Apache
/
druid
mirror of https://github.com/apache/druid.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #2610 from dclim/add-combineText-doc 

add combineText property and cleanup batch ingestion doc
This commit is contained in:
Fangjin Yang 2016-03-08 12:54:16 -08:00
parent 9e31e2ce0e df29667a89
commit 8e36e6fa43
1 changed files with 34 additions and 34 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

68
docs/content/ingestion/batch-ingestion.md
View File

@ -8,8 +8,8 @@ Druid can load data from static files through a variety of methods described her
## 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). A sample task is shown below:
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:
```json
{
@ -84,9 +84,7 @@ of a Druid [overlord](../design/indexing-service.html). A sample task is shown b
### DataSchema
This field is required.
See [Ingestion](../ingestion/index.html)
This field is required. See [Ingestion](../ingestion/index.html).
### IOConfig
@ -95,9 +93,9 @@ 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|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
@ -105,7 +103,7 @@ There are multiple types of inputSpecs:
##### `static`
Is a type of inputSpec where a static path to where the data files are located is passed.
A type of inputSpec where a static path to the data files is provided.
|Field|Type|Description|Required|
|-----|----|-----------|--------|
@ -119,16 +117,16 @@ For example, using the static input paths:
##### `granularity`
Is a type of inputSpec that expects data to be laid out in a specific path format. Specifically, it expects it to be segregated by day in this directory format `y=XXXX/m=XX/d=XX/H=XX/M=XX/S=XX` (dates are represented by lowercase, time is represented by uppercase).
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|
|inputPath|String|Base path to append the expected time path to.|yes|
|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|
|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 date-time 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|
|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
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
@ -153,16 +151,17 @@ The tuningConfig is optional and default parameters will be used if no tuningCon
|-----|----|-----------|--------|
|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.|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.More details below.|no (default == 'hashed')|
|maxRowsInMemory|Integer|The number of rows to aggregate before persisting. This number is the post-aggregation rows, so it is not equivalent to the number of input events, but the number of aggregated rows that those events result in. This is used to manage the required JVM heap size.|no (default == 75000)|
|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.|no (default == 75000)|
|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)|
|useCombiner|Boolean|Use hadoop combiner to merge rows at mapper if possible.|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.|no (default == null)|
|buildV9Directly|Boolean|Whether to build v9 index directly instead of building v8 index and convert it to v9 format|no (default = false)|
|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)|
|buildV9Directly|Boolean|Build v9 index directly instead of building v8 index and converting it to v9 format.|no (default = false)|
|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)|
### Partitioning specification
@ -188,11 +187,11 @@ cardinality of the input set and a target partition size.
The configuration options are:
|property|description|required?|
|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|
|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|
#### Single-dimension partitioning
@ -211,18 +210,19 @@ override it with a specific dimension.
The configuration options are:
|property|description|required?|
|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 input data has already been grouped on time and dimensions. Ingestion will run faster, but can choose suboptimal partitions if the assumption is violated.|no|
|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 having dependency problems with your version of Hadoop and the version compiled with Druid, please see [these docs](../operations/other-hadoop.html).
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
@ -231,7 +231,7 @@ 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".
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]
@ -257,8 +257,8 @@ loads](cluster.html#configure-cluster-for-hadoop-data-loads)" using the XML file
}
```
Note that this method uses Hadoop's builtin 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 those
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.
@ -277,7 +277,7 @@ 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.
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.
Having Problems?
----------------