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Import Data

This section is an overview of the various options for importing data into Pinot.

There are multiple options for importing data into Pinot. These guides are ready-made examples that show you step-by-step instructions for importing records into Pinot, supported by our plugin architecture.

These guides are meant to get you up and running with imported data as quick as possible. Pinot supports multiple file input formats without needing to change anything other than the file name. Each example imports a ready-made dataset so you can see how things work without needing to bring your own dataset.

Pinot Batch Ingestion

Spark Hadoop

Pinot Stream Ingestion

This guide will show you how to import data using stream ingestion from Apache Kafka topics.

This guide will show you how to import data using stream ingestion with upsert.

This guide will show you how to import data using stream ingestion with deduplication.

Pinot File Systems

By default, Pinot does not come with a storage layer, so all the data sent, won't be stored in case of system crash. In order to persistently store the generated segments, you will need to change controller and server configs to add a deep storage. Checkout for all the info and related configs.

These guides will show you how to import data as well as persist it in the file systems.

Pinot Input Formats

These guides will show you how to import data from a Pinot supported input format.

This guide will show you how to handle the complex type in the ingested data, such as map and array.

From Query Console

Insert a file into Pinot from Query Console

This feature is supported after the 0.11.0 release. Reference PR: https://github.com/apache/pinot/pull/8557

Prerequisite

Ensure you have available Pinot Minion instances deployed within the cluster.
Pinot version is 0.11.0 or above

How it works

Parse the query with the table name and directory URI along with a list of options for the ingestion job.
Call controller minion task execution API endpoint to schedule the task on minion
Response has the schema of table name and task job id.

Usage Syntax

INSERT INTO [database.]table FROM FILE dataDirURI OPTION ( k=v ) [, OPTION (k=v)]*

Example

Screenshot

Insert Rows into Pinot

We are actively developing this feature...

The details will be revealed soon.

Hadoop

Segment Creation and Push

Pinot supports as a processor to create and push segment files to the database. Pinot distribution is bundled with the Spark code to process your files and convert and upload them to Pinot.

You can follow the [wiki] to build pinot distribution from source. The resulting JAR file can be found in pinot/target/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar

Backfill Data

Introduction

Pinot batch ingestion involves two parts: routing ingestion job(hourly/daily) and backfill. Here are some tutorials on how routine batch ingestion works in Pinot Offline Table:

Dimension Table

Dimension tables in Apache Pinot.

Dimension tables are a special kind of offline tables from which data can be looked up via the lookup UDF, providing join like functionality.

Dimension tables are replicated on all the hosts for a given tenant to allow faster lookups.

To mark an offline table as a dim table, isDimTable should be set to true and segmentsConfig.segementPushType should be set to REFRESH in the table config as shown below:

{
  "OFFLINE": {
    "tableName": "dimBaseballTeams_OFFLINE",
    "tableType": "OFFLINE",
    "segmentsConfig": {
      "schemaName": "dimBaseballTeams",
      "segmentPushType": "REFRESH"
    },
    "metadata": {},
    "quota": {
      "storage": "200M"
    },
    "isDimTable": true
  }
}

As dimension tables are used to perform lookups of dimension values, they are required to have a primary key (can be a composite key).

{
  "dimensionFieldSpecs": [
    {
      "dataType": "STRING",
      "name": "teamID"
    },
    {
      "dataType": "STRING",
      "name": "teamName"
    }
  ],
  "schemaName": "dimBaseballTeams",
  "primaryKeyColumns": ["teamID"]
}

When a table is marked as a dimension table, it will be replicated on all the hosts, which means that these tables must be small in size.

The maximum size quota for a dimension table in a cluster is controlled by the controller.dimTable.maxSize controller property. Table creation will fail if the storage quota exceeds this maximum size.

A dimension table cannot be part of a .

Amazon Kinesis

To ingest events from an Amazon Kinesis stream into Pinot, set the following configs into the table config

where the Kinesis specific properties are:

Property

Description

streamType

This should be set to "kinesis"

stream.kinesis.topic.name

Kinesis stream name

Kinesis supports authentication using the . The credential provider looks for the credentials in the following order -

Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (RECOMMENDED since they are recognized by all the AWS SDKs and CLI except for .NET), or AWS_ACCESS_KEY and AWS_SECRET_KEY (only recognized by Java SDK)
Java System Properties - aws.accessKeyId and aws.secretKey

You can also specify the accessKey and secretKey using the properties. However, this method is not secure and should be used only for POC setups. You can also specify other aws fields such as AWS_SESSION_TOKEN as environment variables and config and it will work.

Limitations

ShardID is of the format "shardId-000000000001". We use the numeric part as partitionId. Our partitionId variable is integer. If shardIds grow beyond Integer.MAX_VALUE, we will overflow
Segment size based thresholds for segment completion will not work. It assumes that partition "0" always exists. However, once the shard 0 is split/merged, we will no longer have partition 0.

Apache Pulsar

Pinot supports consuming data from Apache Pulsar via pinot-pulsar plugin. You need to enable this plugin so that Pulsar specific libraries are present in the classpath.

You can enable pulsar plugin with the following config at the time of Pinot setup -Dplugins.include=pinot-pulsar

pinot-pulsar plugin is not part of official 0.10.0 binary. You can download the plugin from and add it to libs or plugins directory in pinot.

Set up Pulsar table

A sample Pulsar stream config to ingest data should look as follows. You can use the streamConfigs section from this sample and make changes for your corresponding table.

Pulsar configuration options

You can change the following Pulsar specifc configurations for your tables

Property

Description

Authentication

Pinot-Pulsar connector supports authentication using the security tokens. You can generate the token by following the . Once generated, you can add the following property to streamConfigs to add auth token for each request

TLS support

Pinot-pulsar connecor also supports TLS for encrypted connections. You can follow to enable TLS on your pulsar cluster. Once done, you can enable TLS in pulsar connector by providing the trust certificate file location generated in the previous step.

Also, make sure to change the brokers url from pulsar://localhost:6650 to pulsar+ssl://localhost:6650 so that secure connections are used.

For other table and stream configurations, you can headover to

Supported Pulsar versions

PInot currently relies on Pulsar client version 2.7.2. Users should make sure the Pulsar broker is compatible with the this client version.

Stream Ingestion with Upsert

Upsert support in Apache Pinot.

Pinot provides native support of upsert during the real-time ingestion (v0.6.0+). There are scenarios that the records need modifications, such as correcting a ride fare and updating a delivery status.

With the foundation of full upsert support in Pinot, another category of use cases on partial upsert are enabled (v0.8.0+). Partial upsert is convenient to users so that they only need to specify the columns whose value changes, and ignore the others.

To enable upsert on a Pinot table, there are a couple of configurations to make on the table configurations as well as on the input stream.

Stream Ingestion with Dedup

Deduplication support in Apache Pinot.

Pinot provides native support of Deduplication during the real-time ingestion (v0.11.0+).

To enable dedup on a Pinot table, there are a couple of table configuration and schema changes needed.

Prerequisites for enabling dedup

There are certain mandatory configurations needed in order to be able to enable dedup.

File Systems

This section contains a collection of short guides to show you how to import from a Pinot supported file system.

FileSystem is an abstraction provided by Pinot to access data in distributed file systems (DFS).

Pinot uses distributed file systems for the following purposes:

Batch Ingestion Job - To read the input data (CSV, Avro, Thrift, etc.) and to write generated segments to DFS

Amazon S3

You can enable Filesystem backend by including the plugin pinot-s3 .

By default Pinot loads all the plugins, so you can just drop this plugin there. Also, if you specify -Dplugins.include, you need to put all the plugins you want to use, e.g. pinot-json, pinot-avro , pinot-kafka-2.0...

Azure Data Lake Storage

This guide shows you how to import data from files stored in Azure Data Lake Storage Gen2 (ADLS Gen2)

You can enable the Azure Data Lake Storage using the plugin pinot-adls. In the controller or server, add the config -

Azure Blob Storage provides the following options -

accountName : Name of the azure account under which the storage is created
accessKey : access key required for the authentication
fileSystemName

Each of these properties should be prefixed by pinot.[node].storage.factory.class.adl2. where node is either controller or server depending on the config

e.g.

Examples

Job spec

Controller config

Server config

Minion config

HDFS

This guide shows you how to import data from HDFS.

You can enable the Hadoop DFS using the plugin pinot-hdfs. In the controller or server, add the config:

HDFS implementation provides the following options -

hadoop.conf.path : Absolute path of the directory containing hadoop XML configuration files such as hdfs-site.xml, core-site.xml .
hadoop.write.checksum : create checksum while pushing an object. Default is false

Each of these properties should be prefixed by pinot.[node].storage.factory.class.hdfs. where node is either controller or server depending on the config

The kerberos configs should be used only if your Hadoop installation is secured with Kerberos. Please check on how to generate Kerberos security identification.

You will also need to provide proper Hadoop dependencies jars from your Hadoop installation to your Pinot startup scripts.

Push HDFS segment to Pinot Controller

To push HDFS segment files to Pinot controller, you just need to ensure you have proper Hadoop configuration as we mentioned in the previous part. Then your remote segment creation/push job can send the HDFS path of your newly created segment files to the Pinot Controller and let it download the files.

For example, the following curl requests to Controller will notify it to download segment files to the proper table:

Examples

Job spec

Standalone Job:

Hadoop Job:

Controller config

Server config

Minion config

Google Cloud Storage

This guide shows you how to import data from GCP (Google Cloud Platform).

You can enable the Google Cloud Storage using the plugin pinot-gcs. In the controller or server, add the config -

GCP filesystems provides the following options -

projectId - The name of the Google Cloud Platform project under which you have created your storage bucket.
gcpKey - Location of the json file containing GCP keys. You can refer to download the keys.

Each of these properties should be prefixed by pinot.[node].storage.factory.class.gs. where node is either controller or server depending on the config

e.g.

Examples

Job spec

Controller config

Server config

Minion config

Stream ingestion

Apache Pinot lets users consume data from streams and push it directly into the database, in a process known as stream ingestion. Stream Ingestion makes it possible to query data within seconds of publication.

Stream Ingestion provides support for checkpoints for preventing data loss.

Setting up Stream ingestion involves the following steps:

Create schema configuration
Create table configuration
Upload table and schema spec

Let's take a look at each of the steps in more detail.

Let us assume the data to be ingested is in the following format:

Create Schema Configuration

Schema defines the fields along with their data types. The schema also defines whether fields serve as dimensions , metrics or timestamp. For more details on schema configuration, see .

For our sample data, the schema configuration looks like this:

Create Table Configuration

The next step is to create a table where all the ingested data will flow and can be queried. Unlike batch ingestion, table configuration for real-time ingestion also triggers the data ingestion job. For a more detailed overview of tables, see the reference.

The real-time table configuration consists of the following fields:

tableName - The name of the table where the data should flow
tableType - The internal type for the table. Should always be set to REALTIME for realtime ingestion
segmentsConfig -

Config key

Description

Supported values

The following flush threshold settings are also supported:

Config key

Description

Supported values

You can also specify additional configs for the consumer directly into the streamConfigs.

For our sample data and schema, the table config will look like this:

Upload schema and table config

Now that we have our table and schema configurations, let's upload them to the Pinot cluster. As soon as the configs are uploaded, pinot will start ingesting available records from the topic.

Custom Ingestion Support

We are working on support for other ingestion platforms, but you can also write your own ingestion plugin if it is not supported out of the box. For a walkthrough, see .

Pause Stream Ingestion

There are some scenarios in which you may want to pause the realtime ingestion while your table is available for queries. For example if there is a problem with the stream ingestion, while you are troubleshooting the issue, you still want the queries to be executed on the already ingested data. For these scenarios, you can first issue a Pause request to a Controller host. After troubleshooting with the stream is done, you can issue another request to Controller to resume the consumption.

When a Pause request is issued, Controller instructs the realtime servers hosting your table to commit their consuming segments immediately. However, the commit process may take some time to complete. Please note that Pause and Resume requests are async. OK response means that instructions for pausing or resuming has been successfully sent to the realtime server. If you want to know if the consumptions actually stopped or resumed, you can issue a pause status request.

It's worth noting that consuming segments on realtime servers are stored in volatile memory, and their resources are allocated when the consuming segments are first created. These resources cannot be altered if consumption parameters are changed midway through consumption. It may therefore take hours before these changes take effect. Furthermore, if the parameters are changed in an incompatible way (for example, changing the underlying stream with a completely new set of offsets, or changing the stream endpoint from which to consume messages, etc.), it will result in the table getting into an error state.

Pause and resume feature comes to the rescue here. When a Pause request is issued by the operator, consuming segments are committed without starting new mutables ones. Instead, new mutable segments are started only when the Resume request is issued. This mechanism provides the operators as well as developers with more flexibility. It also enables Pinot to be more resilient to the operational and functional constraints imposed by underlying streams.

There is another feature called "Force Commit" which utilizes the primitives of pause and resume feature. When the operator issues a force commit request, the current mutable segments will be committed and new ones started right away. Operators can now use this feature for all compatible table config parameter changes to take effect immediately.

For incompatible parameter changes, an option is added to the resume request to handle the case of a completely new set of offsets. Operators can now follow a three-step process: First, issue a Pause request. Second, change the consumption parameters. Finally, issue the Resume request with the appropriate option. These steps will preserve the old data and allow the new data to be consumed immediately. All through the operation, queries will continue to be served.

Apache Kafka

This guide shows you how to ingest a stream of records from an Apache Kafka topic into a Pinot table.

Introduction

In this guide, you'll learn how to import data into Pinot using Apache Kafka for real-time stream ingestion. Pinot has out-of-the-box real-time ingestion support for Kafka.

Let's setup a demo Kafka cluster locally, and create a sample topic transcript-topic

Start Kafka

Create a Kafka Topic

Start Kafka

Start Kafka cluster on port 9876 using the same Zookeeper from the .

Create a Kafka topic

Download the latest . Create a topic.

Creating Schema Configuration

We will publish the data in the same format as mentioned in the docs. So you can use the same schema mentioned under .

Creating a table configuration

The real-time table configuration for the transcript table described in the schema from the previous step.

For Kafka, we use streamType as kafka . Currently only JSON format is supported but you can easily write your own decoder by extending the StreamMessageDecoder interface. You can then access your decoder class by putting the jar file in plugins directory

The lowLevel consumer reads data per partition whereas the highLevel consumer utilises Kafka high level consumer to read data from the whole stream. It doesn't have the control over which partition to read at a particular momemt.

For Kafka versions below 2.X, use org.apache.pinot.plugin.stream.kafka09.KafkaConsumerFactory

For Kafka version 2.X and above, use org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory

You can set the offset to -

smallest to start consumer from the earliest offset
largest to start consumer from the latest offset
timestamp in format yyyy-MM-dd'T'HH:mm:ss.SSSZ

The resulting configuration should look as follows -

Upgrade from Kafka 0.9 connector to Kafka 2.x connector

Update table config for both high level and low level consumer: Update config: stream.kafka.consumer.factory.class.name from org.apache.pinot.core.realtime.impl.kafka.KafkaConsumerFactory to org.apache.pinot.core.realtime.impl.kafka2.KafkaConsumerFactory.
If using Stream(High) level consumer: Please also add config stream.kafka.hlc.bootstrap.server into tableIndexConfig.streamConfigs. This config should be the URI of Kafka broker lists, e.g.

How to consume from higher Kafka version?

This connector is also suitable for Kafka lib version higher than 2.0.0. In , change the kafka.lib.version from 2.0.0 to 2.1.1 will make this Connector working with Kafka 2.1.1.

How to consume transactional-committed Kafka messages

The connector with Kafka lib 2.0+ supports Kafka transactions. The transaction support is controlled by config kafka.isolation.level in Kafka stream config, which can be read_committed or read_uncommitted (default). Setting it to read_committed will ingest transactionally committed messages in Kafka stream only.

Upload schema and table

Now that we have our table and schema configurations, let's upload them to the Pinot cluster. As soon as the real-time table is created, it will begin ingesting available records from the Kafka topic.

Add sample data to the Kafka topic

We will publish data in the following format to Kafka. Let us save the data in a file named as transcript.json.

Push sample JSON into the transcript-topic Kafka topic, using the Kafka console producer. This will add 12 records to the topic described in the transcript.json file.

Ingesting streaming data

As soon as data flows into the stream, the Pinot table will consume it and it will be ready for querying. Head over to the to checkout the real-time data.

Some More kafka ingestion configs

Use Kafka Partition(Low) Level Consumer with SSL

Here is an example config which uses SSL based authentication to talk with kafka and schema-registry. Notice there are two sets of SSL options, ones starting with ssl. are for kafka consumer and ones with stream.kafka.decoder.prop.schema.registry. are for SchemaRegistryClient used by KafkaConfluentSchemaRegistryAvroMessageDecoder.

Ingest transactionally committed messages only from Kafka

With Kafka consumer 2.0, you can ingest transactionally committed messages only by configuring kafka.isolation.level to read_committed. For example,

Note that the default value of this config read_uncommitted to read all messages. Also, this config supports low-level consumer only.

Use Kafka Level Consumer with SASL_SSL

Here is an example config which uses SASL_SSL based authentication to talk with kafka and schema-registry. Notice there are two sets of SSL options, some for kafka consumer and ones with stream.kafka.decoder.prop.schema.registry. are for SchemaRegistryClient used by KafkaConfluentSchemaRegistryAvroMessageDecoder.

Post release 0.10.0, we have started shading kafka packages inside Pinot. If you are using our latest tagged docker images or master build, you should replace org.apache.kafka with shaded.org.apache.kafka in your table config.

Input formats

This section contains a collection of guides that will show you how to import data from a Pinot supported input format.

Pinot offers support for various popular input formats during ingestion. By changing the input format, you can reduce the time spent doing serialization-deserialization and speed up the ingestion.

Configuring input formats

The input format can be changed using the recordReaderSpec config in the ingestion job spec.

The config consists of the following keys:

dataFormat - Name of the data format to consume.
className - name of the class that implements the RecordReader interface. This class is used for parsing the data.

To configure input format for realtime ingestion, you can add the following to the table config json

Supported input formats

Pinot supports the multiple input formats out of the box. You just need to specify the corresponding readers and the associated custom configs to switch between the formats.

CSV

Supported Configs

fileFormat - can be one of default, rfc4180, excel, tdf, mysql

header - header of the file. The columnNames should be seperated by the delimiter mentioned in the config

delimiter - The character seperating the columns

multiValueDelimiter - The character seperating multiple values in a single column. This can be used to split a column into a list.

nullValueString - use this to specify how NULL values are represented in your csv files. Default is empty string interpreted as NULL.

Your CSV file may have raw text fields that cannot be reliably delimited using any character. In this case, explicitly set the multiValueDelimeter field to empty in the ingestion config. multiValueDelimiter: ''

AVRO

The Avro record reader converts the data in file to a GenericRecord. A java class or .avro file is not required.

You can also specify Kafka schema registry for avro records in stream.

JSON

Thrift

Thrift requires the generated class using .thrift file to parse the data. The .class file should be available in the Pinot's classpath. You can put the files in the lib/ folder of pinot distribution directory.

Parquet

The above class doesn't read the Parquet INT96 and Decimaltype.

Please use the below class to handle INT96 and Decimaltype.

ORC

ORC record reader supports the following data types -

In LIST and MAP types, the object should only belong to one of the data types supported by Pinot.

Protocol Buffers

The reader requires a descriptor file to deserialize the data present in the files. You can generate the descriptor file (.desc) from the .proto file using the command -

Descriptor file in DFS

The descriptorFile needs to be present on all pinot server machines for ingestion to work. You can also upload the descriptor file to a DFS such as S3, GCS etc. and mention that path in the configs. Do note that you'll also need to specify for the directory in the pinot configuration or ingestion spec as well.

Both proto2 and proto3 formats are supported by the reader.

Schema Registry

Protobuf reader also supports Confluent schema registry. Using schema registry allows you to not create and upload any descriptor file. The schema is fetched from the registry itself using the metadata present in the Kafka message. The only pre-requisite for it to work is that your messages should be serialized using io.confluent.kafka.serializers.protobuf.KafkaProtobufSerializer in producer.

Complex Type (Array, Map) Handling

Complex-type handling in Apache Pinot.

It's common for ingested data to have a complex structure. For example, Avro schemas have records and arrays and JSON supports objects and arrays.

Apache Pinot's data model supports primitive data types (including int, long, float, double, BigDecimal string, bytes), as well as limited multi-value types such as an array of primitive types (multi-valued BigDecimal type is not supported). Such simple data types allow Pinot to build fast indexing structures for good query performance, but it requires some handling of the complex structures.

Support for BIG_DECIMAL type is added after release 0.10.0.

There are in general two options for such handling:

Convert the complex-type data into JSON string and then build a JSON index
Use the inbuilt complex-type handling rules in the ingestion config.

On this page, we'll show how to handle this complex-type structure with these two approaches, to process the example data in the following figure, which is a field group from the .

This object has two child fields and the child group is a nested array with elements of object type.

JSON indexing

Apache Pinot provides a powerful to accelerate the value lookup and filtering for the column. To convert an object group with complex type to JSON, you can add the following config to table config.

The config transformConfigs transforms the object group to a JSON string group_json, which then creates the JSON indexing with config jsonIndexColumns. To read the full spec, see .

Also, note that group is a reserved keyword in SQL and therefore needs to be quoted in transformFunction.

The columnName can't use the same name as any of the fields in the source JSON data e.g. if our source data contains the field group and we want to transform the data in that field before persisting it, the destination column name would need to be something different, like group_json.

Additionally, you need to overwrite the maxLength of the field group_json on the schema, because by default, a string column has a limited length. For example,

For the full spec, see .

With this, you can start to query the nested fields under group. For the details about the supported JSON function, see ).

Ingestion configurations

Though JSON indexing is a handy way to process the complex types, there are some limitations:

It’s not performant to group by or order by a JSON field, because JSON_EXTRACT_SCALAR is needed to extract the values in the GROUP BY and ORDER BY clauses, which invokes the function evaluation.
For cases that you want to use Pinot's such as DISTINCTCOUNTMV

Alternatively, from Pinot 0.8, you can use the complex-type handling in ingestion configurations to flatten and unnest the complex structure and convert them into primitive types. Then you can reduce the complex-type data into a flattened Pinot table, and query it via SQL. With the inbuilt processing rules, you do not need to write ETL jobs in another compute framework such as Flink or Spark.

To process this complex type, you can add the configuration complexTypeConfig to the ingestionConfig. For example:

With the complexTypeConfig , all the map objects will be flattened to direct fields automatically. And with unnestFields , a record with the nested collection will unnest into multiple records. For instance, the example at the beginning will transform into two rows with this configuration example.

Note that

The nested field group_id under group is flattened to group.group_id. The default value of the delimiter is . You can choose another delimiter by specifying the configuration delimiter under complexTypeConfig. This flattening rule also applies to maps in the collections to be unnested.

You can find the full spec of the table config and the table schema .

You can then query the table with primitive values using the following SQL query:

. is a reserved character in SQL, so you need to quote the flattened columns in the query.

Infer the Pinot schema from the Avro schema and JSON data

When there are complex structures, it can be challenging and tedious to figure out the Pinot schema manually. To help with schema inference, Pinot provides utility tools to take the Avro schema or JSON data as input and output the inferred Pinot schema.

To infer the Pinot schema from Avro schema, you can use the command like the following:

Note you can input configurations like fieldsToUnnest similar to the ones in complexTypeConfig. And this will simulate the complex-type handling rules on the Avro schema and output the Pinot schema in the file specified in outputDir.

Similarly, you can use the command like the following to infer the Pinot schema from a file of JSON objects.

You can check out an example of this run in this .

Spark

Pinot supports Apache spark as a processor to create and push segment files to the database. Pinot distribution is bundled with the Spark code to process your files and convert and upload them to Pinot.

We support both Spark 2.X and 3.X

You can follow the wiki to build pinot distribution from source. The resulting JAR file can be found in pinot/target/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar

Next, you need to change the execution config in the job spec to the following -

You can check out the sample job spec here.

To run Spark ingestion, you need the following jars in your classpath

pinot-batch-ingestion-spark plugin jar - available in plugins-external directory in the package
pinot-all jar - available in lib directory in the package

These jars can be specified using spark.driver.extraClassPath or any other option.

For loading any other plugins that you want to use, you can use -

The complete spark-submit command should look as follows

Please ensure environment variables PINOT_ROOT_DIR and PINOT_VERSION are set properly.

Note: You should change the master to yarn and deploy-mode to cluster for production environments.

We have stopped including spark-core dependency in our jars post 0.10.0 release. Users can try 0.11.0-SNAPSHOT and later versions of pinot-batch-ingestion-spark in case of any runtime issues. You can either or download latest master build jars.

Running in Cluster Mode on YARN

If you want to run the spark job in cluster mode on YARN/EMR cluster, the following needs to be done -

Build Pinot from source with option -DuseProvidedHadoop
Copy Pinot binaries to S3, HDFS or any other distributed storage that is accessible from all nodes.
Copy Ingestion spec YAML file to S3, HDFS or any other distributed storage. Mention this path as part of --files

Example

For Spark 3.x, replace pinot-batch-ingestion-spark-2.4 with pinot-batch-ingestion-spark-3.2 in all places in the commands. Also, ensure the classpath in ingestion spec is changed from org.apache.pinot.plugin.ingestion.batch.spark. to org.apache.pinot.plugin.ingestion.batch.spark3.

FAQ

Q - I am getting the following exception - Class has been compiled by a more recent version of the Java Runtime (class file version 55.0), this version of the Java Runtime only recognizes class file versions up to 52.0

Since 0.8.0 release, Pinot binaries are compiled with JDK 11. If you are using Spark along with Hadoop 2.7+, you need to use the java8 version of pinot. Currently, you need to .

Q - I am not able to find pinot-batch-ingestion-spark jar.

For Pinot version prior to 0.10.0, the spark plugin is located in plugin dir of binary distribution. For 0.10.0 and later, it is located in pinot-external dir.

Q - Spark is not able to find the jars leading to java.nio.file.NoSuchFileException

This means the classpath for spark job has not been configured properly. If you are running spark in a distributed environment such as Yarn or k8s, make sure both spark.driver.classpath and spark.executor.classpath are set. Also, the jars in driver.classpath should be added to --jars argument in spark-submit so that spark can distribute those jars to all the nodes in your cluster. You also need to take provide appropriate scheme with the file path when running the jar. In this doc, we have used local:\\ but it can be different dependening on your cluster setup.

Q - Spark job failing while pushing the segments.

It can be because of misconfigured controllerURI in job spec yaml file. If the controllerURI is correct, make sure it is accessible from all the nodes of your YARN or k8s cluster.

Q - My data gets overwritten during ingestion.

Set to APPEND in the tableConfig.

If already set to APPEND, this is likely due to a missing timeColumnName in your table config. If you can't provide a time column, please use our in ingestion spec. Generally using inputFile segment name generator should fix your issue.

Q - I am getting java.lang.RuntimeException: java.io.IOException: Failed to create directory: pinot-plugins-dir-0/plugins/*

Removing -Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins from spark.driver.extraJavaOptions should fix this. As long as plugins are mentioned in classpath and jars argument it should not be an issue.

Q - Getting Class not found: exception

Please check if extraClassPath arguments contain all the plugin jars for both driver and executors. Also, all the plugin jars are mentioned in the --jars argument. If both of these are correct, please check if the extraClassPath contains local filesystem classpaths and not s3 or hdfs or any other distributed file system classpaths.

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 -

Define Schema
Define Table Config
Upload Schema and Table configs
Upload data

Batch Ingestion currently supports the following mechanisms to upload the data -

Standalone

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.

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.

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

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 -

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

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.

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

/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:

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.

You can refer to for more details.

Now that we have the job spec for our table transcript , we can trigger the job using the following command

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:

Upload the entire segment tar file to the Pinot controller.

Pinot controller will:

Save the segment into the controller segment directory(Local or any PinotFS).
Extract segment metadata.
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:

POST this segment Tar URI to the Pinot controller.

Pinot controller will:

Download segment from the URI and save it to controller segment directory(Local or any PinotFS).
Extract segment metadata.
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:

Download the segment based on URI.
Extract metadata.
Upload metadata to the Pinot Controller.

Pinot Controller will:

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

Then use metadata push but add one extra config like below:

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:

Push segment to shared NFS and let pinot pull segment files from the location of that NFS. See .
Push segment to a Web server and let pinot pull segment files from the Web server with HTTP/HTTPS link. See .
Push segment to PinotFS(HDFS/S3/GCS/ADLS) and let pinot pull segment files from PinotFS URI. See and .

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 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 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.

Import Data

hashtagPinot Batch Ingestion

hashtagPinot Stream Ingestion

hashtagPinot File Systems

hashtagPinot Input Formats

From Query Console

hashtagInsert a file into Pinot from Query Console

hashtagPrerequisite

hashtagHow it works

hashtagUsage Syntax

hashtagExample

hashtagInsert Rows into Pinot

Hadoop

hashtagSegment Creation and Push

Backfill Data

hashtagIntroduction

Dimension Table

Amazon Kinesis

hashtagLimitations

Apache Pulsar

hashtagSet up Pulsar table

hashtagPulsar configuration options

hashtagAuthentication

hashtagTLS support

hashtagSupported Pulsar versions

Stream Ingestion with Upsert

hashtag

Stream Ingestion with Dedup

hashtagPrerequisites for enabling dedup

File Systems

Amazon S3

Azure Data Lake Storage

hashtagExamples

hashtagJob spec

hashtagController config

hashtagServer config

hashtagMinion config

HDFS

hashtagPush HDFS segment to Pinot Controller

hashtagExamples

hashtagJob spec

hashtagController config

hashtagServer config

hashtagMinion config

Google Cloud Storage

hashtagExamples

hashtagJob spec

hashtagController config

hashtagServer config

hashtagMinion config

From Query Console

hashtagInsert a file into Pinot from Query Console

hashtagPrerequisite

hashtagHow it works

hashtagUsage Syntax

hashtagExample

hashtagInsert Rows into Pinot

Import Data

hashtagPinot Batch Ingestion

hashtagPinot Stream Ingestion

hashtagPinot File Systems

hashtagPinot Input Formats

Dimension Table

Backfill Data

hashtagIntroduction

hashtagHow to Backfill data in Pinot

hashtagEdge case

Amazon Kinesis

hashtagLimitations

Amazon S3

Stream Ingestion with Dedup

hashtagPrerequisites for enabling dedup

hashtagPartition the input stream by the primary key

hashtagUse strictReplicaGroup for routing

hashtagOther limitations

hashtagEnable dedup in the table configurations

hashtagBest practices

hashtagExamples

hashtagJob spec

hashtagController config

Pinot Batch Ingestion

Pinot Stream Ingestion

Pinot File Systems

Pinot Input Formats

Insert a file into Pinot from Query Console

Prerequisite

How it works

Usage Syntax

Example

Insert Rows into Pinot

Segment Creation and Push

Introduction

Limitations

Set up Pulsar table

Pulsar configuration options

Authentication

TLS support

Supported Pulsar versions

Prerequisites for enabling dedup

Examples

Job spec

Controller config

Server config

Minion config

Push HDFS segment to Pinot Controller

Examples

Job spec

Controller config

Server config

Minion config

Examples

Job spec

Controller config

Server config

Minion config

Insert a file into Pinot from Query Console

Prerequisite

How it works

Usage Syntax

Example

Insert Rows into Pinot

Pinot Batch Ingestion

Pinot Stream Ingestion

Pinot File Systems

Pinot Input Formats

Introduction

How to Backfill data in Pinot

Edge case

Limitations

Prerequisites for enabling dedup

Partition the input stream by the primary key

Use strictReplicaGroup for routing

Other limitations

Enable dedup in the table configurations

Best practices

Examples

Job spec

Controller config

Server config

Minion config

Set up Pulsar table

Pulsar configuration options

Authentication

TLS support

Supported Pulsar versions

Examples

Job spec

Controller config

Server config

Minion config

Segment Creation and Push

Data Preprocessing before Segment Creation

preprocessing.num.reducers

preprocessing.max.num.records.per.file

Supported File Systems

Enabling a File System

Push HDFS segment to Pinot Controller

Examples

Job spec

Controller config