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Ingestion using Parquet format
To use this extension, make sure to include both druid-avro-extensions
and druid-parquet-extensions
.
This extension enables Druid to ingest and understand the Apache Parquet data format offline.
Parquet Hadoop Parser
This is for batch ingestion using the HadoopDruidIndexer. The inputFormat of inputSpec
in ioConfig
must be set to "io.druid.data.input.parquet.DruidParquetInputFormat"
.
Field | Type | Description | Required |
---|---|---|---|
type | String | This should say parquet |
yes |
parseSpec | JSON Object | Specifies the timestamp and dimensions of the data. Should be a timeAndDims parseSpec. | yes |
binaryAsString | Boolean | Specifies if the bytes parquet column should be converted to strings. | no(default == false) |
Example json for overlord
When posting the index job to the overlord, setting the correct inputFormat
is required to switch to parquet ingestion. Make sure to set jobProperties
to make hdfs path timezone unrelated:
{
"type": "index_hadoop",
"spec": {
"ioConfig": {
"type": "hadoop",
"inputSpec": {
"type": "static",
"inputFormat": "io.druid.data.input.parquet.DruidParquetInputFormat",
"paths": "no_metrics"
}
},
"dataSchema": {
"dataSource": "no_metrics",
"parser": {
"type": "parquet",
"parseSpec": {
"format": "timeAndDims",
"timestampSpec": {
"column": "time",
"format": "auto"
},
"dimensionsSpec": {
"dimensions": [
"name"
],
"dimensionExclusions": [],
"spatialDimensions": []
}
}
},
"metricsSpec": [{
"type": "count",
"name": "count"
}],
"granularitySpec": {
"type": "uniform",
"segmentGranularity": "DAY",
"queryGranularity": "ALL",
"intervals": ["2015-12-31/2016-01-02"]
}
},
"tuningConfig": {
"type": "hadoop",
"partitionsSpec": {
"targetPartitionSize": 5000000
},
"jobProperties" : {},
"leaveIntermediate": true
}
}
}
Example json for standalone jvm
When using a standalone JVM instead, additional configuration fields are required. You can just fire a hadoop job with your local compiled jars like:
HADOOP_CLASS_PATH=`hadoop classpath | sed s/*.jar/*/g`
java -Xmx32m -Duser.timezone=UTC -Dfile.encoding=UTF-8 \
-classpath config/overlord:config/_common:lib/*:$HADOOP_CLASS_PATH:extensions/druid-avro-extensions/* \
io.druid.cli.Main index hadoop \
wikipedia_hadoop_parquet_job.json
An example index json when using the standalone JVM:
{
"type": "index_hadoop",
"spec": {
"ioConfig": {
"type": "hadoop",
"inputSpec": {
"type": "static",
"inputFormat": "io.druid.data.input.parquet.DruidParquetInputFormat",
"paths": "no_metrics"
},
"metadataUpdateSpec": {
"type": "postgresql",
"connectURI": "jdbc:postgresql://localhost/druid",
"user" : "druid",
"password" : "asdf",
"segmentTable": "druid_segments"
},
"segmentOutputPath": "tmp/segments"
},
"dataSchema": {
"dataSource": "no_metrics",
"parser": {
"type": "parquet",
"parseSpec": {
"format": "timeAndDims",
"timestampSpec": {
"column": "time",
"format": "auto"
},
"dimensionsSpec": {
"dimensions": [
"name"
],
"dimensionExclusions": [],
"spatialDimensions": []
}
}
},
"metricsSpec": [{
"type": "count",
"name": "count"
}],
"granularitySpec": {
"type": "uniform",
"segmentGranularity": "DAY",
"queryGranularity": "ALL",
"intervals": ["2015-12-31/2016-01-02"]
}
},
"tuningConfig": {
"type": "hadoop",
"workingPath": "tmp/working_path",
"partitionsSpec": {
"targetPartitionSize": 5000000
},
"jobProperties" : {},
"leaveIntermediate": true
}
}
}
Almost all the fields listed above are required, including inputFormat
, metadataUpdateSpec
(type
, connectURI
, user
, password
, segmentTable
).