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doc_page	Ingestion Spec

Ingestion Spec

An Apache Druid (incubating) ingestion spec consists of 3 components:

{
  "dataSchema" : {...},
  "ioConfig" : {...},
  "tuningConfig" : {...}
}

Field	Type	Description	Required
dataSchema	JSON Object	Specifies the the schema of the incoming data. All ingestion specs can share the same dataSchema.	yes
ioConfig	JSON Object	Specifies where the data is coming from and where the data is going. This object will vary with the ingestion method.	yes
tuningConfig	JSON Object	Specifies how to tune various ingestion parameters. This object will vary with the ingestion method.	no

DataSchema

An example dataSchema is shown below:

"dataSchema" : {
  "dataSource" : "wikipedia",
  "parser" : {
    "type" : "string",
    "parseSpec" : {
      "format" : "json",
      "timestampSpec" : {
        "column" : "timestamp",
        "format" : "auto"
      },
      "dimensionsSpec" : {
        "dimensions": [
          "page",
          "language",
          "user",
          "unpatrolled",
          "newPage",
          "robot",
          "anonymous",
          "namespace",
          "continent",
          "country",
          "region",
          "city",
          {
            "type": "long",
            "name": "countryNum"
          },
          {
            "type": "float",
            "name": "userLatitude"
          },
          {
            "type": "float",
            "name": "userLongitude"
          }
        ],
        "dimensionExclusions" : [],
        "spatialDimensions" : []
      }
    }
  },
  "metricsSpec" : [{
    "type" : "count",
    "name" : "count"
  }, {
    "type" : "doubleSum",
    "name" : "added",
    "fieldName" : "added"
  }, {
    "type" : "doubleSum",
    "name" : "deleted",
    "fieldName" : "deleted"
  }, {
    "type" : "doubleSum",
    "name" : "delta",
    "fieldName" : "delta"
  }],
  "granularitySpec" : {
    "segmentGranularity" : "DAY",
    "queryGranularity" : "NONE",
    "intervals" : [ "2013-08-31/2013-09-01" ]
  },
  "transformSpec" : null
}

Field	Type	Description	Required
dataSource	String	The name of the ingested datasource. Datasources can be thought of as tables.	yes
parser	JSON Object	Specifies how ingested data can be parsed.	yes
metricsSpec	JSON Object array	A list of aggregators.	yes
granularitySpec	JSON Object	Specifies how to create segments and roll up data.	yes
transformSpec	JSON Object	Specifes how to filter and transform input data. See transform specs.	no

Parser

If type is not included, the parser defaults to string. For additional data formats, please see our extensions list.

String Parser

Field	Type	Description	Required
type	String	This should say `string` in general, or `hadoopyString` when used in a Hadoop indexing job.	no
parseSpec	JSON Object	Specifies the format, timestamp, and dimensions of the data.	yes

ParseSpec

ParseSpecs serve two purposes:

The String Parser use them to determine the format (i.e. JSON, CSV, TSV) of incoming rows.
All Parsers use them to determine the timestamp and dimensions of incoming rows.

If format is not included, the parseSpec defaults to tsv.

JSON ParseSpec

Use this with the String Parser to load JSON.

Field	Type	Description	Required
format	String	This should say `json`.	no
timestampSpec	JSON Object	Specifies the column and format of the timestamp.	yes
dimensionsSpec	JSON Object	Specifies the dimensions of the data.	yes
flattenSpec	JSON Object	Specifies flattening configuration for nested JSON data. See Flattening JSON for more info.	no

JSON Lowercase ParseSpec

The _jsonLowercase_ parser is deprecated and may be removed in a future version of Druid.

This is a special variation of the JSON ParseSpec that lower cases all the column names in the incoming JSON data. This parseSpec is required if you are updating to Druid 0.7.x from Druid 0.6.x, are directly ingesting JSON with mixed case column names, do not have any ETL in place to lower case those column names, and would like to make queries that include the data you created using 0.6.x and 0.7.x.

Field	Type	Description	Required
format	String	This should say `jsonLowercase`.	yes
timestampSpec	JSON Object	Specifies the column and format of the timestamp.	yes
dimensionsSpec	JSON Object	Specifies the dimensions of the data.	yes

CSV ParseSpec

Use this with the String Parser to load CSV. Strings are parsed using the com.opencsv library.

Field	Type	Description	Required
format	String	This should say `csv`.	yes
timestampSpec	JSON Object	Specifies the column and format of the timestamp.	yes
dimensionsSpec	JSON Object	Specifies the dimensions of the data.	yes
listDelimiter	String	A custom delimiter for multi-value dimensions.	no (default == ctrl+A)
columns	JSON array	Specifies the columns of the data.	yes

TSV / Delimited ParseSpec

Use this with the String Parser to load any delimited text that does not require special escaping. By default, the delimiter is a tab, so this will load TSV.

Field	Type	Description	Required
format	String	This should say `tsv`.	yes
timestampSpec	JSON Object	Specifies the column and format of the timestamp.	yes
dimensionsSpec	JSON Object	Specifies the dimensions of the data.	yes
delimiter	String	A custom delimiter for data values.	no (default == \t)
listDelimiter	String	A custom delimiter for multi-value dimensions.	no (default == ctrl+A)
columns	JSON String array	Specifies the columns of the data.	yes

TimeAndDims ParseSpec

Use this with non-String Parsers to provide them with timestamp and dimensions information. Non-String Parsers handle all formatting decisions on their own, without using the ParseSpec.

Field	Type	Description	Required
format	String	This should say `timeAndDims`.	yes
timestampSpec	JSON Object	Specifies the column and format of the timestamp.	yes
dimensionsSpec	JSON Object	Specifies the dimensions of the data.	yes

TimestampSpec

Field	Type	Description	Required
column	String	The column of the timestamp.	yes
format	String	iso, posix, millis, micro, nano, auto or any Joda time format.	no (default == 'auto')

DimensionsSpec

Field	Type	Description	Required
dimensions	JSON array	A list of dimension schema objects or dimension names. Providing a name is equivalent to providing a String-typed dimension schema with the given name. If this is an empty array, Druid will treat all non-timestamp, non-metric columns that do not appear in "dimensionExclusions" as String-typed dimension columns.	yes
dimensionExclusions	JSON String array	The names of dimensions to exclude from ingestion.	no (default == [])
spatialDimensions	JSON Object array	An array of spatial dimensions	no (default == [])

Dimension Schema

A dimension schema specifies the type and name of a dimension to be ingested.

For string columns, the dimension schema can also be used to enable or disable bitmap indexing by setting the createBitmapIndex boolean. By default, bitmap indexes are enabled for all string columns. Only string columns can have bitmap indexes; they are not supported for numeric columns.

For example, the following dimensionsSpec section from a dataSchema ingests one column as Long (countryNum), two columns as Float (userLatitude, userLongitude), and the other columns as Strings, with bitmap indexes disabled for the comment column.

"dimensionsSpec" : {
  "dimensions": [
    "page",
    "language",
    "user",
    "unpatrolled",
    "newPage",
    "robot",
    "anonymous",
    "namespace",
    "continent",
    "country",
    "region",
    "city",
    {
      "type": "string",
      "name": "comment",
      "createBitmapIndex": false
    },
    {
      "type": "long",
      "name": "countryNum"
    },
    {
      "type": "float",
      "name": "userLatitude"
    },
    {
      "type": "float",
      "name": "userLongitude"
    }
  ],
  "dimensionExclusions" : [],
  "spatialDimensions" : []
}

metricsSpec

The metricsSpec is a list of aggregators. If rollup is false in the granularity spec, the metrics spec should be an empty list and all columns should be defined in the dimensionsSpec instead (without rollup, there isn't a real distinction between dimensions and metrics at ingestion time). This is optional, however.

GranularitySpec

GranularitySpec is to define how to partition a dataSource into time chunks. The default granularitySpec is uniform, and can be changed by setting the type field. Currently, uniform and arbitrary types are supported.

Uniform Granularity Spec

This spec is used to generated segments with uniform intervals.

Field	Type	Description	Required
segmentGranularity	string	The granularity to create time chunks at. Multiple segments can be created per time chunk. For example, with 'DAY' `segmentGranularity`, the events of the same day fall into the same time chunk which can be optionally further partitioned into multiple segments based on other configurations and input size. See Granularity for supported granularities.	no (default == 'DAY')
queryGranularity	string	The minimum granularity to be able to query results at and the granularity of the data inside the segment. E.g. a value of "minute" will mean that data is aggregated at minutely granularity. That is, if there are collisions in the tuple (minute(timestamp), dimensions), then it will aggregate values together using the aggregators instead of storing individual rows. A granularity of 'NONE' means millisecond granularity. See Granularity for supported granularities.	no (default == 'NONE')
rollup	boolean	rollup or not	no (default == true)
intervals	JSON string array	A list of intervals for the raw data being ingested. Ignored for real-time ingestion.	no. If specified, Hadoop and native non-parallel batch ingestion tasks may skip determining partitions phase which results in faster ingestion; native parallel ingestion tasks can request all their locks up-front instead of one by one. Batch ingestion will thrown away any data not in the specified intervals.

Arbitrary Granularity Spec

This spec is used to generate segments with arbitrary intervals (it tries to create evenly sized segments). This spec is not supported for real-time processing.

Field	Type	Description	Required
queryGranularity	string	The minimum granularity to be able to query results at and the granularity of the data inside the segment. E.g. a value of "minute" will mean that data is aggregated at minutely granularity. That is, if there are collisions in the tuple (minute(timestamp), dimensions), then it will aggregate values together using the aggregators instead of storing individual rows. A granularity of 'NONE' means millisecond granularity. See Granularity for supported granularities.	no (default == 'NONE')
rollup	boolean	rollup or not	no (default == true)
intervals	JSON string array	A list of intervals for the raw data being ingested. Ignored for real-time ingestion.	no. If specified, Hadoop and native non-parallel batch ingestion tasks may skip determining partitions phase which results in faster ingestion; native parallel ingestion tasks can request all their locks up-front instead of one by one. Batch ingestion will thrown away any data not in the specified intervals.

Transform Spec

Transform specs allow Druid to transform and filter input data during ingestion. See Transform specs

IO Config

The IOConfig spec differs based on the ingestion task type.

Native Batch ingestion: See Native Batch IOConfig
Hadoop Batch ingestion: See Hadoop Batch IOConfig
Kafka Indexing Service: See Kafka Supervisor IOConfig
Stream Push Ingestion: Stream push ingestion with Tranquility does not require an IO Config.
Stream Pull Ingestion (Deprecated): See Stream pull ingestion.

Tuning Config

The TuningConfig spec differs based on the ingestion task type.

Native Batch ingestion: See Native Batch TuningConfig
Hadoop Batch ingestion: See Hadoop Batch TuningConfig
Kafka Indexing Service: See Kafka Supervisor TuningConfig
Stream Push Ingestion (Tranquility): See Tranquility TuningConfig.
Stream Pull Ingestion (Deprecated): See Stream pull ingestion.

Evaluating Timestamp, Dimensions and Metrics

Druid will interpret dimensions, dimension exclusions, and metrics in the following order:

Any column listed in the list of dimensions is treated as a dimension.
Any column listed in the list of dimension exclusions is excluded as a dimension.
The timestamp column and columns/fieldNames required by metrics are excluded by default.
If a metric is also listed as a dimension, the metric must have a different name than the dimension name.

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