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Find structure APIedit

Finds the structure of text. The text must contain data that is suitable to be ingested into the Elastic Stack.

Requestedit

POST _text_structure/find_structure

Prerequisitesedit

If the Elasticsearch security features are enabled, you must have monitor_text_structure or monitor cluster privileges to use this API. See Security privileges.

Descriptionedit

This API provides a starting point for ingesting data into Elasticsearch in a format that is suitable for subsequent use with other Elastic Stack functionality.

Unlike other Elasticsearch 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 Elasticsearch 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 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.

Query parametersedit

charset

(Optional, string) The text’s character set. It must be a character set that is supported by the JVM that Elasticsearch 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.

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) 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 the Java date/time format documentation for more information about date and time format syntax.

Request bodyedit

The text that you want to analyze. It must contain data that is suitable to be ingested into Elasticsearch. 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 Elasticsearch HTTP receive buffer size, which defaults to 100 Mb.

Examplesedit

Ingesting newline-delimited JSONedit

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

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}

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

{
  "num_lines_analyzed" : 24, 
  "num_messages_analyzed" : 24, 
  "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", 
  "charset" : "UTF-8", 
  "has_byte_order_marker" : false, 
  "format" : "ndjson", 
  "ecs_compatibility" : "disabled", 
  "timestamp_field" : "release_date", 
  "joda_timestamp_formats" : [ 
    "ISO8601"
  ],
  "java_timestamp_formats" : [ 
    "ISO8601"
  ],
  "need_client_timezone" : true, 
  "mappings" : { 
    "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" : { 
    "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
        }
      ]
    }
  }
}

	`num_lines_analyzed` indicates how many lines of the text were analyzed.
	`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.
	`sample_start` reproduces the first two messages in the text verbatim. This may help diagnose parse errors or accidental uploads of the wrong text.
	`charset` indicates the character encoding used to parse the text.
	For UTF character encodings, `has_byte_order_marker` indicates whether the text begins with a byte order marker.
	`format` is one of `ndjson`, `xml`, `delimited` or `semi_structured_text`.
	`ecs_compatibility` is either `disabled` or `v1`, defaults to `disabled`.
	The `timestamp_field` names the field considered most likely to be the primary timestamp of each document.
	`joda_timestamp_formats` are used to tell Logstash how to parse timestamps.
	`java_timestamp_formats` are the Java time formats recognized in the time fields. Elasticsearch mappings and ingest pipelines use this format.
	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.
	`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.
	`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 Elastic Stack functionality.

Finding the structure of NYC yellow cab example dataedit

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.

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 -

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 Elasticsearch, but it must be set.)

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

{
  "num_lines_analyzed" : 20000,
  "num_messages_analyzed" : 19998, 
  "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", 
  "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" : [ 
    "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, 
  "delimiter" : ",", 
  "quote" : "\"", 
  "timestamp_field" : "tpep_pickup_datetime", 
  "joda_timestamp_formats" : [ 
    "YYYY-MM-dd HH:mm:ss"
  ],
  "java_timestamp_formats" : [ 
    "yyyy-MM-dd HH:mm:ss"
  ],
  "need_client_timezone" : true, 
  "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
        }
      ]
    }
  }
}

	`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.
	Unlike the first example, in this case the `format` has been identified as `delimited`.
	Because the `format` is `delimited`, the `column_names` field in the output lists the column names in the order they appear in the sample.
	`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.)
	The `delimiter` for this sample is a comma, as it’s CSV formatted text.
	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.)
	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.
	`joda_timestamp_formats` are used to tell Logstash how to parse timestamps.
	`java_timestamp_formats` are the Java time formats recognized in the time fields. Elasticsearch mappings and ingest pipelines use this format.
	The timestamp format in this sample doesn’t specify a timezone, so to accurately convert them to UTC timestamps to store in Elasticsearch it’s necessary to supply the timezone they relate to. `need_client_timezone` will be `false` for timestamp formats that include the timezone.

Setting the timeout parameteredit

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 Elasticsearch 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:

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 -

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

{
  "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
}

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.

Analyzing Elasticsearch log filesedit

This is an example of analyzing an Elasticsearch log file:

curl -s -H "Content-Type: application/json" -XPOST
"localhost:9200/_text_structure/find_structure?pretty&ecs_compatibility=disabled" -T "$ES_HOME/logs/elasticsearch.log"

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

{
  "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}.*", 
  "ecs_compatibility" : "disabled", 
  "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
        }
      ]
    }
  }
}

	This time the `format` has been identified as `semi_structured_text`.
	The `multiline_start_pattern` is set on the basis that the timestamp appears in the first line of each multi-line log message.
	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.
	The ECS Grok pattern compatibility mode used, may be one of either `disabled` (the default if not specified in the request) or `v1`

Specifying `grok_pattern` as query parameteredit

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 Elasticsearch 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):

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"