Pinot is designed to deliver low latency queries on large datasets. To achieve this performance, Pinot stores data in a columnar format and adds additional indices to perform fast filtering, aggregation and group by.

Raw data is broken into small data shards. Each shard is converted into a unit called a segment. One or more segments together form a table, which is the logical container for querying Pinot using SQL/PQL.

Pinot storage model

Pinot's storage model and infrastructure components include segments, tables, tenants, and clusters.

Segment

Pinot has a distributed systems architecture that scales horizontally. Pinot expects the size of a table to grow infinitely over time. In order to achieve this, all data needs to be distributed across multiple nodes. Pinot achieves this by breaking data into smaller chunks known as segments (similar to shards/partitions in high-availability (HA) relational databases). Another way to describe segments is as time-based partitions.

Table

Similar to traditional databases, Pinot has the concept of a table—a logical abstraction that refers to a collection of related data.

As is the case with relational database management systems (RDBMS), a table is a construct that consists of columns and rows (documents) that are queried using SQL. A table is associated with a schema that defines the columns in a table as well as their data types.

In contrast to RDBMS schemas, multiple tables in Pinot can share a single schema definition. Tables are independently configured for concerns such as indexing strategies, partitioning, tenants, data sources, or replication.

Tenant

Pinot supports multi-tenancy. Every Pinot table is associated with a tenant. This allows all tables belonging to a particular logical namespace to be grouped under a single tenant name and isolated from other tenants. This isolation between tenants provides different namespaces for applications and teams to prevent sharing tables or schemas. Development teams building applications will never have to operate an independent deployment of Pinot. An organization can operate a single cluster and scale it out as new tenants increase the overall volume of queries. Developers can manage their own schemas and tables without being impacted by any other tenant on a cluster.

By default, all tables belong to a default tenant named "default". The concept of tenants is very important, as it satisfies the architectural principle of a "database per service/application" without having to operate many independent data stores. Further, tenants will schedule resources so that segments (shards) are able to restrict a table's data to reside only on a specified set of nodes. Similar to the kind of isolation that is ubiquitously used in Linux containers, compute resources in Pinot can be scheduled to prevent resource contention between tenants.

Cluster

Logically, a cluster is simply a group of tenants. As with the classical definition of a cluster, it is also a grouping of a set of compute nodes. Typically, there is only one cluster per environment/data center. There is no needed to create multiple clusters since Pinot supports the concept of tenants. At LinkedIn, the largest Pinot cluster consists of 1000+ nodes distributed across a data center. The number of nodes in a cluster can be added in a way that will linearly increase performance and availability of queries. The number of nodes and the compute resources per node will reliably predict the QPS for a Pinot cluster, and as such, capacity planning can be easily achieved using SLAs that assert performance expectations for end-user applications.

Pinot components

A Pinot cluster consists of multiple distributed system components. These components are useful to understand for operators that are monitoring system usage or are debugging an issue with a cluster deployment.

• Controller

• Broker

• Server

• Minion (optional)

Pinot's integration with Apache Zookeeper and Apache Helix allow it to be linearly scalable for an unbounded number of nodes.

Pinot Controller

A controller is the core orchestrator that drives the consistency and routing in a Pinot cluster. Controllers are horizontally scaled as an independent component (container) and has visibility of the state of all other components in a cluster. The controller reacts and responds to state changes in the system and schedules the allocation of resources for tables, segments, or nodes. As mentioned earlier, Helix is embedded within the controller as an agent that is a participant responsible for observing and driving state changes that are subscribed to by other components.

In addition to cluster management, resource allocation, and scheduling, the controller is also the HTTP gateway for REST API administration of a Pinot deployment. A web-based query console is also provided for operators to quickly and easily run SQL/PQL queries.

Pinot Broker

A broker receives queries from a client and routes its execution to one or more Pinot servers before returning a consolidated response.

Pinot Server

Servers host segments (shards) that are scheduled and allocated across multiple nodes and routed on an assignment to a tenant (there is a single-tenant by default). Servers are independent containers that scale horizontally and are notified by Helix through state changes driven by the controller. A server can either be a real-time server or an offline server.

A real-time and offline server have very different resource usage requirements, where real-time servers are continually consuming new messages from external systems (such as Kafka topics) that are ingested and allocated on segments of a tenant. Because of this, resource isolation can be used to prioritize high-throughput real-time data streams that are ingested and then made available for query through a broker.

Pinot Minion

Pinot minion is an optional component that can be used to run background tasks such as "purge" for GDPR (General Data Protection Regulation). As Pinot is an immutable aggregate store, records containing sensitive private data need to be purged on a request-by-request basis. Minion provides a solution for this purpose that complies with GDPR while optimizing Pinot segments and building additional indices that guarantee performance in the presence of the possibility of data deletion. One can also write a custom task that runs on a periodic basis. While it's possible to perform these tasks on the Pinot servers directly, having a separate process (Minion) lessens the overall degradation of query latency as segments are impacted by mutable writes.

Pages in this section define and describe the major components and logical abstractions used in Pinot.

For a general overview that ties all these components together, see Basic Concepts.

Operator reference

Developer reference

The Pinot controller is responsible for the following:

• Maintaining global metadata (e.g., configs and schemas) of the system with the help of Zookeeper which is used as the persistent metadata store.

• Hosting the Helix Controller and managing other Pinot components (brokers, servers, minions)

• Maintaining the mapping of which servers are responsible for which segments. This mapping is used by the servers to download the portion of the segments that they are responsible for. This mapping is also used by the broker to decide which servers to route the queries to.

• Serving admin endpoints for viewing, creating, updating, and deleting configs, which are used to manage and operate the cluster.

• Serving endpoints for segment uploads, which are used in offline data pushes. They are responsible for initializing real-time consumption and coordination of persisting real-time segments into the segment store periodically.

• Undertaking other management activities such as managing retention of segments, validations.

For redundancy, there can be multiple instances of Pinot controllers. Pinot expects that all controllers are configured with the same back-end storage system so that they have a common view of the segments (e.g. NFS). Pinot can use other storage systems such as HDFS or ADLS.

Running the periodic task manually

The controller runs several periodic tasks in the background, to perform activities such as management and validation. Each periodic task has its own configuration to define the run frequency and default frequency. Each task runs at its own schedule or can also be triggered manually if needed. The task runs on the lead controller for each table.

For period task configuration details, see Controller configuration reference.

Use the GET /periodictask/names API to fetch the names of all the periodic tasks running on your Pinot cluster.

curl -X GET "http://localhost:9000/periodictask/names" -H "accept: application/json"

[
  "RetentionManager",
  "OfflineSegmentIntervalChecker",
  "RealtimeSegmentValidationManager",
  "BrokerResourceValidationManager",
  "SegmentStatusChecker",
  "SegmentRelocator",
  "StaleInstancesCleanupTask",
  "TaskMetricsEmitter"
]

To manually run a named periodic task, use the GET /periodictask/run API:

curl -X GET "http://localhost:9000/periodictask/run?taskname=SegmentStatusChecker&tableName=jsontypetable&type=OFFLINE" -H "accept: application/json"

{
  "Log Request Id": "api-09630c07",
  "Controllers notified": true
}

The Log Request Id (api-09630c07) can be used to search through pinot-controller log file to see log entries related to execution of the Periodic task that was manually run.

If tableName (and its type OFFLINE or REALTIME) is not provided, the task will run against all tables.

Starting a controller

Make sure you've set up Zookeeper. If you're using Docker, make sure to pull the Pinot Docker image. To start a controller:

docker run \
    --network=pinot-demo \
    --name pinot-controller \
    -p 9000:9000 \
    -d ${PINOT_IMAGE} StartController \
    -zkAddress pinot-zookeeper:2181

bin/pinot-admin.sh StartController \
  -zkAddress localhost:2181 \
  -clusterName PinotCluster \
  -controllerPort 9000

This page introduces you to the guiding principles behind the design of Apache Pinot. Here you will learn the distributed systems architecture that allows Pinot to scale the performance of queries linearly based on the number of nodes in a cluster. You'll also learn about the two different types of tables used to ingest and query data in offline (batch) or real-time (stream) mode.

Guiding design principles

Engineers at LinkedIn and Uber designed Pinot to scale query performance based on the number of nodes in a cluster. As you add more nodes, query performance improves based on the expected query volume per second quota. To achieve horizontal scalability to an unbounded number of nodes and data storage, without performance degradation, we used the following principles:

• Highly available: Pinot is built to serve low latency analytical queries for customer-facing applications. By design, there is no single point of failure in Pinot. The system continues to serve queries when a node goes down.

• Horizontally scalable: Pinot scales by adding new nodes as a workload changes.

• Latency vs. storage: Pinot is built to provide low latency even at high-throughput. Features such as segment assignment strategy, routing strategy, star-tree indexing were developed to achieve this.

• Immutable data: Pinot assumes that all data stored is immutable. For GDPR compliance, we provide an add-on solution for purging data while maintaining performance guarantees.

• Dynamic configuration changes: Operations such as adding new tables, expanding a cluster, ingesting data, modifying indexing config, and re-balancing must not impact query availability or performance.

Core components

As described in the concepts, Pinot has multiple distributed system components: controller, broker, server, and minion.

Pinot uses Apache Helix for cluster management. Helix is embedded as an agent within the different components and uses Apache Zookeeper for coordination and maintaining the overall cluster state and health.

Apache Helix and Zookeeper

Helix, a generic cluster management framework to manage partitions and replicas in a distributed system, manages all Pinot servers and brokers. It's helpful to think of Helix as an event-driven discovery service with push and pull notifications that drives the state of a cluster to an ideal configuration. A finite-state machine maintains a contract of stateful operations that drives the health of the cluster towards its optimal configuration. Helix optimizes query load by updating routing configurations between nodes based on where data is stored in the cluster.

Helix divides nodes into three logical components based on their responsibilities:

• Participant: These are the nodes in the cluster that actually host the distributed storage resources.

• Spectator: These nodes observe the current state of each participant and route requests accordingly. Routers, for example, need to know the instance on which a partition is hosted and its state to route the request to the appropriate endpoint. Routing is continually updated to optimize cluster performance as storage primitives are added and changed.

• Controller: The controller observes and manages the state of participant nodes. The controller is responsible for coordinating all state transitions in the cluster and ensures that state constraints are satisfied while maintaining cluster stability.

Helix uses Zookeeper to maintain cluster state. Each component in a Pinot cluster takes a Zookeeper address as a startup parameter. The various components distributed in a Pinot cluster watch Zookeeper notifications and issue updates via its embedded Helix-defined agent.

Helix agents use Zookeeper to store and update configurations, as well as for distributed coordination. Zookeeper stores the following information about the cluster:

Knowing the ZNode layout structure in Zookeeper for Helix agents in a cluster is useful for operations and/or troubleshooting cluster state and health.

Controller

Pinot's controller acts as the driver of the cluster's overall state and health. Because of its role as a Helix participant and spectator, which drives the state of other components, it's the first component that is typically started after Zookeeper.

Starting a controller requires two parameters: Zookeeper address and cluster name. The controller will automatically create a cluster via Helix if it does not yet exist.

Fault tolerance

To configure fault tolerance, start multiple controllers (typically three) and one of them will act as a leader. If the leader crashes or dies, another leader is automatically elected. Leader election is achieved using Apache Helix. Having at-least one controller is required to perform any DDL equivalent operation on the cluster, such as adding a table or a segment.

The controller does not interfere with query execution. Query execution is not impacted even when all controllers nodes are offline. If all controller nodes are offline, the state of the cluster will stay as it was when the last leader went down. When a new leader comes online, a cluster resumes rebalancing activity and can accept new tables or segments.

Controller REST interface

The controller provides a REST interface to perform CRUD operations on all logical storage resources (servers, brokers, tables, and segments).

See Pinot Data Explorer for more information on the web-based admin tool.

Broker

The broker's responsibility is to route a given query to an appropriate server instance. A broker collects and merges the responses from all servers into a final result, then sends it back to the requesting client. The broker provides HTTP endpoints that accept SQL queries and returns the response in JSON format.

Brokers need three key things to start:

• Cluster name

• Zookeeper address

• Broker instance name

Initially, the broker registers as a Helix Participant and waits for notifications from other Helix agents. The broker handles these notifications for table creation, a new segment being loaded, or a server starting up or going down, in addition to any configuration changes.

Service Discovery/Routing Table

Regardless of the kind of notification, the key responsibility of a broker is to maintain the query routing table. The query routing table is simply a mapping between segments and the servers that a segment resides on. Typically, a segment resides on more than one server. The broker computes multiple routing tables depending on the configured routing strategy for a table. The default strategy is to balance the query load across all available servers.

//This is an example ZNode config for EXTERNAL VIEW in Helix
{
  "id" : "baseballStats_OFFLINE",
  "simpleFields" : {
    ...
  },
  "mapFields" : {
    "baseballStats_OFFLINE_0" : {
      "Server_10.1.10.82_7000" : "ONLINE"
    }
  },
  ...
}

Query processing

For every query, a cluster's broker performs the following:

• Fetches the routes that are computed for a query based on the routing strategy defined in a table's configuration.

• Computes the list of segments to query from on each server. To learn more about this, check out routing.

• Scatter-gather: sends the requests to each server and gathers the responses.

• Merge: merges the query results returned from each server.

• Sends the query result to the client.

// Query: select count(*) from baseballStats limit 10

// RESPONSE
// ========
{
    "resultTable": {
        "dataSchema": {
            "columnDataTypes": ["LONG"],
            "columnNames": ["count(*)"]
        },
        "rows": [
            [97889]
        ]
    },
    "exceptions": [],
    "numServersQueried": 1,
    "numServersResponded": 1,
    "numSegmentsQueried": 1,
    "numSegmentsProcessed": 1,
    "numSegmentsMatched": 1,
    "numConsumingSegmentsQueried": 0,
    "numDocsScanned": 97889,
    "numEntriesScannedInFilter": 0,
    "numEntriesScannedPostFilter": 0,
    "numGroupsLimitReached": false,
    "totalDocs": 97889,
    "timeUsedMs": 5,
    "segmentStatistics": [],
    "traceInfo": {},
    "minConsumingFreshnessTimeMs": 0
}

Fault tolerance

Broker instances scale horizontally without an upper bound. In a majority of cases, only three brokers are required. If most query results that are returned to a client are less than 1 MB in size per query, you can run a broker and servers inside the same instance container. This lowers the overall footprint of a cluster deployment for use cases that don't need to guarantee a strict SLA on query performance in production.

Server

Servers host segments and do most of the heavy lifting during query processing. Though the architecture shows that there are two kinds of servers, real-time and offline, a server doesn't really "know" if it's going to be a real-time server or an offline server. The server's responsibility depends on the table assignment strategy.

Offline servers

Offline servers typically host segments that are immutable. In this case, segments are created outside of a cluster and uploaded via a shell-based curl request. Based on the replication factor and the segment assignment strategy, the controller picks one or more servers to host the segment. Helix notifies the servers about the new segments. Servers fetch the segments from deep store and load them. At this point, the cluster's broker detects that new segments are available and starts including them in query responses.

Real-time servers

Unlike offline servers, real-time server nodes ingest data from streaming sources, such as Kafka, and generate the indexed segments in-memory while flushing segments to disk periodically. In-memory segments are also known as consuming segments. Consuming segments get flushed periodically based on completion threshold (calculated with number of rows, time or segment size). At this point, they become completed segments. Completed segments are similar to offline servers' segments. Queries go over the in-memory (consuming) segments and the completed segments.

Minion

Minion is an optional component used for purging data from a Pinot cluster. For example, you might need to purge data for GDPR compliance in the UK.

Data ingestion overview

Within Pinot, a logical table is modeled as one of two types of physical tables: offline or real-time. Each table type follows a different state model.

Real-time and offline tables provide different configuration options for indexing. For real-time tables, you can also configure the connector properties for the stream data source, like Kafka. The two table types also allow users to use different containers for real-time and offline server nodes. For instance, offline servers might use virtual machines with larger storage capacity, whereas real-time servers might need higher system memory or more CPU cores.

The two types of tables also scale differently.

• Real-time tables have a smaller retention period and scale query performance based on the ingestion rate.

• Offline tables have larger retention and scale performance based on the size of stored data.

When ingesting data from the same source, you can have two tables that ingest the same data that are configured differently for real-time and offline queries. Even though the two tables have the same data, performance will scale differently for queries based on your requirements. In this scenario, real-time and offline tables must share the same schema.

You can configure real-time an offline tables differently depending on usage requirements. For example, you might choose to enable star-tree indexing for an offline table, while the real-time table with the same schema may not need it.

Batch data flow

In batch mode, Pinot ingests data via an ingestion job, which works in the following way:

1. An ingestion job transforms a raw data source (such as a CSV file) into segments.

2. Once segments are generated for the imported data, the ingestion job stores them into the cluster's segment store (also known as deep store) and notifies the controller.

3. The controller processes the notification, resulting in the Helix agent on the controller updating the ideal state configuration in Zookeeper.

4. Helix then notifies the offline server that there are new segments available.

5. In response to the notification from the controller, the offline server downloads the newly created segments directly from the cluster's segment store.

6. The cluster's broker, which watches for state changes in Helix, detects the new segments and adds them to the list of segments to query (segment-to-server routing table).

Real-time data flow

At table creation, a controller creates a new entry in Zookeeper for the consuming segment. Helix notices the new segment and notifies the real-time server, which starts consuming data from the streaming source. The broker, which watches for changes, detects the new segments and adds them to the list of segments to query (segment-to-server routing table).

Whenever the segment is complete (full), the real-time server notifies the controller, which checks with all replicas and picks a winner to commit the segment to. The winner commits the segment and uploads it to the cluster's segment store, updating the state of the segment from "consuming" to "online". The controller then prepares a new segment in a "consuming" state.

Query overview

Queries are received by brokers, which check the request against the segment-to-server routing table and scatter the request between real-time and offline servers.

The two tables process the request by filtering and aggregating the queried data, which is then returned back to the broker. Finally, the broker gathers together all of the pieces of the query response and responds back to the client with the result.

Servers host the data segments and serve queries off the data they host. There are two types of servers:

Offline Offline servers are responsible for downloading segments from the segment store, to host and serve queries off. When a new segment is uploaded to the controller, the controller decides the servers (as many as replication) that will host the new segment and notifies them to download the segment from the segment store. On receiving this notification, the servers download the segment file and load the segment onto the server, to server queries off them.

Real-time Real-time servers directly ingest from a real-time stream (such as Kafka or EventHubs). Periodically, they make segments of the in-memory ingested data, based on certain thresholds. This segment is then persisted onto the segment store.

Pinot servers are modeled as Helix participants, hosting Pinot tables (referred to as resources in Helix terminology). Segments of a table are modeled as Helix partitions (of a resource). Thus, a Pinot server hosts one or more Helix partitions of one or more helix resources (i.e. one or more segments of one or more tables).

Starting a server

Make sure you've set up Zookeeper. If you're using Docker, make sure to pull the Pinot Docker image. To start a server:

Usage: StartServer
	-serverHost               <String>                      : Host name for controller. (required=false)
	-serverPort               <int>                         : Port number to start the server at. (required=false)
	-serverAdminPort          <int>                         : Port number to serve the server admin API at. (required=false)
	-dataDir                  <string>                      : Path to directory containing data. (required=false)
	-segmentDir               <string>                      : Path to directory containing segments. (required=false)
	-zkAddress                <http>                        : Http address of Zookeeper. (required=false)
	-clusterName              <String>                      : Pinot cluster name. (required=false)
	-configFileName           <Config File Name>            : Broker Starter Config file. (required=false)
	-help                                                   : Print this message. (required=false)

docker run \
    --network=pinot-demo \
    --name pinot-server \
    -d ${PINOT_IMAGE} StartServer \
    -zkAddress pinot-zookeeper:2181

bin/pinot-admin.sh StartServer \
    -zkAddress localhost:2181

Apache Pinot is a real-time distributed online analytical processing (OLAP) datastore. Use Pinot to ingest and immediately query data from streaming or batch data sources (including, Apache Kafka, Amazon Kinesis, Hadoop HDFS, Amazon S3, Azure ADLS, and Google Cloud Storage).

Apache Pinot includes the following:

• Ultra low-latency analytics even at extremely high throughput.

• Columnar data store with several smart indexing and pre-aggregation techniques.

• Scaling up and out with no upper bound.

• Consistent performance based on the size of your cluster and an expected query per second (QPS) threshold.

It's perfect for user-facing real-time analytics and other analytical use cases, including internal dashboards, anomaly detection, and ad hoc data exploration.

User-facing real-time analytics

User-facing analytics refers to the analytical tools exposed to the end users of your product. In a user-facing analytics application, all users receive personalized analytics on their devices, resulting in hundreds of thousands of queries per second. Queries triggered by apps may grow quickly in proportion to the number of active users on the app, as many as millions of events per second. Data generated in Pinot is immediately available for analytics in latencies under one second.

User-facing real-time analytics requires the following:

• Fresh data. The system needs to be able to ingest data in real time and make it available for querying, also in real time.

• Support for high-velocity, highly dimensional event data from a wide range of actions and from multiple sources.

• Low latency. Queries are triggered by end users interacting with apps, resulting in hundreds of thousands of queries per second with arbitrary patterns.

• Reliability and high availability.

• Scalability.

• Low cost to serve.

Why Pinot?

Pinot is designed to execute OLAP queries with low latency. It works well where you need fast analytics, such as aggregations, on both mutable and immutable data.

User-facing, real-time analytics

Pinot was originally built at LinkedIn to power rich interactive real-time analytics applications, such as Who Viewed Profile, Company Analytics, Talent Insights, and many more. UberEats Restaurant Manager is another example of a user-facing analytics app built with Pinot.

Real-time dashboards for business metrics

Pinot can perform typical analytical operations such as slice and dice, drill down, roll up, and pivot on large scale multi-dimensional data. For instance, at LinkedIn, Pinot powers dashboards for thousands of business metrics. Connect various business intelligence (BI) tools such as Superset, Tableau, or PowerBI to visualize data in Pinot.

Enterprise business intelligence

For analysts and data scientists, Pinot works well as a highly-scalable data platform for business intelligence. Pinot converges big data platforms with the traditional role of a data warehouse, making it a suitable replacement for analysis and reporting.

Enterprise application development

For application developers, Pinot works well as an aggregate store that sources events from streaming data sources, such as Kafka, and makes it available for a query using SQL. You can also use Pinot to aggregate data across a microservice architecture into one easily queryable view of the domain.

Pinot tenants prevent any possibility of sharing ownership of database tables across microservice teams. Developers can create their own query models of data from multiple systems of record depending on their use case and needs. As with all aggregate stores, query models are eventually consistent.

Get started

If you're new to Pinot, take a look at our Getting Started guide:

To start importing data into Pinot, see how to import batch and stream data:

To start querying data in Pinot, check out our Query guide:

Learn

For a conceptual overview that explains how Pinot works, check out the Concepts guide:

To understand the distributed systems architecture that explains Pinot's operating model, take a look at our basic architecture section:

A cluster is a set of nodes comprising of servers, brokers, controllers and minions.

Pinot uses Apache Helix for cluster management. Helix is a cluster management framework that manages replicated, partitioned resources in a distributed system. Helix uses Zookeeper to store cluster state and metadata.

Cluster configuration

For details of cluster configuration settings, see Cluster configuration reference.

Cluster components

Helix divides nodes into logical components based on their responsibilities:

Participant

Participants are the nodes that host distributed, partitioned resources

Pinot servers are modeled as participants. For details about server nodes, see Server.

Spectator

Spectators are the nodes that observe the current state of each participant and use that information to access the resources. Spectators are notified of state changes in the cluster (state of a participant, or that of a partition in a participant).

Pinot brokers are modeled as spectators. For details about broker nodes, see Broker.

Controller

The node that observes and controls the Participant nodes. It is responsible for coordinating all transitions in the cluster and ensuring that state constraints are satisfied while maintaining cluster stability.

Pinot controllers are modeled as controllers. For details about controller nodes, see Controller.

Logical view

Another way to visualize the cluster is a logical view, where:

• A cluster contains tenants

• Tenants contain tables

• Tables contain segments

Set up a Pinot cluster

Typically, there is only one cluster per environment/data center. There is no need to create multiple Pinot clusters because Pinot supports tenants.

To set up a cluster, see one of the following guides:

• Running Pinot in Docker

• Running Pinot locally

A tenant is a logical component defined as a group of server/broker nodes with the same Helix tag.

In order to support multi-tenancy, Pinot has first-class support for tenants. Every table is associated with a server tenant and a broker tenant. This controls the nodes that will be used by this table as servers and brokers. This allows all tables belonging to a particular use case to be grouped under a single tenant name.

The concept of tenants is very important when the multiple use cases are using Pinot and there is a need to provide quotas or some sort of isolation across tenants. For example, consider we have two tables Table A and Table B in the same Pinot cluster.

We can configure Table A with server tenant Tenant A and Table B with server tenant Tenant B. We can tag some of the server nodes for Tenant A and some for Tenant B. This will ensure that segments of Table A only reside on servers tagged with Tenant A, and segment of Table B only reside on servers tagged with Tenant B. The same isolation can be achieved at the broker level, by configuring broker tenants to the tables.

No need to create separate clusters for every table or use case!

Tenant configuration

This tenant is defined in the tenants section of the table config.

This section contains two main fields broker and server , which decide the tenants used for the broker and server components of this table.

"tenants": {
  "broker": "brokerTenantName",
  "server": "serverTenantName"
}

In the above example:

• The table will be served by brokers that have been tagged as brokerTenantName_BROKER in Helix.

• If this were an offline table, the offline segments for the table will be hosted in Pinot servers tagged in Helix as serverTenantName_OFFLINE

• If this were a real-time table, the real-time segments (both consuming as well as completed ones) will be hosted in pinot servers tagged in Helix as serverTenantName_REALTIME.

Create a tenant

Broker tenant

Here's a sample broker tenant config. This will create a broker tenant sampleBrokerTenant by tagging three untagged broker nodes as sampleBrokerTenant_BROKER.

{
     "tenantRole" : "BROKER",
     "tenantName" : "sampleBrokerTenant",
     "numberOfInstances" : 3
}

To create this tenant use the following command. The creation will fail if number of untagged broker nodes is less than numberOfInstances.

bin/pinot-admin.sh AddTenant \
    -name sampleBrokerTenant 
    -role BROKER 
    -instanceCount 3 -exec

curl -i -X POST -H 'Content-Type: application/json' -d @sample-broker-tenant.json localhost:9000/tenants

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

Server tenant

Here's a sample server tenant config. This will create a server tenant sampleServerTenant by tagging 1 untagged server node as sampleServerTenant_OFFLINE and 1 untagged server node as sampleServerTenant_REALTIME.

{
     "tenantRole" : "SERVER",
     "tenantName" : "sampleServerTenant",
     "offlineInstances" : 1,
     "realtimeInstances" : 1
}

To create this tenant use the following command. The creation will fail if number of untagged server nodes is less than offlineInstances + realtimeInstances.

bin/pinot-admin.sh AddTenant \
    -name sampleServerTenant \
    -role SERVER \
    -offlineInstanceCount 1 \
    -realtimeInstanceCount 1 -exec

curl -i -X POST -H 'Content-Type: application/json' -d @sample-server-tenant.json localhost:9000/tenants

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

Brokers handle Pinot queries. They accept queries from clients and forward them to the right servers. They collect results back from the servers and consolidate them into a single response, to send back to the client.

Pinot brokers are modeled as Helix spectators. They need to know the location of each segment of a table (and each replica of the segments) and route requests to the appropriate server that hosts the segments of the table being queried.

The broker ensures that all the rows of the table are queried exactly once so as to return correct, consistent results for a query. The brokers may optimize to prune some of the segments as long as accuracy is not sacrificed.

Helix provides the framework by which spectators can learn the location in which each partition of a resource (i.e. participant) resides. The brokers use this mechanism to learn the servers that host specific segments of a table.

In the case of hybrid tables, the brokers ensure that the overlap between real-time and offline segment data is queried exactly once, by performing offline and real-time federation.

Let's take this example, we have real-time data for 5 days - March 23 to March 27, and offline data has been pushed until Mar 25, which is 2 days behind real-time. The brokers maintain this time boundary.

Suppose, we get a query to this table : select sum(metric) from table. The broker will split the query into 2 queries based on this time boundary – one for offline and one for real-time. This query becomes select sum(metric) from table_REALTIME where date >= Mar 25 and select sum(metric) from table_OFFLINE where date < Mar 25

The broker merges results from both these queries before returning the result to the client.

Starting a broker

Make sure you've set up Zookeeper. If you're using Docker, make sure to pull the Pinot Docker image. To start a broker:

docker run \
    --network=pinot-demo \
    --name pinot-broker \
    -d ${PINOT_IMAGE} StartBroker \
    -zkAddress pinot-zookeeper:2181

bin/pinot-admin.sh StartBroker \
  -zkAddress localhost:2181 \
  -clusterName PinotCluster \
  -brokerPort 7000

A minion is a standby component that leverages the to offload computationally intensive tasks from other components.

It can be attached to an existing Pinot cluster and then execute tasks as provided by the controller. Custom tasks can be plugged via annotations into the cluster. Some typical minion tasks are:

• Segment creation

• Segment purge

• Segment merge

Starting a Minion

Make sure you've . If you're using Docker, make sure to . To start a minion:

Interfaces

PinotTaskGenerator

PinotTaskGenerator interface defines the APIs for the controller to generate tasks for minions to execute.

PinotTaskExecutorFactory

Factory for PinotTaskExecutor which defines the APIs for Minion to execute the tasks.

MinionEventObserverFactory

Factory for MinionEventObserver which defines the APIs for task event callbacks on minion.

Built-in tasks

SegmentGenerationAndPushTask

The PushTask can fetch files from an input folder e.g. from a S3 bucket and converts them into segments. The PushTask converts one file into one segment and keeps file name in segment metadata to avoid duplicate ingestion. Below is an example task config to put in TableConfig to enable this task. The task is scheduled every 10min to keep ingesting remaining files, with 10 parallel task at max and 1 file per task.

NOTE: You may want to simply omit "tableMaxNumTasks" due to this caveat: the task generates one segment per file, and derives segment name based on the time column of the file. If two files happen to have same time range and are ingested by tasks from different schedules, there might be segment name conflict. To overcome this issue for now, you can omit “tableMaxNumTasks” and by default it’s Integer.MAX_VALUE, meaning to schedule as many tasks as possible to ingest all input files in a single batch. Within one batch, a sequence number suffix is used to ensure no segment name conflict. Because the sequence number suffix is scoped within one batch, tasks from different batches might encounter segment name conflict issue said above.

RealtimeToOfflineSegmentsTask

MergeRollupTask

Enable tasks

Tasks are enabled on a per-table basis. To enable a certain task type (e.g. myTask) on a table, update the table config to include the task type:

Under each enable task type, custom properties can be configured for the task type.

There are also two task configs to be 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).

Schedule tasks

Auto-schedule

There are 2 ways to enable task scheduling:

Controller level schedule for all minion tasks

Tasks can be scheduled periodically for all task types on all enabled tables. Enable auto task scheduling by configuring the schedule frequency in the controller config with the key controller.task.frequencyPeriod. This takes period strings as values, e.g. 2h, 30m, 1d.

Per table and task level schedule

Tasks can also be scheduled based on cron expressions. The cron expression is set in the schedule config for each task type separately. This config in the controller config, controller.task.scheduler.enabled should be set to true to enable cron scheduling.

Manual schedule

Tasks can be manually scheduled using the following controller rest APIs:

Plug-in custom tasks

To plug in a custom task, implement PinotTaskGenerator, PinotTaskExecutorFactory and MinionEventObserverFactory (optional) for the task type (all of them should return the same string for getTaskType()), and annotate them with the following annotations:

After annotating the classes, put them under the package of name org.apache.pinot.*.plugin.minion.tasks.*, then they will be auto-registered by the controller and minion.

Example

Task Manager UI

In the Pinot UI, there is Minion Task Manager tab under Cluster Manager page. From that minion task manager tab, one can find a lot of task related info for troubleshooting. Those info are mainly collected from the Pinot controller that schedules tasks or Helix that tracks task runtime status. There are also buttons to schedule tasks in an ad hoc way. Below are some brief introductions to some pages under the minion task manager tab.

This one shows which types of Minion Task have been used. Essentially which task types have created their task queues in Helix.

Clicking into a task type, one can see the tables using that task. And a few buttons to stop the task queue, cleaning up ended tasks etc.

Then clicking into any table in this list, one can see how the task is configured for that table. And the task metadata if there is one in ZK. For example, MergeRollupTask tracks a watermark in ZK. If the task is cron scheduled, the current and next schedules are also shown in this page like below.

At the bottom of this page is a list of tasks generated for this table for this specific task type. Like here, one MergeRollup task has been generated and completed.

Clicking into a task from that list, we can see start/end time for it, and the sub tasks generated for that task (as context, one minion task can have multiple sub-tasks to process data in parallel). In this example, it happened to have one sub-task here, and it shows when it starts and stops and which minion worker it's running.

Clicking into this subtask, one can see more details about it like the input task configs and error info if the task failed.

Task-related metrics

There is a controller job that runs every 5 minutes by default and emits metrics about Minion tasks scheduled in Pinot. The following metrics are emitted for each task type:

• NumMinionTasksInProgress: Number of running tasks

• NumMinionSubtasksRunning: Number of running sub-tasks

• NumMinionSubtasksWaiting: Number of waiting sub-tasks (unassigned to a minion as yet)

• NumMinionSubtasksError: Number of error sub-tasks (completed with an error/exception)

• PercentMinionSubtasksInQueue: Percent of sub-tasks in waiting or running states

• PercentMinionSubtasksInError: Percent of sub-tasks in error

The controller also emits metrics about how tasks are cron scheduled:

• cronSchedulerJobScheduled: Number of current cron schedules registered to be triggered regularly according their cron expressions. It's a Gauge.

• cronSchedulerJobTrigger: Number of cron scheduled triggered, as a Meter.

• cronSchedulerJobSkipped: Number of late cron scheduled skipped, as a Meter.

• cronSchedulerJobExecutionTimeMs: Time used to complete task generation, as a Timer.

For each task, the minion will emit these metrics:

• TASK_QUEUEING: Task queueing time (task_dequeue_time - task_inqueue_time), assuming the time drift between helix controller and pinot minion is minor, otherwise the value may be negative

• TASK_EXECUTION: Task execution time, which is the time spent on executing the task

• NUMBER_OF_TASKS: number of tasks in progress on that minion. Whenever a Minion starts a task, increase the Gauge by 1, whenever a Minion completes (either succeeded or failed) a task, decrease it by 1

• NUMBER_TASKS_EXECUTED: Number of tasks executed, as a Meter.

• NUMBER_TASKS_COMPLETED: Number of tasks completed, as a Meter.

• NUMBER_TASKS_CANCELLED: Number of tasks cancelled, as a Meter.

• NUMBER_TASKS_FAILED: Number of tasks failed, as a Meter. Different from fatal failure, the task encountered an error which can not be recovered from this run, but it may still succeed by retrying the task.

• NUMBER_TASKS_FATAL_FAILED: Number of tasks fatal failed, as a Meter. Different from failure, the task encountered an error, which will not be recoverable even with retrying the task.

The deep store (or deep storage) is the permanent store for files.

It is used for backup and restore operations. New nodes in a cluster will pull down a copy of segment files from the deep store. If the local segment files on a server gets damaged in some way (or accidentally deleted), a new copy will be pulled down from the deep store on server restart.

The deep store stores a compressed version of the segment files and it typically won't include any indexes. These compressed files can be stored on a local file system or on a variety of other file systems. For more details on supported file systems, see .

Note: Deep store by itself is not sufficient for restore operations. Pinot stores metadata such as table config, schema, segment metadata in Zookeeper. For restore operations, both Deep Store as well as Zookeeper metadata are required.

How do segments get into the deep store?

There are several different ways that segments are persisted in the deep store.

For offline tables, the batch ingestion job writes the segment directly into the deep store, as shown in the diagram below:

The ingestion job then sends a notification about the new segment to the controller, which in turn notifies the appropriate server to pull down that segment.

For real-time tables, by default, a segment is first built-in memory by the server. It is then uploaded to the lead controller (as part of the Segment Completion Protocol sequence), which writes the segment into the deep store, as shown in the diagram below:

When using this configuration, the server will directly write a completed segment to the deep store, as shown in the diagram below:

Configuring the deep store

For hands-on examples of how to configure the deep store, see the following tutorials:

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 .

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 .

A table is a logical abstraction that represents a collection of related data. It is composed of columns and rows (known as documents in Pinot). The columns, data types, and other metadata related to the table are defined using a schema.

Pinot breaks a table into multiple segments and stores these segments in a deep-store such as Hadoop Distributed File System (HDFS) as well as Pinot servers.

In the Pinot cluster, a table is modeled as a Helix resource and each segment of a table is modeled as a Helix Partition.

Pinot supports the following types of tables:

select count(*)
from myTable

Table configuration is used to define the table properties, such as name, type, indexing, routing, and retention. It is written in JSON format and is stored in Zookeeper, along with the table schema.

Use the following properties to make your tables faster or leaner:

• Segment

• Indexing

• Tenants

Segments

A table is comprised of small chunks of data known as segments. Learn more about how Pinot creates and manages segments here.

For offline tables, segments are built outside of Pinot and uploaded using a distributed executor such as Spark or Hadoop. For details, see Batch Ingestion.

For real-time tables, segments are built in a specific interval inside Pinot. You can tune the following for the real-time segments.

Flush

The Pinot real-time consumer ingests the data, creates the segment, and then flushes the in-memory segment to disk. Pinot allows you to configure when to flush the segment in the following ways:

• Number of consumed rows: After consuming the specified number of rows from the stream, Pinot will persist the segment to disk.

• Number of desired rows per segment: Pinot learns and then estimates the number of rows that need to be consumed. The learning phase starts by setting the number of rows to 100,000 (this value can be changed) and adjusts it to reach the appropriate segment size. Because Pinot corrects the estimate as it goes along, the segment size might go significantly over the correct size during the learning phase. You should set this value to optimize the performance of queries.

• Max time duration to wait: Pinot consumers wait for the configured time duration after which segments are persisted to the disk.

Replicas A segment can have multiple replicas to provide higher availability. You can configure the number of replicas for a table segment using the CLI.

Completion Mode By default, if the in-memory segment in the non-winner server is equivalent to the committed segment, then the non-winner server builds and replaces the segment. If the available segment is not equivalent to the committed segment, the server just downloads the committed segment from the controller.

However, in certain scenarios, the segment build can get very memory-intensive. In these cases, you might want to enforce the non-committer servers to just download the segment from the controller instead of building it again. You can do this by setting completionMode: "DOWNLOAD" in the table configuration.

For details, see Completion Config.

Download Scheme

A Pinot server might fail to download segments from the deep store, such as HDFS, after its completion. However, you can configure servers to download these segments from peer servers instead of the deep store. Currently, only HTTP and HTTPS download schemes are supported. More methods, such as gRPC/Thrift, are planned be added in the future.

For more details about peer segment download during real-time ingestion, refer to this design doc on bypass deep store for segment completion.

Indexing

You can create multiple indices on a table to increase the performance of the queries. The following types of indices are supported:

• Forward Index

  • Dictionary-encoded forward index with bit compression

  • Raw value forward index

  • Sorted forward index with run-length encoding

• Inverted Index

  • Bitmap inverted index

  • Sorted inverted index

• Star-tree Index

• Range Index

• Text Index

• Geospatial

For more details on each indexing mechanism and corresponding configurations, see Indexing.

Set up Bloomfilters on columns to make queries faster. You can also keep segments in off-heap instead of on-heap memory for faster queries.

Pre-aggregation

Aggregate the real-time stream data as it is consumed to reduce segment sizes. We add the metric column values of all rows that have the same values for all dimension and time columns and create a single row in the segment. This feature is only available on REALTIME tables.

The only supported aggregation is SUM. The columns to pre-aggregate need to satisfy the following requirements:

• All metrics should be listed in noDictionaryColumns.

• No multi-value dimensions

• All dimension columns are treated to have a dictionary, even if they appear as noDictionaryColumns in the config.

The following table config snippet shows an example of enabling pre-aggregation during real-time ingestion:

    "tableIndexConfig": { 
      "noDictionaryColumns": ["metric1", "metric2"],
      "aggregateMetrics": true,
      ...
    }

Tenants

Each table is associated with a tenant. A segment resides on the server, which has the same tenant as itself. For details, see Tenant.

Optionally, override if a table should move to a server with different tenant based on segment status. The example below adds a tagOverrideConfig under the tenants section for real-time tables to override tags for consuming and completed segments.

  "broker": "brokerTenantName",
  "server": "serverTenantName",
  "tagOverrideConfig" : {
    "realtimeConsuming" : "serverTenantName_REALTIME"
    "realtimeCompleted" : "serverTenantName_OFFLINE"
  }
}

In the above example, the consuming segments will still be assigned to serverTenantName_REALTIME hosts, but once they are completed, the segments will be moved to serverTeantnName_OFFLINE.

You can specify the full name of any tag in this section. For example, you could decide that completed segments for this table should be in Pinot servers tagged as allTables_COMPLETED). To learn more about, see the Moving Completed Segments section.

Hybrid table

A hybrid table is a table composed of two tables, one offline and one real-time, that share the same name. In a hybrid table, offline segments can be pushed periodically. The retention on the offline table can be set to a high value because segments are coming in on a periodic basis, whereas the retention on the real-time part can be small.

Once an offline segment is pushed to cover a recent time period, the brokers automatically switch to using the offline table for segments for that time period and use the real-time table only for data not available in the offline table.

To learn how time boundaries work for hybrid tables, see Broker.

A typical use case for hybrid tables is pushing deduplicated, cleaned-up data into an offline table every day while consuming real-time data as it arrives. Data can remain in offline tables for as long as a few years, while the real-time data would be cleaned every few days.

Examples

Create a table config for your data, or see examples for all possible batch/streaming tables.

Prerequisites

• Set up the cluster

• Create broker and server tenants

Offline table creation

docker run \
    --network=pinot-demo \
    --name pinot-batch-table-creation \
    ${PINOT_IMAGE} AddTable \
    -schemaFile examples/batch/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json \
    -controllerHost pinot-controller \
    -controllerPort 9000 \
    -exec

Executing command: AddTable -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json -schemaFile examples/batch/airlineStats/airlineStats_schema.json -controllerHost pinot-controller -controllerPort 9000 -exec
Sending request: http://pinot-controller:9000/schemas to controller: a413b0013806, version: Unknown
{"status":"Table airlineStats_OFFLINE succesfully added"}

bin/pinot-admin.sh AddTable \
    -schemaFile examples/batch/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json \
    -exec

# add schema
curl -F schemaName=@airlineStats_schema.json  localhost:9000/schemas

# add table
curl -i -X POST -H 'Content-Type: application/json' \
    -d @airlineStats_offline_table_config.json localhost:9000/tables

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

Streaming table creation

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

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

docker run \
    --network=pinot-demo \
    --name pinot-streaming-table-creation \
    ${PINOT_IMAGE} AddTable \
    -schemaFile examples/stream/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/docker/table-configs/airlineStats_realtime_table_config.json \
    -controllerHost pinot-controller \
    -controllerPort 9000 \
    -exec

Executing command: AddTable -tableConfigFile examples/docker/table-configs/airlineStats_realtime_table_config.json -schemaFile examples/stream/airlineStats/airlineStats_schema.json -controllerHost pinot-controller -controllerPort 9000 -exec
Sending request: http://pinot-controller:9000/schemas to controller: 8fbe601012f3, version: Unknown
{"status":"Table airlineStats_REALTIME succesfully added"}

bin/pinot-admin.sh StartZookeeper -zkPort 2191

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

bin/pinot-admin.sh AddTable \
    -schemaFile examples/stream/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/stream/airlineStats/airlineStats_realtime_table_config.json \
    -exec

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

Hybrid table creation

To create a hybrid table, you have to create the offline and real-time tables individually. You don't need to create a separate hybrid table.

"OFFLINE": {
    "tableName": "pinotTable", 
    "tableType": "OFFLINE", 
    "segmentsConfig": {
      ... 
    }, 
    "tableIndexConfig": { 
      ... 
    },  
    "tenants": {
      "broker": "myBrokerTenant", 
      "server": "myServerTenant"
    },
    "metadata": {
      ...
    }
  },
  "REALTIME": { 
    "tableName": "pinotTable", 
    "tableType": "REALTIME", 
    "segmentsConfig": {
      ...
    }, 
    "tableIndexConfig": { 
      ... 
      "streamConfigs": {
        ...
      },  
    },  
    "tenants": {
      "broker": "myBrokerTenant", 
      "server": "myServerTenant"
    },
    "metadata": {
    ...
    }
  }
}

Pinot has the concept of a table, which is a logical abstraction to refer to a collection of related data. Pinot has a distributed architecture and scales horizontally. Pinot expects the size of a table to grow infinitely over time. In order to achieve this, the entire data needs to be distributed across multiple nodes.

Pinot achieves this by breaking the data into smaller chunks known as segments (similar to shards/partitions in relational databases). Segments can be seen as time-based partitions.

A segment is a horizontal shard representing a chunk of table data with some number of rows. The segment stores data for all columns of the table. Each segment packs the data in a columnar fashion, along with the dictionaries and indices for the columns. The segment is laid out in a columnar format so that it can be directly mapped into memory for serving queries.

Columns can be single or multi-valued and the following types are supported: STRING, BOOLEAN, INT, LONG, FLOAT, DOUBLE, TIMESTAMP or BYTES. Only single-valued BIG_DECIMAL data type is supported.

Columns may be declared to be metric or dimension (or specifically as a time dimension) in the schema. Columns can have default null values. For example, the default null value of a integer column can be 0. The default value for bytes columns must be hex-encoded before it's added to the schema.

Pinot uses dictionary encoding to store values as a dictionary ID. Columns may be configured to be “no-dictionary” column in which case raw values are stored. Dictionary IDs are encoded using minimum number of bits for efficient storage (e.g. a column with a cardinality of 3 will use only 2 bits for each dictionary ID).

A forward index is built for each column and compressed for efficient memory use. In addition, you can optionally configure inverted indices for any set of columns. Inverted indices take up more storage, but improve query performance. Specialized indexes like Star-Tree index are also supported. For more details, see Indexing.

Creating a segment

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

Load data in batch

Prerequisites

1. Set up a cluster

2. Create broker and server tenants

3. Create an offline table

Below are instructions to generate and push segments to Pinot via standalone scripts. For a production setup, you should use frameworks such as Hadoop or Spark. For more details on setting up data ingestion jobs, see Import Data.

Job Spec YAML

To generate a segment, we need to first create a job spec YAML file. This file contains all the information regarding data format, input data location, and pinot cluster coordinates. Note that this assumes that the controller is RUNNING to fetch the table config and schema. If not, you will have to configure the spec to point at their location. For full configurations, see Ingestion Job Spec.

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: 'examples/batch/baseballStats/rawdata'
includeFileNamePattern: 'glob:**/*.csv'
excludeFileNamePattern: 'glob:**/*.tmp'
outputDirURI: 'examples/batch/baseballStats/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'
  configs:

tableSpec:
  tableName: 'baseballStats'
  schemaURI: 'http://localhost:9000/tables/baseballStats/schema'
  tableConfigURI: 'http://localhost:9000/tables/baseballStats'
  
segmentNameGeneratorSpec:

pinotClusterSpecs:
  - controllerURI: 'http://localhost:9000'

pushJobSpec:
  pushParallelism: 2
  pushAttempts: 2
  pushRetryIntervalMillis: 1000

Create and push segment

To create and push the segment in one go, use the following:

docker run \
    --network=pinot-demo \
    --name pinot-data-ingestion-job \
    ${PINOT_IMAGE} LaunchDataIngestionJob \
    -jobSpecFile examples/docker/ingestion-job-specs/airlineStats.yaml

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:**/*.avro
inputDirURI: examples/batch/airlineStats/rawdata
jobType: SegmentCreationAndTarPush
outputDirURI: examples/batch/airlineStats/segments
overwriteOutput: true
pinotClusterSpecs:
- {controllerURI: 'http://pinot-controller:9000'}
pinotFSSpecs:
- {className: org.apache.pinot.spi.filesystem.LocalPinotFS, configs: null, scheme: file}
pushJobSpec: {pushAttempts: 2, pushParallelism: 1, pushRetryIntervalMillis: 1000,
  segmentUriPrefix: null, segmentUriSuffix: null}
recordReaderSpec: {className: org.apache.pinot.plugin.inputformat.avro.AvroRecordReader,
  configClassName: null, configs: null, dataFormat: avro}
segmentNameGeneratorSpec: null
tableSpec: {schemaURI: 'http://pinot-controller:9000/tables/airlineStats/schema',
  tableConfigURI: 'http://pinot-controller:9000/tables/airlineStats', tableName: airlineStats}

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 403 documents
Created dictionary for INT column: FlightNum with cardinality: 386, range: 14 to 7389
Using fixed bytes value dictionary for column: Origin, size: 294
Created dictionary for STRING column: Origin with cardinality: 98, max length in bytes: 3, range: ABQ to VPS
Created dictionary for INT column: Quarter with cardinality: 1, range: 1 to 1
Created dictionary for INT column: LateAircraftDelay with cardinality: 50, range: -2147483648 to 303
......
......
Pushing segment: airlineStats_OFFLINE_16085_16085_29 to location: http://pinot-controller:9000 for table airlineStats
Sending request: http://pinot-controller:9000/v2/segments?tableName=airlineStats to controller: a413b0013806, version: Unknown
Response for pushing table airlineStats segment airlineStats_OFFLINE_16085_16085_29 to location http://pinot-controller:9000 - 200: {"status":"Successfully uploaded segment: airlineStats_OFFLINE_16085_16085_29 of table: airlineStats"}
Pushing segment: airlineStats_OFFLINE_16084_16084_30 to location: http://pinot-controller:9000 for table airlineStats
Sending request: http://pinot-controller:9000/v2/segments?tableName=airlineStats to controller: a413b0013806, version: Unknown
Response for pushing table airlineStats segment airlineStats_OFFLINE_16084_16084_30 to location http://pinot-controller:9000 - 200: {"status":"Successfully uploaded segment: airlineStats_OFFLINE_16084_16084_30 of table: airlineStats"}

bin/pinot-admin.sh LaunchDataIngestionJob \
    -jobSpecFile examples/batch/airlineStats/ingestionJobSpec.yaml

Alternately, you can separately create and then push, by changing the jobType to SegmentCreation or SegmenTarPush.

Templating Ingestion Job Spec

The Ingestion job spec supports templating with Groovy Syntax.

This is convenient if you want to generate one ingestion job template file and schedule it on a daily basis with extra parameters updated daily.

e.g. you could set inputDirURI with parameters to indicate the date, so that the ingestion job only processes the data for a particular date. Below is an example that templates the date for input and output directories.

inputDirURI: 'examples/batch/airlineStats/rawdata/${year}/${month}/${day}'
outputDirURI: 'examples/batch/airlineStats/segments/${year}/${month}/${day}'

You can pass in arguments containing values for ${year}, ${month}, ${day} when kicking off the ingestion job: -values $param=value1 $param2=value2...

docker run \
    --network=pinot-demo \
    --name pinot-data-ingestion-job \
    ${PINOT_IMAGE} LaunchDataIngestionJob \
    -jobSpecFile examples/docker/ingestion-job-specs/airlineStats.yaml
    -values year=2014 month=01 day=03

This ingestion job only generates segments for date 2014-01-03

Load data in streaming

Prerequisites

1. Set up a cluster

2. Create broker and server tenants

3. Create a real-time table and set up a real-time stream

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

Kafka

docker run \
  --network pinot-demo \
  --name=loading-airlineStats-data-to-kafka \
  ${PINOT_IMAGE} StreamAvroIntoKafka \
  -avroFile examples/stream/airlineStats/sample_data/airlineStats_data.avro \
  -kafkaTopic flights-realtime -kafkaBrokerList kafka:9092 -zkAddress pinot-zookeeper:2181/kafka

bin/pinot-admin.sh StreamAvroIntoKafka \
  -avroFile examples/stream/airlineStats/sample_data/airlineStats_data.avro \
  -kafkaTopic flights-realtime -kafkaBrokerList localhost:19092 -zkAddress localhost:2191/kafka

Get started setting up a Pinot cluster with Docker using the guide below.

Prerequisites:

• Install Docker

• Configure Docker memory with the following minimum resources:

  • CPUs: 8

  • Memory: 16.00 GB

  • Swap: 4 GB

  • Disk Image size: 60 GB

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

Pull the latest Docker image onto your machine by running the following command:

docker pull apachepinot/pinot:latest

To pull a specific version, modify the command like below:

docker pull apachepinot/pinot:0.12.0

Set up a cluster

Once you'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 command launches Pinot with a baseball dataset pre-loaded:

docker run \
    -p 9000:9000 \
    apachepinot/pinot:0.12.0 QuickStart \
    -type batch

For a list of all available quick start commands, see 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 real-time 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.

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-controller
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:0.12.0
    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:0.12.0
    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:0.12.0
    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

Run the below command to check the 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, see Exploring Pinot to learn how to run queries against the data.

If you have minikube or Docker Kubernetes installed, you can also try running the Kubernetes quick start.

Pinot ships with QuickStart commands that launch Pinot components in a single process and import pre-built datasets. These quick start examples are a good place if you're just getting started with Pinot. The examples begin with the Batch Processing example, after the following notes:

• Prerequisites

  You must have either installed Pinot locally or have Docker installed if you want to use the Pinot Docker image. The examples are available in each option and work the same. The decision of which to choose depends on your installation preference and how you generally like to work. If you don't know which to choose, using Docker will make your cleanup easier after you are done with the examples.

• Pinot versions in examples

  The Docker-based examples on this page use pinot:latest, which instructs Docker to pull and use the most recent release of Apache Pinot. If you prefer to use a specific release instead, you can designate it by replacing latest with the release number, like this: pinot:0.12.1.

  The local install-based examples that are run using the launcher scripts will use the Apache Pinot version you installed.

• Running examples with Docker on a Mac with an M1 or M2 CPU

  Add the -arm64 suffix to the run commands, like this:

  Copy

  docker run \
      -p 9000:9000 \
      apachepinot/pinot:latest-arm64 QuickStart \
      -type batch

• Stopping a running example

  To stop a running example, enter Ctrl+C in the same terminal where you ran the docker run command to start the example.

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 Processing

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:latest 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:latest 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:latest QuickStart \
    -type batch_complex_type

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

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:latest 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:latest 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:latest 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:latest 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:latest 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:latest 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:latest 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:latest QuickStart \
    -type join

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

In this quickstart guide, you will set up a Kubernetes Cluster on Azure Kubernetes Service (AKS)

1. Tooling Installation

1.1 Install Kubectl

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

For Mac users

brew install kubernetes-cli

Check kubectl version after installation.

kubectl version

1.2 Install Helm

To install Helm, see Installing Helm.

For Mac users

brew install kubernetes-helm

Check helm version after installation.

helm version

1.3 Install Azure CLI

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

For Mac users

brew update && brew install azure-cli

2. (Optional) Log in to your Azure account

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

az login

3. (Optional) Create a Resource Group

Use the following script 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

This script will create a 3 node cluster named pinot-quickstart for demo purposes.

Modify the parameters in the following example command with your resource group and cluster details:

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 succeeds, the cluster is ready to be used.

5. Connect to an existing cluster

Run the following command to get the credential for the cluster pinot-quickstart that you just created:

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, run the following:

kubectl get nodes

6. Pinot quickstart

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}

In this quickstart guide, you will set up a Kubernetes Cluster on Amazon Elastic Kubernetes Service (Amazon EKS)

1. Tooling Installation

1.1 Install Kubectl

To install kubectl, see Install kubectl.

For Mac users

brew install kubernetes-cli

Check kubectl version after installation.

kubectl version

1.2 Install Helm

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

For Mac users

brew install kubernetes-helm

Check helm version after installation.

helm version

1.3 Install AWS CLI

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

For Mac users

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

1.4 Install Eksctl

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

For Mac users

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

2. (Optional) Log in to your AWS account

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

Once you have created the account, 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

For k8s 1.23+, run the following commands to allow the containers to provision their storage:

eksctl utils associate-iam-oidc-provider --region=us-east-2 --cluster=pinot-quickstart --approve

eksctl create iamserviceaccount \
  --name ebs-csi-controller-sa \
  --namespace kube-system \
  --cluster pinot-quickstart \
  --attach-policy-arn arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy \
  --approve \
  --role-only \
  --role-name AmazonEKS_EBS_CSI_DriverRole

eksctl create addon --name aws-ebs-csi-driver --cluster pinot-quickstart --service-account-role-arn arn:aws:iam::$(aws sts get-caller-identity --query Account --output text):role/AmazonEKS_EBS_CSI_DriverRole --force

Use the following command to monitor the cluster status:

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

Run the following command to get the credential for the cluster pinot-quickstart that you just created:

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

To verify the connection, run the following:

kubectl get nodes

5. Pinot quickstart

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}

In this quickstart guide, you will set up a Kubernetes Cluster on Google Kubernetes Engine(GKE)

1. Tooling Installation

1.1 Install Kubectl

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

For Mac users

brew install kubernetes-cli

Check kubectl version after installation.

kubectl version

1.2 Install Helm

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

For Mac users

brew install kubernetes-helm

Check helm version after installation.

helm version

1.3 Install Google Cloud SDK

To install Google Cloud SDK, see Install the gcloud CLI

1.3.1 For Mac users

• 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

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

Modify the parameters in the following example command with your gcloud details:

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}

Use the following command do monitor cluster status:

gcloud compute instances list

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

4. Connect to an existing cluster

Run the following command to get the credential for the cluster pinot-quickstart that you just created:

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, run the following:

kubectl get nodes

5. Pinot quickstart

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}

Each table in Pinot is associated with a schema. A schema defines what fields are present in the table along with the data types.

The schema is stored in Zookeeper along with the table configuration.

Categories

A schema also defines what category a column belongs to. Columns in a Pinot table can be categorized into three categories:

Pinot does not enforce strict rules on which of these categories columns belong to, rather the categories can be thought of as hints to Pinot to do internal optimizations.

For example, metrics may be stored without a dictionary and can have a different default null value.

The categories are also relevant when doing segment merge and rollups. Pinot uses the dimension and time fields to identify records against which to apply merge/rollups.

Metrics aggregation is another example where Pinot uses dimensions and time are used as the key, and automatically aggregates values for the metric columns.

For configuration details, see Schema configuration reference.

Date and time fields

Since Pinot doesn't have a dedicated DATETIME datatype support, you need to input time in either STRING, LONG, or INT format. However, Pinot needs to convert the date into an understandable format such as epoch timestamp to do operations. You can refer to DateTime field spec configs for more details on supported formats.

Creating a schema

First, Make sure your cluster is up and running.

Let's create a schema and put it in a JSON file. For this example, we have created a schema for flight data.

{
  "schemaName": "flights",
  "dimensionFieldSpecs": [
    {
      "name": "flightNumber",
      "dataType": "LONG"
    },
    {
      "name": "tags",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": "null"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "price",
      "dataType": "DOUBLE",
      "defaultNullValue": 0
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "millisSinceEpoch",
      "dataType": "LONG",
      "format": "EPOCH",
      "granularity": "15:MINUTES"
    },
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "EPOCH|HOURS",
      "granularity": "1:HOURS"
    },
    {
      "name": "dateString",
      "dataType": "STRING",
      "format": "SIMPLE_DATE_FORMAT|yyyy-MM-dd",
      "granularity": "1:DAYS"
    }
  ]
}

Then, we can upload the sample schema provided above using either a Bash command or REST API call.

bin/pinot-admin.sh AddSchema -schemaFile flights-schema.json -exec

OR

bin/pinot-admin.sh AddTable -schemaFile flights-schema.json -tableFile flights-table.json -exec

curl -F schemaName=@transcript-schema.json  localhost:9000/schemas

Check out the schema in the Rest API to make sure it was successfully uploaded

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

• Download Apache Pinot

• Set up a cluster

• Start a Pinot component in debug mode with IntelliJ

Download Apache Pinot

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

Prerequisites

• Install JDK11 or higher (JDK16 is not yet supported).

• For JDK 8 support, use Pinot 0.7.1 or compile from the source code.

Note that some installations of the JDK do not contain the JNI bindings necessary to run all tests. If you see an error like java.lang.UnsatisfiedLinkError while running tests, you might need to change your JDK.

If using Homebrew, install AdoptOpenJDK 11 using brew install --cask adoptopenjdk11.

Download the distribution or build from source by selecting one of the following tabs:

PINOT_VERSION=0.12.0 #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

tar -zxvf apache-pinot-$PINOT_VERSION-bin.tar.gz

cd apache-pinot-$PINOT_VERSION-bin

OLDER_VERSION="0.10.0"
wget https://archive.apache.org/dist/pinot/apache-pinot-$OLDER_VERSION/apache-pinot-$OLDER_VERSION-bin.tar.gz

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

mvn install package -DskipTests -Pbin-dist

cd build

M1 and M2 Mac Support

Currently, Apache Pinot doesn't provide official binaries for M1 or M2 Mac systems. Follow the instructions below to run on an M1 or M2 Mac:

1. Add the following to your ~/.m2/settings.xml:

<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>