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[role="xpack"]
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[testenv="basic"]
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[[ml-find-file-structure]]
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=== Find File Structure API
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++++
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<titleabbrev>Find File Structure</titleabbrev>
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++++
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experimental[]
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Finds the structure of a text file. The text file must contain data that is
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suitable to be ingested into {es}.
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==== Request
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`POST _xpack/ml/find_file_structure`
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==== Description
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This API provides a starting point for ingesting data into {es} in a format that
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is suitable for subsequent use with other {ml} functionality.
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Unlike other {es} endpoints, the data that is posted to this endpoint does not
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need to be UTF-8 encoded and in JSON format. It must, however, be text; binary
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file formats are not currently supported.
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The response from the API contains:
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* A couple of messages from the beginning of the file.
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* Statistics that reveal the most common values for all fields detected within
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the file and basic numeric statistics for numeric fields.
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* Information about the structure of the file, which is useful when you write
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ingest configurations to index the file contents.
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* Appropriate mappings for an {es} index, which you could use to ingest the file
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contents.
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All this information can be calculated by the structure finder with no guidance.
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However, you can optionally override some of the decisions about the file
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structure by specifying one or more query parameters.
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Details of the output can be seen in the
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<<ml-find-file-structure-examples,examples>>.
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If the structure finder produces unexpected results for a particular file,
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specify the `explain` query parameter. It causes an `explanation` to appear in
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the response, which should help in determining why the returned structure was
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chosen.
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==== Query Parameters
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`charset`::
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(string) The file's character set. It must be a character set that is supported
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by the JVM that {es} uses. For example, `UTF-8`, `UTF-16LE`, `windows-1252`, or
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`EUC-JP`. If this parameter is not specified, the structure finder chooses an
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appropriate character set.
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`column_names`::
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(string) If you have set `format` to `delimited`, you can specify the column names
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in a comma-separated list. If this parameter is not specified, the structure
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finder uses the column names from the header row of the file. If the file does
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not have a header role, columns are named "column1", "column2", "column3", etc.
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`delimiter`::
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(string) If you have set `format` to `delimited`, you can specify the character used
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to delimit the values in each row. Only a single character is supported; the
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delimiter cannot have multiple characters. If this parameter is not specified,
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the structure finder considers the following possibilities: comma, tab,
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semi-colon, and pipe (`|`).
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`explain`::
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(boolean) If this parameter is set to `true`, the response includes a field
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named `explanation`, which is an array of strings that indicate how the
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structure finder produced its result. The default value is `false`.
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`format`::
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(string) The high level structure of the file. Valid values are `json`, `xml`,
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`delimited`, and `semi_structured_text`. If this parameter is not specified,
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the structure finder chooses one.
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`grok_pattern`::
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(string) If you have set `format` to `semi_structured_text`, you can specify a Grok
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pattern that is used to extract fields from every message in the file. The
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name of the timestamp field in the Grok pattern must match what is specified
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in the `timestamp_field` parameter. If that parameter is not specified, the
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name of the timestamp field in the Grok pattern must match "timestamp". If
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`grok_pattern` is not specified, the structure finder creates a Grok pattern.
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`has_header_row`::
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(boolean) If you have set `format` to `delimited`, you can use this parameter to
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indicate whether the column names are in the first row of the file. If this
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parameter is not specified, the structure finder guesses based on the similarity of
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the first row of the file to other rows.
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`lines_to_sample`::
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(unsigned integer) The number of lines to include in the structural analysis,
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starting from the beginning of the file. The minimum is 2; the default
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is 1000. If the value of this parameter is greater than the number of lines in
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the file, the analysis proceeds (as long as there are at least two lines in the
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file) for all of the lines. +
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+
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--
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NOTE: The number of lines and the variation of the lines affects the speed of
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the analysis. For example, if you upload a log file where the first 1000 lines
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are all variations on the same message, the analysis will find more commonality
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than would be seen with a bigger sample. If possible, however, it is more
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efficient to upload a sample file with more variety in the first 1000 lines than
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to request analysis of 100000 lines to achieve some variety.
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--
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`quote`::
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(string) If you have set `format` to `delimited`, you can specify the character used
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to quote the values in each row if they contain newlines or the delimiter
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character. Only a single character is supported. If this parameter is not
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specified, the default value is a double quote (`"`). If your delimited file
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format does not use quoting, a workaround is to set this argument to a
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character that does not appear anywhere in the sample.
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`should_trim_fields`::
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(boolean) If you have set `format` to `delimited`, you can specify whether values
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between delimiters should have whitespace trimmed from them. If this parameter
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is not specified and the delimiter is pipe (`|`), the default value is `true`.
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Otherwise, the default value is `false`.
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`timeout`::
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(time) Sets the maximum amount of time that the structure analysis make take.
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If the analysis is still running when the timeout expires then it will be
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aborted. The default value is 25 seconds.
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`timestamp_field`::
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(string) The name of the field that contains the primary timestamp of each
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record in the file. In particular, if the file were ingested into an index,
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this is the field that would be used to populate the `@timestamp` field. +
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+
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--
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If the `format` is `semi_structured_text`, this field must match the name of the
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appropriate extraction in the `grok_pattern`. Therefore, for semi-structured
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file formats, it is best not to specify this parameter unless `grok_pattern` is
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also specified.
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For structured file formats, if you specify this parameter, the field must exist
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within the file.
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If this parameter is not specified, the structure finder makes a decision about which
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field (if any) is the primary timestamp field. For structured file formats, it
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is not compulsory to have a timestamp in the file.
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--
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`timestamp_format`::
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(string) The time format of the timestamp field in the file. +
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+
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--
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NOTE: Currently there is a limitation that this format must be one that the
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structure finder might choose by itself. The reason for this restriction is that
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to consistently set all the fields in the response the structure finder needs a
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corresponding Grok pattern name and simple regular expression for each timestamp
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format. Therefore, there is little value in specifying this parameter for
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structured file formats. If you know which field contains your primary timestamp,
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it is as good and less error-prone to just specify `timestamp_field`.
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The valuable use case for this parameter is when the format is semi-structured
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text, there are multiple timestamp formats in the file, and you know which
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format corresponds to the primary timestamp, but you do not want to specify the
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full `grok_pattern`.
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If this parameter is not specified, the structure finder chooses the best format from
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the formats it knows, which are these Joda formats and their Java time equivalents:
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* `dd/MMM/YYYY:HH:mm:ss Z`
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* `EEE MMM dd HH:mm zzz YYYY`
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* `EEE MMM dd HH:mm:ss YYYY`
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* `EEE MMM dd HH:mm:ss zzz YYYY`
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* `EEE MMM dd YYYY HH:mm zzz`
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* `EEE MMM dd YYYY HH:mm:ss zzz`
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* `EEE, dd MMM YYYY HH:mm Z`
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* `EEE, dd MMM YYYY HH:mm ZZ`
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* `EEE, dd MMM YYYY HH:mm:ss Z`
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* `EEE, dd MMM YYYY HH:mm:ss ZZ`
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* `ISO8601`
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* `MMM d HH:mm:ss`
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* `MMM d HH:mm:ss,SSS`
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* `MMM d YYYY HH:mm:ss`
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* `MMM dd HH:mm:ss`
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* `MMM dd HH:mm:ss,SSS`
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* `MMM dd YYYY HH:mm:ss`
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* `MMM dd, YYYY h:mm:ss a`
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* `TAI64N`
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* `UNIX`
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* `UNIX_MS`
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* `YYYY-MM-dd HH:mm:ss`
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* `YYYY-MM-dd HH:mm:ss,SSS`
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* `YYYY-MM-dd HH:mm:ss,SSS Z`
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* `YYYY-MM-dd HH:mm:ss,SSSZ`
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* `YYYY-MM-dd HH:mm:ss,SSSZZ`
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* `YYYY-MM-dd HH:mm:ssZ`
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* `YYYY-MM-dd HH:mm:ssZZ`
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* `YYYYMMddHHmmss`
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--
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==== Request Body
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The text file that you want to analyze. It must contain data that is suitable to
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be ingested into {es}. It does not need to be in JSON format and it does not
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need to be UTF-8 encoded. The size is limited to the {es} HTTP receive buffer
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size, which defaults to 100 Mb.
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==== Authorization
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You must have `monitor_ml`, or `monitor` cluster privileges to use this API.
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For more information, see {stack-ov}/security-privileges.html[Security Privileges].
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[[ml-find-file-structure-examples]]
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==== Examples
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Suppose you have a newline-delimited JSON file that contains information about
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some books. You can send the contents to the `find_file_structure` endpoint:
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[source,js]
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----
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POST _xpack/ml/find_file_structure
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{"name": "Leviathan Wakes", "author": "James S.A. Corey", "release_date": "2011-06-02", "page_count": 561}
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{"name": "Hyperion", "author": "Dan Simmons", "release_date": "1989-05-26", "page_count": 482}
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{"name": "Dune", "author": "Frank Herbert", "release_date": "1965-06-01", "page_count": 604}
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{"name": "Dune Messiah", "author": "Frank Herbert", "release_date": "1969-10-15", "page_count": 331}
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{"name": "Children of Dune", "author": "Frank Herbert", "release_date": "1976-04-21", "page_count": 408}
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{"name": "God Emperor of Dune", "author": "Frank Herbert", "release_date": "1981-05-28", "page_count": 454}
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{"name": "Consider Phlebas", "author": "Iain M. Banks", "release_date": "1987-04-23", "page_count": 471}
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{"name": "Pandora's Star", "author": "Peter F. Hamilton", "release_date": "2004-03-02", "page_count": 768}
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{"name": "Revelation Space", "author": "Alastair Reynolds", "release_date": "2000-03-15", "page_count": 585}
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{"name": "A Fire Upon the Deep", "author": "Vernor Vinge", "release_date": "1992-06-01", "page_count": 613}
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{"name": "Ender's Game", "author": "Orson Scott Card", "release_date": "1985-06-01", "page_count": 324}
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{"name": "1984", "author": "George Orwell", "release_date": "1985-06-01", "page_count": 328}
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{"name": "Fahrenheit 451", "author": "Ray Bradbury", "release_date": "1953-10-15", "page_count": 227}
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{"name": "Brave New World", "author": "Aldous Huxley", "release_date": "1932-06-01", "page_count": 268}
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{"name": "Foundation", "author": "Isaac Asimov", "release_date": "1951-06-01", "page_count": 224}
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{"name": "The Giver", "author": "Lois Lowry", "release_date": "1993-04-26", "page_count": 208}
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{"name": "Slaughterhouse-Five", "author": "Kurt Vonnegut", "release_date": "1969-06-01", "page_count": 275}
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{"name": "The Hitchhiker's Guide to the Galaxy", "author": "Douglas Adams", "release_date": "1979-10-12", "page_count": 180}
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{"name": "Snow Crash", "author": "Neal Stephenson", "release_date": "1992-06-01", "page_count": 470}
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{"name": "Neuromancer", "author": "William Gibson", "release_date": "1984-07-01", "page_count": 271}
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{"name": "The Handmaid's Tale", "author": "Margaret Atwood", "release_date": "1985-06-01", "page_count": 311}
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{"name": "Starship Troopers", "author": "Robert A. Heinlein", "release_date": "1959-12-01", "page_count": 335}
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{"name": "The Left Hand of Darkness", "author": "Ursula K. Le Guin", "release_date": "1969-06-01", "page_count": 304}
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{"name": "The Moon is a Harsh Mistress", "author": "Robert A. Heinlein", "release_date": "1966-04-01", "page_count": 288}
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----
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// CONSOLE
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// TEST
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If the request does not encounter errors, you receive the following result:
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[source,js]
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----
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{
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"num_lines_analyzed" : 24, <1>
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"num_messages_analyzed" : 24, <2>
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"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>
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"charset" : "UTF-8", <4>
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"has_byte_order_marker" : false, <5>
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"format" : "json", <6>
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"need_client_timezone" : false, <7>
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"mappings" : { <8>
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"author" : {
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"type" : "keyword"
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},
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"name" : {
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"type" : "keyword"
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},
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"page_count" : {
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"type" : "long"
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},
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"release_date" : {
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"type" : "keyword"
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}
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},
|
|
|
|
|
"field_stats" : { <9>
|
|
|
|
|
"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,
|
2018-09-22 10:48:59 -04:00
|
|
|
"min_value" : 180,
|
|
|
|
|
"max_value" : 768,
|
2018-09-20 07:17:09 -04:00
|
|
|
"mean_value" : 387.0833333333333,
|
|
|
|
|
"median_value" : 329.5,
|
|
|
|
|
"top_hits" : [
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 180,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 208,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 224,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 227,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 268,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 271,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 275,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 288,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 304,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
},
|
|
|
|
|
{
|
2018-09-22 10:48:59 -04:00
|
|
|
"value" : 311,
|
2018-09-20 07:17:09 -04:00
|
|
|
"count" : 1
|
|
|
|
|
}
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"release_date" : {
|
|
|
|
|
"count" : 24,
|
|
|
|
|
"cardinality" : 20,
|
|
|
|
|
"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[s/"sample_start" : ".*",/"sample_start" : "$body.sample_start",/]
|
|
|
|
|
// The substitution is because the "file" 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 file were analyzed.
|
|
|
|
|
<2> `num_messages_analyzed` indicates how many distinct messages the lines contained.
|
|
|
|
|
For ND-JSON, this value is the same as `num_lines_analyzed`. For other file
|
|
|
|
|
formats, messages can span several lines.
|
|
|
|
|
<3> `sample_start` reproduces the first two messages in the file verbatim. This
|
|
|
|
|
may help to diagnose parse errors or accidental uploads of the wrong file.
|
|
|
|
|
<4> `charset` indicates the character encoding used to parse the file.
|
|
|
|
|
<5> For UTF character encodings, `has_byte_order_marker` indicates whether the
|
|
|
|
|
file begins with a byte order marker.
|
|
|
|
|
<6> `format` is one of `json`, `xml`, `delimited` or `semi_structured_text`.
|
|
|
|
|
<7> If a timestamp format is detected that does not include a timezone,
|
|
|
|
|
`need_client_timezone` will be `true`. The server that parses the file must
|
|
|
|
|
therefore be told the correct timezone by the client.
|
|
|
|
|
<8> `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.
|
|
|
|
|
<9> `field_stats` contains the most common values of each field, plus basic
|
2018-09-28 12:32:35 -04:00
|
|
|
numeric statistics for the numeric `page_count` field. This information
|
2018-09-20 07:17:09 -04:00
|
|
|
may provide clues that the data needs to be cleaned or transformed prior
|
|
|
|
|
to use by other {ml} functionality.
|
|
|
|
|
|
2018-09-25 07:52:51 -04:00
|
|
|
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_file_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/_xpack/ml/find_file_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
|
2018-09-28 12:32:35 -04:00
|
|
|
this case the data is not JSON. (Alternatively the `Content-Type` can be set
|
2018-09-25 07:52:51 -04:00
|
|
|
to any other supported by Elasticsearch, but it must be set.)
|
|
|
|
|
--
|
|
|
|
|
|
|
|
|
|
If the request does not encounter errors, you receive the following result:
|
2018-09-28 12:32:35 -04:00
|
|
|
|
2018-09-25 07:52:51 -04:00
|
|
|
[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} \\d{2}:\\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" : {
|
|
|
|
|
"@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"
|
|
|
|
|
},
|
|
|
|
|
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|
|
"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,
|
|
|
|
|
"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,
|
|
|
|
|
"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 a CSV file.
|
|
|
|
|
<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 a
|
|
|
|
|
delimited file 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 Logstash and Ingest pipeline how
|
|
|
|
|
to parse timestamps.
|
|
|
|
|
<9> `java_timestamp_formats` are the Java time formats recognized in the time
|
2018-09-28 12:32:35 -04:00
|
|
|
fields. In future Ingest pipeline will switch to use this format.
|
2018-09-25 07:52:51 -04:00
|
|
|
<10> 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.
|
|
|
|
|
|
2018-09-28 12:32:35 -04:00
|
|
|
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/_xpack/ml/find_file_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.
|
|
|
|
|
--
|
|
|
|
|
|
|
|
|
|
This is an example of analyzing {es}'s own log file:
|
|
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
|
----
|
|
|
|
|
curl -s -H "Content-Type: application/json" -XPOST "localhost:9200/_xpack/ml/find_file_structure?pretty" -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}:\\d{2},\\d{3}", <2>
|
|
|
|
|
"grok_pattern" : "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel}.*", <3>
|
|
|
|
|
"timestamp_field" : "timestamp",
|
|
|
|
|
"joda_timestamp_formats" : [
|
|
|
|
|
"ISO8601"
|
|
|
|
|
],
|
|
|
|
|
"java_timestamp_formats" : [
|
|
|
|
|
"yyyy-MM-dd'T'HH:mm:ss,SSS"
|
|
|
|
|
],
|
|
|
|
|
"need_client_timezone" : true,
|
|
|
|
|
"mappings" : {
|
|
|
|
|
"@timestamp" : {
|
|
|
|
|
"type" : "date"
|
|
|
|
|
},
|
|
|
|
|
"loglevel" : {
|
|
|
|
|
"type" : "keyword"
|
|
|
|
|
},
|
|
|
|
|
"message" : {
|
|
|
|
|
"type" : "text"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"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,
|
|
|
|
|
"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.
|
|
|
|
|
|
|
|
|
|
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 log file again, submitting this `grok_pattern` as a
|
|
|
|
|
query parameter (appropriately URL escaped):
|
|
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
|
----
|
|
|
|
|
curl -s -H "Content-Type: application/json" -XPOST "localhost:9200/_xpack/ml/find_file_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}:\\d{2},\\d{3}",
|
|
|
|
|
"grok_pattern" : "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel} *\\]\\[%{JAVACLASS:class} *\\] \\[%{HOSTNAME:node}\\] %{JAVALOGMESSAGE:message}", <1>
|
|
|
|
|
"timestamp_field" : "timestamp",
|
|
|
|
|
"joda_timestamp_formats" : [
|
|
|
|
|
"ISO8601"
|
|
|
|
|
],
|
|
|
|
|
"java_timestamp_formats" : [
|
|
|
|
|
"yyyy-MM-dd'T'HH:mm:ss,SSS"
|
|
|
|
|
],
|
|
|
|
|
"need_client_timezone" : true,
|
|
|
|
|
"mappings" : {
|
|
|
|
|
"@timestamp" : {
|
|
|
|
|
"type" : "date"
|
|
|
|
|
},
|
|
|
|
|
"class" : {
|
|
|
|
|
"type" : "keyword"
|
|
|
|
|
},
|
|
|
|
|
"loglevel" : {
|
|
|
|
|
"type" : "keyword"
|
|
|
|
|
},
|
|
|
|
|
"message" : {
|
|
|
|
|
"type" : "text"
|
|
|
|
|
},
|
|
|
|
|
"node" : {
|
|
|
|
|
"type" : "keyword"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"field_stats" : { <1>
|
|
|
|
|
"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,
|
|
|
|
|
"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
|
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|
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|
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|
|
<1> The `grok_pattern` in the output is now the overridden one supplied in the
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|
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|
|
query parameter.
|
|
|
|
|
<2> 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
|
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|
|
|
the {ml} UI!
|
