This document provides the basic instruction to set up a Kubernetes Cluster on Azure Kubernetes Service (AKS)

1. Tooling Installation

1.1 Install Kubectl

Please follow this link (https://kubernetes.io/docs/tasks/tools/install-kubectl) to install kubectl.

For Mac User

brew install kubernetes-cli

Please check kubectl version after installation.

kubectl version

1.2 Install Helm

Please follow this link (https://helm.sh/docs/using_helm/#installing-helm) to install helm.

For Mac User

brew install kubernetes-helm

Please check helm version after installation.

helm version

1.3 Install Azure CLI

Please follow this link (https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) to install Azure CLI.

For Mac User

brew update && brew install azure-cli

2. (Optional) Login to your Azure account

Below script will open default browser to sign-in to your Azure Account.

az login

3. (Optional) Create a Resource Group

Below script will create a resource group in location eastus.

AKS_RESOURCE_GROUP=pinot-demo
AKS_RESOURCE_GROUP_LOCATION=eastus
az group create --name ${AKS_RESOURCE_GROUP} \
                --location ${AKS_RESOURCE_GROUP_LOCATION}

4. (Optional) Create a Kubernetes cluster(AKS) in Azure

Below script will create a 3 nodes cluster named pinot-quickstart for demo purposes.

Please modify the parameters in the example command below:

AKS_RESOURCE_GROUP=pinot-demo
AKS_CLUSTER_NAME=pinot-quickstart
az aks create --resource-group ${AKS_RESOURCE_GROUP} \
              --name ${AKS_CLUSTER_NAME} \
              --node-count 3

Once the command is succeed, it's ready to be used.

5. Connect to an existing cluster

Simply run below command to get the credential for the cluster pinot-quickstart that you just created or your existing cluster.

AKS_RESOURCE_GROUP=pinot-demo
AKS_CLUSTER_NAME=pinot-quickstart
az aks get-credentials --resource-group ${AKS_RESOURCE_GROUP} \
                       --name ${AKS_CLUSTER_NAME}

To verify the connection, you can run:

kubectl get nodes

6. Pinot Quickstart

Please follow this Kubernetes QuickStart to deploy your Pinot Demo.

7. Delete a Kubernetes Cluster

AKS_RESOURCE_GROUP=pinot-demo
AKS_CLUSTER_NAME=pinot-quickstart
az aks delete --resource-group ${AKS_RESOURCE_GROUP} \
              --name ${AKS_CLUSTER_NAME}

This document provides the basic instruction to set up a Kubernetes Cluster on Google Kubernetes Engine(GKE)

1. Tooling Installation

1.1 Install Kubectl

Please follow this link (https://kubernetes.io/docs/tasks/tools/install-kubectl) to install kubectl.

For Mac User

brew install kubernetes-cli

Please check kubectl version after installation.

kubectl version

1.2 Install Helm

Please follow this link (https://helm.sh/docs/using_helm/#installing-helm) to install helm.

For Mac User

brew install kubernetes-helm

Please check helm version after installation.

helm version

1.3 Install Google Cloud SDK

Please follow this link (https://cloud.google.com/sdk/install) to install Google Cloud SDK.

1.3.1 For Mac User

• Install Google Cloud SDK

curl https://sdk.cloud.google.com | bash

• Restart your shell

exec -l $SHELL

2. (Optional) Initialize Google Cloud Environment

gcloud init

3. (Optional) Create a Kubernetes cluster(GKE) in Google Cloud

Below script will create a 3 nodes cluster named pinot-quickstart in us-west1-b with n1-standard-2 machines for demo purposes.

Please modify the parameters in the example command below:

GCLOUD_PROJECT=[your gcloud project name]
GCLOUD_ZONE=us-west1-b
GCLOUD_CLUSTER=pinot-quickstart
GCLOUD_MACHINE_TYPE=n1-standard-2
GCLOUD_NUM_NODES=3
gcloud container clusters create ${GCLOUD_CLUSTER} \
  --num-nodes=${GCLOUD_NUM_NODES} \
  --machine-type=${GCLOUD_MACHINE_TYPE} \
  --zone=${GCLOUD_ZONE} \
  --project=${GCLOUD_PROJECT}

You can monitor cluster status by command:

gcloud compute instances list

Once the cluster is in RUNNING status, it's ready to be used.

4. Connect to an existing cluster

Simply run below command to get the credential for the cluster pinot-quickstart that you just created or your existing cluster.

GCLOUD_PROJECT=[your gcloud project name]
GCLOUD_ZONE=us-west1-b
GCLOUD_CLUSTER=pinot-quickstart
gcloud container clusters get-credentials ${GCLOUD_CLUSTER} --zone ${GCLOUD_ZONE} --project ${GCLOUD_PROJECT}

To verify the connection, you can run:

kubectl get nodes

5. Pinot Quickstart

Please follow this Kubernetes QuickStart to deploy your Pinot Demo.

6. Delete a Kubernetes Cluster

GCLOUD_ZONE=us-west1-b
gcloud container clusters delete pinot-quickstart --zone=${GCLOUD_ZONE}

In this guide, you'll learn how to download and install Apache Pinot as a standalone instance.

Download Apache Pinot

First, let's download the Pinot distribution for this tutorial. You can either download a packaged release or build a distribution from the source code.

You can build from source or download the distribution:

PINOT_VERSION=0.10.0 #set to the Pinot version you decide to use

wget https://downloads.apache.org/pinot/apache-pinot-$PINOT_VERSION/apache-pinot-$PINOT_VERSION-bin.tar.gz

# untar it
tar -zxvf apache-pinot-$PINOT_VERSION-bin.tar.gz

# navigate to directory containing the launcher scripts
cd apache-pinot-$PINOT_VERSION-bin

# checkout pinot
git clone https://github.com/apache/pinot.git
cd pinot

# build pinot
mvn install package -DskipTests -Pbin-dist

# navigate to directory containing the setup scripts
cd build

M1 Mac Support

Currently Apache Pinot doesn't provide official binaries for M1 Mac. You can however build from source using the steps provided above. In addition to the steps, you will need to add the following in your ~/.m2/settings.xml prior to the build.

<settings>
  <activeProfiles>
    <activeProfile>
      apple-silicon
    </activeProfile>
  </activeProfiles>
  <profiles>
    <profile>
      <id>apple-silicon</id>
      <properties>
        <os.detected.classifier>osx-x86_64</os.detected.classifier>
      </properties>
    </profile>
  </profiles>
</settings>  

Also make sure to install rosetta

softwareupdate --install-rosetta

Note that some installations of the JDK do not contain the JNI bindings that are necessary to run all tests, if you see any java.lang.UnsatisfiedLinkError while running tests, you may need to change your JDK. If using Homebrew, you may install AdoptOpenJDK 11 using: brew install --cask adoptopenjdk11

Now that we've downloaded Pinot, it's time to set up a cluster. There are two ways to do this:

Quick Start

Pinot comes with quick-start commands that launch instances of Pinot components in the same process and import pre-built datasets.

For example, the following quick-start launches Pinot with a baseball dataset pre-loaded:

./bin/pinot-admin.sh QuickStart -type batch

For a list of all the available quick starts, see the Quick Start Examples.

Manual Cluster

If you want to play with bigger datasets (more than a few MB), you can launch all the components individually.

The video below is a step-by-step walk through for launching the individual components of Pinot and scaling them to multiple instances.

You can find the commands that are shown in this video in the github.com/npawar/pinot-tutorial GitHub repository.

export JAVA_OPTS="-Xms4G -Xmx8G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-controller.log"

export JAVA_OPTS="-Xms4G -Xmx8G"

Start Zookeeper

./bin/pinot-admin.sh StartZookeeper \
  -zkPort 2191

You can use Zooinspector to browse the Zookeeper instance.

Start Pinot Controller

export JAVA_OPTS="-Xms4G -Xmx8G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-controller.log"
./bin/pinot-admin.sh StartController \
    -zkAddress localhost:2191 \
    -controllerPort 9000

Start Pinot Broker

export JAVA_OPTS="-Xms4G -Xmx4G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-broker.log"
./bin/pinot-admin.sh StartBroker \
    -zkAddress localhost:2191

Start Pinot Server

export JAVA_OPTS="-Xms4G -Xmx16G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-server.log"
./bin/pinot-admin.sh StartServer \
    -zkAddress localhost:2191

Start Kafka

./bin/pinot-admin.sh  StartKafka \ 
  -zkAddress=localhost:2191/kafka \
  -port 19092

Once your cluster is up and running, you can head over to Exploring Pinot to learn how to run queries against the data.

Start Pinot Component in Debug Mode with IntelliJ

Starting a pinot component of interest in IntelliJ using debug mode can be useful for development purposes. You can set break points and inspect variables. Take debugging server for example, one can start zookeeper , controller, and broker using the steps in Manual Cluster. Then use the following configuration put under $PROJECT_DIR$\.run ) to start server. This commit is an example of how it can be used. Please replace the metrics-core version and cluster name as needed.

<component name="ProjectRunConfigurationManager">
  <configuration default="false" name="HelixServerStarter" type="Application" factoryName="Application" nameIsGenerated="true">
    <classpathModifications>
      <entry path="$PROJECT_DIR$/pinot-plugins/pinot-metrics/pinot-yammer/target/classes" />
      <entry path="$MAVEN_REPOSITORY$/com/yammer/metrics/metrics-core/2.2.0/metrics-core-2.2.0.jar" />
    </classpathModifications>
    <option name="MAIN_CLASS_NAME" value="org.apache.pinot.server.starter.helix.HelixServerStarter" />
    <module name="pinot-server" />
    <extension name="coverage">
      <pattern>
        <option name="PATTERN" value="org.apache.pinot.server.starter.helix.*" />
        <option name="ENABLED" value="true" />
      </pattern>
    </extension>
    <method v="2">
      <option name="Make" enabled="true" />
    </method>
  </configuration>
</component>

So far, we setup our cluster, ran some queries on the demo tables and explored the admin endpoints. We also uploaded some sample batch data for transcript table.

Now, it's time to ingest from a sample stream into Pinot. The rest of the instructions assume you're using Pinot in Docker.

Data Stream

First, we need to setup a stream. Pinot has out-of-the-box realtime ingestion support for Kafka. Other streams can be plugged in, more details in Pluggable Streams.

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

docker run \
    --network pinot-demo --name=kafka \
    -e KAFKA_ZOOKEEPER_CONNECT=manual-zookeeper:2181/kafka \
    -e KAFKA_BROKER_ID=0 \
    -e KAFKA_ADVERTISED_HOST_NAME=kafka \
    -d bitnami/kafka:latest

docker exec \
  -t kafka \
  /opt/kafka/bin/kafka-topics.sh \
  --zookeeper manual-zookeeper:2181/kafka \
  --partitions=1 --replication-factor=1 \
  --create --topic transcript-topic

bin/pinot-admin.sh  StartKafka -zkAddress=localhost:2123/kafka -port 9876

bin/kafka-topics.sh --create --bootstrap-server localhost:9876 --replication-factor 1 --partitions 1 --topic transcript-topic

Creating a Schema

If you followed the Batch upload sample data, you have already pushed a schema for your sample table. If not, head over to Creating a schema on that page, to learn how to create a schema for your sample data.

Creating a table config

If you followed Batch upload sample data, you learnt how to push an offline table and schema. Similar to the offline table config, we will create a realtime table config for the sample. Here's the realtime table config for the transcript table. For a more detailed overview about table, checkout Table.

{
  "tableName": "transcript",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "timestampInEpoch",
    "timeType": "MILLISECONDS",
    "schemaName": "transcript",
    "replicasPerPartition": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
    "streamConfigs": {
      "streamType": "kafka",
      "stream.kafka.consumer.type": "lowlevel",
      "stream.kafka.topic.name": "transcript-topic",
      "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
      "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
      "stream.kafka.broker.list": "kafka:9092",
      "realtime.segment.flush.threshold.rows": "0",
      "realtime.segment.flush.threshold.time": "24h",
      "realtime.segment.flush.threshold.segment.size": "50M",
      "stream.kafka.consumer.prop.auto.offset.reset": "smallest"
    }
  },
  "metadata": {
    "customConfigs": {}
  }
}

Uploading your schema and table config

Now that we have our table and schema, let's upload them to the cluster. As soon as the realtime table is created, it will begin ingesting from the Kafka topic.

docker run \
    --network=pinot-demo \
    -v /tmp/pinot-quick-start:/tmp/pinot-quick-start \
    --name pinot-streaming-table-creation \
    apachepinot/pinot:latest AddTable \
    -schemaFile /tmp/pinot-quick-start/transcript-schema.json \
    -tableConfigFile /tmp/pinot-quick-start/transcript-table-realtime.json \
    -controllerHost manual-pinot-controller \
    -controllerPort 9000 \
    -exec

bin/pinot-admin.sh AddTable \
    -schemaFile /tmp/pinot-quick-start/transcript-schema.json \
    -tableConfigFile /tmp/pinot-quick-start/transcript-table-realtime.json \
    -exec

Loading sample data into stream

Here's a JSON file for transcript table data:

{"studentID":205,"firstName":"Natalie","lastName":"Jones","gender":"Female","subject":"Maths","score":3.8,"timestampInEpoch":1571900400000}
{"studentID":205,"firstName":"Natalie","lastName":"Jones","gender":"Female","subject":"History","score":3.5,"timestampInEpoch":1571900400000}
{"studentID":207,"firstName":"Bob","lastName":"Lewis","gender":"Male","subject":"Maths","score":3.2,"timestampInEpoch":1571900400000}
{"studentID":207,"firstName":"Bob","lastName":"Lewis","gender":"Male","subject":"Chemistry","score":3.6,"timestampInEpoch":1572418800000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"Geography","score":3.8,"timestampInEpoch":1572505200000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"English","score":3.5,"timestampInEpoch":1572505200000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"Maths","score":3.2,"timestampInEpoch":1572678000000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"Physics","score":3.6,"timestampInEpoch":1572678000000}
{"studentID":211,"firstName":"John","lastName":"Doe","gender":"Male","subject":"Maths","score":3.8,"timestampInEpoch":1572678000000}
{"studentID":211,"firstName":"John","lastName":"Doe","gender":"Male","subject":"English","score":3.5,"timestampInEpoch":1572678000000}
{"studentID":211,"firstName":"John","lastName":"Doe","gender":"Male","subject":"History","score":3.2,"timestampInEpoch":1572854400000}
{"studentID":212,"firstName":"Nick","lastName":"Young","gender":"Male","subject":"History","score":3.6,"timestampInEpoch":1572854400000}

Push sample JSON into Kafka topic, using the Kafka script from the Kafka download

bin/kafka-console-producer.sh \
    --broker-list localhost:9876 \
    --topic transcript-topic < /tmp/pinot-quick-start/rawData/transcript.json

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 realtime data

In this guide we will learn about running Pinot in Docker.

This guide assumes that you have installed Docker and have configured it with enough memory. A sample config is shown below:

The latest Pinot Docker image is published at apachepinot/pinot:latest and you can see a list of all published tags on Docker Hub.

You can pull the Docker image onto your machine by running the following command:

docker pull apachepinot/pinot:latest

Or if you want to use a specific version:

docker pull apachepinot/pinot:0.9.3

Now that we've downloaded the Pinot Docker image, it's time to set up a cluster. There are two ways to do this:

Quick Start

Pinot comes with quick-start commands that launch instances of Pinot components in the same process and import pre-built datasets.

For example, the following quick-start launches Pinot with a baseball dataset pre-loaded:

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type batch

For a list of all the available quick starts, see the Quick Start Examples.

Manual Cluster

The quick start scripts launch Pinot with minimal resources. If you want to play with bigger datasets (more than a few MB), you can launch each of the Pinot components individually.

Docker

Create a Network

Create an isolated bridge network in docker

docker network create -d bridge pinot-demo

Start Zookeeper

Start Zookeeper in daemon mode. This is a single node zookeeper setup. Zookeeper is the central metadata store for Pinot and should be set up with replication for production use. For more information, see Running Replicated Zookeeper.

docker run \
    --network=pinot-demo \
    --name pinot-zookeeper \
    --restart always \
    -p 2181:2181 \
    -d zookeeper:3.5.6

Start Pinot Controller

Start Pinot Controller in daemon and connect to Zookeeper.

docker run --rm -ti \
    --network=pinot-demo \
    --name pinot-controller \
    -p 9000:9000 \
    -e JAVA_OPTS="-Dplugins.dir=/opt/pinot/plugins -Xms1G -Xmx4G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-controller.log" \
    -d ${PINOT_IMAGE} StartController \
    -zkAddress pinot-zookeeper:2181

Start Pinot Broker

Start Pinot Broker in daemon and connect to Zookeeper.

docker run --rm -ti \
    --network=pinot-demo \
    --name pinot-broker \
    -p 8099:8099 \
    -e JAVA_OPTS="-Dplugins.dir=/opt/pinot/plugins -Xms4G -Xmx4G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-broker.log" \
    -d ${PINOT_IMAGE} StartBroker \
    -zkAddress pinot-zookeeper:2181

Start Pinot Server

Start Pinot Server in daemon and connect to Zookeeper.

docker run --rm -ti \
    --network=pinot-demo \
    --name pinot-server \
    -p 8098:8098 \
    -e JAVA_OPTS="-Dplugins.dir=/opt/pinot/plugins -Xms4G -Xmx16G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-server.log" \
    -d ${PINOT_IMAGE} StartServer \
    -zkAddress pinot-zookeeper:2181

Start Kafka

Optionally, you can also start Kafka for setting up realtime streams. This brings up the Kafka broker on port 9092.

docker run --rm -ti \
    --network pinot-demo --name=kafka \
    -e KAFKA_ZOOKEEPER_CONNECT=pinot-zookeeper:2181/kafka \
    -e KAFKA_BROKER_ID=0 \
    -e KAFKA_ADVERTISED_HOST_NAME=kafka \
    -p 9092:9092 \
    -d bitnami/kafka:latest

Now all Pinot related components are started as an empty cluster.

You can run the below command to check container status.

docker container ls -a

Sample Console Output

CONTAINER ID        IMAGE                       COMMAND                  CREATED             STATUS              PORTS                                                  NAMES
9ec20e4463fa        bitnami/kafka:latest        "start-kafka.sh"         43 minutes ago      Up 43 minutes                                                              kafka
0775f5d8d6bf        apachepinot/pinot:latest    "./bin/pinot-admin.s…"   44 minutes ago      Up 44 minutes       8096-8099/tcp, 9000/tcp                                pinot-server
64c6392b2e04        apachepinot/pinot:latest    "./bin/pinot-admin.s…"   44 minutes ago      Up 44 minutes       8096-8099/tcp, 9000/tcp                                pinot-broker
b6d0f2bd26a3        apachepinot/pinot:latest    "./bin/pinot-admin.s…"   45 minutes ago      Up 45 minutes       8096-8099/tcp, 0.0.0.0:9000->9000/tcp                  pinot-quickstart
570416fc530e        zookeeper:3.5.6             "/docker-entrypoint.…"   45 minutes ago      Up 45 minutes       2888/tcp, 3888/tcp, 0.0.0.0:2181->2181/tcp, 8080/tcp   pinot-zookeeper

Docker Compose

Create a file called docker-compose.yml that contains the following:

version: '3.7'
services:
  pinot-zookeeper:
    image: zookeeper:3.5.6
    container_name: pinot-zookeeper
    ports:
      - "2181:2181"
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000
  pinot-controller:
    image: apachepinot/pinot:0.9.3
    command: "StartController -zkAddress pinot-zookeeper:2181"
    container_name: pinot-controller
    restart: unless-stopped
    ports:
      - "9000:9000"
    environment:
      JAVA_OPTS: "-Dplugins.dir=/opt/pinot/plugins -Xms1G -Xmx4G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-controller.log"
    depends_on:
      - pinot-zookeeper
  pinot-broker:
    image: apachepinot/pinot:0.9.3
    command: "StartBroker -zkAddress pinot-zookeeper:2181"
    restart: unless-stopped
    container_name: "pinot-broker"
    ports:
      - "8099:8099"
    environment:
      JAVA_OPTS: "-Dplugins.dir=/opt/pinot/plugins -Xms4G -Xmx4G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-broker.log"
    depends_on:
      - pinot-controller
  pinot-server:
    image: apachepinot/pinot:0.9.3
    command: "StartServer -zkAddress pinot-zookeeper:2181"
    restart: unless-stopped
    container_name: "pinot-server"
    ports:
      - "8098:8098"
    environment:
      JAVA_OPTS: "-Dplugins.dir=/opt/pinot/plugins -Xms4G -Xmx16G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:gc-pinot-server.log"
    depends_on:
      - pinot-broker

Run the following command to launch all the components:

docker-compose --project-name pinot-demo up

You can run the below command to check container status.

docker container ls 

Sample Console Output

CONTAINER ID   IMAGE                     COMMAND                  CREATED              STATUS              PORTS                                                                     NAMES
ba5cb0868350   apachepinot/pinot:0.9.3   "./bin/pinot-admin.s…"   About a minute ago   Up About a minute   8096-8099/tcp, 9000/tcp                                                   pinot-server
698f160852f9   apachepinot/pinot:0.9.3   "./bin/pinot-admin.s…"   About a minute ago   Up About a minute   8096-8098/tcp, 9000/tcp, 0.0.0.0:8099->8099/tcp, :::8099->8099/tcp        pinot-broker
b1ba8cf60d69   apachepinot/pinot:0.9.3   "./bin/pinot-admin.s…"   About a minute ago   Up About a minute   8096-8099/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp                  pinot-controller
54e7e114cd53   zookeeper:3.5.6           "/docker-entrypoint.…"   About a minute ago   Up About a minute   2888/tcp, 3888/tcp, 0.0.0.0:2181->2181/tcp, :::2181->2181/tcp, 8080/tcp   pinot-zookeeper

Once your cluster is up and running, you can head over to Exploring Pinot to learn how to run queries against the data.

Note: These are sample configs to be used as reference. For production setup, you may want to customize it to your needs.

The following quick start guides will show you how to run an Apache Pinot cluster using Kubernetes on different public cloud providers.

To use HDFS as deep storage you need to include HDFS dependency jars and plugins.

Server Setup

Configuration.

pinot.server.instance.enable.split.commit=true
pinot.server.storage.factory.class.hdfs=org.apache.pinot.plugin.filesystem.HadoopPinotFS
pinot.server.storage.factory.hdfs.hadoop.conf.path=/path/to/hadoop/conf/directory/
pinot.server.segment.fetcher.protocols=file,http,hdfs
pinot.server.segment.fetcher.hdfs.class=org.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
pinot.server.segment.fetcher.hdfs.hadoop.kerberos.principle=<your kerberos principal>
pinot.server.segment.fetcher.hdfs.hadoop.kerberos.keytab=<your kerberos keytab>
pinot.set.instance.id.to.hostname=true
pinot.server.instance.dataDir=/path/in/local/filesystem/for/pinot/data/server/index
pinot.server.instance.segmentTarDir=/path/in/local/filesystem/for/pinot/data/server/segment
pinot.server.grpc.enable=true
pinot.server.grpc.port=8090

Executable.

export HADOOP_HOME=/path/to/hadoop/home
export HADOOP_VERSION=2.7.1
export HADOOP_GUAVA_VERSION=11.0.2
export HADOOP_GSON_VERSION=2.2.4
export GC_LOG_LOCATION=/path/to/gc/log/file
export PINOT_VERSION=0.10.0
export PINOT_DISTRIBUTION_DIR=/path/to/apache-pinot-${PINOT_VERSION}-bin/
export SERVER_CONF_DIR=/path/to/pinot/conf/dir/
export ZOOKEEPER_ADDRESS=localhost:2181


export CLASSPATH_PREFIX="${HADOOP_HOME}/share/hadoop/hdfs/hadoop-hdfs-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-annotations-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-auth-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/hadoop-common-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/guava-${HADOOP_GUAVA_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/gson-${HADOOP_GSON_VERSION}.jar"
export JAVA_OPTS="-Xms4G -Xmx16G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:${GC_LOG_LOCATION}/gc-pinot-server.log"
${PINOT_DISTRIBUTION_DIR}/bin/start-server.sh  -zkAddress ${ZOOKEEPER_ADDRESS} -configFileName ${SERVER_CONF_DIR}/server.conf

Controller Setup

Configuration.

controller.data.dir=hdfs://path/in/hdfs/for/controller/segment
controller.local.temp.dir=/tmp/pinot/
controller.zk.str=<ZOOKEEPER_HOST:ZOOKEEPER_PORT>
controller.enable.split.commit=true
controller.access.protocols.http.port=9000
controller.helix.cluster.name=PinotCluster
pinot.controller.storage.factory.class.hdfs=org.apache.pinot.plugin.filesystem.HadoopPinotFS
pinot.controller.storage.factory.hdfs.hadoop.conf.path=/path/to/hadoop/conf/directory/
pinot.controller.segment.fetcher.protocols=file,http,hdfs
pinot.controller.segment.fetcher.hdfs.class=org.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
pinot.controller.segment.fetcher.hdfs.hadoop.kerberos.principle=<your kerberos principal>
pinot.controller.segment.fetcher.hdfs.hadoop.kerberos.keytab=<your kerberos keytab>
controller.vip.port=9000
controller.port=9000
pinot.set.instance.id.to.hostname=true
pinot.server.grpc.enable=true

Executable.

export HADOOP_HOME=/path/to/hadoop/home
export HADOOP_VERSION=2.7.1
export HADOOP_GUAVA_VERSION=11.0.2
export HADOOP_GSON_VERSION=2.2.4
export GC_LOG_LOCATION=/path/to/gc/log/file
export PINOT_VERSION=0.10.0
export PINOT_DISTRIBUTION_DIR=/path/to/apache-pinot-${PINOT_VERSION}-bin/
export SERVER_CONF_DIR=/path/to/pinot/conf/dir/
export ZOOKEEPER_ADDRESS=localhost:2181


export CLASSPATH_PREFIX="${HADOOP_HOME}/share/hadoop/hdfs/hadoop-hdfs-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-annotations-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-auth-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/hadoop-common-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/guava-${HADOOP_GUAVA_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/gson-${HADOOP_GSON_VERSION}.jar"
export JAVA_OPTS="-Xms8G -Xmx12G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:${GC_LOG_LOCATION}/gc-pinot-controller.log"
${PINOT_DISTRIBUTION_DIR}/bin/start-controller.sh -configFileName ${SERVER_CONF_DIR}/controller.conf

Broker Setup

Configuration.

pinot.set.instance.id.to.hostname=true
pinot.server.grpc.enable=true

Executable.

export HADOOP_HOME=/path/to/hadoop/home
export HADOOP_VERSION=2.7.1
export HADOOP_GUAVA_VERSION=11.0.2
export HADOOP_GSON_VERSION=2.2.4
export GC_LOG_LOCATION=/path/to/gc/log/file
export PINOT_VERSION=0.10.0
export PINOT_DISTRIBUTION_DIR=/path/to/apache-pinot-${PINOT_VERSION}-bin/
export SERVER_CONF_DIR=/path/to/pinot/conf/dir/
export ZOOKEEPER_ADDRESS=localhost:2181


export CLASSPATH_PREFIX="${HADOOP_HOME}/share/hadoop/hdfs/hadoop-hdfs-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-annotations-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-auth-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/hadoop-common-${HADOOP_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/guava-${HADOOP_GUAVA_VERSION}.jar:${HADOOP_HOME}/share/hadoop/common/lib/gson-${HADOOP_GSON_VERSION}.jar"
export JAVA_OPTS="-Xms4G -Xmx4G -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -Xloggc:${GC_LOG_LOCATION}/gc-pinot-broker.log"
${PINOT_DISTRIBUTION_DIR}/bin/start-broker.sh -zkAddress ${ZOOKEEPER_ADDRESS} -configFileName  ${SERVER_CONF_DIR}/broker.conf

This document provides the basic instruction to set up a Kubernetes Cluster on Amazon Elastic Kubernetes Service (Amazon EKS)

1. Tooling Installation

1.1 Install Kubectl

Please follow this link (https://kubernetes.io/docs/tasks/tools/install-kubectl) to install kubectl.

For Mac User

brew install kubernetes-cli

Please check kubectl version after installation.

kubectl version

1.2 Install Helm

Please follow this link (https://helm.sh/docs/using_helm/#installing-helm) to install helm.

For Mac User

brew install kubernetes-helm

Please check helm version after installation.

helm version

1.3 Install AWS CLI

Please follow this link (https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html#install-tool-bundled) to install AWS CLI.

For Mac User

curl "https://d1vvhvl2y92vvt.cloudfront.net/awscli-exe-macos.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

1.4 Install Eksctl

Please follow this link (https://docs.aws.amazon.com/eks/latest/userguide/eksctl.html#installing-eksctl) to install AWS CLI.

For Mac User

brew tap weaveworks/tap
brew install weaveworks/tap/eksctl

2. (Optional) Login to your AWS account.

For first time AWS user, please register your account at https://aws.amazon.com/.

Once created the account, you can go to AWS Identity and Access Management (IAM) to create a user and create access keys under Security Credential tab.

aws configure

3. (Optional) Create a Kubernetes cluster(EKS) in AWS

The script below will create a 1 node cluster named pinot-quickstart in us-west-2 with a t3.xlarge machine for demo purposes:

EKS_CLUSTER_NAME=pinot-quickstart
eksctl create cluster \
--name ${EKS_CLUSTER_NAME} \
--version 1.16 \
--region us-west-2 \
--nodegroup-name standard-workers \
--node-type t3.xlarge \
--nodes 1 \
--nodes-min 1 \
--nodes-max 1

You can monitor the cluster status via this command:

EKS_CLUSTER_NAME=pinot-quickstart
aws eks describe-cluster --name ${EKS_CLUSTER_NAME} --region us-west-2

Once the cluster is in ACTIVE status, it's ready to be used.

4. Connect to an existing cluster

Simply run below command to get the credential for the cluster pinot-quickstart that you just created or your existing cluster.

EKS_CLUSTER_NAME=pinot-quickstart
aws eks update-kubeconfig --name ${EKS_CLUSTER_NAME}

To verify the connection, you can run:

kubectl get nodes

5. Pinot Quickstart

Please follow this Kubernetes QuickStart to deploy your Pinot Demo.

6. Delete a Kubernetes Cluster

EKS_CLUSTER_NAME=pinot-quickstart
aws eks delete-cluster --name ${EKS_CLUSTER_NAME}

Pinot ships with QuickStart commands that launch Pinot components in a single process and import pre-built datasets. These QuickStarts are a good place if you're just getting started with Pinot.

Failed to start a Pinot [SERVER]
java.lang.RuntimeException: java.net.BindException: Address already in use
	at org.apache.pinot.core.transport.QueryServer.start(QueryServer.java:103) ~[pinot-all-0.9.0-jar-with-dependencies.jar:0.9.0-cf8b84e8b0d6ab62374048de586ce7da21132906]
	at org.apache.pinot.server.starter.ServerInstance.start(ServerInstance.java:158) ~[pinot-all-0.9.0-jar-with-dependencies.jar:0.9.0-cf8b84e8b0d6ab62374048de586ce7da21132906]
	at org.apache.helix.manager.zk.ParticipantManager.handleNewSession(ParticipantManager.java:110) ~[pinot-all-0.9.0-jar-with-dependencies.jar:0.9.0-cf8b84e8b0d6ab62374048de586ce7da2113

Batch

This example demonstrates how to do batch processing with Pinot. The command:

• Starts Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates the baseballStats table

• Launches a standalone data ingestion job that builds one segment for a given CSV data file for the baseballStats table and pushes the segment to the Pinot Controller.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type batch

./bin/pinot-admin.sh QuickStart -type batch

Batch JSON

This example demonstrates how to import and query JSON documents in Pinot. The command:

• Starts Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates the githubEvents table

• Launches a standalone data ingestion job that builds one segment for a given JSON data file for the githubEvents table and pushes the segment to the Pinot Controller.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type batch_json_index

./bin/pinot-admin.sh QuickStart -type batch_json_index

Batch with complex data types

This example demonstrates how to do batch processing in Pinot where the the data items have complex fields that need to be unnested. The command:

• Starts Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates the githubEvents table

• Launches a standalone data ingestion job that builds one segment for a given JSON data file for the githubEvents table and pushes the segment to the Pinot Controller.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type batch_json_index

./bin/pinot-admin.sh QuickStart -type batch_json_index

Streaming

This example demonstrates how to do stream processing with Pinot. The command:

• Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates meetupRsvp table

• Launches a meetup stream

• Publishes data to a Kafka topic meetupRSVPEvents that is subscribed to by Pinot.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type stream

./bin/pinot-admin.sh QuickStart -type stream

Streaming JSON

This example demonstrates how to do stream processing with JSON documents in Pinot. The command:

• Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates meetupRsvp table

• Launches a meetup stream

• Publishes data to a Kafka topic meetupRSVPEvents that is subscribed to by Pinot

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type stream_json_index

./bin/pinot-admin.sh QuickStart -type stream_json_index

Streaming with minion cleanup

This example demonstrates how to do stream processing in Pinot with RealtimeToOfflineSegmentsTask and MergeRollupTask minion tasks continuously optimizing segments as data gets ingested. The command:

• Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, Pinot Minion, and Pinot Server.

• Creates githubEvents table

• Launches a GitHub events stream

• Publishes data to a Kafka topic githubEvents that is subscribed to by Pinot.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type realtime_minion

./bin/pinot-admin.sh QuickStart -type realtime_minion

Streaming with complex data types

This example demonstrates how to do stream processing in Pinot where the stream contains items that have complex fields that need to be unnested. The command:

• Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, Pinot Minion, and Pinot Server.

• Creates meetupRsvp table

• Launches a meetup stream

• Publishes data to a Kafka topic meetupRSVPEvents that is subscribed to by Pinot.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type stream_complex_type

./bin/pinot-admin.sh QuickStart -type stream_complex_type

Upsert

This example demonstrates how to do stream processing with upsert with Pinot. The command:

• Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates meetupRsvp table

• Launches a meetup stream

• Publishes data to a Kafka topic meetupRSVPEvents that is subscribed to by Pinot

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type upsert

./bin/pinot-admin.sh QuickStart -type upsert

Upsert JSON

This example demonstrates how to do stream processing with upsert with JSON documents in Pinot. The command:

• Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

• Creates meetupRsvp table

• Launches a meetup stream

• Publishes data to a Kafka topic meetupRSVPEvents that is subscribed to by Pinot

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type upsert_json_index

./bin/pinot-admin.sh QuickStart -type upsert_json_index

Hybrid

This example demonstrates how to do hybrid stream and batch processing with Pinot. The command:

1. Starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server.

2. Creates airlineStats table

3. Launches a standalone data ingestion job that builds segments under a given directory of Avro files for the airlineStats table and pushes the segments to the Pinot Controller.

4. Launches a stream of flights stats

5. Publishes data to a Kafka topic airlineStatsEvents that is subscribed to by Pinot.

6. Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type hybrid

./bin/pinot-admin.sh QuickStart -type hybrid

Join

This example demonstrates how to do joins in Pinot using the Lookup UDF. The command:

• Starts Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server in the same container.

• Creates the baseballStats table

• Launches a data ingestion job that builds one segment for a given CSV data file for the baseballStats table and pushes the segment to the Pinot Controller.

• Creates the dimBaseballTeams table

• Launches a data ingestion job that builds one segment for a given CSV data file for the dimBaseballStats table and pushes the segment to the Pinot Controller.

• Issues sample queries to Pinot

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.9.3 QuickStart \
    -type join

./bin/pinot-admin.sh QuickStart -type join

How does Pinot use deep storage?

When data is pushed in to Pinot, it makes a backup copy of the data and stores it on the configured deep-storage (S3/GCP/ADLS/NFS/etc). This copy is stored as tar.gz Pinot segments. Note, that pinot servers keep a (untarred) copy of the segments on their local disk as well. This is done for performance reasons.

How does Pinot use Zookeeper?

Pinot uses Apache Helix for cluster management, which in turn is built on top of Zookeeper. Helix uses Zookeeper to store the cluster state, including Ideal State, External View, Participants, etc. Besides that, Pinot uses Zookeeper to store other information such as Table configs, schema, Segment Metadata, etc.

Why am I getting "Could not find or load class" error when running Quickstart using 0.8.0 release?

Please check the JDK version you are using. The release 0.8.0 binary is on JDK 11. You may be getting this error if you are using JDK8. In that case, please consider using JDK11, or you will need to download the source code for the release and build it locally.

1. Prerequisites

2. Setting up a Pinot cluster in Kubernetes

Before continuing, please make sure that you've downloaded Apache Pinot. The scripts for the setup in this guide can be found in our open source project on GitHub.

The scripts can be found in the Pinot source at ./pinot/kubernetes/helm

# checkout pinot
git clone https://github.com/apache/pinot.git
cd pinot/kubernetes/helm

2.1 Start Pinot with Helm

helm repo add pinot https://raw.githubusercontent.com/apache/pinot/master/kubernetes/helm
kubectl create ns pinot-quickstart
helm install pinot pinot/pinot \
    -n pinot-quickstart \
    --set cluster.name=pinot \
    --set server.replicaCount=2

helm dependency update

helm init --service-account tiller

helm install --namespace "pinot-quickstart" --name "pinot" pinot

kubectl create ns pinot-quickstart
helm install -n pinot-quickstart pinot pinot

Error: could not find tiller.

kubectl -n kube-system delete deployment tiller-deploy
kubectl -n kube-system delete service/tiller-deploy
helm init --service-account tiller

kubectl apply -f helm-rbac.yaml

2.2 Check Pinot deployment status

kubectl get all -n pinot-quickstart

3. Load data into Pinot using Kafka

3.1 Bring up a Kafka cluster for real-time data ingestion

helm repo add kafka https://charts.bitnami.com/bitnami
helm install -n pinot-quickstart kafka kafka/kafka --set replicas=1,zookeeper.image.tag=latest

helm repo add incubator https://charts.helm.sh/incubator
helm install --namespace "pinot-quickstart"  --name kafka incubator/kafka --set zookeeper.image.tag=latest 

3.2 Check Kafka deployment status

kubectl get all -n pinot-quickstart | grep kafka

Ensure the Kafka deployment is ready before executing the scripts in the following next steps.

pod/kafka-0                                                 1/1     Running     0          2m
pod/kafka-zookeeper-0                                       1/1     Running     0          10m
pod/kafka-zookeeper-1                                       1/1     Running     0          9m
pod/kafka-zookeeper-2                                       1/1     Running     0          8m

3.3 Create Kafka topics

The scripts below will create two Kafka topics for data ingestion:

kubectl -n pinot-quickstart exec kafka-0 -- kafka-topics.sh --bootstrap-server kafka-0:9092 --topic flights-realtime --create --partitions 1 --replication-factor 1
kubectl -n pinot-quickstart exec kafka-0 -- kafka-topics.sh --bootstrap-server kafka-0:9092 --topic flights-realtime-avro --create --partitions 1 --replication-factor 1

3.4 Load data into Kafka and create Pinot schema/tables

The script below will deploy 3 batch jobs.

• Ingest 19492 JSON messages to Kafka topic flights-realtime at a speed of 1 msg/sec

• Ingest 19492 Avro messages to Kafka topic flights-realtime-avro at a speed of 1 msg/sec

• Upload Pinot schema airlineStats

• Create Pinot table airlineStats to ingest data from JSON encoded Kafka topic flights-realtime

• Create Pinot table airlineStatsAvro to ingest data from Avro encoded Kafka topic flights-realtime-avro

kubectl apply -f pinot/pinot-realtime-quickstart.yml

4. Query using Pinot Data Explorer

4.1 Pinot Data Explorer

Please use the script below to perform local port-forwarding, which will also open Pinot query console in your default web browser.

This script can be found in the Pinot source at ./pinot/kubernetes/helm/pinot

./query-pinot-data.sh

5. Using Superset to query Pinot

5.1 Bring up Superset using helm

Install SuperSet Helm Repo

helm repo add superset https://apache.github.io/superset

Get Helm values config file:

helm inspect values superset/superset > /tmp/superset-values.yaml

Edit /tmp/superset-values.yaml file and add pinotdb pip dependency into bootstrapScript field, so Superset will install pinot dependencies during bootstrap time.

You can also build your own image with this dependency or just use image: apachepinot/pinot-superset:latest instead.

Also remember to change the admin credential inside the init section with meaningful user profile and stronger password.

Install Superset using helm

kubectl create ns superset
helm upgrade --install --values /tmp/superset-values.yaml superset superset/superset -n superset

Ensure your cluster is up by running:

kubectl get all -n superset

5.2 Access Superset UI

You can run the below command to port forward superset to your localhost:18088. Then you can navigate superset in your browser with the previous set admin credential.

kubectl port-forward service/superset 18088:8088 -n superset

Create Pinot Database using URI:

pinot+http://pinot-broker.pinot-quickstart:8099/query?controller=http://pinot-controller.pinot-quickstart:9000/

Once the database is added, you can add more data sets and explore the dashboarding.

6. Access Pinot using Trino

6.1 Deploy Trino

You can run the command below to deploy Trino with the Pinot plugin installed.

helm repo add trino https://trinodb.github.io/charts/

The above command adds Trino HelmChart repo. You can then run the below command to see the charts.

helm search repo trino

In order to connect Trino to Pinot, we need to add Pinot catalog, which requires extra configurations. You can run the below command to get all the configurable values.

helm inspect values trino/trino > /tmp/trino-values.yaml

To add Pinot catalog, you can edit the additionalCatalogs section by adding:

additionalCatalogs:
  pinot: |
    connector.name=pinot
    pinot.controller-urls=pinot-controller.pinot-quickstart:9000

After modifying the /tmp/trino-values.yaml file, you can deploy Trino with:

kubectl create ns trino-quickstart
helm install my-trino trino/trino --version 0.2.0 -n trino-quickstart --values /tmp/trino-values.yaml

Once you deployed the Trino, You can check Trino deployment status by:

kubectl get pods -n trino-quickstart

6.2 Query Trino using Trino CLI

Once Trino is deployed, you can run the below command to get a runnable Trino CLI.

6.2.1 Download Trino CLI

curl -L https://repo1.maven.org/maven2/io/trino/trino-cli/363/trino-cli-363-executable.jar -o /tmp/trino && chmod +x /tmp/trino

6.2.2 Port forward Trino service to your local if it's not already exposed

echo "Visit http://127.0.0.1:18080 to use your application"
kubectl port-forward service/my-trino 18080:8080 -n trino-quickstart

6.2.3 Use Trino console client to connect to Trino service

/tmp/trino --server localhost:18080 --catalog pinot --schema default

6.2.4 Query Pinot data using Trino CLI

6.3 Sample queries to execute

• List all catalogs

trino:default> show catalogs;

  Catalog
---------
 pinot
 system
 tpcds
 tpch
(4 rows)

Query 20211025_010256_00002_mxcvx, FINISHED, 2 nodes
Splits: 36 total, 36 done (100.00%)
0.70 [0 rows, 0B] [0 rows/s, 0B/s]

• List All tables

trino:default> show tables;

    Table
--------------
 airlinestats
(1 row)

Query 20211025_010326_00003_mxcvx, FINISHED, 3 nodes
Splits: 36 total, 36 done (100.00%)
0.28 [1 rows, 29B] [3 rows/s, 104B/s]

• Show schema

trino:default> DESCRIBE airlinestats;

        Column        |      Type      | Extra | Comment
----------------------+----------------+-------+---------
 flightnum            | integer        |       |
 origin               | varchar        |       |
 quarter              | integer        |       |
 lateaircraftdelay    | integer        |       |
 divactualelapsedtime | integer        |       |
 divwheelsons         | array(integer) |       |
 divwheelsoffs        | array(integer) |       |
......

Query 20211025_010414_00006_mxcvx, FINISHED, 3 nodes
Splits: 36 total, 36 done (100.00%)
0.37 [79 rows, 5.96KB] [212 rows/s, 16KB/s]

• Count total documents

trino:default> select count(*) as cnt from airlinestats limit 10;

 cnt
------
 9746
(1 row)

Query 20211025_015607_00009_mxcvx, FINISHED, 2 nodes
Splits: 17 total, 17 done (100.00%)
0.24 [1 rows, 9B] [4 rows/s, 38B/s]

7. Access Pinot using Presto

7.1 Deploy Presto using Pinot plugin

You can run the command below to deploy a customized Presto with the Pinot plugin installed.

helm install presto pinot/presto -n pinot-quickstart

kubectl apply -f presto-coordinator.yaml

The above command deploys Presto with default configs. For customizing your deployment, you can run the below command to get all the configurable values.

helm inspect values pinot/presto > /tmp/presto-values.yaml

After modifying the /tmp/presto-values.yaml file, you can deploy Presto with:

helm install presto pinot/presto -n pinot-quickstart --values /tmp/presto-values.yaml

Once you deployed the Presto, You can check Presto deployment status by:

kubectl get pods -n pinot-quickstart

7.2 Query Presto using Presto CLI

Once Presto is deployed, you can run the below command from here, or just follow steps 6.2.1 to 6.2.3.

./pinot-presto-cli.sh

6.2.1 Download Presto CLI

curl -L https://repo1.maven.org/maven2/com/facebook/presto/presto-cli/0.246/presto-cli-0.246-executable.jar -o /tmp/presto-cli && chmod +x /tmp/presto-cli

6.2.2 Port forward presto-coordinator port 8080 to localhost port 18080

kubectl port-forward service/presto-coordinator 18080:8080 -n pinot-quickstart> /dev/null &

6.2.3 Start Presto CLI with pinot catalog to query it then query it

/tmp/presto-cli --server localhost:18080 --catalog pinot --schema default

6.2.4 Query Pinot data using Presto CLI

7.3 Sample queries to execute

• List all catalogs

presto:default> show catalogs;

 Catalog
---------
 pinot
 system
(2 rows)

Query 20191112_050827_00003_xkm4g, FINISHED, 1 node
Splits: 19 total, 19 done (100.00%)
0:01 [0 rows, 0B] [0 rows/s, 0B/s]

• List All tables

presto:default> show tables;

    Table
--------------
 airlinestats
(1 row)

Query 20191112_050907_00004_xkm4g, FINISHED, 1 node
Splits: 19 total, 19 done (100.00%)
0:01 [1 rows, 29B] [1 rows/s, 41B/s]

• Show schema

presto:default> DESCRIBE pinot.dontcare.airlinestats;

        Column        |  Type   | Extra | Comment
----------------------+---------+-------+---------
 flightnum            | integer |       |
 origin               | varchar |       |
 quarter              | integer |       |
 lateaircraftdelay    | integer |       |
 divactualelapsedtime | integer |       |
......

Query 20191112_051021_00005_xkm4g, FINISHED, 1 node
Splits: 19 total, 19 done (100.00%)
0:02 [80 rows, 6.06KB] [35 rows/s, 2.66KB/s]

• Count total documents

presto:default> select count(*) as cnt from pinot.dontcare.airlinestats limit 10;

 cnt
------
 9745
(1 row)

Query 20191112_051114_00006_xkm4g, FINISHED, 1 node
Splits: 17 total, 17 done (100.00%)
0:00 [1 rows, 8B] [2 rows/s, 19B/s]

8. Deleting the Pinot cluster in Kubernetes

kubectl delete ns pinot-quickstart

Note: These are sample configs to be used as reference. For production setup, you may want to customize it to your needs.

Memory

How much heap should I allocate for my Pinot instances?

Typically, Pinot components try to use as much off-heap (MMAP/DirectMemory) wherever possible. For example, Pinot servers load segments in memory-mapped files in MMAP mode (recommended), or direct memory in HEAP mode. Heap memory is used mostly for query execution and storing some metadata. We have seen production deployments with high throughput and low-latency work well with just 16 GB of heap for Pinot servers and brokers. Pinot controller may also cache some metadata (table configs etc) in heap, so if there are just a few tables in the Pinot cluster, a few GB of heap should suffice.

DR

Does Pinot provide any backup/restore mechanism?

Pinot relies on deep-storage for storing backup copy of segments (offline as well as realtime). It relies on Zookeeper to store metadata (table configs, schema, cluster state, etc). It does not explicitly provide tools to take backups or restore these data, but relies on the deep-storage (ADLS/S3/GCP/etc), and ZK to persist these data/metadata.

Alter Table

Can I change a column name in my table, without losing data?

Changing a column name or data type is considered backward incompatible change. While Pinot does support schema evolution for backward compatible changes, it does not support backward incompatible changes like changing name/data-type of a column.

How to change number of replicas of a table?

You can change the number of replicas by updating the table config's segmentsConfig section. Make sure you have at least as many servers as the replication.

For OFFLINE table, update replication

{ 
    "tableName": "pinotTable", 
    "tableType": "OFFLINE", 
    "segmentsConfig": {
      "replication": "3", 
      ... 
    }
    ..

For REALTIME table update replicasPerPartition

{ 
    "tableName": "pinotTable", 
    "tableType": "REALTIME", 
    "segmentsConfig": {
      "replicasPerPartition": "3", 
      ... 
    }
    ..

After changing the replication, run a table rebalance.

Rebalance

How to run a rebalance on a table?

Refer to Rebalance.

Why does my REALTIME table not use the new nodes I added to the cluster?

Likely explanation: num partitions * num replicas < num servers

In realtime tables, segments of the same partition always continue to remain on the same node. This sticky assignment is needed for replica groups and is critical if using upserts. For instance, if you have 3 partitions, 1 replica, and 4 nodes, only ¾ nodes will be used, and all of p0 segments will be on 1 node, p1 on 1 node, and p2 on 1 node. One server will be unused, and will remain unused through rebalances.

There’s nothing we can do about CONSUMING segments, they will continue to use only 3 nodes if you have 3 partitions. But we can rebalance such that completed segments use all nodes. If you want to force the completed segments of the table to use the new server, use this config

"instanceAssignmentConfigMap": {
      "COMPLETED": {
        "tagPoolConfig": {
          "tag": "DefaultTenant_OFFLINE"
        },
        "replicaGroupPartitionConfig": {
        }
      }
    },

Segments

How to control number of segments generated?

The number of segments generated depends on the number of input files. If you provide only 1 input file, you will get 1 segment. If you break up the input file into multiple files, you will get as many segments as the input files.

What are the common reasons my segment is in a BAD state ?

This typically happens when the server is unable to load the segment. Possible causes: Out-Of-Memory, no-disk space, unable to download segment from deep-store, and similar other errors. Please check server logs for more information.

How to reset a segment when it runs into a BAD state?

Use the segment reset controller REST API to reset the segment:

curl -X POST "{host}/segments/{tableNameWithType}/{segmentName}/reset"

How to pause realtime ingestion?

Refer to Pause Stream Ingestion.

What's the difference to Reset, Refresh, or Reload a segment?

RESET: this gets a segment in ERROR state back to ONLINE or CONSUMING state. Behind the scenes, Pinot controller takes the segment to OFFLINE state, waits for External View to stabilize, and then moves it back to ONLINE/CONSUMING state, thus effectively resetting segments or consumers in error states.

REFRESH: this replaces the segment with a new one, with the same name but often different data. Under the hood, Pinot controller sets new segment metadata in Zookeeper, and notifies brokers and servers to check their local states about this segment and update accordingly. Servers also download the new segment to replace the old one, when both have different checksums. There is no separate rest API for refreshing, and it is done as part of SegmentUpload API today.

RELOAD: this reloads the segment, often to generate a new index as updated in table config. Underlying, Pinot server gets the new table config from Zookeeper, and uses it to guide the segment reloading. In fact, the last step of REFRESH as explained above is to load the segment into memory to serve queries. There is a dedicated rest API for reloading. By default, it doesn't download segment. But option is provided to force server to download segment to replace the local one cleanly.

In addition, RESET brings the segment OFFLINE temporarily; while REFRESH and RELOAD swap the segment on server atomically without bringing down the segment or affecting ongoing queries.

Tenants

How can I make brokers/servers join the cluster without the DefaultTenant tag?

Set this property in your controller.conf file

cluster.tenant.isolation.enable=false

Now your brokers and servers should join the cluster as broker_untagged and server_untagged . You can then directly use the POST /tenants API to create the desired tenants

curl -X POST "http://localhost:9000/tenants" 
-H "accept: application/json" 
-H "Content-Type: application/json" 
-d "{\"tenantRole\":\"BROKER\",\"tenantName\":\"foo\",\"numberOfInstances\":1}"

Minion

How to tune minion task timeout and parallelism on each worker

There are two task configs but set as part of cluster configs like below. One controls task's overall timeout (1hr by default) and one for how many tasks to run on a single minion worker (1 by default). The <taskType> is the task to tune, e.g. MergeRollupTask or RealtimeToOfflineSegmentsTask etc.

Using "POST /cluster/configs" API on CLUSTER tab in Swagger, with this payload
{
	"<taskType>.timeoutMs": "600000",
	"<taskType>.numConcurrentTasksPerInstance": "4"
}

How to I manually run a Periodic Task

Refer to Running a Periodic Task Manually

Tuning and Optimizations

Do replica groups work for real-time?

Yes, replica groups work for realtime. There's 2 parts to enabling replica groups:

1. Replica groups segment assignment

2. Replica group query routing

Replica group segment assignment

Replica group segment assignment is achieved in realtime, if number of servers is a multiple of number of replicas. The partitions get uniformly sprayed across the servers, creating replica groups. For example, consider we have 6 partitions, 2 replicas, and 4 servers.

As you can see, the set (S0, S2) contains r1 of every partition, and (s1, S3) contains r2 of every partition. The query will only be routed to one of the sets, and not span every server. If you are are adding/removing servers from an existing table setup, you have to run rebalance for segment assignment changes to take effect.

Replica group query routing

Once replica group segment assignment is in effect, the query routing can take advantage of it. For replica group based query routing, set the following in the table config's routing section, and then restart brokers

{
    "tableName": "pinotTable", 
    "tableType": "REALTIME",
    "routing": {
        "instanceSelectorType": "replicaGroup"
    }
    ..
}

Running Pinot

To simplify the getting started experience, Pinot ships with quick start guides that launch Pinot components in a single process and import pre-built datasets.

For a full list of these guides, see Quick Start Examples.

Deploy to a public cloud

Data import examples

Getting data into Pinot is easy. Take a look at these two quick start guides which will help you get up and running with sample data for offline and real-time tables.

Is there any debug information available in Pinot?

Pinot offers various ways to assist with troubleshooting and debugging problems that might happen. It is recommended to start off with the debug api which may quickly surface some of the commonly occurring problems. The debug api provides information such as tableSize, ingestion status, any error messages related to state transition in server, among other things.

The table debug api can be invoked via the Swagger UI as follows:

It can also be invoked directly by accessing the URL as follows. The api requires the tableName, and can optionally take tableType (offline|realtime) and verbosity level.

curl -X GET "http://localhost:9000/debug/tables/airlineStats?verbosity=0" -H "accept: application/json"

Pinot also provides a wide-variety of operational metrics that can be used for creating dashboards, alerting and monitoring. Also, all pinot components log debug information related to error conditions that can be used for troubleshooting.

How do I debug a slow query or a query which keeps timing out

Please use these steps:

1. If the query executes, look at the query result. Specifically look at numEntriesScannedInFilter and numDocsScanned.

   1. If numEntriesScannedInFilter is very high, consider adding indexes for the corresponding columns being used in the filter predicates. You should also think about partitioning the incoming data based on the dimension most heavily used in your filter queries.

   2. If numDocsScanned is very high, that means the selectivity for the query is low and lots of documents need to be processed after the filtering. Consider refining the filter to increase the selectivity of the query.

2. If the query is not executing, you can extend the query timeout by appending a timeoutMs parameter to the query (eg: select * from mytable limit 10 option(timeoutMs=60000)). Then you can repeat step 1.

3. You can also look at GC stats for the corresponding Pinot servers. If a particular server seems to be running full GC all the time, you can do a couple of things such as

   1. Increase JVM heap (Xmx)

   2. Consider using off-heap memory for segments

   3. Decrease the total number of segments per server (by partitioning the data in a better way)

So far, we have set up our cluster, ran some queries, and explored the admin endpoints. Now, it's time to get our own data into Pinot. The rest of the instructions assume you're using Pinot in Docker.

Preparing your data

Let's gather our data files and put them in pinot-quick-start/rawdata.

mkdir -p /tmp/pinot-quick-start/rawdata

Supported file formats are CSV, JSON, AVRO, PARQUET, THRIFT, ORC. If you don't have sample data, you can use this sample CSV.

studentID,firstName,lastName,gender,subject,score,timestampInEpoch
200,Lucy,Smith,Female,Maths,3.8,1570863600000
200,Lucy,Smith,Female,English,3.5,1571036400000
201,Bob,King,Male,Maths,3.2,1571900400000
202,Nick,Young,Male,Physics,3.6,1572418800000

Creating a schema

Schema is used to define the columns and data types of the Pinot table. A detailed overview of the schema can be found in Schema.

Briefly, we categorize our columns into 3 types

For example, in our sample table, the studentID,firstName,lastName,gender,subject columns are the dimensions, the score column is the metric and timestampInEpoch is the time column.

Once you have identified the dimensions, metrics and time columns, create a schema for your data, using the reference below.

{
  "schemaName": "transcript",
  "dimensionFieldSpecs": [
    {
      "name": "studentID",
      "dataType": "INT"
    },
    {
      "name": "firstName",
      "dataType": "STRING"
    },
    {
      "name": "lastName",
      "dataType": "STRING"
    },
    {
      "name": "gender",
      "dataType": "STRING"
    },
    {
      "name": "subject",
      "dataType": "STRING"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "score",
      "dataType": "FLOAT"
    }
  ],
  "dateTimeFieldSpecs": [{
    "name": "timestampInEpoch",
    "dataType": "LONG",
    "format" : "1:MILLISECONDS:EPOCH",
    "granularity": "1:MILLISECONDS"
  }]
}

Creating a table config

A table config is used to define the config related to the Pinot table. A detailed overview of the table can be found in Table.

Here's the table config for the sample CSV file. You can use this as a reference to build your own table config. Simply edit the tableName and schemaName.

{
  "tableName": "transcript",
  "segmentsConfig" : {
    "timeColumnName": "timestampInEpoch",
    "timeType": "MILLISECONDS",
    "replication" : "1",
    "schemaName" : "transcript"
  },
  "tableIndexConfig" : {
    "invertedIndexColumns" : [],
    "loadMode"  : "MMAP"
  },
  "tenants" : {
    "broker":"DefaultTenant",
    "server":"DefaultTenant"
  },
  "tableType":"OFFLINE",
  "metadata": {}
}

Uploading your table config and schema

Check the directory structure so far

$ ls /tmp/pinot-quick-start
rawdata			transcript-schema.json	transcript-table-offline.json

$ ls /tmp/pinot-quick-start/rawdata 
transcript.csv

Upload the table config using the following command

docker run --rm -ti \
    --network=pinot-demo \
    -v /tmp/pinot-quick-start:/tmp/pinot-quick-start \
    --name pinot-batch-table-creation \
    apachepinot/pinot:latest AddTable \
    -schemaFile /tmp/pinot-quick-start/transcript-schema.json \
    -tableConfigFile /tmp/pinot-quick-start/transcript-table-offline.json \
    -controllerHost manual-pinot-controller \
    -controllerPort 9000 -exec

bin/pinot-admin.sh AddTable \
  -tableConfigFile /tmp/pinot-quick-start/transcript-table-offline.json \
  -schemaFile /tmp/pinot-quick-start/transcript-schema.json -exec

Check out the table config and schema in the Rest API to make sure it was successfully uploaded.

Creating a segment

A Pinot table's data is stored as Pinot segments. A detailed overview of the segment can be found in Segment.

To generate a segment, we need to first create a job spec yaml file. JobSpec yaml file has all the information regarding data format, input data location and pinot cluster coordinates. You can just copy over this job spec file. If you're using your own data, be sure to 1) replace transcript with your table name 2) set the right recordReaderSpec

executionFrameworkSpec:
  name: 'standalone'
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/tmp/pinot-quick-start/rawdata/'
includeFileNamePattern: 'glob:**/*.csv'
outputDirURI: '/tmp/pinot-quick-start/segments/'
overwriteOutput: true
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'csv'
  className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
  configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
  tableName: 'transcript'
  schemaURI: 'http://manual-pinot-controller:9000/tables/transcript/schema'
  tableConfigURI: 'http://manual-pinot-controller:9000/tables/transcript'
pinotClusterSpecs:
  - controllerURI: 'http://manual-pinot-controller:9000'

executionFrameworkSpec:
  name: 'standalone'
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/tmp/pinot-quick-start/rawdata/'
includeFileNamePattern: 'glob:**/*.csv'
outputDirURI: '/tmp/pinot-quick-start/segments/'
overwriteOutput: true
pushJobSpec:
  pushFileNamePattern: 'glob:**/*.tar.gz'
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'csv'
  className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
  configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
  tableName: 'transcript'
  schemaURI: 'http://localhost:9000/tables/transcript/schema'
  tableConfigURI: 'http://localhost:9000/tables/transcript'
pinotClusterSpecs:
  - controllerURI: 'http://localhost:9000'

Use the following command to generate a segment and upload it

docker run --rm -ti \
    --network=pinot-demo \
    -v /tmp/pinot-quick-start:/tmp/pinot-quick-start \
    --name pinot-data-ingestion-job \
    apachepinot/pinot:latest LaunchDataIngestionJob \
    -jobSpecFile /tmp/pinot-quick-start/docker-job-spec.yml

bin/pinot-admin.sh LaunchDataIngestionJob \
    -jobSpecFile /tmp/pinot-quick-start/batch-job-spec.yml

Sample output

SegmentGenerationJobSpec: 
!!org.apache.pinot.spi.ingestion.batch.spec.SegmentGenerationJobSpec
excludeFileNamePattern: null
executionFrameworkSpec: {extraConfigs: null, name: standalone, segmentGenerationJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner,
  segmentTarPushJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner,
  segmentUriPushJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner}
includeFileNamePattern: glob:**\/*.csv
inputDirURI: /tmp/pinot-quick-start/rawdata/
jobType: SegmentCreationAndTarPush
outputDirURI: /tmp/pinot-quick-start/segments
overwriteOutput: true
pinotClusterSpecs:
- {controllerURI: 'http://localhost:9000'}
pinotFSSpecs:
- {className: org.apache.pinot.spi.filesystem.LocalPinotFS, configs: null, scheme: file}
pushJobSpec: null
recordReaderSpec: {className: org.apache.pinot.plugin.inputformat.csv.CSVRecordReader,
  configClassName: org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig,
  configs: null, dataFormat: csv}
segmentNameGeneratorSpec: null
tableSpec: {schemaURI: 'http://localhost:9000/tables/transcript/schema', tableConfigURI: 'http://localhost:9000/tables/transcript',
  tableName: transcript}

Trying to create instance for class org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner
Initializing PinotFS for scheme file, classname org.apache.pinot.spi.filesystem.LocalPinotFS
Finished building StatsCollector!
Collected stats for 4 documents
Using fixed bytes value dictionary for column: studentID, size: 9
Created dictionary for STRING column: studentID with cardinality: 3, max length in bytes: 3, range: 200 to 202
Using fixed bytes value dictionary for column: firstName, size: 12
Created dictionary for STRING column: firstName with cardinality: 3, max length in bytes: 4, range: Bob to Nick
Using fixed bytes value dictionary for column: lastName, size: 15
Created dictionary for STRING column: lastName with cardinality: 3, max length in bytes: 5, range: King to Young
Created dictionary for FLOAT column: score with cardinality: 4, range: 3.2 to 3.8
Using fixed bytes value dictionary for column: gender, size: 12
Created dictionary for STRING column: gender with cardinality: 2, max length in bytes: 6, range: Female to Male
Using fixed bytes value dictionary for column: subject, size: 21
Created dictionary for STRING column: subject with cardinality: 3, max length in bytes: 7, range: English to Physics
Created dictionary for LONG column: timestampInEpoch with cardinality: 4, range: 1570863600000 to 1572418800000
Start building IndexCreator!
Finished records indexing in IndexCreator!
Finished segment seal!
Converting segment: /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0 to v3 format
v3 segment location for segment: transcript_OFFLINE_1570863600000_1572418800000_0 is /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0/v3
Deleting files in v1 segment directory: /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0
Starting building 1 star-trees with configs: [StarTreeV2BuilderConfig[splitOrder=[studentID, firstName],skipStarNodeCreation=[],functionColumnPairs=[org.apache.pinot.core.startree.v2.AggregationFunctionColumnPair@3a48efdc],maxLeafRecords=1]] using OFF_HEAP builder
Starting building star-tree with config: StarTreeV2BuilderConfig[splitOrder=[studentID, firstName],skipStarNodeCreation=[],functionColumnPairs=[org.apache.pinot.core.startree.v2.AggregationFunctionColumnPair@3a48efdc],maxLeafRecords=1]
Generated 3 star-tree records from 4 segment records
Finished constructing star-tree, got 9 tree nodes and 4 records under star-node
Finished creating aggregated documents, got 6 aggregated records
Finished building star-tree in 10ms
Finished building 1 star-trees in 27ms
Computed crc = 3454627653, based on files [/var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0/v3/columns.psf, /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0/v3/index_map, /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0/v3/metadata.properties, /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0/v3/star_tree_index, /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0/v3/star_tree_index_map]
Driver, record read time : 0
Driver, stats collector time : 0
Driver, indexing time : 0
Tarring segment from: /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0 to: /var/folders/3z/qn6k60qs6ps1bb6s2c26gx040000gn/T/pinot-1583443148720/output/transcript_OFFLINE_1570863600000_1572418800000_0.tar.gz
Size for segment: transcript_OFFLINE_1570863600000_1572418800000_0, uncompressed: 6.73KB, compressed: 1.89KB
Trying to create instance for class org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner
Initializing PinotFS for scheme file, classname org.apache.pinot.spi.filesystem.LocalPinotFS
Start pushing segments: [/tmp/pinot-quick-start/segments/transcript_OFFLINE_1570863600000_1572418800000_0.tar.gz]... to locations: [org.apache.pinot.spi.ingestion.batch.spec.PinotClusterSpec@243c4f91] for table transcript
Pushing segment: transcript_OFFLINE_1570863600000_1572418800000_0 to location: http://localhost:9000 for table transcript
Sending request: http://localhost:9000/v2/segments?tableName=transcript to controller: nehas-mbp.hsd1.ca.comcast.net, version: Unknown
Response for pushing table transcript segment transcript_OFFLINE_1570863600000_1572418800000_0 to location http://localhost:9000 - 200: {"status":"Successfully uploaded segment: transcript_OFFLINE_1570863600000_1572418800000_0 of table: transcript"}

Check that your segment made it to the table using the Rest API

Querying your data

You're all set! You should see your table in the Query Console and be able to run queries against it now.

How to increase server disk size on AWS

Below is an example of AWS EKS.

1. Update Storage Class

In the K8s cluster, check the storage class: in AWS, it should be gp2.

Then update StorageClass to ensure:

allowVolumeExpansion: true

Once StorageClass is updated, it should be like:

2. Update PVC

Once the storage class is updated, then we can update PVC for the server disk size.

Now we want to double the disk size for pinot-server-3.

Below is an example of current disks:

Below is the output of data-pinot-server-3

Now, let's change the PVC size to 2T by editing the server PVC.

kubectl edit pvc data-pinot-server-3 -n pinot

Once updated, the spec's PVC size is updated to 2T, but the status's PVC size is still 1T.

3. Restart pod to let it reflect

Restart pinot-server-3 pod:

Recheck PVC size:

Data processing

What is a good segment size?

While Pinot can work with segments of various sizes, for optimal use of Pinot, you want to get your segments sized in the 100MB to 500MB (un-tarred/uncompressed) range. Please note that having too many (thousands or more) of tiny segments for a single table just creates more overhead in terms of the metadata storage in Zookeeper as well as in the Pinot servers' heap. At the same time, having too few really large (GBs) segments reduces parallelism of query execution, as on the server side, the thread parallelism of query execution is at segment level.

Can multiple Pinot tables consume from the same Kafka topic?

Yes. Each table can be independently configured to consume from any given Kafka topic, regardless of whether there are other tables that are also consuming from the same Kafka topic.

If I add a partition to a Kafka topic, will Pinot automatically ingest data from this partition?

Pinot automatically detects new partitions in Kafka topics. It checks for new partitions whenever RealtimeSegmentValidationManager periodic job runs and starts consumers for new partitions.

You can configure the interval for this job using thecontroller.realtime.segment.validation.frequencyPeriod property in controller configuration.

How do I enable partitioning in Pinot, when using Kafka stream?

Setup partitioner in the Kafka producer:

The partitioning logic in the stream should match the partitioning config in Pinot. Kafka uses murmur2, and the equivalent in Pinot is Murmur function.

Set partitioning config as below using same column used in Kafka

and also set

How do I store BYTES column in JSON data?

For JSON, you can use hex encoded string to ingest BYTES

How do I flatten my JSON Kafka stream?

How do I escape Unicode in my Job Spec YAML file?

Is there a limit on the maximum length of a string column in Pinot?

By default, Pinot limits the length of a String column to 512 bytes. If you want to overwrite this value, you can set the maxLength attribute in the schema as follows:

When can new events become queryable when getting ingested into a real-time table?

Events are available to queries as soon as they are ingested. This is because events are instantly indexed in memory upon ingestion.

The ingestion of events into the real-time table is not transactional, so replicas of the open segment are not immediately consistent. Pinot trades consistency for availability upon network partitioning (CAP theorem) to provide ultra-low ingestion latencies at high throughput.

How to reset a CONSUMING segment stuck on an offset which has expired from the stream?

This typically happens if

1. The consumer is lagging a lot

2. The consumer was down (server down, cluster down), and the stream moved on, resulting in offset not found when consumer comes back up.

Indexing

How to set inverted indexes?

How to apply an inverted index to existing segments?

The output from this API should look something like the following:

Can I retrospectively add an index to any segment?

Not all indexes can be retrospectively applied to existing segments.

How to create star-tree indexes?

The new segments will have star-tree indexes generated after applying the star-tree index configs to the table config. Currently, Pinot does not support adding star-tree indexes to the existing segments.

Handling time in Pinot

How does Pinot’s real-time ingestion handle out-of-order events?

Pinot does not require ordering of event time stamps. Out of order events are still consumed and indexed into the "currently consuming" segment. In a pathological case, if you have a 2 day old event come in "now", it will still be stored in the segment that is open for consumption "now". There is no strict time-based partitioning for segments, but star-indexes and hybrid tables will handle this as appropriate.

When generating star-indexes, the time column will be part of the star-tree so the tree can still be efficiently queried for segments with multiple time intervals.

What is the purpose of a hybrid table not using max(OfflineTime) to determine the time-boundary, and instead using an offset?

This lets you have an old event up come in without building complex offline pipelines that perfectly partition your events by event timestamps. With this offset, even if your offline data pipeline produces segments with a maximum timestamp, Pinot will not use the offline dataset for that last chunk of segments. The expectation is if you process offline the next time-range of data, your data pipeline will include any late events.

Why are segments not strictly time-partitioned?

It might seem odd that segments are not strictly time-partitioned, unlike similar systems such as Apache Druid. This allows real-time ingestion to consume out-of-order events. Even though segments are not strictly time-partitioned, Pinot will still index, prune, and query segments intelligently by time intervals for the performance of hybrid tables and time-filtered data.

When generating offline segments, the segments generated such that segments only contain one time interval and are well partitioned by the time column.

Querying

I get the following error when running a query, what does it mean?

This essentially implies that the Pinot Broker assigned to the table specified in the query was not found. A common root cause for this is a typo in the table name in the query. Another uncommon reason could be if there wasn't actually a broker with required broker tenant tag for the table.

What are all the fields in the Pinot query's JSON response?

Here's the page explaining the Pinot response format:

SQL Query fails with "Encountered 'timestamp' was expecting one of..."

"timestamp" is a reserved keyword in SQL. Escape timestamp with double quotes.

Other commonly encountered reserved keywords are date, time, table.

Filtering on STRING column WHERE column = "foo" does not work?

For filtering on STRING columns, use single quotes

ORDER BY using an alias doesn't work?

The fields in the ORDER BY clause must be one of the group by clauses or aggregations, BEFORE applying the alias. Therefore, this will not work

Instead, this will work

Does pagination work in GROUP BY queries?

No. Pagination only works for SELECTION queries

How do I increase timeout for a query ?

You can add this at the end of your query: option(timeoutMs=X). For eg: the following example will use a timeout of 20 seconds for the query:

You can also use SET "timeoutMs" = 20000; SELECT COUNT(*) from myTable

For changing timeout on the entire cluster, set this property pinot.broker.timeoutMs in either broker configs or cluster configs (using POST /cluster/configs API from swagger)

How do I cancel a query?

Add these two configs for Pinot server and broker to start tracking of running queries. The query tracks are added and cleaned as query starts and ends, so should not consume much resource.

Then use the Rest APIs on Pinot controller to list running queries and cancel them via the query ID and broker ID (as query ID is only local to broker), like below:

How do I optimize my Pinot table for doing aggregations and group-by on high cardinality columns ?

How do I verify that an index is created on a particular column ?

There are 2 ways to verify this:

1. Log in to a server that hosts segments of this table. Inside the data directory, locate the segment directory for this table. In this directory, there is a file named index_map which lists all the indexes and other data structures created for each segment. Verify that the requested index is present here.

2. During query: Use the column in the filter predicate and check the value of numEntriesScannedInFilter . If this value is 0, then indexing is working as expected (works for Inverted index)

Does Pinot use a default value for LIMIT in queries?

Yes, Pinot uses a default value of LIMIT 10 in queries. The reason behind this default value is to avoid unintentionally submitting expensive queries that end up fetching or processing a lot of data from Pinot. Users can always overwrite this by explicitly specifying a LIMIT value.

Does Pinot cache query results?

Pinot does not cache query results, each query is computed in its entirety. Note though, running the same or similar query multiple times will naturally pull in segment pages into memory making subsequent calls faster. Also, for realtime systems, the data is changing in realtime, so results cannot be cached. For offline-only systems, caching layer can be built on top of Pinot, with invalidation mechanism built-in to invalidate the cache when data is pushed into Pinot.

I'm noticing that the first query is slower than subsequent queries, why is that?

Pinot memory maps segments. It warms up during the first query, when segments are pulled into the memory by the OS. Subsequent queries will have the segment already loaded in memory, and hence will be faster. The OS is responsible for bringing the segments into memory, and also removing them in favor of other segments when other segments not already in memory are accessed.

How do I determine if StarTree index is being used for my query?

The query execution engine will prefer to use StarTree index for all queries where it can be used. The criteria to determine whether StarTree index can be used is as follows:

• All aggregation function + column pairs in the query must exist in the StarTree index.

• All dimensions that appear in filter predicates and group-by should be StarTree dimensions.

For queries where above is true, StarTree index is used. For other queries, the execution engine will default to using the next best index available.