Profile API

The query section contains detailed timing of the query tree executed by Lucene on a particular shard. The overall structure of this query tree will resemble your original Elasticsearch query, but may be slightly (or sometimes very) different. It will also use similar but not always identical naming. Using our previous match query example, let’s analyze the query section:

"query": [
    {
       "type": "BooleanQuery",
       "description": "message:get message:search",
       "time_in_nanos": "11972972",
       "breakdown": {...},               
       "children": [
          {
             "type": "TermQuery",
             "description": "message:get",
             "time_in_nanos": "3801935",
             "breakdown": {...}
          },
          {
             "type": "TermQuery",
             "description": "message:search",
             "time_in_nanos": "205654",
             "breakdown": {...}
          }
       ]
    }
]

Based on the profile structure, we can see that our match query was rewritten by Lucene into a BooleanQuery with two clauses (both holding a TermQuery). The type field displays the Lucene class name, and often aligns with the equivalent name in Elasticsearch. The description field displays the Lucene explanation text for the query, and is made available to help differentiating between parts of your query (e.g. both message:get and message:search are TermQuery’s and would appear identical otherwise.

The time_in_nanos field shows that this query took ~11.9ms for the entire BooleanQuery to execute. The recorded time is inclusive of all children.

The breakdown field will give detailed stats about how the time was spent, we’ll look at that in a moment. Finally, the children array lists any sub-queries that may be present. Because we searched for two values ("get search"), our BooleanQuery holds two children TermQueries. They have identical information (type, time, breakdown, etc). Children are allowed to have their own children.

The breakdown component lists detailed timing statistics about low-level Lucene execution:

"breakdown": {
  "set_min_competitive_score_count": 0,
  "match_count": 5,
  "shallow_advance_count": 0,
  "set_min_competitive_score": 0,
  "next_doc": 39022,
  "match": 4456,
  "next_doc_count": 5,
  "score_count": 5,
  "compute_max_score_count": 0,
  "compute_max_score": 0,
  "advance": 84525,
  "advance_count": 1,
  "score": 37779,
  "build_scorer_count": 2,
  "create_weight": 4694895,
  "shallow_advance": 0,
  "create_weight_count": 1,
  "build_scorer": 7112295,
  "count_weight": 0,
  "count_weight_count": 0
}

Timings are listed in wall-clock nanoseconds and are not normalized at all. All caveats about the overall time_in_nanos apply here. The intention of the breakdown is to give you a feel for A) what machinery in Lucene is actually eating time, and B) the magnitude of differences in times between the various components. Like the overall time, the breakdown is inclusive of all children times.

The meaning of the stats are as follows:

The Collectors portion of the response shows high-level execution details. Lucene works by defining a "Collector" which is responsible for coordinating the traversal, scoring, and collection of matching documents. Collectors are also how a single query can record aggregation results, execute unscoped "global" queries, execute post-query filters, etc.

Looking at the previous example:

"collector": [
  {
    "name": "SimpleTopScoreDocCollector",
    "reason": "search_top_hits",
    "time_in_nanos": 775274
  }
]

We see a single collector named SimpleTopScoreDocCollector wrapped into CancellableCollector. SimpleTopScoreDocCollector is the default "scoring and sorting" Collector used by Elasticsearch. The reason field attempts to give a plain English description of the class name. The time_in_nanos is similar to the time in the Query tree: a wall-clock time inclusive of all children. Similarly, children lists all sub-collectors. The CancellableCollector that wraps SimpleTopScoreDocCollector is used by Elasticsearch to detect if the current search was cancelled and stop collecting documents as soon as it occurs.

It should be noted that Collector times are independent from the Query times. They are calculated, combined, and normalized independently! Due to the nature of Lucene’s execution, it is impossible to "merge" the times from the Collectors into the Query section, so they are displayed in separate portions.

For reference, the various collector reasons are:

All queries in Lucene undergo a "rewriting" process. A query (and its sub-queries) may be rewritten one or more times, and the process continues until the query stops changing. This process allows Lucene to perform optimizations, such as removing redundant clauses, replacing one query for a more efficient execution path, etc. For example a Boolean → Boolean → TermQuery can be rewritten to a TermQuery, because all the Booleans are unnecessary in this case.

The rewriting process is complex and difficult to display, since queries can change drastically. Rather than showing the intermediate results, the total rewrite time is simply displayed as a value (in nanoseconds). This value is cumulative and contains the total time for all queries being rewritten.

To demonstrate a slightly more complex query and the associated results, we can profile the following query:

response = client.search(
  index: 'my-index-000001',
  body: {
    profile: true,
    query: {
      term: {
        "user.id": {
          value: 'elkbee'
        }
      }
    },
    aggregations: {
      my_scoped_agg: {
        terms: {
          field: 'http.response.status_code'
        }
      },
      my_global_agg: {
        global: {},
        aggregations: {
          my_level_agg: {
            terms: {
              field: 'http.response.status_code'
            }
          }
        }
      }
    },
    post_filter: {
      match: {
        message: 'search'
      }
    }
  }
)
puts response

GET /my-index-000001/_search
{
  "profile": true,
  "query": {
    "term": {
      "user.id": {
        "value": "elkbee"
      }
    }
  },
  "aggs": {
    "my_scoped_agg": {
      "terms": {
        "field": "http.response.status_code"
      }
    },
    "my_global_agg": {
      "global": {},
      "aggs": {
        "my_level_agg": {
          "terms": {
            "field": "http.response.status_code"
          }
        }
      }
    }
  },
  "post_filter": {
    "match": {
      "message": "search"
    }
  }
}

This example has:

The API returns the following result:

{
  ...
  "profile": {
    "shards": [
      {
        "id": "[P6-vulHtQRWuD4YnubWb7A][my-index-000001][0]",
        "searches": [
          {
            "query": [
              {
                "type": "TermQuery",
                "description": "message:search",
                "time_in_nanos": 141618,
                "breakdown": {
                  "set_min_competitive_score_count": 0,
                  "match_count": 0,
                  "shallow_advance_count": 0,
                  "set_min_competitive_score": 0,
                  "next_doc": 0,
                  "match": 0,
                  "next_doc_count": 0,
                  "score_count": 0,
                  "compute_max_score_count": 0,
                  "compute_max_score": 0,
                  "advance": 3942,
                  "advance_count": 4,
                  "count_weight_count": 0,
                  "score": 0,
                  "build_scorer_count": 2,
                  "create_weight": 38380,
                  "shallow_advance": 0,
                  "count_weight": 0,
                  "create_weight_count": 1,
                  "build_scorer": 99296
                }
              },
              {
                "type": "TermQuery",
                "description": "user.id:elkbee",
                "time_in_nanos": 163081,
                "breakdown": {
                  "set_min_competitive_score_count": 0,
                  "match_count": 0,
                  "shallow_advance_count": 0,
                  "set_min_competitive_score": 0,
                  "next_doc": 2447,
                  "match": 0,
                  "next_doc_count": 4,
                  "score_count": 4,
                  "compute_max_score_count": 0,
                  "compute_max_score": 0,
                  "advance": 3552,
                  "advance_count": 1,
                  "score": 5027,
                  "count_weight_count": 0,
                  "build_scorer_count": 2,
                  "create_weight": 107840,
                  "shallow_advance": 0,
                  "count_weight": 0,
                  "create_weight_count": 1,
                  "build_scorer": 44215
                }
              }
            ],
            "rewrite_time": 4769,
            "collector": [
              {
                "name": "MultiCollector",
                "reason": "search_multi",
                "time_in_nanos": 1945072,
                "children": [
                  {
                    "name": "FilteredCollector",
                    "reason": "search_post_filter",
                    "time_in_nanos": 500850,
                    "children": [
                      {
                        "name": "SimpleTopScoreDocCollector",
                        "reason": "search_top_hits",
                        "time_in_nanos": 22577
                      }
                    ]
                  },
                  {
                    "name": "BucketCollectorWrapper: [BucketCollectorWrapper[bucketCollector=[my_scoped_agg, my_global_agg]]]",
                    "reason": "aggregation",
                    "time_in_nanos": 867617
                  }
                ]
              }
            ]
          }
        ],
        "aggregations": [...], 
        "fetch": {...}
      }
    ]
  }
}

As you can see, the output is significantly more verbose than before. All the major portions of the query are represented:

The Collector tree is fairly straightforward, showing how a single CancellableCollector wraps a MultiCollector which also wraps a FilteredCollector to execute the post_filter (and in turn wraps the normal scoring SimpleCollector), a BucketCollector to run all scoped aggregations.

A special note needs to be made about the MultiTermQuery class of queries. This includes wildcards, regex, and fuzzy queries. These queries emit very verbose responses, and are not overly structured.

Essentially, these queries rewrite themselves on a per-segment basis. If you imagine the wildcard query b*, it technically can match any token that begins with the letter "b". It would be impossible to enumerate all possible combinations, so Lucene rewrites the query in context of the segment being evaluated, e.g., one segment may contain the tokens [bar, baz], so the query rewrites to a BooleanQuery combination of "bar" and "baz". Another segment may only have the token [bakery], so the query rewrites to a single TermQuery for "bakery".

Due to this dynamic, per-segment rewriting, the clean tree structure becomes distorted and no longer follows a clean "lineage" showing how one query rewrites into the next. At present time, all we can do is apologize, and suggest you collapse the details for that query’s children if it is too confusing. Luckily, all the timing statistics are correct, just not the physical layout in the response, so it is sufficient to just analyze the top-level MultiTermQuery and ignore its children if you find the details too tricky to interpret.

Hopefully this will be fixed in future iterations, but it is a tricky problem to solve and still in-progress. :)

The breakdown component lists detailed statistics about low-level execution:

"breakdown": {
  "reduce": 0,
  "build_aggregation": 30885,
  "build_aggregation_count": 1,
  "initialize": 2623,
  "initialize_count": 1,
  "reduce_count": 0,
  "collect": 45786,
  "collect_count": 4,
  "build_leaf_collector": 18211,
  "build_leaf_collector_count": 1
}

Each property in the breakdown component corresponds to an internal method for the aggregation. For example, the build_leaf_collector property measures nanoseconds spent running the aggregation’s getLeafCollector() method. Properties ending in _count record the number of invocations of the particular method. For example, "collect_count": 2 means the aggregation called the collect() on two different documents. The reduce property is reserved for future use and always returns 0.

Timings are listed in wall-clock nanoseconds and are not normalized at all. All caveats about the overall time apply here. The intention of the breakdown is to give you a feel for A) what machinery in Elasticsearch is actually eating time, and B) the magnitude of differences in times between the various components. Like the overall time, the breakdown is inclusive of all children times.

All shards that fetched documents will have a fetch section in the profile. Let’s execute a small search and have a look at the fetch profile:

response = client.search(
  index: 'my-index-000001',
  filter_path: 'profile.shards.fetch',
  body: {
    profile: true,
    query: {
      term: {
        "user.id": {
          value: 'elkbee'
        }
      }
    }
  }
)
puts response

GET /my-index-000001/_search?filter_path=profile.shards.fetch
{
  "profile": true,
  "query": {
    "term": {
      "user.id": {
        "value": "elkbee"
      }
    }
  }
}

And here is the fetch profile:

{
  "profile": {
    "shards": [
      {
        "fetch": {
          "type": "fetch",
          "description": "",
          "time_in_nanos": 660555,
          "breakdown": {
            "next_reader": 7292,
            "next_reader_count": 1,
            "load_stored_fields": 299325,
            "load_stored_fields_count": 5,
            "load_source": 3863,
            "load_source_count": 5
          },
          "debug": {
            "stored_fields": ["_id", "_routing", "_source"]
          },
          "children": [
            {
              "type": "FetchSourcePhase",
              "description": "",
              "time_in_nanos": 20443,
              "breakdown": {
                "next_reader": 745,
                "next_reader_count": 1,
                "process": 19698,
                "process_count": 5
              },
              "debug": {
                "fast_path": 4
              }
            },
            {
              "type": "StoredFieldsPhase",
              "description": "",
              "time_in_nanos": 5310,
              "breakdown": {
                "next_reader": 745,
                "next_reader_count": 1,
                "process": 4445,
                "process_count": 5
              }
            }
          ]
        }
      }
    ]
  }
}

Since this is debugging information about the way that Elasticsearch executes the fetch it can change from request to request and version to version. Even patch versions may change the output here. That lack of consistency is what makes it useful for debugging.

Anyway! time_in_nanos measures the time total time of the fetch phase. The breakdown counts and times the our per-segment preparation in next_reader and the time taken loading stored fields in load_stored_fields. Debug contains miscellaneous non-timing information, specifically stored_fields lists the stored fields that fetch will have to load. If it is an empty list then fetch will entirely skip loading stored fields.

The children section lists the sub-phases that do the actual fetching work and the breakdown has counts and timings for the per-segment preparation in next_reader and the per document fetching in process.

The DFS phase runs before the query phase to collect global information relevant to the query. It’s currently used in two cases:

Both of these cases can be profiled by setting profile to true as part of the search request.

PUT my-dfs-index
{
  "settings": {
    "number_of_shards": 2, 
    "number_of_replicas": 1
  },
  "mappings": {
      "properties": {
        "my-keyword": { "type": "keyword" }
      }
    }
}

POST my-dfs-index/_bulk?refresh=true
{ "index" : { "_id" : "1" } }
{ "my-keyword" : "a" }
{ "index" : { "_id" : "2" } }
{ "my-keyword" : "b" }

response = client.search(
  index: 'my-dfs-index',
  search_type: 'dfs_query_then_fetch',
  pretty: true,
  size: 0,
  body: {
    profile: true,
    query: {
      term: {
        "my-keyword": {
          value: 'a'
        }
      }
    }
  }
)
puts response

GET /my-dfs-index/_search?search_type=dfs_query_then_fetch&pretty&size=0 
{
  "profile": true, 
  "query": {
    "term": {
      "my-keyword": {
        "value": "a"
      }
    }
  }
}

"dfs" : {
    "statistics" : {
        "type" : "statistics",
        "description" : "collect term statistics",
        "time_in_nanos" : 236955,
        "breakdown" : {
            "term_statistics" : 4815,
            "collection_statistics" : 27081,
            "collection_statistics_count" : 1,
            "create_weight" : 153278,
            "term_statistics_count" : 1,
            "rewrite_count" : 0,
            "create_weight_count" : 1,
            "rewrite" : 0
        }
    }
}

PUT my-knn-index
{
  "mappings": {
    "properties": {
      "my-vector": {
        "type": "dense_vector",
        "dims": 3,
        "index": true,
        "similarity": "l2_norm"
      }
    }
  }
}

POST my-knn-index/_bulk?refresh=true
{ "index": { "_id": "1" } }
{ "my-vector": [1, 5, -20] }
{ "index": { "_id": "2" } }
{ "my-vector": [42, 8, -15] }
{ "index": { "_id": "3" } }
{ "my-vector": [15, 11, 23] }

response = client.search(
  index: 'my-knn-index',
  body: {
    profile: true,
    knn: {
      field: 'my-vector',
      query_vector: [
        -5,
        9,
        -12
      ],
      k: 3,
      num_candidates: 100
    }
  }
)
puts response

POST my-knn-index/_search
{
  "profile": true, 
  "knn": {
    "field": "my-vector",
    "query_vector": [-5, 9, -12],
    "k": 3,
    "num_candidates": 100
  }
}

"dfs" : {
    "knn" : [
        {
        "query" : [
            {
                "type" : "DocAndScoreQuery",
                "description" : "DocAndScore[100]",
                "time_in_nanos" : 444414,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 1688,
                  "match" : 0,
                  "next_doc_count" : 3,
                  "score_count" : 3,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 4153,
                  "advance_count" : 1,
                  "score" : 2099,
                  "build_scorer_count" : 2,
                  "create_weight" : 128879,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 307595,
                  "count_weight": 0,
                  "count_weight_count": 0
                }
            }
        ],
        "rewrite_time" : 1275732,
        "collector" : [
            {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 17163
            }
        ]
    }   ]
}

Like any profiler, the Profile API introduces a non-negligible overhead to search execution. The act of instrumenting low-level method calls such as collect, advance, and next_doc can be fairly expensive, since these methods are called in tight loops. Therefore, profiling should not be enabled in production settings by default, and should not be compared against non-profiled query times. Profiling is just a diagnostic tool.

There are also cases where special Lucene optimizations are disabled, since they are not amenable to profiling. This could cause some queries to report larger relative times than their non-profiled counterparts, but in general should not have a drastic effect compared to other components in the profiled query.