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.
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.
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.
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.
You can follow the 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 to the following -
You can check out the sample job spec here.
Now, add the pinot jar to spark's classpath using following options -
Please ensure environment variables PINOT_ROOT_DIR and PINOT_VERSION are set properly.
Finally execute the spark job using the command -
Note: You should change the master to yarn and deploy-mode to cluster for production.
# executionFrameworkSpec: Defines ingestion jobs to be running.
executionFrameworkSpec:
# name: execution framework name
name: 'spark'
# segmentGenerationJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentGenerationJobRunner'
# segmentTarPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentTarPushJobRunner'
# segmentUriPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentUriPushJobRunner'
#segmentMetadataPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface
segmentMetadataPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentMetadataPushJobRunner'
# extraConfigs: extra configs for execution framework.
extraConfigs:
# stagingDir is used in distributed filesystem to host all the segments then move this directory entirely to output directory.
stagingDir: your/local/dir/staging
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:
Organize raw data into buckets (eg: /var/pinot/airlineStats/rawdata/2014/01/01). Each bucket typically contains several files (eg: /var/pinot/airlineStats/rawdata/2014/01/01/airlineStats_data_2014-01-01_0.avro)
Run a Pinot batch ingestion job, which points to a specific date folder like ‘/var/pinot/airlineStats/rawdata/2014/01/01’. The segment generation job will convert each such avro file into a Pinot segment for that day and give it a unique name.
Run Pinot segment push job to upload those segments with those uniques names via a Controller API
IMPORTANT: The segment name is the unique identifier used to uniquely identify that segment in Pinot. If the controller gets an upload request for a segment with the same name - it will attempt to replace it with the new one.
This newly uploaded data can now be queried in Pinot. However, sometimes users will make changes to the raw data which need to be reflected in Pinot. This process is known as 'Backfill'.
How to Backfill data in Pinot
Pinot supports data modification only at the segment level, which means we should update entire segments for doing backfills. The high level idea is to repeat steps 2 (segment generation) and 3 (segment upload) mentioned above:
Backfill jobs must run at the same granularity as the daily job. E.g., if you need to backfill data for 2014/01/01, specify that input folder for your backfill job (e.g.: ‘/var/pinot/airlineStats/rawdata/2014/01/01’)
The backfill job will then generate segments with the same name as the original job (with the new data).
When uploading those segments to Pinot, the controller will replace the old segments with the new ones (segment names act like primary keys within Pinot) one by one.
Edge case
Backfill jobs expect the same number of (or more) data files on the backfill date. So the segment generation job will create the same number of (or more) segments than the original run.
E.g. assuming table airlineStats has 2 segments(airlineStats_2014-01-01_2014-01-01_0, airlineStats_2014-01-01_2014-01-01_1) on date 2014/01/01 and the backfill input directory contains only 1 input file. Then the segment generation job will create just one segment: airlineStats_2014-01-01_2014-01-01_0. After the segment push job, only segment airlineStats_2014-01-01_2014-01-01_0 got replaced and stale data in segment airlineStats_2014-01-01_2014-01-01_1 are still there.
In case the raw data is modified in such a way that the original time bucket has fewer input files than the first ingestion run, backfill will fail.
Hadoop
Segment Creation and Push
Pinot supports Apache Hadoop 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
Next, you need to change the execution config in the job spec to the following -
You can check out the sample job spec here.
Finally execute the hadoop job using the command -
Please ensure environment variables PINOT_ROOT_DIR and PINOT_VERSION are set properly.
Data Preprocessing before Segment Creation
We’ve seen some requests that data should be massaged (like partitioning, sorting, resizing) before creating and pushing segments to Pinot.
The MapReduce job called SegmentPreprocessingJob would be the best fit for this use case, regardless of whether the input data is of AVRO or ORC format.
Check the below example to see how to use SegmentPreprocessingJob.
In Hadoop properties, set the following to enable this job:
In table config, specify the operations in preprocessing.operations that you'd like to enable in the MR job, and then specify the exact configs regarding those operations:
preprocessing.num.reducers
Minimum number of reducers. Optional. Fetched when partitioning gets disabled and resizing is enabled. This parameter is to avoid having too many small input files for Pinot, which leads to the case where Pinot server is holding too many small segments, causing too many threads.
preprocessing.max.num.records.per.file
Maximum number of records per reducer. Optional.Unlike, “preprocessing.num.reducers”, this parameter is to avoid having too few large input files for Pinot, which misses the advantage of muti-threading when querying. When not set, each reducer will finally generate one output file. When set (e.g. M), the original output file will be split into multiple files and each new output file contains at most M records. It does not matter whether partitioning is enabled or not.
For more details on this MR job, please refer to this .
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 a join like functionality. These 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 the configuration isDimTable should be set to true in the table config as shown below
As dimension table are used to perform lookups of dimension values, they are required to have a primary key (can be a composite key).
As mentioned above, when a table is marked as a dimension table it will be replicated on all the hosts, because of this the size of the dim table has to be small. The maximum size quota for a dimension table in a cluster is controlled by controller.dimTable.maxSize controller property. Table creation will fail if the storage quota exceeds this maximum size.
# executionFrameworkSpec: Defines ingestion jobs to be running.
executionFrameworkSpec:
# name: execution framework name
name: 'hadoop'
# segmentGenerationJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentGenerationJobRunner'
# segmentTarPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentTarPushJobRunner'
# segmentUriPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentUriPushJobRunner'
# segmentMetadataPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
segmentMetadataPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentMetadataPushJobRunner'
# extraConfigs: extra configs for execution framework.
extraConfigs:
# stagingDir is used in distributed filesystem to host all the segments then move this directory entirely to output directory.
stagingDir: your/local/dir/staging
#CONTROLLER
pinot.controller.storage.factory.class.[scheme]=className of the pinot file systems
pinot.controller.segment.fetcher.protocols=file,http,[scheme]
pinot.controller.segment.fetcher.[scheme].class=org.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
#SERVER
pinot.server.storage.factory.class.[scheme]=className of the pinotfile systems
pinot.server.segment.fetcher.protocols=file,http,[scheme]
pinot.server.segment.fetcher.[scheme].class=org.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
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.
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.
Amazon Kinesis
This is not tested in production. You may hit some snags while trying to use this.
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
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.
Web Identity Token credentials from the environment or container
Credential profiles file at the default location (~/.aws/credentials) shared by all AWS SDKs and the AWS CLI
Credentials delivered through the Amazon EC2 container service if AWS_CONTAINER_CREDENTIALS_RELATIVE_URI environment variable is set and security manager has permission to access the variable,
Instance profile credentials delivered through the Amazon EC2 metadata service
Description
streamType
This should be set to "kinesis"
stream.kinesis.topic.name
Kinesis stream name
region
Kinesis region e.g. us-west-1
accessKey
Kinesis access key
secretKey
Kinesis secret key
shardIteratorType
Set to "LATEST" for largest offset (default),TRIM_HORIZONfor earliest offset.
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:
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...
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
Amazon S3
You can enable Amazon S3 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...
You can also configure the S3 filesystem using the following options:
Each of these properties should be prefixed by pinot.[node].storage.factory.s3. where node is either controller or server depending on the config
e.g.
S3 Filesystem 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.
Examples
Job spec
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 -
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...
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
Azure Data Lake Storage
This guide shows you how to import data from files stored in Azure Data Lake Storage (ADLS)
You can enable the Azure Data Lake Storage using the plugin pinot-adls. In the controller or server, add the config -
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 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.abfss. where node is either controller or server depending on the config
e.g.
Examples
Job spec
Controller config
Server config
Minion config
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.
(Optional) The server-side encryption algorithm used when storing this object in Amazon S3 (Now supports aws:kms), set to null to disable SSE.
ssekmsKeyId
(Optional, but required when serverSideEncryption=aws:kms) Specifies the AWS KMS key ID to use for object encryption. All GET and PUT requests for an object protected by AWS KMS will fail if not made via SSL or using SigV4.
ssekmsEncryptionContext
(Optional) Specifies the AWS KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.
Web Identity Token credentials from the environment or container
Credential profiles file at the default location (~/.aws/credentials) shared by all AWS SDKs and the AWS CLI
Credentials delivered through the Amazon EC2 container service if AWS_CONTAINER_CREDENTIALS_RELATIVE_URI environment variable is set and security manager has permission to access the variable,
Instance profile credentials delivered through the Amazon EC2 metadata service
Configuration
Description
region
The AWS Data center region in which the bucket is located
accessKey
(Optional) AWS access key required for authentication. This should only be used for testing purposes as we don't store these keys in secret.
secretKey
(Optional) AWS secret key required for authentication. This should only be used for testing purposes as we don't store these keys in secret.
endpoint
(Optional) Override endpoint for s3 client.
disableAcl
If this is set tofalse, bucket owner is granted full access to the objects created by pinot. Default value is true.
To update a record, a primary key is needed to uniquely identify the record. To define a primary key, add the field primaryKeyColumns to the schema definition. For example, the schema definition of UpsertMeetupRSVP in the quick start example has this definition.
Note this field expects a list of columns, as the primary key can be composite.
When two records of the same primary key are ingested, the record with the greater event time (as defined by the time column) is used. When records with the same primary key and event time, then the order is not determined. In most cases, the later ingested record will be used, but may not be so in the cases when the table has a column to sort by.
Partition the input stream by the primary key
An important requirement for the Pinot upsert table is to partition the input stream by the primary key. For Kafka messages, this means the producer shall set the key in the send API. If the original stream is not partitioned, then a streaming processing job (e.g. Flink) is needed to shuffle and repartition the input stream into a partitioned one for Pinot's ingestion.
Enable upsert in the table configurations
There are a few configurations needed in the table configurations to enable upsert.
Upsert mode
For append-only tables, the upsert mode defaults to NONE. To enable the full upsert, set the mode to FULL for the full update. For example:
Pinot also added the partial update support in v0.8.0+. To enable the partial upsert, set the mode to PARTIAL and specify partialUpsertStrategies for partial upsert columns. For example:
Pinot supports the following partial upsert strategies -
Strategy
Description
OVERWRITE
Overwrite the column of the last record
INCREMENT
Add the new value to the existing values
APPEND
Add the new item to the Pinot unordered set
UNION
Add the new item to the Pinot unordered set if not exists
Comparison Column
By default, Pinot uses the value in the time column to determine the latest record. That means, for two records with the same primary key, the record with the larger value of the time column is picked as the latest update. However, there are cases when users need to use another column to determine the order. In such case, you can use option comparisonColumn to override the column used for comparison. For example,
Use strictReplicaGroup for routing
The upsert Pinot table can use only the low-level consumer for the input streams. As a result, it uses the partitioned replica-group assignment for the segments. Moreover,upsert poses the additional requirement that all segments of the same partition must be served from the same server to ensure the data consistency across the segments. Accordingly, it requires to use strictReplicaGroup as the routing strategy. To use that, configure instanceSelectorType in Routing as the following:
Limitations
There are some limitations for the upsert Pinot tables.
First, the high-level consumer is not allowed for the input stream ingestion, which means stream.kafka.consumer.type must be lowLevel.
Second, the star-tree index cannot be used for indexing, as the star-tree index performs pre-aggregation during the ingestion.
Example
Putting these together, you can find the table configurations of the quick start example as the following:
Quick Start
To illustrate how the full upsert works, the Pinot binary comes with a quick start example. Use the following command to creates a realtime upsert table meetupRSVP.
You can also run partial upsert demo with the following command
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 Query Console to checkout the realtime data.
Query the upsert table
For partial upsert you can see only the value from configured column changed based on specified partial upsert strategy.
Query the partial upsert table
An example for partial upsert is shown below, each of the event_id kept being unique during ingestion, meanwhile the value of rsvp_count incremented.
Explain partial upsert table
To see the difference from the append-only table, you can use a query option skipUpsert to skip the upsert effect in the query result.
Disable the upsert during query via query option
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
Stream ingestion
Apache Pinot allows user to consume data from streams and push it directly to pinot database. This process is known as Stream Ingestion. Stream Ingestion allows user to query data within seconds of publishing.
Stream Ingestion provides support for checkpoints out of the box for preventing data loss.
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 milliseconds to start the consumer from the offset after the timestamp.
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. localhost:9092.
How to consume from higher Kafka version?
This connector is also suitable for Kafka lib version higher than 2.0.0. In Kafka 2.0 connector pom.xml, change the kafka.lib.version from 2.0.0 to 2.1.1 will make this Connector working with Kafka 2.1.1.
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 Query Console 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.
dockerrun\--networkpinot-demo--name=kafka
dockerexec\-tkafka\
Create table configuration
Upload table and schema spec
Let's take a look at each of the following steps in a bit 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 which are available in the datasource. Schema also defines the fields which serve as dimensions , metrics and timestamp respectively.
Follow creating a schema for more details on schema configuration. For our sample data, the schema configuration should look as follows
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 realtime ingestion also triggers the data ingestion job.For a more detailed overview about tables, check out the table reference.
The realtime table configuration consists of the 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 -
tableIndexConfig - defines which column to use for indexing along with the type of index. You can refer [Indexing Configs] for full configuration. It consists of the following required fields -
loadMode - specifies how the segments should be loaded. Should be one of heap or mmap. Here's the difference between both the configs
Config key
Description
Supported values
streamType
the streaming platform from which to consume the data
kafka
stream.[streamType].consumer.type
whether to use per partition low-level consumer or high-level stream consumer
lowLevel or highLevel
stream.[streamType].topic.name
the datasource (e.g. topic, data stream) from which to consume the data
String
stream.[streamType].decoder.class.name
You can also specify additional configs for the consumer by prefixing the key with stream.[streamType] where streamType is the name of the streaming platform. For our sample data and schema, the table config will look like -
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 adding more integrations such as Kinesis out of the box. You can easily write your on ingestion plugin in case it is not supported out of the box. Follow Stream Ingestion Plugin for a walkthrough.
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 that goes in serialization-deserialization and speed up the ingestion.
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.
configClassName - name of the class that implements the RecordReaderConfig interface. This class is used the parse the values mentioned in configs
configs - Key value pair for format specific configs. This field can be left out.
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
CSV Record Reader supports the following 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.
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.
JSON
Thrift
Note: 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 -
Complex Type (Array, Map) Handling
Complex-type handling in Apache Pinot.
It's common for the ingested data to have complex structure. For example, Avro schema has and , and JSON data has and . In Apache Pinot, the data model supports primitive data types (including int, long, float, double, string, bytes), as well as limited multi-value types such as an array of primitive types. Such simple data types allow Pinot to build fast indexing structures for good query performance, but it requires some handling on the complex structures. There are in general two options for such handling: convert the complex-type data into JSON string and then build JSON index; or use the inbuilt complex-type handling rules in the ingestion config.
In 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 . Note this object has two child fields, and the child group is a nested array with the element of object type.
streamConfig - specifies the datasource along with the necessary configs to start consuming the realtime data. The streamConfig can be thought of as equivalent of job spec in case of batch ingestion. The following options are supported in this config -
name of the class to be used for parsing the data. The class should implement org.apache.pinot.spi.stream.StreamMessageDecoder interface
String
stream.[streamType].consumer.factory.class.name
name of the factory class to be used to provide the appropriate implementation of low level and high level consumer as well as the metadata
determines the offset from which to start the ingestion
smallestlargest or timestamp in milliseconds
realtime.segment.flush.threshold.time
Time threshold that will keep the realtime segment open for before we complete the segment
realtime.segment.flush.threshold.size
Row count flush threshold for realtime segments. This behaves in a similar way for HLC and LLC. For HLC,
since there is only one consumer per server, this size is used as the size of the consumption buffer and determines after how many rows we flush to disk. For example, if this threshold is set to two million rows,
then a high level consumer would have a buffer size of two million.
If this value is set to 0, then the consumers adjust the number of rows consumed by a partition such that the size of the completed segment is the desired size (unless
threshold.time is reached first)
realtime.segment.flush.desired.size
The desired size of a completed realtime segment.This config is used only if threshold.size is set to 0.
Double
STRING
String
VARCHAR
String
CHAR
String
LIST
Object[]
MAP
Map<Object, Object>
DATE
Long
TIMESTAMP
Long
BINARY
byte[]
BYTE
Integer
Parquet Data Type
Java Data Type
Comment
INT96
INT64
ParquetINT96 type converts nanoseconds
to Pinot INT64 type of milliseconds
DECIMAL
DOUBLE
ORC Data Type
Java Data Type
BOOLEAN
String
SHORT
Integer
INT
Integer
LONG
Integer
FLOAT
Float
DOUBLE
heap: Segments are loaded on direct-memory. Note, 'heap' here is a legacy misnomer, and it does not
imply JVM heap. This mode should only be used when we want faster performance than memory-mapped files,
and are also sure that we will never run into OOM.
mmap: Segments are loaded on memory-mapped file. This is the default mode.
dataFormat: 'csv'
className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
configs:
fileFormat: 'default' #should be one of default, rfc4180, excel, tdf, mysql
header: 'columnName seperated by delimiter'
delimiter: ','
multiValueDelimiter: '-'
Apache Pinot provides powerful JSON index 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.
Note 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, please check out this file. Also note that group is a reserved keyword in SQL, and that's why it's quoted in the transformFunction.
Additionall, 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,
With this, you can start to query the nested fields under group. For the deatils about the supported JSON function, please check out this guide).
Handle the complex type with 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.
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 in the beginning will transform into two rows with this configuration example.
Flattened/unnested data
Note that
The nested field group_id under group is flattened to field group.group_id. The default value of the delimiter is ., you can choose other delimiter by changing the configuration delimiter under complexTypeConfig. This flattening rule also apllies on the maps in the collections to be unnested.
The nested array group_topics under group is unnested into the top-level, and convert the output to a collection of two rows. Note the handling of the nested field within group_topics, and the eventual top-level field of group.group_topics.urlkey. All the collections to unnest shall be included in configuration fieldsToUnnest.
For the collections not in specified in fieldsToUnnest, the ingestion by default will serialize them into JSON string, except for the array of primitive values, which will be ingested as multi-value column by default. The behavior is defined in config collectionNotUnnestedToJson with default value to NON_PRIMITIVE. Other behaviors include (1) ALL, which aslo convert the array of primitive values to JSON string; (2) NONE, this does not do conversion, but leave it to the users to use transform functions for handling.
You can find the full spec of the table config here and the table schema here.
With the flattening/unnesting, you can then query the table with primitive values using the SQL query like:
Note . is a reserved character in SQL, so you need to quote the flattened column.
Infer the Pinot schema from the Avro schema and JSON data
When there are complex structures, it could be challenging and tedious to figure out the Pinot schema manually. To help the 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 PR.
