|hadoopDependencyCoordinates|A JSON array of Hadoop dependency coordinates that Druid will use, this property will override the default Hadoop coordinates. Once specified, Druid will look for those Hadoop dependencies from the location specified by `druid.extensions.hadoopDependenciesDir`|no|
|classpathPrefix|Classpath that will be pre-appended for the peon process.|no|
also note that, druid automatically computes the classpath for hadoop job containers that run in hadoop cluster. But, in case of conflicts between hadoop and druid's dependencies, you can manually specify the classpath by setting `druid.extensions.hadoopContainerDruidClasspath` property. See the extensions config in [base druid configuration](../configuration/index.html#extensions).
|metadataUpdateSpec|Object|A specification of how to update the metadata for the druid cluster these segments belong to.|Only used by the [CLI Hadoop Indexer](../ingestion/command-line-hadoop-indexer.html). This field must be null otherwise.|
A type of inputSpec that expects data to be organized in directories according to datetime using the path format: `y=XXXX/m=XX/d=XX/H=XX/M=XX/S=XX` (where date is represented by lowercase and time is represented by uppercase).
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|dataGranularity|String|Specifies the granularity to expect the data at, e.g. hour means to expect directories `y=XXXX/m=XX/d=XX/H=XX`.|yes|
|inputFormat|String|Specifies the Hadoop InputFormat class to use. e.g. `org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat` |no|
|inputPath|String|Base path to append the datetime path to.|yes|
|filePattern|String|Pattern that files should match to be included.|yes|
|pathFormat|String|Joda datetime format for each directory. Default value is `"'y'=yyyy/'m'=MM/'d'=dd/'H'=HH"`, or see [Joda documentation](http://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html)|no|
For example, if the sample config were run with the interval 2012-06-01/2012-06-02, it would expect data at the paths:
|workingPath|String|The working path to use for intermediate results (results between Hadoop jobs).|Only used by the [CLI Hadoop Indexer](../ingestion/command-line-hadoop-indexer.html). The default is '/tmp/druid-indexing'. This field must be null otherwise.|
|version|String|The version of created segments. Ignored for HadoopIndexTask unless useExplicitVersion is set to true|no (default == datetime that indexing starts at)|
|partitionsSpec|Object|A specification of how to partition each time bucket into segments. Absence of this property means no partitioning will occur. See 'Partitioning specification' below.|no (default == 'hashed')|
|maxRowsInMemory|Integer|The number of rows to aggregate before persisting. Note that this is the number of post-aggregation rows which may not be equal to the number of input events due to roll-up. This is used to manage the required JVM heap size. Normally user does not need to set this, but depending on the nature of data, if rows are short in terms of bytes, user may not want to store a million rows in memory and this value should be set.|no (default == 1000000)|
|maxBytesInMemory|Long|The number of bytes to aggregate in heap memory before persisting. Normally this is computed internally and user does not need to set it. This is based on a rough estimate of memory usage and not actual usage. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists).|no (default == One-sixth of max JVM memory)|
|leaveIntermediate|Boolean|Leave behind intermediate files (for debugging) in the workingPath when a job completes, whether it passes or fails.|no (default == false)|
|cleanupOnFailure|Boolean|Clean up intermediate files when a job fails (unless leaveIntermediate is on).|no (default == true)|
|overwriteFiles|Boolean|Override existing files found during indexing.|no (default == false)|
|ignoreInvalidRows|Boolean|Ignore rows found to have problems.|no (default == false)|
|combineText|Boolean|Use CombineTextInputFormat to combine multiple files into a file split. This can speed up Hadoop jobs when processing a large number of small files.|no (default == false)|
|useCombiner|Boolean|Use Hadoop combiner to merge rows at mapper if possible.|no (default == false)|
|jobProperties|Object|A map of properties to add to the Hadoop job configuration, see below for details.|no (default == null)|
|indexSpec|Object|Tune how data is indexed. See below for more information.|no|
|numBackgroundPersistThreads|Integer|The number of new background threads to use for incremental persists. Using this feature causes a notable increase in memory pressure and cpu usage but will make the job finish more quickly. If changing from the default of 0 (use current thread for persists), we recommend setting it to 1.|no (default == 0)|
|forceExtendableShardSpecs|Boolean|Forces use of extendable shardSpecs. Experimental feature intended for use with the [Kafka indexing service extension](../development/extensions-core/kafka-ingestion.html).|no (default = false)|
|useExplicitVersion|Boolean|Forces HadoopIndexTask to use version.|no (default = false)|
### jobProperties field of TuningConfig
```json
"tuningConfig" : {
"type": "hadoop",
"jobProperties": {
"<hadoop-property-a>": "<value-a>",
"<hadoop-property-b>": "<value-b>"
}
}
```
Hadoop's [MapReduce documentation](https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml) lists the possible configuration parameters.
With some Hadoop distributions, it may be necessary to set `mapreduce.job.classpath` or `mapreduce.job.user.classpath.first`
to avoid class loading issues. See the [working with different Hadoop versions documentation](../operations/other-hadoop.html)
for more details.
### IndexSpec
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|bitmap|Object|Compression format for bitmap indexes. Should be a JSON object; see below for options.|no (defaults to Concise)|
|dimensionCompression|String|Compression format for dimension columns. Choose from `LZ4`, `LZF`, or `uncompressed`.|no (default == `LZ4`)|
|metricCompression|String|Compression format for metric columns. Choose from `LZ4`, `LZF`, `uncompressed`, or `none`.|no (default == `LZ4`)|
|longEncoding|String|Encoding format for metric and dimension columns with type long. Choose from `auto` or `longs`. `auto` encodes the values using offset or lookup table depending on column cardinality, and store them with variable size. `longs` stores the value as is with 8 bytes each.|no (default == `longs`)|
#### Bitmap types
For Concise bitmaps:
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|type|String|Must be `concise`.|yes|
For Roaring bitmaps:
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|type|String|Must be `roaring`.|yes|
|compressRunOnSerialization|Boolean|Use a run-length encoding where it is estimated as more space efficient.|no (default == `true`)|
## Partitioning specification
Segments are always partitioned based on timestamp (according to the granularitySpec) and may be further partitioned in
some other way depending on partition type. Druid supports two types of partitioning strategies: "hashed" (based on the
hash of all dimensions in each row), and "dimension" (based on ranges of a single dimension).
Hashed partitioning is recommended in most cases, as it will improve indexing performance and create more uniformly
sized data segments relative to single-dimension partitioning.
### Hash-based partitioning
```json
"partitionsSpec": {
"type": "hashed",
"targetPartitionSize": 5000000
}
```
Hashed partitioning works by first selecting a number of segments, and then partitioning rows across those segments
according to the hash of all dimensions in each row. The number of segments is determined automatically based on the
cardinality of the input set and a target partition size.
The configuration options are:
|Field|Description|Required|
|--------|-----------|---------|
|type|Type of partitionSpec to be used.|"hashed"|
|targetPartitionSize|Target number of rows to include in a partition, should be a number that targets segments of 500MB\~1GB.|either this or numShards|
|numShards|Specify the number of partitions directly, instead of a target partition size. Ingestion will run faster, since it can skip the step necessary to select a number of partitions automatically.|either this or targetPartitionSize|
|partitionDimensions|The dimensions to partition on. Leave blank to select all dimensions. Only used with numShards, will be ignored when targetPartitionSize is set|no|
### Single-dimension partitioning
```json
"partitionsSpec": {
"type": "dimension",
"targetPartitionSize": 5000000
}
```
Single-dimension partitioning works by first selecting a dimension to partition on, and then separating that dimension
into contiguous ranges. Each segment will contain all rows with values of that dimension in that range. For example,
your segments may be partitioned on the dimension "host" using the ranges "a.example.com" to "f.example.com" and
"f.example.com" to "z.example.com". By default, the dimension to use is determined automatically, although you can
override it with a specific dimension.
The configuration options are:
|Field|Description|Required|
|--------|-----------|---------|
|type|Type of partitionSpec to be used.|"dimension"|
|targetPartitionSize|Target number of rows to include in a partition, should be a number that targets segments of 500MB\~1GB.|yes|
|maxPartitionSize|Maximum number of rows to include in a partition. Defaults to 50% larger than the targetPartitionSize.|no|
|partitionDimension|The dimension to partition on. Leave blank to select a dimension automatically.|no|
|assumeGrouped|Assume that input data has already been grouped on time and dimensions. Ingestion will run faster, but may choose sub-optimal partitions if this assumption is violated.|no|
## Remote Hadoop Cluster
If you have a remote Hadoop cluster, make sure to include the folder holding your configuration `*.xml` files in your Druid `_common` configuration folder.
If you are having dependency problems with your version of Hadoop and the version compiled with Druid, please see [these docs](../operations/other-hadoop.html).
## Using Elastic MapReduce
If your cluster is running on Amazon Web Services, you can use Elastic MapReduce (EMR) to index data
from S3. To do this:
- Create a persistent, [long-running cluster](http://docs.aws.amazon.com/ElasticMapReduce/latest/ManagementGuide/emr-plan-longrunning-transient.html).
- When creating your cluster, enter the following configuration. If you're using the wizard, this
should be in advanced mode under "Edit software settings":
