[role="xpack"]
[[find-structure]]
= Find structure API

Finds the structure of text. The text must
contain data that is suitable to be ingested into the
{stack}.

[discrete]
[[find-structure-request]]
== {api-request-title}

`POST _text_structure/find_structure`

[discrete]
[[find-structure-prereqs]]
== {api-prereq-title}

* If the {es} {security-features} are enabled, you must have `monitor_text_structure` or
`monitor` cluster privileges to use this API. See
<<security-privileges>>.

[discrete]
[[find-structure-desc]]
== {api-description-title}

This API provides a starting point for ingesting data into {es} in a format that
is suitable for subsequent use with other {stack} functionality.

Unlike other {es} endpoints, the data that is posted to this endpoint does not
need to be UTF-8 encoded and in JSON format. It must, however, be text; binary
text formats are not currently supported.

The response from the API contains:

* A couple of messages from the beginning of the text.
* Statistics that reveal the most common values for all fields detected within
the text and basic numeric statistics for numeric fields.
* Information about the structure of the text, which is useful when you write
ingest configurations to index it or similarly formatted text.
* Appropriate mappings for an {es} index, which you could use to ingest the text.

All this information can be calculated by the structure finder with no guidance.
However, you can optionally override some of the decisions about the text
structure by specifying one or more query parameters.

Details of the output can be seen in the <<find-structure-examples,examples>>.

If the structure finder produces unexpected results for some text,
specify the `explain` query parameter. It causes an `explanation` to appear in
the response, which should help in determining why the returned structure was
chosen.

[discrete]
[[find-structure-query-parms]]
== {api-query-parms-title}

`charset`::
(Optional, string) The text's character set. It must be a character set that is
supported by the JVM that {es} uses. For example, `UTF-8`, `UTF-16LE`,
`windows-1252`, or `EUC-JP`. If this parameter is not specified, the structure
finder chooses an appropriate character set.

`column_names`::
(Optional, string) If you have set `format` to `delimited`, you can specify the
column names in a comma-separated list. If this parameter is not specified, the
structure finder uses the column names from the header row of the text. If the
text does not have a header role, columns are named "column1", "column2",
"column3", etc.

`delimiter`::
(Optional, string) If you have set `format` to `delimited`, you can specify the
character used to delimit the values in each row. Only a single character is
supported; the delimiter cannot have multiple characters. By default, the API
considers the following possibilities: comma, tab, semi-colon, and pipe (`|`).
In this default scenario, all rows must have the same number of fields for the
delimited format to be detected. If you specify a delimiter, up to 10% of the
rows can have a different number of columns than the first row.

`explain`::
(Optional, Boolean) If this parameter is set to `true`, the response includes a
field named `explanation`, which is an array of strings that indicate how the
structure finder produced its result. The default value is `false`.

`format`::
(Optional, string) The high level structure of the text. Valid values are
`ndjson`, `xml`, `delimited`, and `semi_structured_text`. By default, the API
chooses the format. In this default scenario, all rows must have the same number
of fields for a delimited format to be detected. If the `format` is set to
`delimited` and the `delimiter` is not set, however, the API tolerates up to 5%
of rows that have a different number of columns than the first row.

`grok_pattern`::
(Optional, string) If you have set `format` to `semi_structured_text`, you can
specify a Grok pattern that is used to extract fields from every message in the
text. The name of the timestamp field in the Grok pattern must match what is
specified in the `timestamp_field` parameter. If that parameter is not
specified, the name of the timestamp field in the Grok pattern must match
"timestamp". If `grok_pattern` is not specified, the structure finder creates a
Grok pattern.

`ecs_compatibility`::
(Optional, string) The mode of compatibility with ECS compliant Grok patterns.
Use this parameter to specify whether to use ECS Grok patterns instead of
legacy ones when the structure finder creates a Grok pattern. Valid values
are `disabled` and `v1`. The default value is `disabled`. This setting primarily
has an impact when a whole message Grok pattern such as `%{CATALINALOG}`
matches the input. If the structure finder identifies a common structure but
has no idea of meaning then generic field names such as `path`, `ipaddress`,
`field1` and `field2` are used in the `grok_pattern` output, with the intention
that a user who knows the meanings rename these fields before using it.
`has_header_row`::
(Optional, Boolean) If you have set `format` to `delimited`, you can use this
parameter to indicate whether the column names are in the first row of the text.
If this parameter is not specified, the structure finder guesses based on the
similarity of the first row of the text to other rows.

`line_merge_size_limit`::
(Optional, unsigned integer) The maximum number of characters in a message when
lines are merged to form messages while analyzing semi-structured text. The
default is `10000`. If you have extremely long messages you may need to increase
this, but be aware that this may lead to very long processing times if the way
to group lines into messages is misdetected.

`lines_to_sample`::
(Optional, unsigned integer) The number of lines to include in the structural
analysis, starting from the beginning of the text. The minimum is 2; the default
is `1000`. If the value of this parameter is greater than the number of lines in
the text, the analysis proceeds (as long as there are at least two lines in the
text) for all of the lines.
+
--
NOTE: The number of lines and the variation of the lines affects the speed of
the analysis. For example, if you upload text where the first 1000 lines
are all variations on the same message, the analysis will find more commonality
than would be seen with a bigger sample. If possible, however, it is more
efficient to upload sample text with more variety in the first 1000 lines than
to request analysis of 100000 lines to achieve some variety.

--

`quote`::
(Optional, string) If you have set `format` to `delimited`, you can specify the
character used to quote the values in each row if they contain newlines or the
delimiter character. Only a single character is supported. If this parameter is
not specified, the default value is a double quote (`"`). If your delimited text
format does not use quoting, a workaround is to set this argument to a character
that does not appear anywhere in the sample.

`should_trim_fields`::
(Optional, Boolean) If you have set `format` to `delimited`, you can specify
whether values between delimiters should have whitespace trimmed from them. If
this parameter is not specified and the delimiter is pipe (`|`), the default
value is `true`. Otherwise, the default value is `false`.

`timeout`::
(Optional, <<time-units,time units>>) Sets the maximum amount of time that the
structure analysis make take. If the analysis is still running when the timeout
expires then it will be aborted. The default value is 25 seconds.

`timestamp_field`::
(Optional, string) The name of the field that contains the primary timestamp of
each record in the text. In particular, if the text were ingested into an index,
this is the field that would be used to populate the `@timestamp` field.
+
--
If the `format` is `semi_structured_text`, this field must match the name of the
appropriate extraction in the `grok_pattern`. Therefore, for semi-structured
text, it is best not to specify this parameter unless `grok_pattern` is
also specified.

For structured text, if you specify this parameter, the field must exist
within the text.

If this parameter is not specified, the structure finder makes a decision about
which field (if any) is the primary timestamp field. For structured text,
it is not compulsory to have a timestamp in the text.
--

`timestamp_format`::
(Optional, string) The Java time format of the timestamp field in the text.
+
--
Only a subset of Java time format letter groups are supported:

* `a`
* `d`
* `dd`
* `EEE`
* `EEEE`
* `H`
* `HH`
* `h`
* `M`
* `MM`
* `MMM`
* `MMMM`
* `mm`
* `ss`
* `XX`
* `XXX`
* `yy`
* `yyyy`
* `zzz`

Additionally `S` letter groups (fractional seconds) of length one to nine are
supported providing they occur after `ss` and separated from the `ss` by a `.`,
`,` or `:`. Spacing and punctuation is also permitted with the exception of `?`,
newline and carriage return, together with literal text enclosed in single
quotes. For example, `MM/dd HH.mm.ss,SSSSSS 'in' yyyy` is a valid override
format.

One valuable use case for this parameter is when the format is semi-structured
text, there are multiple timestamp formats in the text, and you know which
format corresponds to the primary timestamp, but you do not want to specify the
full `grok_pattern`. Another is when the timestamp format is one that the
structure finder does not consider by default.

If this parameter is not specified, the structure finder chooses the best
format from a built-in set.

If the special value `null` is specified the structure finder will not look
for a primary timestamp in the text. When the format is semi-structured text
this will result in the structure finder treating the text as single-line
messages.

The following table provides the appropriate `timeformat` values for some example timestamps:

|===
| Timeformat                 | Presentation

| yyyy-MM-dd HH:mm:ssZ       | 2019-04-20 13:15:22+0000
| EEE, d MMM yyyy HH:mm:ss Z | Sat, 20 Apr 2019 13:15:22 +0000
| dd.MM.yy HH:mm:ss.SSS      | 20.04.19 13:15:22.285
|===

See
https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html[the Java date/time format documentation]
for more information about date and time format syntax.

--

[discrete]
[[find-structure-request-body]]
== {api-request-body-title}

The text that you want to analyze. It must contain data that is suitable to
be ingested into {es}. It does not need to be in JSON format and it does not
need to be UTF-8 encoded. The size is limited to the {es} HTTP receive buffer
size, which defaults to 100 Mb.

[discrete]
[[find-structure-examples]]
== {api-examples-title}

[discrete]
[[find-structure-example-nld-json]]
=== Ingesting newline-delimited JSON

Suppose you have newline-delimited JSON text that contains information about
some books. You can send the contents to the `find_structure` endpoint:

[source,console]
----
POST _text_structure/find_structure
{"name": "Leviathan Wakes", "author": "James S.A. Corey", "release_date": "2011-06-02", "page_count": 561}
{"name": "Hyperion", "author": "Dan Simmons", "release_date": "1989-05-26", "page_count": 482}
{"name": "Dune", "author": "Frank Herbert", "release_date": "1965-06-01", "page_count": 604}
{"name": "Dune Messiah", "author": "Frank Herbert", "release_date": "1969-10-15", "page_count": 331}
{"name": "Children of Dune", "author": "Frank Herbert", "release_date": "1976-04-21", "page_count": 408}
{"name": "God Emperor of Dune", "author": "Frank Herbert", "release_date": "1981-05-28", "page_count": 454}
{"name": "Consider Phlebas", "author": "Iain M. Banks", "release_date": "1987-04-23", "page_count": 471}
{"name": "Pandora's Star", "author": "Peter F. Hamilton", "release_date": "2004-03-02", "page_count": 768}
{"name": "Revelation Space", "author": "Alastair Reynolds", "release_date": "2000-03-15", "page_count": 585}
{"name": "A Fire Upon the Deep", "author": "Vernor Vinge", "release_date": "1992-06-01", "page_count": 613}
{"name": "Ender's Game", "author": "Orson Scott Card", "release_date": "1985-06-01", "page_count": 324}
{"name": "1984", "author": "George Orwell", "release_date": "1985-06-01", "page_count": 328}
{"name": "Fahrenheit 451", "author": "Ray Bradbury", "release_date": "1953-10-15", "page_count": 227}
{"name": "Brave New World", "author": "Aldous Huxley", "release_date": "1932-06-01", "page_count": 268}
{"name": "Foundation", "author": "Isaac Asimov", "release_date": "1951-06-01", "page_count": 224}
{"name": "The Giver", "author": "Lois Lowry", "release_date": "1993-04-26", "page_count": 208}
{"name": "Slaughterhouse-Five", "author": "Kurt Vonnegut", "release_date": "1969-06-01", "page_count": 275}
{"name": "The Hitchhiker's Guide to the Galaxy", "author": "Douglas Adams", "release_date": "1979-10-12", "page_count": 180}
{"name": "Snow Crash", "author": "Neal Stephenson", "release_date": "1992-06-01", "page_count": 470}
{"name": "Neuromancer", "author": "William Gibson", "release_date": "1984-07-01", "page_count": 271}
{"name": "The Handmaid's Tale", "author": "Margaret Atwood", "release_date": "1985-06-01", "page_count": 311}
{"name": "Starship Troopers", "author": "Robert A. Heinlein", "release_date": "1959-12-01", "page_count": 335}
{"name": "The Left Hand of Darkness", "author": "Ursula K. Le Guin", "release_date": "1969-06-01", "page_count": 304}
{"name": "The Moon is a Harsh Mistress", "author": "Robert A. Heinlein", "release_date": "1966-04-01", "page_count": 288}
----
// TEST

If the request does not encounter errors, you receive the following result:

[source,console-result]
----
{
  "num_lines_analyzed" : 24, <1>
  "num_messages_analyzed" : 24, <2>
  "sample_start" : "{\"name\": \"Leviathan Wakes\", \"author\": \"James S.A. Corey\", \"release_date\": \"2011-06-02\", \"page_count\": 561}\n{\"name\": \"Hyperion\", \"author\": \"Dan Simmons\", \"release_date\": \"1989-05-26\", \"page_count\": 482}\n", <3>
  "charset" : "UTF-8", <4>
  "has_byte_order_marker" : false, <5>
  "format" : "ndjson", <6>
  "ecs_compatibility" : "disabled", <7>
  "timestamp_field" : "release_date", <8>
  "joda_timestamp_formats" : [ <9>
    "ISO8601"
  ],
  "java_timestamp_formats" : [ <10>
    "ISO8601"
  ],
  "need_client_timezone" : true, <11>
  "mappings" : { <12>
    "properties" : {
      "@timestamp" : {
        "type" : "date"
      },
      "author" : {
        "type" : "keyword"
      },
      "name" : {
        "type" : "keyword"
      },
      "page_count" : {
        "type" : "long"
      },
      "release_date" : {
        "type" : "date",
        "format" : "iso8601"
      }
    }
  },
  "ingest_pipeline" : {
    "description" : "Ingest pipeline created by text structure finder",
    "processors" : [
      {
        "date" : {
          "field" : "release_date",
          "timezone" : "{{ event.timezone }}",
          "formats" : [
            "ISO8601"
          ]
        }
      }
    ]
  },
  "field_stats" : { <13>
    "author" : {
      "count" : 24,
      "cardinality" : 20,
      "top_hits" : [
        {
          "value" : "Frank Herbert",
          "count" : 4
        },
        {
          "value" : "Robert A. Heinlein",
          "count" : 2
        },
        {
          "value" : "Alastair Reynolds",
          "count" : 1
        },
        {
          "value" : "Aldous Huxley",
          "count" : 1
        },
        {
          "value" : "Dan Simmons",
          "count" : 1
        },
        {
          "value" : "Douglas Adams",
          "count" : 1
        },
        {
          "value" : "George Orwell",
          "count" : 1
        },
        {
          "value" : "Iain M. Banks",
          "count" : 1
        },
        {
          "value" : "Isaac Asimov",
          "count" : 1
        },
        {
          "value" : "James S.A. Corey",
          "count" : 1
        }
      ]
    },
    "name" : {
      "count" : 24,
      "cardinality" : 24,
      "top_hits" : [
        {
          "value" : "1984",
          "count" : 1
        },
        {
          "value" : "A Fire Upon the Deep",
          "count" : 1
        },
        {
          "value" : "Brave New World",
          "count" : 1
        },
        {
          "value" : "Children of Dune",
          "count" : 1
        },
        {
          "value" : "Consider Phlebas",
          "count" : 1
        },
        {
          "value" : "Dune",
          "count" : 1
        },
        {
          "value" : "Dune Messiah",
          "count" : 1
        },
        {
          "value" : "Ender's Game",
          "count" : 1
        },
        {
          "value" : "Fahrenheit 451",
          "count" : 1
        },
        {
          "value" : "Foundation",
          "count" : 1
        }
      ]
    },
    "page_count" : {
      "count" : 24,
      "cardinality" : 24,
      "min_value" : 180,
      "max_value" : 768,
      "mean_value" : 387.0833333333333,
      "median_value" : 329.5,
      "top_hits" : [
        {
          "value" : 180,
          "count" : 1
        },
        {
          "value" : 208,
          "count" : 1
        },
        {
          "value" : 224,
          "count" : 1
        },
        {
          "value" : 227,
          "count" : 1
        },
        {
          "value" : 268,
          "count" : 1
        },
        {
          "value" : 271,
          "count" : 1
        },
        {
          "value" : 275,
          "count" : 1
        },
        {
          "value" : 288,
          "count" : 1
        },
        {
          "value" : 304,
          "count" : 1
        },
        {
          "value" : 311,
          "count" : 1
        }
      ]
    },
    "release_date" : {
      "count" : 24,
      "cardinality" : 20,
      "earliest" : "1932-06-01",
      "latest" : "2011-06-02",
      "top_hits" : [
        {
          "value" : "1985-06-01",
          "count" : 3
        },
        {
          "value" : "1969-06-01",
          "count" : 2
        },
        {
          "value" : "1992-06-01",
          "count" : 2
        },
        {
          "value" : "1932-06-01",
          "count" : 1
        },
        {
          "value" : "1951-06-01",
          "count" : 1
        },
        {
          "value" : "1953-10-15",
          "count" : 1
        },
        {
          "value" : "1959-12-01",
          "count" : 1
        },
        {
          "value" : "1965-06-01",
          "count" : 1
        },
        {
          "value" : "1966-04-01",
          "count" : 1
        },
        {
          "value" : "1969-10-15",
          "count" : 1
        }
      ]
    }
  }
}
----
// TESTRESPONSE[skip:"AwaitsFix https://github.com/elastic/elasticsearch/issues/92141"]
// original (put back after unmuting)[s/"sample_start" : ".*",/"sample_start" : "$body.sample_start",/]
// The substitution is because the text is pre-processed by the test harness,
// so the fields may get reordered in the JSON the endpoint sees

<1> `num_lines_analyzed` indicates how many lines of the text were analyzed.
<2> `num_messages_analyzed` indicates how many distinct messages the lines
contained. For NDJSON, this value is the same as `num_lines_analyzed`. For other
text formats, messages can span several lines.
<3> `sample_start` reproduces the first two messages in the text verbatim. This
may help diagnose parse errors or accidental uploads of the wrong text.
<4> `charset` indicates the character encoding used to parse the text.
<5> For UTF character encodings, `has_byte_order_marker` indicates whether the
text begins with a byte order marker.
<6> `format` is one of `ndjson`, `xml`, `delimited` or `semi_structured_text`.
<7> `ecs_compatibility` is either `disabled` or `v1`, defaults to `disabled`.
<8> The `timestamp_field` names the field considered most likely to be the
primary timestamp of each document.
<9> `joda_timestamp_formats` are used to tell {ls} how to parse timestamps.
<10> `java_timestamp_formats` are the Java time formats recognized in the time
fields. {es} mappings and ingest pipelines use this format.
<11> If a timestamp format is detected that does not include a timezone,
`need_client_timezone` will be `true`. The server that parses the text must
therefore be told the correct timezone by the client.
<12> `mappings` contains some suitable mappings for an index into which the data
could be ingested. In this case, the `release_date` field has been given a
`keyword` type as it is not considered specific enough to convert to the `date`
type.
<13> `field_stats` contains the most common values of each field, plus basic
numeric statistics for the numeric `page_count` field. This information may
provide clues that the data needs to be cleaned or transformed prior to use by
other {stack} functionality.

[discrete]
[[find-structure-example-nyc]]
=== Finding the structure of NYC yellow cab example data

The next example shows how it's possible to find the structure of some New York
City yellow cab trip data. The first `curl` command downloads the data, the
first 20000 lines of which are then piped into the `find_structure`
endpoint. The `lines_to_sample` query parameter of the endpoint is set to 20000
to match what is specified in the `head` command.

[source,js]
----
curl -s "s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2018-06.csv" | head -20000 | curl -s -H "Content-Type: application/json" -XPOST "localhost:9200/_text_structure/find_structure?pretty&lines_to_sample=20000" -T -
----
// NOTCONSOLE
// Not converting to console because this shows how curl can be used

--
NOTE: The `Content-Type: application/json` header must be set even though in
this case the data is not JSON. (Alternatively the `Content-Type` can be set
to any other supported by {es}, but it must be set.)

--

If the request does not encounter errors, you receive the following result:

[source,js]
----
{
  "num_lines_analyzed" : 20000,
  "num_messages_analyzed" : 19998, <1>
  "sample_start" : "VendorID,tpep_pickup_datetime,tpep_dropoff_datetime,passenger_count,trip_distance,RatecodeID,store_and_fwd_flag,PULocationID,DOLocationID,payment_type,fare_amount,extra,mta_tax,tip_amount,tolls_amount,improvement_surcharge,total_amount\n\n1,2018-06-01 00:15:40,2018-06-01 00:16:46,1,.00,1,N,145,145,2,3,0.5,0.5,0,0,0.3,4.3\n",
  "charset" : "UTF-8",
  "has_byte_order_marker" : false,
  "format" : "delimited", <2>
  "multiline_start_pattern" : "^.*?,\"?\\d{4}-\\d{2}-\\d{2}[T ]\\d{2}:\\d{2}",
  "exclude_lines_pattern" : "^\"?VendorID\"?,\"?tpep_pickup_datetime\"?,\"?tpep_dropoff_datetime\"?,\"?passenger_count\"?,\"?trip_distance\"?,\"?RatecodeID\"?,\"?store_and_fwd_flag\"?,\"?PULocationID\"?,\"?DOLocationID\"?,\"?payment_type\"?,\"?fare_amount\"?,\"?extra\"?,\"?mta_tax\"?,\"?tip_amount\"?,\"?tolls_amount\"?,\"?improvement_surcharge\"?,\"?total_amount\"?",
  "column_names" : [ <3>
    "VendorID",
    "tpep_pickup_datetime",
    "tpep_dropoff_datetime",
    "passenger_count",
    "trip_distance",
    "RatecodeID",
    "store_and_fwd_flag",
    "PULocationID",
    "DOLocationID",
    "payment_type",
    "fare_amount",
    "extra",
    "mta_tax",
    "tip_amount",
    "tolls_amount",
    "improvement_surcharge",
    "total_amount"
  ],
  "has_header_row" : true, <4>
  "delimiter" : ",", <5>
  "quote" : "\"", <6>
  "timestamp_field" : "tpep_pickup_datetime", <7>
  "joda_timestamp_formats" : [ <8>
    "YYYY-MM-dd HH:mm:ss"
  ],
  "java_timestamp_formats" : [ <9>
    "yyyy-MM-dd HH:mm:ss"
  ],
  "need_client_timezone" : true, <10>
  "mappings" : {
    "properties" : {
      "@timestamp" : {
        "type" : "date"
      },
      "DOLocationID" : {
        "type" : "long"
      },
      "PULocationID" : {
        "type" : "long"
      },
      "RatecodeID" : {
        "type" : "long"
      },
      "VendorID" : {
        "type" : "long"
      },
      "extra" : {
        "type" : "double"
      },
      "fare_amount" : {
        "type" : "double"
      },
      "improvement_surcharge" : {
        "type" : "double"
      },
      "mta_tax" : {
        "type" : "double"
      },
      "passenger_count" : {
        "type" : "long"
      },
      "payment_type" : {
        "type" : "long"
      },
      "store_and_fwd_flag" : {
        "type" : "keyword"
      },
      "tip_amount" : {
        "type" : "double"
      },
      "tolls_amount" : {
        "type" : "double"
      },
      "total_amount" : {
        "type" : "double"
      },
      "tpep_dropoff_datetime" : {
        "type" : "date",
        "format" : "yyyy-MM-dd HH:mm:ss"
      },
      "tpep_pickup_datetime" : {
        "type" : "date",
        "format" : "yyyy-MM-dd HH:mm:ss"
      },
      "trip_distance" : {
        "type" : "double"
      }
    }
  },
  "ingest_pipeline" : {
    "description" : "Ingest pipeline created by text structure finder",
    "processors" : [
      {
        "csv" : {
          "field" : "message",
          "target_fields" : [
            "VendorID",
            "tpep_pickup_datetime",
            "tpep_dropoff_datetime",
            "passenger_count",
            "trip_distance",
            "RatecodeID",
            "store_and_fwd_flag",
            "PULocationID",
            "DOLocationID",
            "payment_type",
            "fare_amount",
            "extra",
            "mta_tax",
            "tip_amount",
            "tolls_amount",
            "improvement_surcharge",
            "total_amount"
          ]
        }
      },
      {
        "date" : {
          "field" : "tpep_pickup_datetime",
          "timezone" : "{{ event.timezone }}",
          "formats" : [
            "yyyy-MM-dd HH:mm:ss"
          ]
        }
      },
      {
        "convert" : {
          "field" : "DOLocationID",
          "type" : "long"
        }
      },
      {
        "convert" : {
          "field" : "PULocationID",
          "type" : "long"
        }
      },
      {
        "convert" : {
          "field" : "RatecodeID",
          "type" : "long"
        }
      },
      {
        "convert" : {
          "field" : "VendorID",
          "type" : "long"
        }
      },
      {
        "convert" : {
          "field" : "extra",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "fare_amount",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "improvement_surcharge",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "mta_tax",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "passenger_count",
          "type" : "long"
        }
      },
      {
        "convert" : {
          "field" : "payment_type",
          "type" : "long"
        }
      },
      {
        "convert" : {
          "field" : "tip_amount",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "tolls_amount",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "total_amount",
          "type" : "double"
        }
      },
      {
        "convert" : {
          "field" : "trip_distance",
          "type" : "double"
        }
      },
      {
        "remove" : {
          "field" : "message"
        }
      }
    ]
  },
  "field_stats" : {
    "DOLocationID" : {
      "count" : 19998,
      "cardinality" : 240,
      "min_value" : 1,
      "max_value" : 265,
      "mean_value" : 150.26532653265312,
      "median_value" : 148,
      "top_hits" : [
        {
          "value" : 79,
          "count" : 760
        },
        {
          "value" : 48,
          "count" : 683
        },
        {
          "value" : 68,
          "count" : 529
        },
        {
          "value" : 170,
          "count" : 506
        },
        {
          "value" : 107,
          "count" : 468
        },
        {
          "value" : 249,
          "count" : 457
        },
        {
          "value" : 230,
          "count" : 441
        },
        {
          "value" : 186,
          "count" : 432
        },
        {
          "value" : 141,
          "count" : 409
        },
        {
          "value" : 263,
          "count" : 386
        }
      ]
    },
    "PULocationID" : {
      "count" : 19998,
      "cardinality" : 154,
      "min_value" : 1,
      "max_value" : 265,
      "mean_value" : 153.4042404240424,
      "median_value" : 148,
      "top_hits" : [
        {
          "value" : 79,
          "count" : 1067
        },
        {
          "value" : 230,
          "count" : 949
        },
        {
          "value" : 148,
          "count" : 940
        },
        {
          "value" : 132,
          "count" : 897
        },
        {
          "value" : 48,
          "count" : 853
        },
        {
          "value" : 161,
          "count" : 820
        },
        {
          "value" : 234,
          "count" : 750
        },
        {
          "value" : 249,
          "count" : 722
        },
        {
          "value" : 164,
          "count" : 663
        },
        {
          "value" : 114,
          "count" : 646
        }
      ]
    },
    "RatecodeID" : {
      "count" : 19998,
      "cardinality" : 5,
      "min_value" : 1,
      "max_value" : 5,
      "mean_value" : 1.0656565656565653,
      "median_value" : 1,
      "top_hits" : [
        {
          "value" : 1,
          "count" : 19311
        },
        {
          "value" : 2,
          "count" : 468
        },
        {
          "value" : 5,
          "count" : 195
        },
        {
          "value" : 4,
          "count" : 17
        },
        {
          "value" : 3,
          "count" : 7
        }
      ]
    },
    "VendorID" : {
      "count" : 19998,
      "cardinality" : 2,
      "min_value" : 1,
      "max_value" : 2,
      "mean_value" : 1.59005900590059,
      "median_value" : 2,
      "top_hits" : [
        {
          "value" : 2,
          "count" : 11800
        },
        {
          "value" : 1,
          "count" : 8198
        }
      ]
    },
    "extra" : {
      "count" : 19998,
      "cardinality" : 3,
      "min_value" : -0.5,
      "max_value" : 0.5,
      "mean_value" : 0.4815981598159816,
      "median_value" : 0.5,
      "top_hits" : [
        {
          "value" : 0.5,
          "count" : 19281
        },
        {
          "value" : 0,
          "count" : 698
        },
        {
          "value" : -0.5,
          "count" : 19
        }
      ]
    },
    "fare_amount" : {
      "count" : 19998,
      "cardinality" : 208,
      "min_value" : -100,
      "max_value" : 300,
      "mean_value" : 13.937719771977209,
      "median_value" : 9.5,
      "top_hits" : [
        {
          "value" : 6,
          "count" : 1004
        },
        {
          "value" : 6.5,
          "count" : 935
        },
        {
          "value" : 5.5,
          "count" : 909
        },
        {
          "value" : 7,
          "count" : 903
        },
        {
          "value" : 5,
          "count" : 889
        },
        {
          "value" : 7.5,
          "count" : 854
        },
        {
          "value" : 4.5,
          "count" : 802
        },
        {
          "value" : 8.5,
          "count" : 790
        },
        {
          "value" : 8,
          "count" : 789
        },
        {
          "value" : 9,
          "count" : 711
        }
      ]
    },
    "improvement_surcharge" : {
      "count" : 19998,
      "cardinality" : 3,
      "min_value" : -0.3,
      "max_value" : 0.3,
      "mean_value" : 0.29915991599159913,
      "median_value" : 0.3,
      "top_hits" : [
        {
          "value" : 0.3,
          "count" : 19964
        },
        {
          "value" : -0.3,
          "count" : 22
        },
        {
          "value" : 0,
          "count" : 12
        }
      ]
    },
    "mta_tax" : {
      "count" : 19998,
      "cardinality" : 3,
      "min_value" : -0.5,
      "max_value" : 0.5,
      "mean_value" : 0.4962246224622462,
      "median_value" : 0.5,
      "top_hits" : [
        {
          "value" : 0.5,
          "count" : 19868
        },
        {
          "value" : 0,
          "count" : 109
        },
        {
          "value" : -0.5,
          "count" : 21
        }
      ]
    },
    "passenger_count" : {
      "count" : 19998,
      "cardinality" : 7,
      "min_value" : 0,
      "max_value" : 6,
      "mean_value" : 1.6201620162016201,
      "median_value" : 1,
      "top_hits" : [
        {
          "value" : 1,
          "count" : 14219
        },
        {
          "value" : 2,
          "count" : 2886
        },
        {
          "value" : 5,
          "count" : 1047
        },
        {
          "value" : 3,
          "count" : 804
        },
        {
          "value" : 6,
          "count" : 523
        },
        {
          "value" : 4,
          "count" : 406
        },
        {
          "value" : 0,
          "count" : 113
        }
      ]
    },
    "payment_type" : {
      "count" : 19998,
      "cardinality" : 4,
      "min_value" : 1,
      "max_value" : 4,
      "mean_value" : 1.315631563156316,
      "median_value" : 1,
      "top_hits" : [
        {
          "value" : 1,
          "count" : 13936
        },
        {
          "value" : 2,
          "count" : 5857
        },
        {
          "value" : 3,
          "count" : 160
        },
        {
          "value" : 4,
          "count" : 45
        }
      ]
    },
    "store_and_fwd_flag" : {
      "count" : 19998,
      "cardinality" : 2,
      "top_hits" : [
        {
          "value" : "N",
          "count" : 19910
        },
        {
          "value" : "Y",
          "count" : 88
        }
      ]
    },
    "tip_amount" : {
      "count" : 19998,
      "cardinality" : 717,
      "min_value" : 0,
      "max_value" : 128,
      "mean_value" : 2.010959095909593,
      "median_value" : 1.45,
      "top_hits" : [
        {
          "value" : 0,
          "count" : 6917
        },
        {
          "value" : 1,
          "count" : 1178
        },
        {
          "value" : 2,
          "count" : 624
        },
        {
          "value" : 3,
          "count" : 248
        },
        {
          "value" : 1.56,
          "count" : 206
        },
        {
          "value" : 1.46,
          "count" : 205
        },
        {
          "value" : 1.76,
          "count" : 196
        },
        {
          "value" : 1.45,
          "count" : 195
        },
        {
          "value" : 1.36,
          "count" : 191
        },
        {
          "value" : 1.5,
          "count" : 187
        }
      ]
    },
    "tolls_amount" : {
      "count" : 19998,
      "cardinality" : 26,
      "min_value" : 0,
      "max_value" : 35,
      "mean_value" : 0.2729697969796978,
      "median_value" : 0,
      "top_hits" : [
        {
          "value" : 0,
          "count" : 19107
        },
        {
          "value" : 5.76,
          "count" : 791
        },
        {
          "value" : 10.5,
          "count" : 36
        },
        {
          "value" : 2.64,
          "count" : 21
        },
        {
          "value" : 11.52,
          "count" : 8
        },
        {
          "value" : 5.54,
          "count" : 4
        },
        {
          "value" : 8.5,
          "count" : 4
        },
        {
          "value" : 17.28,
          "count" : 4
        },
        {
          "value" : 2,
          "count" : 2
        },
        {
          "value" : 2.16,
          "count" : 2
        }
      ]
    },
    "total_amount" : {
      "count" : 19998,
      "cardinality" : 1267,
      "min_value" : -100.3,
      "max_value" : 389.12,
      "mean_value" : 17.499898989898995,
      "median_value" : 12.35,
      "top_hits" : [
        {
          "value" : 7.3,
          "count" : 478
        },
        {
          "value" : 8.3,
          "count" : 443
        },
        {
          "value" : 8.8,
          "count" : 420
        },
        {
          "value" : 6.8,
          "count" : 406
        },
        {
          "value" : 7.8,
          "count" : 405
        },
        {
          "value" : 6.3,
          "count" : 371
        },
        {
          "value" : 9.8,
          "count" : 368
        },
        {
          "value" : 5.8,
          "count" : 362
        },
        {
          "value" : 9.3,
          "count" : 332
        },
        {
          "value" : 10.3,
          "count" : 332
        }
      ]
    },
    "tpep_dropoff_datetime" : {
      "count" : 19998,
      "cardinality" : 9066,
      "earliest" : "2018-05-31 06:18:15",
      "latest" : "2018-06-02 02:25:44",
      "top_hits" : [
        {
          "value" : "2018-06-01 01:12:12",
          "count" : 10
        },
        {
          "value" : "2018-06-01 00:32:15",
          "count" : 9
        },
        {
          "value" : "2018-06-01 00:44:27",
          "count" : 9
        },
        {
          "value" : "2018-06-01 00:46:42",
          "count" : 9
        },
        {
          "value" : "2018-06-01 01:03:22",
          "count" : 9
        },
        {
          "value" : "2018-06-01 01:05:13",
          "count" : 9
        },
        {
          "value" : "2018-06-01 00:11:20",
          "count" : 8
        },
        {
          "value" : "2018-06-01 00:16:03",
          "count" : 8
        },
        {
          "value" : "2018-06-01 00:19:47",
          "count" : 8
        },
        {
          "value" : "2018-06-01 00:25:17",
          "count" : 8
        }
      ]
    },
    "tpep_pickup_datetime" : {
      "count" : 19998,
      "cardinality" : 8760,
      "earliest" : "2018-05-31 06:08:31",
      "latest" : "2018-06-02 01:21:21",
      "top_hits" : [
        {
          "value" : "2018-06-01 00:01:23",
          "count" : 12
        },
        {
          "value" : "2018-06-01 00:04:31",
          "count" : 10
        },
        {
          "value" : "2018-06-01 00:05:38",
          "count" : 10
        },
        {
          "value" : "2018-06-01 00:09:50",
          "count" : 10
        },
        {
          "value" : "2018-06-01 00:12:01",
          "count" : 10
        },
        {
          "value" : "2018-06-01 00:14:17",
          "count" : 10
        },
        {
          "value" : "2018-06-01 00:00:34",
          "count" : 9
        },
        {
          "value" : "2018-06-01 00:00:40",
          "count" : 9
        },
        {
          "value" : "2018-06-01 00:02:53",
          "count" : 9
        },
        {
          "value" : "2018-06-01 00:05:40",
          "count" : 9
        }
      ]
    },
    "trip_distance" : {
      "count" : 19998,
      "cardinality" : 1687,
      "min_value" : 0,
      "max_value" : 64.63,
      "mean_value" : 3.6521062106210715,
      "median_value" : 2.16,
      "top_hits" : [
        {
          "value" : 0.9,
          "count" : 335
        },
        {
          "value" : 0.8,
          "count" : 320
        },
        {
          "value" : 1.1,
          "count" : 316
        },
        {
          "value" : 0.7,
          "count" : 304
        },
        {
          "value" : 1.2,
          "count" : 303
        },
        {
          "value" : 1,
          "count" : 296
        },
        {
          "value" : 1.3,
          "count" : 280
        },
        {
          "value" : 1.5,
          "count" : 268
        },
        {
          "value" : 1.6,
          "count" : 268
        },
        {
          "value" : 0.6,
          "count" : 256
        }
      ]
    }
  }
}
----
// NOTCONSOLE

<1> `num_messages_analyzed` is 2 lower than `num_lines_analyzed` because only
data records count as messages. The first line contains the column names and in
this sample the second line is blank.
<2> Unlike the first example, in this case the `format` has been identified as
`delimited`.
<3> Because the `format` is `delimited`, the `column_names` field in the output
lists the column names in the order they appear in the sample.
<4> `has_header_row` indicates that for this sample the column names were in
the first row of the sample. (If they hadn't been then it would have been a good
idea to specify them in the `column_names` query parameter.)
<5> The `delimiter` for this sample is a comma, as it's CSV formatted text.
<6> The `quote` character is the default double quote. (The structure finder
does not attempt to deduce any other quote character, so if you have delimited
text that's quoted with some other character you must specify it using the
`quote` query parameter.)
<7> The `timestamp_field` has been chosen to be `tpep_pickup_datetime`.
`tpep_dropoff_datetime` would work just as well, but `tpep_pickup_datetime` was
chosen because it comes first in the column order. If you prefer
`tpep_dropoff_datetime` then force it to be chosen using the
`timestamp_field` query parameter.
<8> `joda_timestamp_formats` are used to tell {ls} how to parse timestamps.
<9> `java_timestamp_formats` are the Java time formats recognized in the time
fields. {es} mappings and ingest pipelines use this format.
<10> The timestamp format in this sample doesn't specify a timezone, so to
accurately convert them to UTC timestamps to store in {es} it's necessary to
supply the timezone they relate to. `need_client_timezone` will be `false` for
timestamp formats that include the timezone.

[discrete]
[[find-structure-example-timeout]]
=== Setting the timeout parameter

If you try to analyze a lot of data then the analysis will take a long time. If
you want to limit the amount of processing your {es} cluster performs for a
request, use the `timeout` query parameter. The analysis will be aborted and an
error returned when the timeout expires. For example, you can replace 20000
lines in the previous example with 200000 and set a 1 second timeout on the
analysis:

[source,js]
----
curl -s "s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2018-06.csv" | head -200000 | curl -s -H "Content-Type: application/json" -XPOST "localhost:9200/_text_structure/find_structure?pretty&lines_to_sample=200000&timeout=1s" -T -
----
// NOTCONSOLE
// Not converting to console because this shows how curl can be used

Unless you are using an incredibly fast computer you'll receive a timeout error:

[source,js]
----
{
  "error" : {
    "root_cause" : [
      {
        "type" : "timeout_exception",
        "reason" : "Aborting structure analysis during [delimited record parsing] as it has taken longer than the timeout of [1s]"
      }
    ],
    "type" : "timeout_exception",
    "reason" : "Aborting structure analysis during [delimited record parsing] as it has taken longer than the timeout of [1s]"
  },
  "status" : 500
}
----
// NOTCONSOLE

--
NOTE: If you try the example above yourself you will note that the overall
running time of the `curl` commands is considerably longer than 1 second. This
is because it takes a while to download 200000 lines of CSV from the internet,
and the timeout is measured from the time this endpoint starts to process the
data.

--

[discrete]
[[find-structure-example-eslog]]
=== Analyzing {es} log files

This is an example of analyzing an {es} log file:

[source,js]
----
curl -s -H "Content-Type: application/json" -XPOST
"localhost:9200/_text_structure/find_structure?pretty&ecs_compatibility=disabled" -T "$ES_HOME/logs/elasticsearch.log"
----
// NOTCONSOLE
// Not converting to console because this shows how curl can be used

If the request does not encounter errors, the result will look something like
this:

[source,js]
----
{
  "num_lines_analyzed" : 53,
  "num_messages_analyzed" : 53,
  "sample_start" : "[2018-09-27T14:39:28,518][INFO ][o.e.e.NodeEnvironment    ] [node-0] using [1] data paths, mounts [[/ (/dev/disk1)]], net usable_space [165.4gb], net total_space [464.7gb], types [hfs]\n[2018-09-27T14:39:28,521][INFO ][o.e.e.NodeEnvironment    ] [node-0] heap size [494.9mb], compressed ordinary object pointers [true]\n",
  "charset" : "UTF-8",
  "has_byte_order_marker" : false,
  "format" : "semi_structured_text", <1>
  "multiline_start_pattern" : "^\\[\\b\\d{4}-\\d{2}-\\d{2}[T ]\\d{2}:\\d{2}", <2>
  "grok_pattern" : "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel}.*", <3>
  "ecs_compatibility" : "disabled", <4>
  "timestamp_field" : "timestamp",
  "joda_timestamp_formats" : [
    "ISO8601"
  ],
  "java_timestamp_formats" : [
    "ISO8601"
  ],
  "need_client_timezone" : true,
  "mappings" : {
    "properties" : {
      "@timestamp" : {
        "type" : "date"
      },
      "loglevel" : {
        "type" : "keyword"
      },
      "message" : {
        "type" : "text"
      }
    }
  },
  "ingest_pipeline" : {
    "description" : "Ingest pipeline created by text structure finder",
    "processors" : [
      {
        "grok" : {
          "field" : "message",
          "patterns" : [
            "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel}.*"
          ]
        }
      },
      {
        "date" : {
          "field" : "timestamp",
          "timezone" : "{{ event.timezone }}",
          "formats" : [
            "ISO8601"
          ]
        }
      },
      {
        "remove" : {
          "field" : "timestamp"
        }
      }
    ]
  },
  "field_stats" : {
    "loglevel" : {
      "count" : 53,
      "cardinality" : 3,
      "top_hits" : [
        {
          "value" : "INFO",
          "count" : 51
        },
        {
          "value" : "DEBUG",
          "count" : 1
        },
        {
          "value" : "WARN",
          "count" : 1
        }
      ]
    },
    "timestamp" : {
      "count" : 53,
      "cardinality" : 28,
      "earliest" : "2018-09-27T14:39:28,518",
      "latest" : "2018-09-27T14:39:37,012",
      "top_hits" : [
        {
          "value" : "2018-09-27T14:39:29,859",
          "count" : 10
        },
        {
          "value" : "2018-09-27T14:39:29,860",
          "count" : 9
        },
        {
          "value" : "2018-09-27T14:39:29,858",
          "count" : 6
        },
        {
          "value" : "2018-09-27T14:39:28,523",
          "count" : 3
        },
        {
          "value" : "2018-09-27T14:39:34,234",
          "count" : 2
        },
        {
          "value" : "2018-09-27T14:39:28,518",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:28,521",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:28,522",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:29,861",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:32,786",
          "count" : 1
        }
      ]
    }
  }
}
----
// NOTCONSOLE

<1> This time the `format` has been identified as `semi_structured_text`.
<2> The `multiline_start_pattern` is set on the basis that the timestamp appears
in the first line of each multi-line log message.
<3> A very simple `grok_pattern` has been created, which extracts the timestamp
and recognizable fields that appear in every analyzed message. In this case the
only field that was recognized beyond the timestamp was the log level.
<4> The ECS Grok pattern compatibility mode used, may be one of either `disabled`
(the default if not specified in the request) or `v1`

[discrete]
[[find-structure-example-grok]]
=== Specifying `grok_pattern` as query parameter

If you recognize more fields than the simple `grok_pattern` produced by the
structure finder unaided then you can resubmit the request specifying a more
advanced `grok_pattern` as a query parameter and the structure finder will
calculate `field_stats` for your additional fields.

In the case of the {es} log a more complete Grok pattern is
`\[%{TIMESTAMP_ISO8601:timestamp}\]\[%{LOGLEVEL:loglevel} *\]\[%{JAVACLASS:class} *\] \[%{HOSTNAME:node}\] %{JAVALOGMESSAGE:message}`.
You can analyze the same text again, submitting this `grok_pattern` as a
query parameter (appropriately URL escaped):

[source,js]
----
curl -s -H "Content-Type: application/json" -XPOST "localhost:9200/_text_structure/find_structure?pretty&format=semi_structured_text&grok_pattern=%5C%5B%25%7BTIMESTAMP_ISO8601:timestamp%7D%5C%5D%5C%5B%25%7BLOGLEVEL:loglevel%7D%20*%5C%5D%5C%5B%25%7BJAVACLASS:class%7D%20*%5C%5D%20%5C%5B%25%7BHOSTNAME:node%7D%5C%5D%20%25%7BJAVALOGMESSAGE:message%7D" -T "$ES_HOME/logs/elasticsearch.log"
----
// NOTCONSOLE
// Not converting to console because this shows how curl can be used

If the request does not encounter errors, the result will look something like
this:

[source,js]
----
{
  "num_lines_analyzed" : 53,
  "num_messages_analyzed" : 53,
  "sample_start" : "[2018-09-27T14:39:28,518][INFO ][o.e.e.NodeEnvironment    ] [node-0] using [1] data paths, mounts [[/ (/dev/disk1)]], net usable_space [165.4gb], net total_space [464.7gb], types [hfs]\n[2018-09-27T14:39:28,521][INFO ][o.e.e.NodeEnvironment    ] [node-0] heap size [494.9mb], compressed ordinary object pointers [true]\n",
  "charset" : "UTF-8",
  "has_byte_order_marker" : false,
  "format" : "semi_structured_text",
  "multiline_start_pattern" : "^\\[\\b\\d{4}-\\d{2}-\\d{2}[T ]\\d{2}:\\d{2}",
  "grok_pattern" : "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel} *\\]\\[%{JAVACLASS:class} *\\] \\[%{HOSTNAME:node}\\] %{JAVALOGMESSAGE:message}", <1>
  "ecs_compatibility" : "disabled", <2>
  "timestamp_field" : "timestamp",
  "joda_timestamp_formats" : [
    "ISO8601"
  ],
  "java_timestamp_formats" : [
    "ISO8601"
  ],
  "need_client_timezone" : true,
  "mappings" : {
    "properties" : {
      "@timestamp" : {
        "type" : "date"
      },
      "class" : {
        "type" : "keyword"
      },
      "loglevel" : {
        "type" : "keyword"
      },
      "message" : {
        "type" : "text"
      },
      "node" : {
        "type" : "keyword"
      }
    }
  },
  "ingest_pipeline" : {
    "description" : "Ingest pipeline created by text structure finder",
    "processors" : [
      {
        "grok" : {
          "field" : "message",
          "patterns" : [
            "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel} *\\]\\[%{JAVACLASS:class} *\\] \\[%{HOSTNAME:node}\\] %{JAVALOGMESSAGE:message}"
          ]
        }
      },
      {
        "date" : {
          "field" : "timestamp",
          "timezone" : "{{ event.timezone }}",
          "formats" : [
            "ISO8601"
          ]
        }
      },
      {
        "remove" : {
          "field" : "timestamp"
        }
      }
    ]
  },
  "field_stats" : { <3>
    "class" : {
      "count" : 53,
      "cardinality" : 14,
      "top_hits" : [
        {
          "value" : "o.e.p.PluginsService",
          "count" : 26
        },
        {
          "value" : "o.e.c.m.MetadataIndexTemplateService",
          "count" : 8
        },
        {
          "value" : "o.e.n.Node",
          "count" : 7
        },
        {
          "value" : "o.e.e.NodeEnvironment",
          "count" : 2
        },
        {
          "value" : "o.e.a.ActionModule",
          "count" : 1
        },
        {
          "value" : "o.e.c.s.ClusterApplierService",
          "count" : 1
        },
        {
          "value" : "o.e.c.s.MasterService",
          "count" : 1
        },
        {
          "value" : "o.e.d.DiscoveryModule",
          "count" : 1
        },
        {
          "value" : "o.e.g.GatewayService",
          "count" : 1
        },
        {
          "value" : "o.e.l.LicenseService",
          "count" : 1
        }
      ]
    },
    "loglevel" : {
      "count" : 53,
      "cardinality" : 3,
      "top_hits" : [
        {
          "value" : "INFO",
          "count" : 51
        },
        {
          "value" : "DEBUG",
          "count" : 1
        },
        {
          "value" : "WARN",
          "count" : 1
        }
      ]
    },
    "message" : {
      "count" : 53,
      "cardinality" : 53,
      "top_hits" : [
        {
          "value" : "Using REST wrapper from plugin org.elasticsearch.xpack.security.Security",
          "count" : 1
        },
        {
          "value" : "adding template [.monitoring-alerts] for index patterns [.monitoring-alerts-6]",
          "count" : 1
        },
        {
          "value" : "adding template [.monitoring-beats] for index patterns [.monitoring-beats-6-*]",
          "count" : 1
        },
        {
          "value" : "adding template [.monitoring-es] for index patterns [.monitoring-es-6-*]",
          "count" : 1
        },
        {
          "value" : "adding template [.monitoring-kibana] for index patterns [.monitoring-kibana-6-*]",
          "count" : 1
        },
        {
          "value" : "adding template [.monitoring-logstash] for index patterns [.monitoring-logstash-6-*]",
          "count" : 1
        },
        {
          "value" : "adding template [.triggered_watches] for index patterns [.triggered_watches*]",
          "count" : 1
        },
        {
          "value" : "adding template [.watch-history-9] for index patterns [.watcher-history-9*]",
          "count" : 1
        },
        {
          "value" : "adding template [.watches] for index patterns [.watches*]",
          "count" : 1
        },
        {
          "value" : "starting ...",
          "count" : 1
        }
      ]
    },
    "node" : {
      "count" : 53,
      "cardinality" : 1,
      "top_hits" : [
        {
          "value" : "node-0",
          "count" : 53
        }
      ]
    },
    "timestamp" : {
      "count" : 53,
      "cardinality" : 28,
      "earliest" : "2018-09-27T14:39:28,518",
      "latest" : "2018-09-27T14:39:37,012",
      "top_hits" : [
        {
          "value" : "2018-09-27T14:39:29,859",
          "count" : 10
        },
        {
          "value" : "2018-09-27T14:39:29,860",
          "count" : 9
        },
        {
          "value" : "2018-09-27T14:39:29,858",
          "count" : 6
        },
        {
          "value" : "2018-09-27T14:39:28,523",
          "count" : 3
        },
        {
          "value" : "2018-09-27T14:39:34,234",
          "count" : 2
        },
        {
          "value" : "2018-09-27T14:39:28,518",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:28,521",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:28,522",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:29,861",
          "count" : 1
        },
        {
          "value" : "2018-09-27T14:39:32,786",
          "count" : 1
        }
      ]
    }
  }
}
----
// NOTCONSOLE

<1> The `grok_pattern` in the output is now the overridden one supplied in the
query parameter.
<2> The ECS Grok pattern compatibility mode used, may be one of either `disabled`
(the default if not specified in the request) or `v1`
<3> The returned `field_stats` include entries for the fields from the
overridden `grok_pattern`.

The URL escaping is hard, so if you are working interactively it is best to use
the UI!
