6.8 KiB

Raw Blame History

layout
doc_page

Avro

This extension enables Druid to ingest and understand the Apache Avro data format. Make sure to include druid-avro-extensions as an extension.

Avro Stream Parser

This is for streaming/realtime ingestion.

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 a timeAndDims parseSpec.	yes

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": "timeAndDims",
    "timestampSpec": <standard timestampSpec>,
    "dimensionsSpec": <standard dimensionsSpec>
  }
}

Avro Bytes Decoder

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

Inline Schema Based Avro Bytes Decoder

This decoder can be used if all the input events can be read using the same schema. In that case schema can be specified in the input task json itself as described below.

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

Multiple Inline Schemas Based Avro Bytes Decoder

This decoder can be used if different input events can have different read schema. In that case schema can be specified 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": "io.druid.data",
      "name": "User",
      "type": "record",
      "fields": [
        { "name": "FullName", "type": "string" },
        { "name": "Country", "type": "string" }
      ]
    },
    "2": {
      "namespace": "io.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 extract subject and id from input message bytes, then use them to lookup the Avro schema with which to decode Avro record from bytes. Details can be found in schema repo and AVRO-1124. You will need an http service like schema repo to hold the avro schema. Towards schema registration on the message producer side, you can refer to io.druid.data.input.AvroStreamInputRowParserTest#testParse().

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

Avro-1124 Subject And Id Converter

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

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's Schema Registry

This Avro bytes decoder first extract unique id from input message bytes, then use them it lookup in the Schema Registry for the related schema, with which to decode Avro record from bytes. Details can be found in 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

Avro Hadoop Parser

This is for batch ingestion using the HadoopDruidIndexer. The inputFormat of inputSpec in ioConfig must be set to "io.druid.data.input.avro.AvroValueInputFormat". You may want to set Avro reader's schema in jobProperties in tuningConfig, eg: "avro.schema.input.value.path": "/path/to/your/schema.avsc" or "avro.schema.input.value": "your_schema_JSON_object", if reader's schema is not set, the schema in Avro object container file will be used, see Avro specification. Make sure to include "io.druid.extensions:druid-avro-extensions" as an extension.

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

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

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

6.8 KiB Raw Blame History