dclim closed pull request #6120: Separate hadoop and native batch docs more
URL: https://github.com/apache/incubator-druid/pull/6120
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diff --git a/docs/content/ingestion/batch-ingestion.md
b/docs/content/ingestion/batch-ingestion.md
index 41695995af9..fb5b4fcaf04 100644
--- a/docs/content/ingestion/batch-ingestion.md
+++ b/docs/content/ingestion/batch-ingestion.md
@@ -6,341 +6,13 @@ layout: doc_page
Druid can load data from static files through a variety of methods described
here.
-## Hadoop-based Batch Ingestion
+## Native Batch Ingestion
-Hadoop-based batch ingestion in Druid is supported via a Hadoop-ingestion
task. These tasks can be posted to a running
-instance of a Druid [overlord](../design/indexing-service.html). A sample task
is shown below:
+Druid has built-in batch ingestion functionality. See
[here](../ingestion/native_tasks.html) for more info.
-```json
-{
- "type" : "index_hadoop",
- "spec" : {
- "dataSchema" : {
- "dataSource" : "wikipedia",
- "parser" : {
- "type" : "hadoopyString",
- "parseSpec" : {
- "format" : "json",
- "timestampSpec" : {
- "column" : "timestamp",
- "format" : "auto"
- },
- "dimensionsSpec" : {
- "dimensions":
["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"],
- "dimensionExclusions" : [],
- "spatialDimensions" : []
- }
- }
- },
- "metricsSpec" : [
- {
- "type" : "count",
- "name" : "count"
- },
- {
- "type" : "doubleSum",
- "name" : "added",
- "fieldName" : "added"
- },
- {
- "type" : "doubleSum",
- "name" : "deleted",
- "fieldName" : "deleted"
- },
- {
- "type" : "doubleSum",
- "name" : "delta",
- "fieldName" : "delta"
- }
- ],
- "granularitySpec" : {
- "type" : "uniform",
- "segmentGranularity" : "DAY",
- "queryGranularity" : "NONE",
- "intervals" : [ "2013-08-31/2013-09-01" ]
- }
- },
- "ioConfig" : {
- "type" : "hadoop",
- "inputSpec" : {
- "type" : "static",
- "paths" : "/MyDirectory/example/wikipedia_data.json"
- }
- },
- "tuningConfig" : {
- "type": "hadoop"
- }
- },
- "hadoopDependencyCoordinates": <my_hadoop_version>
-}
-```
+## Hadoop Batch Ingestion
-|property|description|required?|
-|--------|-----------|---------|
-|type|The task type, this should always be "index_hadoop".|yes|
-|spec|A Hadoop Index Spec. See [Batch
Ingestion](../ingestion/batch-ingestion.html)|yes|
-|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).
-
-### DataSchema
-
-This field is required. See [Ingestion](../ingestion/index.html).
-
-### IOConfig
-
-This field is required.
-
-|Field|Type|Description|Required|
-|-----|----|-----------|--------|
-|type|String|This should always be 'hadoop'.|yes|
-|inputSpec|Object|A specification of where to pull the data in from. See
below.|yes|
-|segmentOutputPath|String|The path to dump segments into.|yes|
-|metadataUpdateSpec|Object|A specification of how to update the metadata for
the druid cluster these segments belong to.|yes|
-
-#### InputSpec specification
-
-There are multiple types of inputSpecs:
-
-##### `static`
-
-A type of inputSpec where a static path to the data files is provided.
-
-|Field|Type|Description|Required|
-|-----|----|-----------|--------|
-|inputFormat|String|Specifies the Hadoop InputFormat class to use. e.g.
`org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat` |no|
-|paths|Array of String|A String of input paths indicating where the raw data
is located.|yes|
-
-For example, using the static input paths:
-
-```
-"paths" :
"s3n://billy-bucket/the/data/is/here/data.gz,s3n://billy-bucket/the/data/is/here/moredata.gz,s3n://billy-bucket/the/data/is/here/evenmoredata.gz"
-```
-
-##### `granularity`
-
-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:
-
-```
-s3n://billy-bucket/the/data/is/here/y=2012/m=06/d=01/H=00
-s3n://billy-bucket/the/data/is/here/y=2012/m=06/d=01/H=01
-...
-s3n://billy-bucket/the/data/is/here/y=2012/m=06/d=01/H=23
-```
-
-##### `dataSource`
-
-Read Druid segments. See [here](../ingestion/update-existing-data.html) for
more information.
-
-##### `multi`
-
-Read multiple sources of data. See
[here](../ingestion/update-existing-data.html) for more information.
-
-### TuningConfig
-
-The tuningConfig is optional and default parameters will be used if no
tuningConfig is specified.
-
-|Field|Type|Description|Required|
-|-----|----|-----------|--------|
-|workingPath|String|The working path to use for intermediate results (results
between Hadoop jobs).|no (default == '/tmp/druid-indexing')|
-|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":
-
-```
-classification=yarn-site,properties=[mapreduce.reduce.memory.mb=6144,mapreduce.reduce.java.opts=-server
-Xms2g -Xmx2g -Duser.timezone=UTC -Dfile.encoding=UTF-8 -XX:+PrintGCDetails
-XX:+PrintGCTimeStamps,mapreduce.map.java.opts=758,mapreduce.map.java.opts=-server
-Xms512m -Xmx512m -Duser.timezone=UTC -Dfile.encoding=UTF-8
-XX:+PrintGCDetails -XX:+PrintGCTimeStamps,mapreduce.task.timeout=1800000]
-```
-
-- Follow the instructions under "[Configure Hadoop for data
-loads](../tutorials/cluster.html#configure-cluster-for-hadoop-data-loads)"
using the XML files from
-`/etc/hadoop/conf` on your EMR master.
-
-### Secured Hadoop Cluster
-
-By default druid can use the exisiting TGT kerberos ticket available in local
kerberos key cache.
-Although TGT ticket has a limited life cycle,
-therefore you need to call `kinit` command periodically to ensure validity of
TGT ticket.
-To avoid this extra external cron job script calling `kinit` periodically,
- you can provide the principal name and keytab location and druid will do the
authentication transparently at startup and job launching time.
-
-|Property|Possible Values|Description|Default|
-|--------|---------------|-----------|-------|
-|`druid.hadoop.security.kerberos.principal`|`dr...@example.com`| Principal
user name |empty|
-|`druid.hadoop.security.kerberos.keytab`|`/etc/security/keytabs/druid.headlessUser.keytab`|Path
to keytab file|empty|
-
-#### Loading from S3 with EMR
-
-- In the `jobProperties` field in the `tuningConfig` section of your Hadoop
indexing task, add:
-
-```
-"jobProperties" : {
- "fs.s3.awsAccessKeyId" : "YOUR_ACCESS_KEY",
- "fs.s3.awsSecretAccessKey" : "YOUR_SECRET_KEY",
- "fs.s3.impl" : "org.apache.hadoop.fs.s3native.NativeS3FileSystem",
- "fs.s3n.awsAccessKeyId" : "YOUR_ACCESS_KEY",
- "fs.s3n.awsSecretAccessKey" : "YOUR_SECRET_KEY",
- "fs.s3n.impl" : "org.apache.hadoop.fs.s3native.NativeS3FileSystem",
- "io.compression.codecs" :
"org.apache.hadoop.io.compress.GzipCodec,org.apache.hadoop.io.compress.DefaultCodec,org.apache.hadoop.io.compress.BZip2Codec,org.apache.hadoop.io.compress.SnappyCodec"
-}
-```
-
-Note that this method uses Hadoop's built-in S3 filesystem rather than
Amazon's EMRFS, and is not compatible
-with Amazon-specific features such as S3 encryption and consistent views. If
you need to use these
-features, you will need to make the Amazon EMR Hadoop JARs available to Druid
through one of the
-mechanisms described in the [Using other Hadoop
distributions](#using-other-hadoop-distributions) section.
-
-## Using other Hadoop distributions
-
-Druid works out of the box with many Hadoop distributions.
-
-If you are having dependency conflicts between Druid and your version of
Hadoop, you can try
-searching for a solution in the [Druid user
groups](https://groups.google.com/forum/#!forum/druid-
-user), or reading the Druid [Different Hadoop
Versions](../operations/other-hadoop.html) documentation.
-
-## Command Line Hadoop Indexer
-
-If you don't want to use a full indexing service to use Hadoop to get data
into Druid, you can also use the standalone command line Hadoop indexer.
-See [here](../ingestion/command-line-hadoop-indexer.html) for more info.
-
-## IndexTask-based Batch Ingestion
-
-If you do not want to have a dependency on Hadoop for batch ingestion, you can
also use the index task. This task will be much slower and less scalable than
the Hadoop-based method. See [here](../ingestion/tasks.html) for more info.
+Hadoop can be used for batch ingestion. The Hadoop-based batch ingestion will
be faster and more scalable than the native batch ingestion. See
[here](../ingestion/hadoop.html) for more details.
Having Problems?
----------------
diff --git a/docs/content/ingestion/hadoop.md b/docs/content/ingestion/hadoop.md
new file mode 100644
index 00000000000..1115804636b
--- /dev/null
+++ b/docs/content/ingestion/hadoop.md
@@ -0,0 +1,340 @@
+---
+layout: doc_page
+---
+
+# Hadoop-based Batch Ingestion
+
+Hadoop-based batch ingestion in Druid is supported via a Hadoop-ingestion
task. These tasks can be posted to a running
+instance of a Druid [overlord](../design/indexing-service.html).
+
+## Command Line Hadoop Indexer
+
+If you don't want to use a full indexing service to use Hadoop to get data
into Druid, you can also use the standalone command line Hadoop indexer.
+See [here](../ingestion/command-line-hadoop-indexer.html) for more info.
+
+## Task syntax
+
+A sample task is shown below:
+
+```json
+{
+ "type" : "index_hadoop",
+ "spec" : {
+ "dataSchema" : {
+ "dataSource" : "wikipedia",
+ "parser" : {
+ "type" : "hadoopyString",
+ "parseSpec" : {
+ "format" : "json",
+ "timestampSpec" : {
+ "column" : "timestamp",
+ "format" : "auto"
+ },
+ "dimensionsSpec" : {
+ "dimensions":
["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"],
+ "dimensionExclusions" : [],
+ "spatialDimensions" : []
+ }
+ }
+ },
+ "metricsSpec" : [
+ {
+ "type" : "count",
+ "name" : "count"
+ },
+ {
+ "type" : "doubleSum",
+ "name" : "added",
+ "fieldName" : "added"
+ },
+ {
+ "type" : "doubleSum",
+ "name" : "deleted",
+ "fieldName" : "deleted"
+ },
+ {
+ "type" : "doubleSum",
+ "name" : "delta",
+ "fieldName" : "delta"
+ }
+ ],
+ "granularitySpec" : {
+ "type" : "uniform",
+ "segmentGranularity" : "DAY",
+ "queryGranularity" : "NONE",
+ "intervals" : [ "2013-08-31/2013-09-01" ]
+ }
+ },
+ "ioConfig" : {
+ "type" : "hadoop",
+ "inputSpec" : {
+ "type" : "static",
+ "paths" : "/MyDirectory/example/wikipedia_data.json"
+ }
+ },
+ "tuningConfig" : {
+ "type": "hadoop"
+ }
+ },
+ "hadoopDependencyCoordinates": <my_hadoop_version>
+}
+```
+
+|property|description|required?|
+|--------|-----------|---------|
+|type|The task type, this should always be "index_hadoop".|yes|
+|spec|A Hadoop Index Spec. See [Batch
Ingestion](../ingestion/batch-ingestion.html)|yes|
+|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).
+
+## DataSchema
+
+This field is required. See [Ingestion](../ingestion/index.html).
+
+## IOConfig
+
+This field is required.
+
+|Field|Type|Description|Required|
+|-----|----|-----------|--------|
+|type|String|This should always be 'hadoop'.|yes|
+|inputSpec|Object|A specification of where to pull the data in from. See
below.|yes|
+|segmentOutputPath|String|The path to dump segments into.|yes|
+|metadataUpdateSpec|Object|A specification of how to update the metadata for
the druid cluster these segments belong to.|yes|
+
+### InputSpec specification
+
+There are multiple types of inputSpecs:
+
+#### `static`
+
+A type of inputSpec where a static path to the data files is provided.
+
+|Field|Type|Description|Required|
+|-----|----|-----------|--------|
+|inputFormat|String|Specifies the Hadoop InputFormat class to use. e.g.
`org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat` |no|
+|paths|Array of String|A String of input paths indicating where the raw data
is located.|yes|
+
+For example, using the static input paths:
+
+```
+"paths" :
"s3n://billy-bucket/the/data/is/here/data.gz,s3n://billy-bucket/the/data/is/here/moredata.gz,s3n://billy-bucket/the/data/is/here/evenmoredata.gz"
+```
+
+#### `granularity`
+
+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:
+
+```
+s3n://billy-bucket/the/data/is/here/y=2012/m=06/d=01/H=00
+s3n://billy-bucket/the/data/is/here/y=2012/m=06/d=01/H=01
+...
+s3n://billy-bucket/the/data/is/here/y=2012/m=06/d=01/H=23
+```
+
+#### `dataSource`
+
+Read Druid segments. See [here](../ingestion/update-existing-data.html) for
more information.
+
+#### `multi`
+
+Read multiple sources of data. See
[here](../ingestion/update-existing-data.html) for more information.
+
+## TuningConfig
+
+The tuningConfig is optional and default parameters will be used if no
tuningConfig is specified.
+
+|Field|Type|Description|Required|
+|-----|----|-----------|--------|
+|workingPath|String|The working path to use for intermediate results (results
between Hadoop jobs).|no (default == '/tmp/druid-indexing')|
+|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":
+
+```
+classification=yarn-site,properties=[mapreduce.reduce.memory.mb=6144,mapreduce.reduce.java.opts=-server
-Xms2g -Xmx2g -Duser.timezone=UTC -Dfile.encoding=UTF-8 -XX:+PrintGCDetails
-XX:+PrintGCTimeStamps,mapreduce.map.java.opts=758,mapreduce.map.java.opts=-server
-Xms512m -Xmx512m -Duser.timezone=UTC -Dfile.encoding=UTF-8
-XX:+PrintGCDetails -XX:+PrintGCTimeStamps,mapreduce.task.timeout=1800000]
+```
+
+- Follow the instructions under "[Configure Hadoop for data
+loads](../tutorials/cluster.html#configure-cluster-for-hadoop-data-loads)"
using the XML files from
+`/etc/hadoop/conf` on your EMR master.
+
+## Secured Hadoop Cluster
+
+By default druid can use the exisiting TGT kerberos ticket available in local
kerberos key cache.
+Although TGT ticket has a limited life cycle,
+therefore you need to call `kinit` command periodically to ensure validity of
TGT ticket.
+To avoid this extra external cron job script calling `kinit` periodically,
+ you can provide the principal name and keytab location and druid will do the
authentication transparently at startup and job launching time.
+
+|Property|Possible Values|Description|Default|
+|--------|---------------|-----------|-------|
+|`druid.hadoop.security.kerberos.principal`|`dr...@example.com`| Principal
user name |empty|
+|`druid.hadoop.security.kerberos.keytab`|`/etc/security/keytabs/druid.headlessUser.keytab`|Path
to keytab file|empty|
+
+### Loading from S3 with EMR
+
+- In the `jobProperties` field in the `tuningConfig` section of your Hadoop
indexing task, add:
+
+```
+"jobProperties" : {
+ "fs.s3.awsAccessKeyId" : "YOUR_ACCESS_KEY",
+ "fs.s3.awsSecretAccessKey" : "YOUR_SECRET_KEY",
+ "fs.s3.impl" : "org.apache.hadoop.fs.s3native.NativeS3FileSystem",
+ "fs.s3n.awsAccessKeyId" : "YOUR_ACCESS_KEY",
+ "fs.s3n.awsSecretAccessKey" : "YOUR_SECRET_KEY",
+ "fs.s3n.impl" : "org.apache.hadoop.fs.s3native.NativeS3FileSystem",
+ "io.compression.codecs" :
"org.apache.hadoop.io.compress.GzipCodec,org.apache.hadoop.io.compress.DefaultCodec,org.apache.hadoop.io.compress.BZip2Codec,org.apache.hadoop.io.compress.SnappyCodec"
+}
+```
+
+Note that this method uses Hadoop's built-in S3 filesystem rather than
Amazon's EMRFS, and is not compatible
+with Amazon-specific features such as S3 encryption and consistent views. If
you need to use these
+features, you will need to make the Amazon EMR Hadoop JARs available to Druid
through one of the
+mechanisms described in the [Using other Hadoop
distributions](#using-other-hadoop-distributions) section.
+
+## Using other Hadoop distributions
+
+Druid works out of the box with many Hadoop distributions.
+
+If you are having dependency conflicts between Druid and your version of
Hadoop, you can try
+searching for a solution in the [Druid user
groups](https://groups.google.com/forum/#!forum/druid-
+user), or reading the Druid [Different Hadoop
Versions](../operations/other-hadoop.html) documentation.
+
diff --git a/docs/content/ingestion/native_tasks.md
b/docs/content/ingestion/native_tasks.md
index 0e9353c4703..8ef79624bff 100644
--- a/docs/content/ingestion/native_tasks.md
+++ b/docs/content/ingestion/native_tasks.md
@@ -514,7 +514,7 @@ For Roaring bitmaps:
While ingesting data using the Index task, it creates segments from the input
data and pushes them. For segment pushing,
the Index task supports two segment pushing modes, i.e., _bulk pushing mode_
and _incremental pushing mode_ for
-[perfect rollup and best-effort rollup](./design/index.html), respectively.
+[perfect rollup and best-effort rollup](../design/index.html), respectively.
In the bulk pushing mode, every segment is pushed at the very end of the index
task. Until then, created segments
are stored in the memory and local storage of the node running the index task.
As a result, this mode might cause a
diff --git a/docs/content/toc.md b/docs/content/toc.md
index a9d91c8b4fd..bd221394138 100644
--- a/docs/content/toc.md
+++ b/docs/content/toc.md
@@ -17,6 +17,8 @@ layout: toc
* [Schema Design](/docs/VERSION/ingestion/schema-design.html)
* [Schema Changes](/docs/VERSION/ingestion/schema-changes.html)
* [Batch File Ingestion](/docs/VERSION/ingestion/batch-ingestion.html)
+ * [Native Batch Ingestion](docs/VERSION/ingestion/native_tasks.html)
+ * [Hadoop Batch Ingestion](docs/VERSION/ingestion/hadoop.html)
* [Stream Ingestion](/docs/VERSION/ingestion/stream-ingestion.html)
* [Stream Push](/docs/VERSION/ingestion/stream-push.html)
* [Stream Pull](/docs/VERSION/ingestion/stream-pull.html)
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