the documents into buckets.
|
||
`post_collection`| Contains the time spent running the aggregation’s `postCollection()` callback method.
|
||
`build_aggregation`| Contains the time spent running the aggregation’s `buildAggregations()` method, which builds the results of this aggregation.
|
||
`reduce`| Contains the time spent in the `reduce` phase.
|
||
`<method>_count` | Contains the number of invocations of a `<method>`. For example, `build_leaf_collector_count` contains the number of invocations of the `build_leaf_collector` method. |