[role="xpack"]
[testenv="basic"]
[[ml-find-file-structure]]
=== Find file structure API
++++
Find file structure
++++
experimental[]
Finds the structure of a text file. The text file must contain data that is
suitable to be ingested into {es}.
==== Request
`POST _ml/find_file_structure`
==== Description
This API provides a starting point for ingesting data into {es} in a format that
is suitable for subsequent use with other {ml} functionality.
Unlike other {es} endpoints, the data that is posted to this endpoint does not
need to be UTF-8 encoded and in JSON format. It must, however, be text; binary
file formats are not currently supported.
The response from the API contains:
* A couple of messages from the beginning of the file.
* Statistics that reveal the most common values for all fields detected within
the file and basic numeric statistics for numeric fields.
* Information about the structure of the file, which is useful when you write
ingest configurations to index the file contents.
* Appropriate mappings for an {es} index, which you could use to ingest the file
contents.
All this information can be calculated by the structure finder with no guidance.
However, you can optionally override some of the decisions about the file
structure by specifying one or more query parameters.
Details of the output can be seen in the
<>.
If the structure finder produces unexpected results for a particular file,
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 Parameters
`charset`::
(string) The file's character set. It must be a character set that is supported
by the JVM that {es} uses. For example, `UTF-8`, `UTF-16LE`, `windows-1252`, or
`EUC-JP`. If this parameter is not specified, the structure finder chooses an
appropriate character set.
`column_names`::
(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 file. If the file does
not have a header role, columns are named "column1", "column2", "column3", etc.
`delimiter`::
(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. If this parameter is not specified,
the structure finder considers the following possibilities: comma, tab,
semi-colon, and pipe (`|`).
`explain`::
(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`::
(string) The high level structure of the file. Valid values are `ndjson`, `xml`,
`delimited`, and `semi_structured_text`. If this parameter is not specified,
the structure finder chooses one.
`grok_pattern`::
(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 file. 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.
`has_header_row`::
(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 file. If this
parameter is not specified, the structure finder guesses based on the similarity of
the first row of the file to other rows.
`lines_to_sample`::
(unsigned integer) The number of lines to include in the structural analysis,
starting from the beginning of the file. The minimum is 2; the default
is 1000. If the value of this parameter is greater than the number of lines in
the file, the analysis proceeds (as long as there are at least two lines in the
file) for all of the lines. +
+
--
NOTE: The number of lines and the variation of the lines affects the speed of
the analysis. For example, if you upload a log file 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 a sample file with more variety in the first 1000 lines than
to request analysis of 100000 lines to achieve some variety.
--
`quote`::
(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 file
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`::
(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`::
(time) 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`::
(string) The name of the field that contains the primary timestamp of each
record in the file. In particular, if the file 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
file formats, it is best not to specify this parameter unless `grok_pattern` is
also specified.
For structured file formats, if you specify this parameter, the field must exist
within the file.
If this parameter is not specified, the structure finder makes a decision about which
field (if any) is the primary timestamp field. For structured file formats, it
is not compulsory to have a timestamp in the file.
--
`timestamp_format`::
(string) The Java time format of the timestamp field in the file. +
+
--
NOTE: 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 file, 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.
--
==== Request Body
The text file that you want to analyze. It must contain data that is suitable to
be ingested into {es}. It does not need to be in JSON format and it does not
need to be UTF-8 encoded. The size is limited to the {es} HTTP receive buffer
size, which defaults to 100 Mb.
==== Authorization
You must have `monitor_ml`, or `monitor` cluster privileges to use this API.
For more information, see {stack-ov}/security-privileges.html[Security Privileges].
[[ml-find-file-structure-examples]]
==== Examples
Suppose you have a newline-delimited JSON file that contains information about
some books. You can send the contents to the `find_file_structure` endpoint:
[source,js]
----
POST _ml/find_file_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}
----
// CONSOLE
// TEST
If the request does not encounter errors, you receive the following result:
[source,js]
----
{
"num_lines_analyzed" : 24, <1>
"num_messages_analyzed" : 24, <2>
"sample_start" : "{\"name\": \"Leviathan Wakes\", \"author\": \"James S.A. Corey\", \"release_date\": \"2011-06-02\", \"page_count\": 561}\n{\"name\": \"Hyperion\", \"author\": \"Dan Simmons\", \"release_date\": \"1989-05-26\", \"page_count\": 482}\n", <3>
"charset" : "UTF-8", <4>
"has_byte_order_marker" : false, <5>
"format" : "ndjson", <6>
"timestamp_field" : "release_date", <7>
"joda_timestamp_formats" : [ <8>
"ISO8601"
],
"java_timestamp_formats" : [ <9>
"ISO8601"
],
"need_client_timezone" : true, <10>
"mappings" : { <11>
"@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 file structure finder",
"processors" : [
{
"date" : {
"field" : "release_date",
"timezone" : "{{ beat.timezone }}",
"formats" : [
"ISO8601"
]
}
}
]
},
"field_stats" : { <12>
"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,
"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 NDJSON, 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 `ndjson`, `xml`, `delimited` or `semi_structured_text`.
<7> The `timestamp_field` names the field considered most likely to be the
primary timestamp of each document.
<8> `joda_timestamp_formats` are used to tell Logstash how to parse timestamps.
<9> `java_timestamp_formats` are the Java time formats recognized in the time
fields. Elasticsearch mappings and Ingest pipeline use this format.
<10> 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.
<11> `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.
<12> `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 {ml} functionality.
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/_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
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:
[source,js]
----
{
"num_lines_analyzed" : 20000,
"num_messages_analyzed" : 19998, <1>
"sample_start" : "VendorID,tpep_pickup_datetime,tpep_dropoff_datetime,passenger_count,trip_distance,RatecodeID,store_and_fwd_flag,PULocationID,DOLocationID,payment_type,fare_amount,extra,mta_tax,tip_amount,tolls_amount,improvement_surcharge,total_amount\n\n1,2018-06-01 00:15:40,2018-06-01 00:16:46,1,.00,1,N,145,145,2,3,0.5,0.5,0,0,0.3,4.3\n",
"charset" : "UTF-8",
"has_byte_order_marker" : false,
"format" : "delimited", <2>
"multiline_start_pattern" : "^.*?,\"?\\d{4}-\\d{2}-\\d{2}[T ]\\d{2}:\\d{2}",
"exclude_lines_pattern" : "^\"?VendorID\"?,\"?tpep_pickup_datetime\"?,\"?tpep_dropoff_datetime\"?,\"?passenger_count\"?,\"?trip_distance\"?,\"?RatecodeID\"?,\"?store_and_fwd_flag\"?,\"?PULocationID\"?,\"?DOLocationID\"?,\"?payment_type\"?,\"?fare_amount\"?,\"?extra\"?,\"?mta_tax\"?,\"?tip_amount\"?,\"?tolls_amount\"?,\"?improvement_surcharge\"?,\"?total_amount\"?",
"column_names" : [ <3>
"VendorID",
"tpep_pickup_datetime",
"tpep_dropoff_datetime",
"passenger_count",
"trip_distance",
"RatecodeID",
"store_and_fwd_flag",
"PULocationID",
"DOLocationID",
"payment_type",
"fare_amount",
"extra",
"mta_tax",
"tip_amount",
"tolls_amount",
"improvement_surcharge",
"total_amount"
],
"has_header_row" : true, <4>
"delimiter" : ",", <5>
"quote" : "\"", <6>
"timestamp_field" : "tpep_pickup_datetime", <7>
"joda_timestamp_formats" : [ <8>
"YYYY-MM-dd HH:mm:ss"
],
"java_timestamp_formats" : [ <9>
"yyyy-MM-dd HH:mm:ss"
],
"need_client_timezone" : true, <10>
"mappings" : {
"@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 file structure finder",
"processors" : [
{
"date" : {
"field" : "tpep_pickup_datetime",
"timezone" : "{{ beat.timezone }}",
"formats" : [
"yyyy-MM-dd HH:mm:ss"
]
}
}
]
},
"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,
"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 how to parse timestamps.
<9> `java_timestamp_formats` are the Java time formats recognized in the time
fields. Elasticsearch mappings and Ingest pipeline use this format.
<10> The timestamp format in this sample doesn't specify a timezone, so to
accurately convert them to UTC timestamps to store in Elasticsearch it's
necessary to supply the timezone they relate to. `need_client_timezone`
will be `false` for timestamp formats that include the timezone.
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/_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/_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}", <2>
"grok_pattern" : "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel}.*", <3>
"timestamp_field" : "timestamp",
"joda_timestamp_formats" : [
"ISO8601"
],
"java_timestamp_formats" : [
"ISO8601"
],
"need_client_timezone" : true,
"mappings" : {
"@timestamp" : {
"type" : "date"
},
"loglevel" : {
"type" : "keyword"
},
"message" : {
"type" : "text"
}
},
"ingest_pipeline" : {
"description" : "Ingest pipeline created by file structure finder",
"processors" : [
{
"grok" : {
"field" : "message",
"patterns" : [
"\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel}.*"
]
}
},
{
"date" : {
"field" : "timestamp",
"timezone" : "{{ beat.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,
"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/_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}",
"grok_pattern" : "\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel} *\\]\\[%{JAVACLASS:class} *\\] \\[%{HOSTNAME:node}\\] %{JAVALOGMESSAGE:message}", <1>
"timestamp_field" : "timestamp",
"joda_timestamp_formats" : [
"ISO8601"
],
"java_timestamp_formats" : [
"ISO8601"
],
"need_client_timezone" : true,
"mappings" : {
"@timestamp" : {
"type" : "date"
},
"class" : {
"type" : "keyword"
},
"loglevel" : {
"type" : "keyword"
},
"message" : {
"type" : "text"
},
"node" : {
"type" : "keyword"
}
},
"ingest_pipeline" : {
"description" : "Ingest pipeline created by file structure finder",
"processors" : [
{
"grok" : {
"field" : "message",
"patterns" : [
"\\[%{TIMESTAMP_ISO8601:timestamp}\\]\\[%{LOGLEVEL:loglevel} *\\]\\[%{JAVACLASS:class} *\\] \\[%{HOSTNAME:node}\\] %{JAVALOGMESSAGE:message}"
]
}
},
{
"date" : {
"field" : "timestamp",
"timezone" : "{{ beat.timezone }}",
"formats" : [
"ISO8601"
]
}
},
{
"remove" : {
"field" : "timestamp"
}
}
]
},
"field_stats" : { <2>
"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
<1> The `grok_pattern` in the output is now the overridden one supplied in the
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
the {ml} UI!