druid/data-formats.md at 46beaa06408f814936ce5171bbec25887d7aec94

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data-formats	Data formats

Apache Druid can ingest denormalized data in JSON, CSV, or a delimited form such as TSV, or any custom format. While most examples in the documentation use data in JSON format, it is not difficult to configure Druid to ingest any other delimited data. We welcome any contributions to new formats.

This page lists all default and core extension data formats supported by Druid. For additional data formats supported with community extensions, please see our community extensions list.

Formatting the Data

The following samples show data formats that are natively supported in Druid:

JSON

{"timestamp": "2013-08-31T01:02:33Z", "page": "Gypsy Danger", "language" : "en", "user" : "nuclear", "unpatrolled" : "true", "newPage" : "true", "robot": "false", "anonymous": "false", "namespace":"article", "continent":"North America", "country":"United States", "region":"Bay Area", "city":"San Francisco", "added": 57, "deleted": 200, "delta": -143}
{"timestamp": "2013-08-31T03:32:45Z", "page": "Striker Eureka", "language" : "en", "user" : "speed", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Australia", "country":"Australia", "region":"Cantebury", "city":"Syndey", "added": 459, "deleted": 129, "delta": 330}
{"timestamp": "2013-08-31T07:11:21Z", "page": "Cherno Alpha", "language" : "ru", "user" : "masterYi", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"article", "continent":"Asia", "country":"Russia", "region":"Oblast", "city":"Moscow", "added": 123, "deleted": 12, "delta": 111}
{"timestamp": "2013-08-31T11:58:39Z", "page": "Crimson Typhoon", "language" : "zh", "user" : "triplets", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"China", "region":"Shanxi", "city":"Taiyuan", "added": 905, "deleted": 5, "delta": 900}
{"timestamp": "2013-08-31T12:41:27Z", "page": "Coyote Tango", "language" : "ja", "user" : "cancer", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"Japan", "region":"Kanto", "city":"Tokyo", "added": 1, "deleted": 10, "delta": -9}

CSV

2013-08-31T01:02:33Z,"Gypsy Danger","en","nuclear","true","true","false","false","article","North America","United States","Bay Area","San Francisco",57,200,-143
2013-08-31T03:32:45Z,"Striker Eureka","en","speed","false","true","true","false","wikipedia","Australia","Australia","Cantebury","Syndey",459,129,330
2013-08-31T07:11:21Z,"Cherno Alpha","ru","masterYi","false","true","true","false","article","Asia","Russia","Oblast","Moscow",123,12,111
2013-08-31T11:58:39Z,"Crimson Typhoon","zh","triplets","true","false","true","false","wikipedia","Asia","China","Shanxi","Taiyuan",905,5,900
2013-08-31T12:41:27Z,"Coyote Tango","ja","cancer","true","false","true","false","wikipedia","Asia","Japan","Kanto","Tokyo",1,10,-9

TSV (Delimited)

2013-08-31T01:02:33Z  "Gypsy Danger"  "en"  "nuclear" "true"  "true"  "false" "false" "article" "North America" "United States" "Bay Area"  "San Francisco" 57  200 -143
2013-08-31T03:32:45Z  "Striker Eureka"  "en"  "speed" "false" "true"  "true"  "false" "wikipedia" "Australia" "Australia" "Cantebury" "Syndey"  459 129 330
2013-08-31T07:11:21Z  "Cherno Alpha"  "ru"  "masterYi"  "false" "true"  "true"  "false" "article" "Asia"  "Russia"  "Oblast"  "Moscow"  123 12  111
2013-08-31T11:58:39Z  "Crimson Typhoon" "zh"  "triplets"  "true"  "false" "true"  "false" "wikipedia" "Asia"  "China" "Shanxi"  "Taiyuan" 905 5 900
2013-08-31T12:41:27Z  "Coyote Tango"  "ja"  "cancer"  "true"  "false" "true"  "false" "wikipedia" "Asia"  "Japan" "Kanto" "Tokyo" 1 10  -9

Note that the CSV and TSV data do not contain column heads. This becomes important when you specify the data for ingesting.

Besides text formats, Druid also supports binary formats such as Orc and Parquet formats.

Custom Formats

Druid supports custom data formats and can use the Regex parser or the JavaScript parsers to parse these formats. Please note that using any of these parsers for parsing data will not be as efficient as writing a native Java parser or using an external stream processor. We welcome contributions of new Parsers.

Input Format

The Input Format is a new way to specify the data format of your input data which was introduced in 0.17.0. Unfortunately, the Input Format doesn't support all data formats or ingestion methods supported by Druid yet. Especially if you want to use the Hadoop ingestion, you still need to use the Parser. If your data is formatted in some format not listed in this section, please consider using the Parser instead.

All forms of Druid ingestion require some form of schema object. The format of the data to be ingested is specified using the inputFormat entry in your ioConfig.

JSON

The inputFormat to load data of JSON format. An example is:

"ioConfig": {
  "inputFormat": {
    "type": "json"
  },
  ...
}

The JSON inputFormat has the following components:

Field	Type	Description	Required
type	String	This should say `json`.	yes
flattenSpec	JSON Object	Specifies flattening configuration for nested JSON data. See `flattenSpec` for more info.	no
featureSpec	JSON Object	JSON parser features supported by Jackson library. Those features will be applied when parsing the input JSON data.	no

CSV

The inputFormat to load data of the CSV format. An example is:

"ioConfig": {
  "inputFormat": {
    "type": "csv",
    "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"]
  },
  ...
}

The CSV inputFormat has the following components:

Field	Type	Description	Required
type	String	This should say `csv`.	yes
listDelimiter	String	A custom delimiter for multi-value dimensions.	no (default = ctrl+A)
columns	JSON array	Specifies the columns of the data. The columns should be in the same order with the columns of your data.	yes if `findColumnsFromHeader` is false or missing
findColumnsFromHeader	Boolean	If this is set, the task will find the column names from the header row. Note that `skipHeaderRows` will be applied before finding column names from the header. For example, if you set `skipHeaderRows` to 2 and `findColumnsFromHeader` to true, the task will skip the first two lines and then extract column information from the third line. `columns` will be ignored if this is set to true.	no (default = false if `columns` is set; otherwise null)
skipHeaderRows	Integer	If this is set, the task will skip the first `skipHeaderRows` rows.	no (default = 0)

TSV (Delimited)

"ioConfig": {
  "inputFormat": {
    "type": "tsv",
    "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"],
    "delimiter":"|"
  },
  ...
}

The inputFormat to load data of a delimited format. An example is:

Field	Type	Description	Required
type	String	This should say `tsv`.	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 array	Specifies the columns of the data. The columns should be in the same order with the columns of your data.	yes if `findColumnsFromHeader` is false or missing
findColumnsFromHeader	Boolean	If this is set, the task will find the column names from the header row. Note that `skipHeaderRows` will be applied before finding column names from the header. For example, if you set `skipHeaderRows` to 2 and `findColumnsFromHeader` to true, the task will skip the first two lines and then extract column information from the third line. `columns` will be ignored if this is set to true.	no (default = false if `columns` is set; otherwise null)
skipHeaderRows	Integer	If this is set, the task will skip the first `skipHeaderRows` rows.	no (default = 0)

Be sure to change the delimiter to the appropriate delimiter for your data. Like CSV, you must specify the columns and which subset of the columns you want indexed.

ORC

You need to include the druid-orc-extensions as an extension to use the ORC input format.

If you are considering upgrading from earlier than 0.15.0 to 0.15.0 or a higher version, please read Migration from 'contrib' extension carefully.

The inputFormat to load data of ORC format. An example is:

"ioConfig": {
  "inputFormat": {
    "type": "orc",
    "flattenSpec": {
      "useFieldDiscovery": true,
      "fields": [
        {
          "type": "path",
          "name": "nested",
          "expr": "$.path.to.nested"
        }
      ]
    },
    "binaryAsString": false
  },
  ...
}

The ORC inputFormat has the following components:

Field	Type	Description	Required
type	String	This should say `orc`.	yes
flattenSpec	JSON Object	Specifies flattening configuration for nested ORC data. See `flattenSpec` for more info.	no
binaryAsString	Boolean	Specifies if the binary orc column which is not logically marked as a string should be treated as a UTF-8 encoded string.	no (default = false)

Parquet

You need to include the druid-parquet-extensions as an extension to use the Parquet input format.

The inputFormat to load data of Parquet format. An example is:

"ioConfig": {
  "inputFormat": {
    "type": "parquet",
    "flattenSpec": {
      "useFieldDiscovery": true,
      "fields": [
        {
          "type": "path",
          "name": "nested",
          "expr": "$.path.to.nested"
        }
      ]
    }
    "binaryAsString": false
  },
  ...
}

The Parquet inputFormat has the following components:

Field	Type	Description	Required
type	String	This should be set to `parquet` to read Parquet file	yes
flattenSpec	JSON Object	Define a `flattenSpec` to extract nested values from a Parquet file. Note that only 'path' expression are supported ('jq' is unavailable).	no (default will auto-discover 'root' level properties)
binaryAsString	Boolean	Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string.	no (default = false)

FlattenSpec

The flattenSpec is located in inputFormat → flattenSpec and is responsible for bridging the gap between potentially nested input data (such as JSON, Avro, etc) and Druid's flat data model. An example flattenSpec is:

"flattenSpec": {
  "useFieldDiscovery": true,
  "fields": [
    { "name": "baz", "type": "root" },
    { "name": "foo_bar", "type": "path", "expr": "$.foo.bar" },
    { "name": "first_food", "type": "jq", "expr": ".thing.food[1]" }
  ]
}

Conceptually, after input data records are read, the flattenSpec is applied first before any other specs such as timestampSpec, transformSpec, dimensionsSpec, or metricsSpec. Keep this in mind when writing your ingestion spec.

Flattening is only supported for data formats that support nesting, including avro, json, orc, and parquet.

A flattenSpec can have the following components:

Field	Description	Default
useFieldDiscovery	If true, interpret all root-level fields as available fields for usage by `timestampSpec`, `transformSpec`, `dimensionsSpec`, and `metricsSpec`. If false, only explicitly specified fields (see `fields`) will be available for use.	`true`
fields	Specifies the fields of interest and how they are accessed. See below for details.	`[]`

Field flattening specifications

Each entry in the fields list can have the following components:

Field	Description	Default
type	Options are as follows: `root`, referring to a field at the root level of the record. Only really useful if `useFieldDiscovery` is false. `path`, referring to a field using JsonPath notation. Supported by most data formats that offer nesting, including `avro`, `json`, `orc`, and `parquet`. `jq`, referring to a field using jackson-jq notation. Only supported for the `json` format.	none (required)
name	Name of the field after flattening. This name can be referred to by the `timestampSpec`, `transformSpec`, `dimensionsSpec`, and `metricsSpec`.	none (required)
expr	Expression for accessing the field while flattening. For type `path`, this should be JsonPath. For type `jq`, this should be jackson-jq notation. For other types, this parameter is ignored.	none (required for types `path` and `jq`)

Notes on flattening

For convenience, when defining a root-level field, it is possible to define only the field name, as a string, instead of a JSON object. For example, {"name": "baz", "type": "root"} is equivalent to "baz".
Enabling useFieldDiscovery will only automatically detect "simple" fields at the root level that correspond to data types that Druid supports. This includes strings, numbers, and lists of strings or numbers. Other types will not be automatically detected, and must be specified explicitly in the fields list.
Duplicate field names are not allowed. An exception will be thrown.
If useFieldDiscovery is enabled, any discovered field with the same name as one already defined in the fields list will be skipped, rather than added twice.
http://jsonpath.herokuapp.com/ is useful for testing path-type expressions.
jackson-jq supports a subset of the full jq syntax. Please refer to the jackson-jq documentation for details.

Parser

The Parser is deprecated for native batch tasks, Kafka indexing service, and Kinesis indexing service. Consider using the input format instead for these types of ingestion.

This section lists all default and core extension parsers. For community extension parsers, please see our community extensions list.

String Parser

string typed parsers operate on text based inputs that can be split into individual records by newlines. Each line can be further parsed using parseSpec.

Field	Type	Description	Required
type	String	This should say `string` in general, or `hadoopyString` when used in a Hadoop indexing job.	yes
parseSpec	JSON Object	Specifies the format, timestamp, and dimensions of the data.	yes

Avro Hadoop Parser

You need to include the druid-avro-extensions as an extension to use the Avro Hadoop Parser.

This parser is for Hadoop batch ingestion. The inputFormat of inputSpec in ioConfig must be set to "org.apache.druid.data.input.avro.AvroValueInputFormat". You may want to set Avro reader's schema in jobProperties in tuningConfig, e.g.: "avro.schema.input.value.path": "/path/to/your/schema.avsc" or "avro.schema.input.value": "your_schema_JSON_object". If the Avro reader's schema is not set, the schema in Avro object container file will be used. See Avro specification for more information.

Field	Type	Description	Required
type	String	This should say `avro_hadoop`.	yes
parseSpec	JSON Object	Specifies the timestamp and dimensions of the data. Should be an "avro" parseSpec.	yes
fromPigAvroStorage	Boolean	Specifies whether the data file is stored using AvroStorage.	no(default == false)

An Avro parseSpec can contain a flattenSpec using either the "root" or "path" field types, which can be used to read nested Avro records. The "jq" field type is not currently supported for Avro.

For example, using Avro Hadoop parser with custom reader's schema file:

{
  "type" : "index_hadoop",
  "spec" : {
    "dataSchema" : {
      "dataSource" : "",
      "parser" : {
        "type" : "avro_hadoop",
        "parseSpec" : {
          "format": "avro",
          "timestampSpec": <standard timestampSpec>,
          "dimensionsSpec": <standard dimensionsSpec>,
          "flattenSpec": <optional>
        }
      }
    },
    "ioConfig" : {
      "type" : "hadoop",
      "inputSpec" : {
        "type" : "static",
        "inputFormat": "org.apache.druid.data.input.avro.AvroValueInputFormat",
        "paths" : ""
      }
    },
    "tuningConfig" : {
       "jobProperties" : {
          "avro.schema.input.value.path" : "/path/to/my/schema.avsc"
      }
    }
  }
}

ORC Hadoop Parser

You need to include the druid-orc-extensions as an extension to use the ORC Hadoop Parser.

If you are considering upgrading from earlier than 0.15.0 to 0.15.0 or a higher version, please read Migration from 'contrib' extension carefully.

This parser is for Hadoop batch ingestion. The inputFormat of inputSpec in ioConfig must be set to "org.apache.orc.mapreduce.OrcInputFormat".

Field	Type	Description	Required
type	String	This should say `orc`	yes
parseSpec	JSON Object	Specifies the timestamp and dimensions of the data (`timeAndDims` and `orc` format) and a `flattenSpec` (`orc` format)	yes

The parser supports two parseSpec formats: orc and timeAndDims.

orc supports auto field discovery and flattening, if specified with a flattenSpec. If no flattenSpec is specified, useFieldDiscovery will be enabled by default. Specifying a dimensionSpec is optional if useFieldDiscovery is enabled: if a dimensionSpec is supplied, the list of dimensions it defines will be the set of ingested dimensions, if missing the discovered fields will make up the list.

timeAndDims parse spec must specify which fields will be extracted as dimensions through the dimensionSpec.

All column types are supported, with the exception of union types. Columns of list type, if filled with primitives, may be used as a multi-value dimension, or specific elements can be extracted with flattenSpec expressions. Likewise, primitive fields may be extracted from map and struct types in the same manner. Auto field discovery will automatically create a string dimension for every (non-timestamp) primitive or list of primitives, as well as any flatten expressions defined in the flattenSpec.

Hadoop job properties

Like most Hadoop jobs, the best outcomes will add "mapreduce.job.user.classpath.first": "true" or "mapreduce.job.classloader": "true" to the jobProperties section of tuningConfig. Note that it is likely if using "mapreduce.job.classloader": "true" that you will need to set mapreduce.job.classloader.system.classes to include -org.apache.hadoop.hive. to instruct Hadoop to load org.apache.hadoop.hive classes from the application jars instead of system jars, e.g.

...
    "mapreduce.job.classloader": "true",
    "mapreduce.job.classloader.system.classes" : "java., javax.accessibility., javax.activation., javax.activity., javax.annotation., javax.annotation.processing., javax.crypto., javax.imageio., javax.jws., javax.lang.model., -javax.management.j2ee., javax.management., javax.naming., javax.net., javax.print., javax.rmi., javax.script., -javax.security.auth.message., javax.security.auth., javax.security.cert., javax.security.sasl., javax.sound., javax.sql., javax.swing., javax.tools., javax.transaction., -javax.xml.registry., -javax.xml.rpc., javax.xml., org.w3c.dom., org.xml.sax., org.apache.commons.logging., org.apache.log4j., -org.apache.hadoop.hbase., -org.apache.hadoop.hive., org.apache.hadoop., core-default.xml, hdfs-default.xml, mapred-default.xml, yarn-default.xml",
...

This is due to the hive-storage-api dependency of the orc-mapreduce library, which provides some classes under the org.apache.hadoop.hive package. If instead using the setting "mapreduce.job.user.classpath.first": "true", then this will not be an issue.

Examples

`orc` parser, `orc` parseSpec, auto field discovery, flatten expressions

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat",
        "paths": "path/to/file.orc"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "orc",
        "parseSpec": {
          "format": "orc",
          "flattenSpec": {
            "useFieldDiscovery": true,
            "fields": [
              {
                "type": "path",
                "name": "nestedDim",
                "expr": "$.nestedData.dim1"
              },
              {
                "type": "path",
                "name": "listDimFirstItem",
                "expr": "$.listDim[1]"
              }
            ]
          },
          "timestampSpec": {
            "column": "timestamp",
            "format": "millis"
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
    }
  }
}

`orc` parser, `orc` parseSpec, field discovery with no flattenSpec or dimensionSpec

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat",
        "paths": "path/to/file.orc"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "orc",
        "parseSpec": {
          "format": "orc",
          "timestampSpec": {
            "column": "timestamp",
            "format": "millis"
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
    }
  }
}

`orc` parser, `orc` parseSpec, no autodiscovery

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat",
        "paths": "path/to/file.orc"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "orc",
        "parseSpec": {
          "format": "orc",
          "flattenSpec": {
            "useFieldDiscovery": false,
            "fields": [
              {
                "type": "path",
                "name": "nestedDim",
                "expr": "$.nestedData.dim1"
              },
              {
                "type": "path",
                "name": "listDimFirstItem",
                "expr": "$.listDim[1]"
              }
            ]
          },
          "timestampSpec": {
            "column": "timestamp",
            "format": "millis"
          },
          "dimensionsSpec": {
            "dimensions": [
              "dim1",
              "dim3",
              "nestedDim",
              "listDimFirstItem"
            ],
            "dimensionExclusions": [],
            "spatialDimensions": []
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
    }
  }
}

`orc` parser, `timeAndDims` parseSpec

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat",
        "paths": "path/to/file.orc"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "orc",
        "parseSpec": {
          "format": "timeAndDims",
          "timestampSpec": {
            "column": "timestamp",
            "format": "auto"
          },
          "dimensionsSpec": {
            "dimensions": [
              "dim1",
              "dim2",
              "dim3",
              "listDim"
            ],
            "dimensionExclusions": [],
            "spatialDimensions": []
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
  }
}

Parquet Hadoop Parser

You need to include the druid-parquet-extensions as an extension to use the Parquet Hadoop Parser.

The Parquet Hadoop parser is for Hadoop batch ingestion and parses Parquet files directly. The inputFormat of inputSpec in ioConfig must be set to org.apache.druid.data.input.parquet.DruidParquetInputFormat.

The Parquet Hadoop Parser supports auto field discovery and flattening if provided with a flattenSpec with the parquet parseSpec. Parquet nested list and map logical types should operate correctly with JSON path expressions for all supported types.

Field	Type	Description	Required
type	String	This should say `parquet`.	yes
parseSpec	JSON Object	Specifies the timestamp and dimensions of the data, and optionally, a flatten spec. Valid parseSpec formats are `timeAndDims` and `parquet`	yes
binaryAsString	Boolean	Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string.	no(default = false)

When the time dimension is a DateType column, a format should not be supplied. When the format is UTF8 (String), either auto or a explicitly defined format is required.

Parquet Hadoop Parser vs Parquet Avro Hadoop Parser

Both parsers read from Parquet files, but slightly differently. The main differences are:

The Parquet Hadoop Parser uses a simple conversion while the Parquet Avro Hadoop Parser converts Parquet data into avro records first with the parquet-avro library and then parses avro data using the druid-avro-extensions module to ingest into Druid.
The Parquet Hadoop Parser sets a hadoop job property parquet.avro.add-list-element-records to false (which normally defaults to true), in order to 'unwrap' primitive list elements into multi-value dimensions.
The Parquet Hadoop Parser supports int96 Parquet values, while the Parquet Avro Hadoop Parser does not. There may also be some subtle differences in the behavior of JSON path expression evaluation of flattenSpec.

Based on those differences, we suggest using the Parquet Hadoop Parser over the Parquet Avro Hadoop Parser to allow ingesting data beyond the schema constraints of Avro conversion. However, the Parquet Avro Hadoop Parser was the original basis for supporting the Parquet format, and as such it is a bit more mature.

Examples

`parquet` parser, `parquet` parseSpec

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.druid.data.input.parquet.DruidParquetInputFormat",
        "paths": "path/to/file.parquet"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "parquet",
        "parseSpec": {
          "format": "parquet",
          "flattenSpec": {
            "useFieldDiscovery": true,
            "fields": [
              {
                "type": "path",
                "name": "nestedDim",
                "expr": "$.nestedData.dim1"
              },
              {
                "type": "path",
                "name": "listDimFirstItem",
                "expr": "$.listDim[1]"
              }
            ]
          },
          "timestampSpec": {
            "column": "timestamp",
            "format": "auto"
          },
          "dimensionsSpec": {
            "dimensions": [],
            "dimensionExclusions": [],
            "spatialDimensions": []
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
    }
  }
}

`parquet` parser, `timeAndDims` parseSpec

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.druid.data.input.parquet.DruidParquetInputFormat",
        "paths": "path/to/file.parquet"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "parquet",
        "parseSpec": {
          "format": "timeAndDims",
          "timestampSpec": {
            "column": "timestamp",
            "format": "auto"
          },
          "dimensionsSpec": {
            "dimensions": [
              "dim1",
              "dim2",
              "dim3",
              "listDim"
            ],
            "dimensionExclusions": [],
            "spatialDimensions": []
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
  }
}

Parquet Avro Hadoop Parser

Consider using the Parquet Hadoop Parser over this parser to ingest Parquet files. See Parquet Hadoop Parser vs Parquet Avro Hadoop Parser for the differences between those parsers.

You need to include both the druid-parquet-extensions [druid-avro-extensions] as extensions to use the Parquet Avro Hadoop Parser.

The Parquet Avro Hadoop Parser is for Hadoop batch ingestion. This parser first converts the Parquet data into Avro records, and then parses them to ingest into Druid. The inputFormat of inputSpec in ioConfig must be set to org.apache.druid.data.input.parquet.DruidParquetAvroInputFormat.

The Parquet Avro Hadoop Parser supports auto field discovery and flattening if provided with a flattenSpec with the avro parseSpec. Parquet nested list and map logical types should operate correctly with JSON path expressions for all supported types. This parser sets a hadoop job property parquet.avro.add-list-element-records to false (which normally defaults to true), in order to 'unwrap' primitive list elements into multi-value dimensions.

Note that the int96 Parquet value type is not supported with this parser.

Field	Type	Description	Required
type	String	This should say `parquet-avro`.	yes
parseSpec	JSON Object	Specifies the timestamp and dimensions of the data, and optionally, a flatten spec. Should be `avro`.	yes
binaryAsString	Boolean	Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string.	no(default = false)

When the time dimension is a DateType column, a format should not be supplied. When the format is UTF8 (String), either auto or an explicitly defined format is required.

Example

{
  "type": "index_hadoop",
  "spec": {
    "ioConfig": {
      "type": "hadoop",
      "inputSpec": {
        "type": "static",
        "inputFormat": "org.apache.druid.data.input.parquet.DruidParquetAvroInputFormat",
        "paths": "path/to/file.parquet"
      },
      ...
    },
    "dataSchema": {
      "dataSource": "example",
      "parser": {
        "type": "parquet-avro",
        "parseSpec": {
          "format": "avro",
          "flattenSpec": {
            "useFieldDiscovery": true,
            "fields": [
              {
                "type": "path",
                "name": "nestedDim",
                "expr": "$.nestedData.dim1"
              },
              {
                "type": "path",
                "name": "listDimFirstItem",
                "expr": "$.listDim[1]"
              }
            ]
          },
          "timestampSpec": {
            "column": "timestamp",
            "format": "auto"
          },
          "dimensionsSpec": {
            "dimensions": [],
            "dimensionExclusions": [],
            "spatialDimensions": []
          }
        }
      },
      ...
    },
    "tuningConfig": <hadoop-tuning-config>
    }
  }
}

Avro Stream Parser

You need to include the druid-avro-extensions as an extension to use the Avro Stream Parser.

This parser is for stream ingestion and reads Avro data from a stream directly.

Field	Type	Description	Required
type	String	This should say `avro_stream`.	no
avroBytesDecoder	JSON Object	Specifies how to decode bytes to Avro record.	yes
parseSpec	JSON Object	Specifies the timestamp and dimensions of the data. Should be an "avro" parseSpec.	yes

An Avro parseSpec can contain a flattenSpec using either the "root" or "path" field types, which can be used to read nested Avro records. The "jq" field type is not currently supported for Avro.

For example, using Avro stream parser with schema repo Avro bytes decoder:

"parser" : {
  "type" : "avro_stream",
  "avroBytesDecoder" : {
    "type" : "schema_repo",
    "subjectAndIdConverter" : {
      "type" : "avro_1124",
      "topic" : "${YOUR_TOPIC}"
    },
    "schemaRepository" : {
      "type" : "avro_1124_rest_client",
      "url" : "${YOUR_SCHEMA_REPO_END_POINT}",
    }
  },
  "parseSpec" : {
    "format": "avro",
    "timestampSpec": <standard timestampSpec>,
    "dimensionsSpec": <standard dimensionsSpec>,
    "flattenSpec": <optional>
  }
}

Avro Bytes Decoder

If type is not included, the avroBytesDecoder defaults to schema_repo.

Inline Schema Based Avro Bytes Decoder

The "schema_inline" decoder reads Avro records using a fixed schema and does not support schema migration. If you may need to migrate schemas in the future, consider one of the other decoders, all of which use a message header that allows the parser to identify the proper Avro schema for reading records.

This decoder can be used if all the input events can be read using the same schema. In this case, specify the schema in the input task JSON itself, as described below.

...
"avroBytesDecoder": {
  "type": "schema_inline",
  "schema": {
    //your schema goes here, for example
    "namespace": "org.apache.druid.data",
    "name": "User",
    "type": "record",
    "fields": [
      { "name": "FullName", "type": "string" },
      { "name": "Country", "type": "string" }
    ]
  }
}
...

Multiple Inline Schemas Based Avro Bytes Decoder

Use this decoder if different input events can have different read schemas. In this case, specify the schema in the input task JSON itself, as described below.

...
"avroBytesDecoder": {
  "type": "multiple_schemas_inline",
  "schemas": {
    //your id -> schema map goes here, for example
    "1": {
      "namespace": "org.apache.druid.data",
      "name": "User",
      "type": "record",
      "fields": [
        { "name": "FullName", "type": "string" },
        { "name": "Country", "type": "string" }
      ]
    },
    "2": {
      "namespace": "org.apache.druid.otherdata",
      "name": "UserIdentity",
      "type": "record",
      "fields": [
        { "name": "Name", "type": "string" },
        { "name": "Location", "type": "string" }
      ]
    },
    ...
    ...
  }
}
...

Note that it is essentially a map of integer schema ID to avro schema object. This parser assumes that record has following format. first 1 byte is version and must always be 1. next 4 bytes are integer schema ID serialized using big-endian byte order. remaining bytes contain serialized avro message.

SchemaRepo Based Avro Bytes Decoder

This Avro bytes decoder first extracts subject and id from the input message bytes, and then uses them to look up the Avro schema used to decode the Avro record from bytes. For details, see the schema repo and AVRO-1124. You will need an http service like schema repo to hold the avro schema. For information on registering a schema on the message producer side, see org.apache.druid.data.input.AvroStreamInputRowParserTest#testParse().

Field	Type	Description	Required
type	String	This should say `schema_repo`.	no
subjectAndIdConverter	JSON Object	Specifies how to extract the subject and id from message bytes.	yes
schemaRepository	JSON Object	Specifies how to look up the Avro schema from subject and id.	yes

Avro-1124 Subject And Id Converter

This section describes the format of the subjectAndIdConverter object for the schema_repo Avro bytes decoder.

Field	Type	Description	Required
type	String	This should say `avro_1124`.	no
topic	String	Specifies the topic of your Kafka stream.	yes

Avro-1124 Schema Repository

This section describes the format of the schemaRepository object for the schema_repo Avro bytes decoder.

Field	Type	Description	Required
type	String	This should say `avro_1124_rest_client`.	no
url	String	Specifies the endpoint url of your Avro-1124 schema repository.	yes

Confluent Schema Registry-based Avro Bytes Decoder

This Avro bytes decoder first extracts a unique id from input message bytes, and then uses it to look up the schema in the Schema Registry used to decode the Avro record from bytes. For details, see the Schema Registry documentation and repository.

Field	Type	Description	Required
type	String	This should say `schema_registry`.	no
url	String	Specifies the url endpoint of the Schema Registry.	yes
capacity	Integer	Specifies the max size of the cache (default = Integer.MAX_VALUE).	no

...
"avroBytesDecoder" : {
   "type" : "schema_registry",
   "url" : <schema-registry-url>
}
...

Protobuf Parser

You need to include the druid-protobuf-extensions as an extension to use the Protobuf Parser.

This parser is for stream ingestion and reads Protocol buffer data from a stream directly.

Field	Type	Description	Required
type	String	This should say `protobuf`.	yes
descriptor	String	Protobuf descriptor file name in the classpath or URL.	yes
protoMessageType	String	Protobuf message type in the descriptor. Both short name and fully qualified name are accepted. The parser uses the first message type found in the descriptor if not specified.	no
parseSpec	JSON Object	Specifies the timestamp and dimensions of the data. The format must be JSON. See JSON ParseSpec for more configuration options. Note that timeAndDims parseSpec is no longer supported.	yes

Sample spec:

"parser": {
  "type": "protobuf",
  "descriptor": "file:///tmp/metrics.desc",
  "protoMessageType": "Metrics",
  "parseSpec": {
    "format": "json",
    "timestampSpec": {
      "column": "timestamp",
      "format": "auto"
    },
    "dimensionsSpec": {
      "dimensions": [
        "unit",
        "http_method",
        "http_code",
        "page",
        "metricType",
        "server"
      ],
      "dimensionExclusions": [
        "timestamp",
        "value"
      ]
    }
  }
}

See the extension description for more details and examples.

ParseSpec

The Parser is deprecated for native batch tasks, Kafka indexing service, and Kinesis indexing service. Consider using the input format instead for these types of ingestion.

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 `flattenSpec` for more info.	no

Sample spec:

"parseSpec": {
  "format" : "json",
  "timestampSpec" : {
    "column" : "timestamp"
  },
  "dimensionSpec" : {
    "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"]
  }
}

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

Sample spec:

"parseSpec": {
  "format" : "csv",
  "timestampSpec" : {
    "column" : "timestamp"
  },
  "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"],
  "dimensionsSpec" : {
    "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"]
  }
}

CSV Index Tasks

If your input files contain a header, the columns field is optional and you don't need to set. Instead, you can set the hasHeaderRow field to true, which makes Druid automatically extract the column information from the header. Otherwise, you must set the columns field and ensure that field must match the columns of your input data in the same order.

Also, you can skip some header rows by setting skipHeaderRows in your parseSpec. If both skipHeaderRows and hasHeaderRow options are set, skipHeaderRows is first applied. For example, if you set skipHeaderRows to 2 and hasHeaderRow to true, Druid will skip the first two lines and then extract column information from the third line.

Note that hasHeaderRow and skipHeaderRows are effective only for non-Hadoop batch index tasks. Other types of index tasks will fail with an exception.

Other CSV Ingestion Tasks

The columns field must be included and and ensure that the order of the fields matches the columns of your input data in the same order.

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

Sample spec:

"parseSpec": {
  "format" : "tsv",
  "timestampSpec" : {
    "column" : "timestamp"
  },
  "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"],
  "delimiter":"|",
  "dimensionsSpec" : {
    "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"]
  }
}

Be sure to change the delimiter to the appropriate delimiter for your data. Like CSV, you must specify the columns and which subset of the columns you want indexed.

TSV (Delimited) Index Tasks

If your input files contain a header, the columns field is optional and doesn't need to be set. Instead, you can set the hasHeaderRow field to true, which makes Druid automatically extract the column information from the header. Otherwise, you must set the columns field and ensure that field must match the columns of your input data in the same order.

Note that hasHeaderRow and skipHeaderRows are effective only for non-Hadoop batch index tasks. Other types of index tasks will fail with an exception.

Other TSV (Delimited) Ingestion Tasks

The columns field must be included and and ensure that the order of the fields matches the columns of your input data in the same order.

Multi-value dimensions

Dimensions can have multiple values for TSV and CSV data. To specify the delimiter for a multi-value dimension, set the listDelimiter in the parseSpec.

JSON data can contain multi-value dimensions as well. The multiple values for a dimension must be formatted as a JSON array in the ingested data. No additional parseSpec configuration is needed.

Regex ParseSpec

"parseSpec":{
  "format" : "regex",
  "timestampSpec" : {
    "column" : "timestamp"
  },
  "dimensionsSpec" : {
    "dimensions" : [<your_list_of_dimensions>]
  },
  "columns" : [<your_columns_here>],
  "pattern" : <regex pattern for partitioning data>
}

The columns field must match the columns of your regex matching groups in the same order. If columns are not provided, default columns names ("column_1", "column2", ... "column_n") will be assigned. Ensure that your column names include all your dimensions.

JavaScript ParseSpec

"parseSpec":{
  "format" : "javascript",
  "timestampSpec" : {
    "column" : "timestamp"
  },
  "dimensionsSpec" : {
    "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"]
  },
  "function" : "function(str) { var parts = str.split(\"-\"); return { one: parts[0], two: parts[1] } }"
}

Note with the JavaScript parser that data must be fully parsed and returned as a {key:value} format in the JS logic. This means any flattening or parsing multi-dimensional values must be done here.

JavaScript-based functionality is disabled by default. Please refer to the Druid JavaScript programming guide for guidelines about using Druid's JavaScript functionality, including instructions on how to enable it.

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

Orc ParseSpec

Use this with the Hadoop ORC Parser to load ORC files.

Field	Type	Description	Required
format	String	This should say `orc`.	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 `flattenSpec` for more info.	no

Parquet ParseSpec

Use this with the Hadoop Parquet Parser to load Parquet files.

Field	Type	Description	Required
format	String	This should say `parquet`.	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 `flattenSpec` for more info.	no

53 KiB Raw Blame History

Formatting the Data

Custom Formats

Input Format

JSON

CSV

TSV (Delimited)

ORC

Parquet

FlattenSpec

Field flattening specifications

Notes on flattening

Parser

String Parser

Avro Hadoop Parser

ORC Hadoop Parser

Hadoop job properties

Examples

orc parser, orc parseSpec, auto field discovery, flatten expressions

orc parser, orc parseSpec, field discovery with no flattenSpec or dimensionSpec

orc parser, orc parseSpec, no autodiscovery

orc parser, timeAndDims parseSpec

Parquet Hadoop Parser

Parquet Hadoop Parser vs Parquet Avro Hadoop Parser

Examples

parquet parser, parquet parseSpec

parquet parser, timeAndDims parseSpec

Parquet Avro Hadoop Parser

Example

Avro Stream Parser

Avro Bytes Decoder

Inline Schema Based Avro Bytes Decoder

Multiple Inline Schemas Based Avro Bytes Decoder

SchemaRepo Based Avro Bytes Decoder

Avro-1124 Subject And Id Converter

Avro-1124 Schema Repository

Confluent Schema Registry-based Avro Bytes Decoder

Protobuf Parser

ParseSpec

JSON ParseSpec

JSON Lowercase ParseSpec

CSV ParseSpec

CSV Index Tasks

Other CSV Ingestion Tasks

TSV / Delimited ParseSpec

TSV (Delimited) Index Tasks

Other TSV (Delimited) Ingestion Tasks

Multi-value dimensions

Regex ParseSpec

JavaScript ParseSpec

TimeAndDims ParseSpec

Orc ParseSpec

Parquet ParseSpec

53 KiB

Raw Blame History

`orc` parser, `orc` parseSpec, auto field discovery, flatten expressions

`orc` parser, `orc` parseSpec, field discovery with no flattenSpec or dimensionSpec

`orc` parser, `orc` parseSpec, no autodiscovery

`orc` parser, `timeAndDims` parseSpec

`parquet` parser, `parquet` parseSpec

`parquet` parser, `timeAndDims` parseSpec