Batch Ingestion

Batch ingestion allows users to create a table using data already present in a file system such as S3. This is particularly useful for the cases where the user wants to utilize Pinot's ability to query large data with minimal latency or test out new features using a simple data file.
Ingesting data from a filesystem involves the following steps -
1.Define Schema
2.Define Table Config
3.Upload Schema and Table configs
4.Upload data
Batch Ingestion currently supports the following mechanisms to upload the data -
Standalone
​Hadoop​
​Spark​
Here we'll take a look at the standalone local processing to get you started.
Let's create a table for the following CSV data source.
studentID,firstName,lastName,gender,subject,score,timestampInEpoch
200,Lucy,Smith,Female,Maths,3.8,1570863600000
200,Lucy,Smith,Female,English,3.5,1571036400000
201,Bob,King,Male,Maths,3.2,1571900400000
202,Nick,Young,Male,Physics,3.6,1572418800000
Create Schema Configuration
In our data, the only column on which aggregations can be performed is score. Secondly, timestampInEpoch is the only timestamp column. So, on our schema, we keep score as metric and timestampInEpoch as timestamp column.
{
  "schemaName": "transcript",
  "dimensionFieldSpecs": [
    {
      "name": "studentID",
      "dataType": "INT"
    },
    {
      "name": "firstName",
      "dataType": "STRING"
    },
    {
      "name": "lastName",
      "dataType": "STRING"
    },
    {
      "name": "gender",
      "dataType": "STRING"
    },
    {
      "name": "subject",
      "dataType": "STRING"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "score",
      "dataType": "FLOAT"
    }
  ],
  "dateTimeFieldSpecs": [{
    "name": "timestampInEpoch",
    "dataType": "LONG",
    "format" : "1:MILLISECONDS:EPOCH",
    "granularity": "1:MILLISECONDS"
  }]
}
Here, we have also defined two extra fields - format and granularity. The format specifies the formatting of our timestamp column in the data source. Currently, it is in milliseconds hence we have specified 1:MILLISECONDS:EPOCH
Create Table Configuration
We define a tabletranscriptand map the schema created in the previous step to the table. For batch data, we keep the tableType as OFFLINE
{
  "tableName": "transcript",
  "tableType": "OFFLINE",
  "segmentsConfig": {
    "replication": 1,
    "timeColumnName": "timestampInEpoch",
    "timeType": "MILLISECONDS",
    "retentionTimeUnit": "DAYS",
    "retentionTimeValue": 365
  },
  "tenants": {
    "broker":"DefaultTenant",
    "server":"DefaultTenant"
  },
  "tableIndexConfig": {
    "loadMode": "MMAP"
  },
  "ingestionConfig": {
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "DAILY"
    }
  },
  "metadata": {}
}
Upload Schema and Table
Now that we have both the configs, we can simply upload them and create a table. To achieve that, just run the command -
bin/pinot-admin.sh AddTable \\
  -tableConfigFile /path/to/table-config.json \\
  -schemaFile /path/to/table-schema.json -exec
Check out the table config and schema in the [Rest API] to make sure it was successfully uploaded.
Upload data
We now have an empty table in pinot. So as the next step we will upload our CSV file to this table. 
A table is composed of multiple segments. The segments can be created using three ways
1) Minion based ingestion
2) Upload API
3) Ingestion jobs
Minion Based Ingestion
Refer to SegmentGenerationAndPushTask​
Upload API
There are 2 Controller APIs that can be used for a quick ingestion test using a small file. 
When these APIs are invoked, the controller has to download the file and build the segment locally.
Hence, these APIs are NOT meant for production environments and for large input files. 
/ingestFromFile
This API creates a segment using the given file and pushes it to Pinot. All steps happen on the controller. Example usage:
To upload a JSON file data.json to a table called foo_OFFLINE, use below command 
Note that query params need to be URLEncoded. For example, {"inputFormat":"json"} in the command below needs to be converted to %7B%22inputFormat%22%3A%22json%22%7D. 
curl -X POST -F [email protected] \
  -H "Content-Type: multipart/form-data" \
  "http://localhost:9000/ingestFromFile?tableNameWithType=foo_OFFLINE&
  batchConfigMapStr={"inputFormat":"json"}"
The batchConfigMapStr can be used to pass in additional properties needed for decoding the file. For example, in case of csv, you may need to provide the delimiter
curl -X POST -F [email protected] \
  -H "Content-Type: multipart/form-data" \
  "http://localhost:9000/ingestFromFile?tableNameWithType=foo_OFFLINE&
batchConfigMapStr={
  "inputFormat":"csv",
  "recordReader.prop.delimiter":"|"
}"
/ingestFromURI
This API creates a segment using file at the given URI and pushes it to Pinot. Properties to access the FS need to be provided in the batchConfigMap. All steps happen on the controller.
Example usage: 
curl -X POST "http://localhost:9000/ingestFromURI?tableNameWithType=foo_OFFLINE
&batchConfigMapStr={
  "inputFormat":"json",
  "input.fs.className":"org.apache.pinot.plugin.filesystem.S3PinotFS",
  "input.fs.prop.region":"us-central",
  "input.fs.prop.accessKey":"foo",
  "input.fs.prop.secretKey":"bar"
}
&sourceURIStr=s3://test.bucket/path/to/json/data/data.json"
Ingestion Jobs
Segments can be created and uploaded using tasks known as DataIngestionJobs. A job also needs a config of its own. We call this config the JobSpec.
For our CSV file and table, the job spec should look like below.
executionFrameworkSpec:
  name: 'standalone'
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
  segmentMetadataPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentMetadataPushJobRunner'
​
# Recommended to set jobType to SegmentCreationAndMetadataPush for production environment where Pinot Deep Store is configured  
jobType: SegmentCreationAndTarPush
​
inputDirURI: '/tmp/pinot-quick-start/rawdata/'
includeFileNamePattern: 'glob:**/*.csv'
outputDirURI: '/tmp/pinot-quick-start/segments/'
overwriteOutput: true
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'csv'
  className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
  configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
  tableName: 'transcript'
pinotClusterSpecs:
  - controllerURI: 'http://localhost:9000'
pushJobSpec:
  pushAttempts: 2
  pushRetryIntervalMillis: 1000
You can refer to Ingestion Job Spec for more details.
Now that we have the job spec for our table transcript , we can trigger the job using the following command
bin/pinot-admin.sh LaunchDataIngestionJob \\
    -jobSpecFile /tmp/pinot-quick-start/batch-job-spec.yaml
Once the job has successfully finished, you can head over to the [query console] and start playing with the data.
Segment Push Job Type
There are 3 ways to upload a Pinot segment:
1. Segment Tar Push
This is the original and default push mechanism.
Tar push requires the segment to be stored locally or can be opened as an InputStream on PinotFS. So we can stream the entire segment tar file to the controller.
The push job will:
1.Upload the entire segment tar file to the Pinot controller. 
Pinot controller will:
1.Save the segment into the controller segment directory(Local or any PinotFS). 
2.Extract segment metadata.
3.Add the segment to the table.
2. Segment URI Push
This push mechanism requires the segment Tar file stored on a deep store with a globally accessible segment tar URI.
URI push is light-weight on the client-side, and the controller side requires equivalent work as the Tar push.
The push job will:
1.POST this segment Tar URI to the Pinot controller. 
Pinot controller will:
1.Download segment from the URI and save it to controller segment directory(Local or any PinotFS).
2.Extract segment metadata.
3.Add the segment to the table.
3. Segment Metadata Push
This push mechanism also requires the segment Tar file stored on a deep store with a globally accessible segment tar URI.
Metadata push is light-weight on the controller side, there is no deep store download involves from the controller side.
The push job will:
1.Download the segment based on URI.
2.Extract metadata.
3.Upload metadata to the Pinot Controller.
Pinot Controller will:
1.Add the segment to the table based on the metadata.
4. Segment Metadata Push with copyToDeepStore
This extends the original Segment Metadata Push for cases, where the segments are pushed to a location not used as deep store. The ingestion job can still do metadata push but ask Pinot Controller to copy the segments into deep store. Those use cases usually happen when the ingestion jobs don't have direct access to deep store but still want to use metadata push for its efficiency, thus using a staging location to keep the segments temporarily.
NOTE: the staging location and deep store have to use same storage scheme, like both on s3. This is because the copy is done via PinotFS.copyDir interface that assumes so; but also because this does copy at storage system side, so segments don't need to go through Pinot Controller at all.
To make this work, firstly, grant Pinot controllers access to the staging location. e.g. on AWS, this may be to add access policy like below for the controller EC2 instances
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "s3:ListAllMyBuckets",
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": "s3:*",
            "Resource": [
                "arn:aws:s3:::metadata-push-staging",
                "arn:aws:s3:::metadata-push-staging/*"
            ]
        }
    ]
}
Then use metadata push but add one extra config like below:
...
jobType: SegmentCreationAndMetadataPush
...
outputDirURI: 's3://metadata-push-staging/stagingDir/'
...
pushJobSpec:
  copyToDeepStoreForMetadataPush: true
...
Consistent Data Push and Rollback
Pinot supports atomic update on segment level, which means that when data consisting of multiple segments are pushed to a table, as segments are replaced one at a time, queries to the broker during this upload phase may produce inconsistent result due to interleaving of old and new data.
See Consistent Push and Rollback for how to enable this feature. 
Segment Fetchers
When pinot segment files are created in external systems (Hadoop/spark/etc), there are several ways to push those data to the Pinot Controller and Server:
1.Push segment to shared NFS and let pinot pull segment files from the location of that NFS. See Segment URI Push.
2.Push segment to a Web server and let pinot pull segment files from the Web server with HTTP/HTTPS link. See Segment URI Push.
3.Push segment to PinotFS(HDFS/S3/GCS/ADLS) and let pinot pull segment files from PinotFS URI. See Segment URI Push and Segment Metadata Push.
4.Push segment to other systems and implement your own segment fetcher to pull data from those systems.
The first three options are supported out of the box within the Pinot package. As long your remote jobs send Pinot controller with the corresponding URI to the files it will pick up the file and allocate it to proper Pinot Servers and brokers. To enable Pinot support for PinotFS, you will need to provide PinotFS configuration and proper Hadoop dependencies.
Persistence
By default, Pinot does not come with a storage layer, so all the data sent, won't be stored in case of a system crash. In order to persistently store the generated segments, you will need to change controller and server configs to add deep storage. Checkout File systems for all the info and related configs.
Tuning
Standalone
Since pinot is written in Java, you can set the following basic java configurations to tune the segment runner job -
Log4j2 file location with -Dlog4j2.configurationFile
Plugin directory location with -Dplugins.dir=/opt/pinot/plugins
JVM props, like -Xmx8g -Xms4G
If you are using the docker, you can set the following under JAVA_OPTS variable.
Hadoop
You can set -D mapreduce.map.memory.mb=8192 to set the mapper memory size when submitting the Hadoop job.
Spark
You can add config spark.executor.memory to tune the memory usage for segment creation when submitting the Spark job.
From Query Console
Spark
Last modified 4mo ago