Segment
Pinot has the concept of table, which is a logical abstraction to refer to a collection of related data. Pinot has a distributed architecture and scales horizontally. Pinot expects the size of a table to grow infinitely over time. In order to achieve this, the entire data needs to be distributed across multiple nodes. Pinot achieve this by breaking the data into smaller chunks known as segment (this is similar to shards/partitions in relational databases). Segments can also be seen as time based partitions.
Thus, a segment is a horizontal shard representing a chunk of table data with some number of rows. The segment stores data for all columns of the table. Each segment packs the data in a columnar fashion, along with the dictionaries and indices for the columns. The segment is laid out in a columnar format so that it can be directly mapped into memory for serving queries.
Columns may be single or multi-valued. Column types may be STRING, INT, LONG, FLOAT, DOUBLE or BYTES. Columns may be declared to be metric or dimension (or specifically as a time dimension) in the schema. Columns can have default null value. For example, the default null value of a integer column can be 0. Note: The default value of byte column has to be hex-encoded before adding to the schema.
Pinot uses dictionary encoding to store values as a dictionary ID. Columns may be configured to be “no-dictionary” column in which case raw values are stored. Dictionary IDs are encoded using minimum number of bits for efficient storage (e.g. a column with cardinality of 3 will use only 2 bits for each dictionary ID).
There is a forward index built for each column and compressed appropriately for efficient memory use. In addition, optional inverted indices can be configured for any set of columns. Inverted indices, while take up more storage, offer better query performance. Specialized indexes like Star-Tree index is also supported. Check out Indexing for more details.

Creating a segment

Once the table is configured, we can load some data. Loading data involves generating pinot segments from raw data and pushing them to the pinot cluster. Data can be loaded in batch mode or streaming mode. See ingestion overview page for details.

Load Data in Batch

Prerequisites

Below are instructions to generate and push segments to Pinot via standalone scripts. For a production setup, you should use frameworks such as Hadoop or Spark. See this page for more details on setting up Data Ingestion Jobs.

Job Spec YAML

To generate a segment, we need to first create a job spec yaml file. JobSpec yaml file has all the information regarding data format, input data location, and pinot cluster coordinates. Note that this assumes that the controller is RUNNING to fetch the table config and schema. If not, you will have to configure the spec to point at their location. Please refer to Ingestion Job Spec for full configurations.
job-spec.yml
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executionFrameworkSpec:
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name: 'standalone'
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segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
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segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
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segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
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jobType: SegmentCreationAndTarPush
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inputDirURI: 'examples/batch/baseballStats/rawdata'
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includeFileNamePattern: 'glob:**/*.csv'
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excludeFileNamePattern: 'glob:**/*.tmp'
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outputDirURI: 'examples/batch/baseballStats/segments'
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overwriteOutput: true
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pinotFSSpecs:
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- scheme: file
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className: org.apache.pinot.spi.filesystem.LocalPinotFS
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recordReaderSpec:
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dataFormat: 'csv'
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className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
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configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
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configs:
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tableSpec:
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tableName: 'baseballStats'
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schemaURI: 'http://localhost:9000/tables/baseballStats/schema'
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tableConfigURI: 'http://localhost:9000/tables/baseballStats'
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segmentNameGeneratorSpec:
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pinotClusterSpecs:
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- controllerURI: 'http://localhost:9000'
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pushJobSpec:
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pushParallelism: 2
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pushAttempts: 2
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pushRetryIntervalMillis: 1000
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Create and push segment

To create and push the segment in one go, use
Docker
Using launcher scripts
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docker run \
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--network=pinot-demo \
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--name pinot-data-ingestion-job \
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${PINOT_IMAGE} LaunchDataIngestionJob \
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-jobSpecFile examples/docker/ingestion-job-specs/airlineStats.yaml
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Sample Console Output
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SegmentGenerationJobSpec:
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!!org.apache.pinot.spi.ingestion.batch.spec.SegmentGenerationJobSpec
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excludeFileNamePattern: null
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executionFrameworkSpec: {extraConfigs: null, name: standalone, segmentGenerationJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner,
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segmentTarPushJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner,
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segmentUriPushJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner}
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includeFileNamePattern: glob:**/*.avro
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inputDirURI: examples/batch/airlineStats/rawdata
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jobType: SegmentCreationAndTarPush
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outputDirURI: examples/batch/airlineStats/segments
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overwriteOutput: true
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pinotClusterSpecs:
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- {controllerURI: 'http://pinot-controller:9000'}
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pinotFSSpecs:
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- {className: org.apache.pinot.spi.filesystem.LocalPinotFS, configs: null, scheme: file}
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pushJobSpec: {pushAttempts: 2, pushParallelism: 1, pushRetryIntervalMillis: 1000,
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segmentUriPrefix: null, segmentUriSuffix: null}
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recordReaderSpec: {className: org.apache.pinot.plugin.inputformat.avro.AvroRecordReader,
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configClassName: null, configs: null, dataFormat: avro}
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segmentNameGeneratorSpec: null
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tableSpec: {schemaURI: 'http://pinot-controller:9000/tables/airlineStats/schema',
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tableConfigURI: 'http://pinot-controller:9000/tables/airlineStats', tableName: airlineStats}
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Trying to create instance for class org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner
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Initializing PinotFS for scheme file, classname org.apache.pinot.spi.filesystem.LocalPinotFS
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Finished building StatsCollector!
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Collected stats for 403 documents
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Created dictionary for INT column: FlightNum with cardinality: 386, range: 14 to 7389
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Using fixed bytes value dictionary for column: Origin, size: 294
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Created dictionary for STRING column: Origin with cardinality: 98, max length in bytes: 3, range: ABQ to VPS
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Created dictionary for INT column: Quarter with cardinality: 1, range: 1 to 1
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Created dictionary for INT column: LateAircraftDelay with cardinality: 50, range: -2147483648 to 303
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......
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......
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Pushing segment: airlineStats_OFFLINE_16085_16085_29 to location: http://pinot-controller:9000 for table airlineStats
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Sending request: http://pinot-controller:9000/v2/segments?tableName=airlineStats to controller: a413b0013806, version: Unknown
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Response for pushing table airlineStats segment airlineStats_OFFLINE_16085_16085_29 to location http://pinot-controller:9000 - 200: {"status":"Successfully uploaded segment: airlineStats_OFFLINE_16085_16085_29 of table: airlineStats"}
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Pushing segment: airlineStats_OFFLINE_16084_16084_30 to location: http://pinot-controller:9000 for table airlineStats
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Sending request: http://pinot-controller:9000/v2/segments?tableName=airlineStats to controller: a413b0013806, version: Unknown
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Response for pushing table airlineStats segment airlineStats_OFFLINE_16084_16084_30 to location http://pinot-controller:9000 - 200: {"status":"Successfully uploaded segment: airlineStats_OFFLINE_16084_16084_30 of table: airlineStats"}
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bin/pinot-admin.sh LaunchDataIngestionJob \
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-jobSpecFile examples/batch/airlineStats/ingestionJobSpec.yaml
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Alternately, you can separately create and then push, by changing the jobType to SegmentCreation or SegmenTarPush.

Templating Ingestion Job Spec

Ingestion job spec supports templating with Groovy Syntax.
This would be convenient for users to generate one ingestion job template file and schedule it in a daily basis with extra parameters updated daily.
E.g. users can set inputDirURI with parameters to indicate date, so that ingestion job only process the data for a particular date.
Below is an example to specify the date templating for input and output path.
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inputDirURI: 'examples/batch/airlineStats/rawdata/${year}/${month}/${day}'
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outputDirURI: 'examples/batch/airlineStats/segments/${year}/${month}/${day}'
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Then specify the value of ${year}, ${month}, ${day} when kicking off the ingestion job with arguments: -values $param=value1 $param2=value2...
Docker
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docker run \
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--network=pinot-demo \
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--name pinot-data-ingestion-job \
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${PINOT_IMAGE} LaunchDataIngestionJob \
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-jobSpecFile examples/docker/ingestion-job-specs/airlineStats.yaml
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-values year=2014 month=01 day=03
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This ingestion job only generates segment for date 2014-01-03

Load Data in Streaming

Prerequisites
Below is an example of how to publish sample data to your stream. As soon as data is available to the realtime stream, it starts getting consumed by the realtime servers

Kafka

Docker
Using launcher scripts
Run below command to stream JSON data into Kafka topic: flights-realtime
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docker run \
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--network pinot-demo \
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--name=loading-airlineStats-data-to-kafka \
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${PINOT_IMAGE} StreamAvroIntoKafka \
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-avroFile examples/stream/airlineStats/sample_data/airlineStats_data.avro \
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-kafkaTopic flights-realtime -kafkaBrokerList kafka:9092 -zkAddress pinot-zookeeper:2181/kafka
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Run below command to stream JSON data into Kafka topic: flights-realtime
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bin/pinot-admin.sh StreamAvroIntoKafka \
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-avroFile examples/stream/airlineStats/sample_data/airlineStats_data.avro \
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-kafkaTopic flights-realtime -kafkaBrokerList localhost:19092 -zkAddress localhost:2191/kafka
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Last modified 3mo ago