Druid indexing plugin for using Spark in batch jobs
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README.md

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druid-spark-batch

Druid indexing plugin for using Spark in batch jobs

This repository holds a Druid extension for using Spark as the engine for running batch jobs

To build issue the command sbt clean test publish-local publish-m2

Default Properties

The default properties injected into spark are as follows:

    .set("spark.executor.memory", "7G")
    .set("spark.executor.cores", "1")
    .set("spark.kryo.referenceTracking", "false")
    .set("user.timezone", "UTC")
    .set("file.encoding", "UTF-8")
    .set("java.util.logging.manager", "org.apache.logging.log4j.jul.LogManager")
    .set("org.jboss.logging.provider", "slf4j")

How to use

There are four key things that need configured to use this extension

  1. Overlord needs the druid-spark-batch extension added.
  2. MiddleManager (if present) needs the druid-spark-batch extension added.
  3. A task json needs configured.
  4. Spark is included in the default hadoop coordinates similar to druid.indexer.task.defaultHadoopCoordinates=["org.apache.spark:spark-core_2.10:1.5.2-mmx1"]

To load the extension, use the appropriate coordinates (for druid 0.8.x the following should be added to druid.extensions.coordinates : io.druid.extensions:druid-spark-batch_2.10:jar:assembly:0.0.13) or make certain the extension jars are located in the proper directories (druid 0.9.0 with version 0.9.0.x of this library, druid 0.9.1 with the 0.9.1.x version)

The recommended method of pulling down the extensions is to use pull-deps to pull down the versions of interest. A Hadoop coordinate and an extension should be specified as per -h org.apache.spark:spark-core_2.10:1.5.2-mmx4 and -c io.druid.extensions:druid-spark-batch_2.10:0.9.1-0 (with the appropriate versions of course)

Task JSON

The following is an example spark batch task for the indexing service:

{
    "paths":["/<your-druid-spark-batch-dir>/src/test/resources/lineitem.small.tbl"],
    "dataSchema": {
        "dataSource": "sparkTest",
        "granularitySpec": {
            "intervals": [
                "1992-01-01T00:00:00.000Z/1999-01-01T00:00:00.000Z"
            ],
            "queryGranularity": {
                "type": "all"
            },
            "segmentGranularity": "YEAR",
            "type": "uniform"
        },
        "metricsSpec": [
            {
                "name": "count",
                "type": "count"
            },
            {
                "fieldName": "l_quantity",
                "name": "L_QUANTITY_longSum",
                "type": "longSum"
            },
            {
                "fieldName": "l_extendedprice",
                "name": "L_EXTENDEDPRICE_doubleSum",
                "type": "doubleSum"
            },
            {
                "fieldName": "l_discount",
                "name": "L_DISCOUNT_doubleSum",
                "type": "doubleSum"
            },
            {
                "fieldName": "l_tax",
                "name": "L_TAX_doubleSum",
                "type": "doubleSum"
            }
        ],
        "parser": {
            "encoding": "UTF-8",
            "parseSpec": {
                "columns": [
                    "l_orderkey",
                    "l_partkey",
                    "l_suppkey",
                    "l_linenumber",
                    "l_quantity",
                    "l_extendedprice",
                    "l_discount",
                    "l_tax",
                    "l_returnflag",
                    "l_linestatus",
                    "l_shipdate",
                    "l_commitdate",
                    "l_receiptdate",
                    "l_shipinstruct",
                    "l_shipmode",
                    "l_comment"
                ],
                "delimiter": "|",
                "dimensionsSpec": {
                    "dimensionExclusions": [
                        "l_tax",
                        "l_quantity",
                        "count",
                        "l_extendedprice",
                        "l_shipdate",
                        "l_discount"
                    ],
                    "dimensions": [
                        "l_comment",
                        "l_commitdate",
                        "l_linenumber",
                        "l_linestatus",
                        "l_orderkey",
                        "l_receiptdate",
                        "l_returnflag",
                        "l_shipinstruct",
                        "l_shipmode",
                        "l_suppkey"
                    ],
                    "spatialDimensions": []
                },
                "format": "tsv",
                "listDelimiter": ",",
                "timestampSpec": {
                    "column": "l_shipdate",
                    "format": "yyyy-MM-dd",
                    "missingValue": null
                }
            },
            "type": "string"
        }
    },
    "indexSpec": {
        "bitmap": {
            "type": "concise"
        },
        "dimensionCompression": "lz4",
        "metricCompression": "lz4"
    },
    "intervals": ["1992-01-01T00:00:00.000Z/1999-01-01T00:00:00.000Z"],
    "master": "local[1]",
    "properties": {
        "some.property": "someValue",
        "spark.io.compression.codec":"org.apache.spark.io.LZ4CompressionCodec"
    },
    "targetPartitionSize": 10000000,
    "type": "index_spark_2.11"
}

The json keys accepted by the spark batch indexer are described below

Batch indexer json fields

Field Type Required Default Description
type String Yes, index_spark N/A Must be index_spark
paths List of strings Yes N/A A list of hadoop-readable input files. The values are joined with a , and used as a SparkContext.textFile
dataSchema DataSchema Yes N/A The data schema to use
intervals List of strings Yes N/A A list of ISO intervals to be indexed. ALL data for these intervals MUST be present in paths
maxRowsInMemory positive integer No 75000 Maximum number of rows to store in memory before an intermediate flush to disk
targetPartitionSize positive integer No 5000000 The target number of rows per partition per segment granularity
master String No master[1] The spark master URI
properties Map No none A map of string key/value pairs to inject into the SparkContext properties overriding any prior set values
id String No Assigned based on dataSource, intervals, and DateTime.now() The ID for the task. If not provied it will be assigned
indexSpec InputSpec No concise, lz4, lz4 The InputSpec containing the various compressions to be used
context Map No none The task context
hadoopDependencyCoordinates List of strings No null (use default set by druid config) The spark dependency coordinates to load in the ClassLoader when launching the task
buildV9Directly Boolean No False Build v9 index directly instead of building v8 index and converting it to v9 format.

Deploying this project

This project uses cross-building in SBT. Both 2.10 and 2.11 versions can be built and deployed with sbt release

For setting repository credentials to be able to publish a release, refer to https://stackoverflow.com/a/19598435

Upgrading to 0.9.2

There is now a version for scala 2.10 and scala 2.11. Only ONE of which may be used at any given time.