This section is a reference for the definition of major components and logical abstractions used in Pinot.

For a general overview that ties together all of the reference material in this section, see Basic Concepts.

Operator reference

Developer reference

Join us in our Slack channel for questions, troubleshooting, and feedback. You can request an invite from - https://communityinviter.com/apps/apache-pinot/apache-pinot.

Pinot is a real-time distributed OLAP datastore, purpose-built to provide ultra low-latency analytics, even at extremely high throughput. It can ingest directly from streaming data sources - such as Apache Kafka and Amazon Kinesis - and make the events available for querying instantly. It can also ingest from batch data sources such as Hadoop HDFS, Amazon S3, Azure ADLS, and Google Cloud Storage.

At the heart of the system is a columnar store, with several smart indexing and pre-aggregation techniques for low latency. This makes Pinot the most perfect fit for user-facing realtime analytics. At the same time, Pinot is also a great choice for other analytical use-cases, such as internal dashboards, anomaly detection, and ad-hoc data exploration.

Pinot was built by engineers at LinkedIn and Uber and is designed to scale up and out with no upper bound. Performance always remains constant based on the size of your cluster and an expected query per second (QPS) threshold.

User-Facing Real-Time Analytics

User-facing analytics, or site-facing analytics, is the analytical tools and applications that you would expose directly to the end-users of your product. In a user-facing analytics application, think of the user-base as ALL end users of an App. This App could be a social networking app, or a food delivery app - anything at all. It’s not just a few analysts doing offline analysis, or a handful of data scientists in a company running ad-hoc queries. This is ALL end-users, receiving personalized analytics on their personal devices (think 100s of 1000s of queries per second). These queries are triggered by apps, and not written by people, and so the scale will be as much as the active users on that App (think millions of events/sec)

And, this is for all the freshest possible data, which touches on the other aspect here - realtime analytics. "Yesterday" might be a long time ago for some businesses and they cannot wait for ETLs and batch jobs. The data needs to be used for analytics, as soon as it is generated (think latencies < 1s).

Why is user-facing real-time analytics is so challenging?

Wanting such a user-facing analytics application, using realtime events, sounds great. But what does it mean for the underlying infrastructure, to support such an analytical workload?

1. Such applications require the freshest possible data, and so the system needs to be able to ingest data in real time and make it available for querying, also in real time.

2. Data for such apps tend to be event data, for a wide range of actions, coming from multiple sources, and so the data comes in at a very high velocity and tends to be highly dimensional.

3. Queries are triggered by end-users interacting with apps - with queries per second in hundreds of thousands, with arbitrary query patterns, and latencies are expected to be in milliseconds for good user-experience.

4. And further do all of the above, while being scalable, reliable, highly available, and having a low cost to serve.

This video talks more about user-facing real-time analytics, and how Pinot is used to achieve that.

Here's another great video that goes into the details of how Pinot tackles some of the challenges faced in handling a user-facing analytics workload.

Companies using Pinot

Pinot originated at LinkedIn which currently has one of the largest deployment powering more than 50+ user facing applications such as Viewed My Profile, Talent Analytics, Company Analytics, Ad Analytics and many more. At LinkedIn, Pinot also serves as the backend to visualize and monitor 10,000+ business metrics.

With Pinot's growing popularity, several companies are now using it in production to power a variety of analytics use cases. A detailed list of companies using Pinot can be found here.​

Features

• A column-oriented database with various compression schemes such as Run Length, Fixed Bit Length

• Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index, StarTree Index, Bloom Filter, Range Index, Text Search Index(Lucence/FST), Json Index, Geospatial Index

• Ability to optimize query/execution plan based on query and segment metadata

• Near real-time ingestion from streams such as Kafka, Kinesis and batch ingestion from sources such as Hadoop, S3, Azure, GCS

• SQL-like language that supports selection, aggregation, filtering, group by, order by, distinct queries on data

• Support for multi-valued fields

• Horizontally scalable and fault-tolerant

When should I use it?

Pinot is designed to execute OLAP queries with low latency. It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion.

User facing Analytics Products

Pinot is the perfect choice for user-facing analytics products. Pinot was originally built at LinkedIn to power rich interactive real-time analytic applications such as Who Viewed Profile, Company Analytics, Talent Insights, and many more. UberEats Restaurant Manager is another example of a customer-facing Analytics App. At LinkedIn, Pinot powers 50+ user-facing products, ingesting millions of events per second and serving 100k+ queries per second at millisecond latency.

Real-time Dashboard for Business Metrics

Pinot can be also be used to 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. One can connect various BI tools such as Superset, Tableau, or PowerBI to visualize data in Pinot.

Instructions to connect Pinot with Superset can be found here.

Anomaly Detection

In addition to visualizing data in Pinot, one can run Machine Learning Algorithms to detect Anomalies in the data stored in Pinot. See ThirdEye for more information on how to use Pinot for Anomaly Detection and Root Cause Analysis.

Frequently asked questions when getting started

Is Pinot a data warehouse or a database?

While Pinot doesn't match the typical mold of a database product, it is best understood based on your role as either an analyst, data scientist, or application developer.

Enterprise business intelligence

For analysts and data scientists, Pinot is best viewed as a highly-scalable data platform for business intelligence. In this view, 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 is best viewed as an immutable aggregate store that sources events from streaming data sources, such as Kafka, and makes it available for a query using SQL.

As is the case with a microservice architecture, data encapsulation ends up requiring each application to provide its own data store, as opposed to sharing one OLTP database for reads and writes. In this case, it becomes difficult to query the complete view of a domain because it becomes stored in many different databases. This is costly in terms of performance since it requires joins across multiple microservices that expose their data over HTTP under a REST API. To prevent this, Pinot can be used to aggregate all of the 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 and immutable.

Get started

Our documentation is structured to let you quickly get to the content you need and is organized around the different concerns of users, operators, and developers. If you're new to Pinot and want to learn things by example, please take a look at our getting started section.

Starter guides

To start importing data into Pinot, check out our guides on batch import and stream ingestion based on our plugin architecture.

Query example

Pinot works very well for querying time series data with many dimensions and metrics over a vast unbounded space of records that scales linearly on a per-node basis. Filters and aggregations are both easy and fast.

SELECT sum(clicks), sum(impressions) FROM AdAnalyticsTable
  WHERE 
       ((daysSinceEpoch >= 17849 AND daysSinceEpoch <= 17856)) AND 
       accountId IN (123456789)
  GROUP BY 
       daysSinceEpoch TOP 100

Pinot supports SQL for querying read-only data. Learn more about querying Pinot for time series data in our PQL (Pinot Query Language) guide.

Installation

Pinot may be deployed to and operated on a cloud provider or a local or virtual machine. You may get started either with a bare-metal installation or a Kubernetes one (either locally or in the cloud). To get immediately started with Pinot, check out these quick start guides for bootstrapping a Pinot cluster using Docker or Kubernetes.

Standalone mode

Cluster mode

Learn

For a high-level overview that explains how Pinot works, please take a look at our basic concepts section.

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

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 components

Helix divides nodes into logical components based on their responsibilities:

Participant

The nodes that host distributed, partitioned resources

Pinot Servers are modeled as Participants. For more details about server nodes, see Server.

Spectator

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 more 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 more 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.

Setup a Pinot Cluster

Typically, there is only one cluster per environment/data center. There is no need to create multiple Pinot clusters since Pinot supports the concept of tenants. At LinkedIn, the largest Pinot cluster consists of 1000+ nodes.

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

• Running Pinot in Docker

• Running Pinot locally

Pinot is designed to deliver low latency queries on large datasets. In order 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 and each shard is converted into a unit known as 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 uses a variety of terms that can refer to either abstractions that model the storage of data or infrastructure components that drive the functionality of the system.

Table

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

As is the case with 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 (real-time or batch) can inherit a single schema definition. Tables are independently configured for concerns such as indexing strategies, partitioning, tenants, data sources, and/or replication.

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 HA relational databases). Segments can also be seen as time-based partitions.

Tenant

In order to support multi-tenancy, Pinot has first class support for tenants. A 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 comprises 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

• Server

• Broker

• Minion (optional)

The benefits of scale that make Pinot linearly scalable for an unbounded number of nodes is made possible through its integration with Apache Zookeeper and Apache Helix.

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 their 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.

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.

Controller periodic tasks

The Controller runs several periodic tasks in the background, to perform activities such as management and validation. Each periodic task has its own configs 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.

Here's a list of all the periodic tasks

BrokerResourceValidationManager

This task rebuilds the BrokerResource if the instance set has changed.

MinionInstancesCleanupTask

TBD

OfflineSegmentIntervalChecker

This task manages the segment ValidationMetrics (missingSegmentCount, offlineSegmentDelayHours, lastPushTimeDelayHours, TotalDocumentCount, NonConsumingPartitionCount, SegmentCount), to ensure that all offline segments are contiguous (no missing segments) and that the offline push delay isn't too high.

PinotTaskManager

TBD

RealtimeSegmentValidationManager

This task validates the ideal state and segment zk metadata of realtime tables,

1. fixing any partitions which have stopped consuming

2. starting consumption from new partitions

3. uploading segments to deep store if segment download url is missing

This task ensures that the consumption of the realtime tables gets fixed and keeps going when met with erroneous conditions.

RetentionManager

This task manages retention of segments for all tables. During the run, it looks at the retentionTimeUnit and retentionTimeValue inside the segmentsConfig of every table, and deletes segments which are older than the retention. The deleted segments are moved to a DeletedSegments folder colocated with the dataDir on segment store, and permanently deleted from that folder in a configurable number of days.

SegmentRelocator

This task is applicable only if you have tierConfig or tagOverrideConfig. It runs rebalance in the background to

1. relocate COMPLETED segments to tag overrides

2. relocate ONLINE segments to tiers if tier configs are set

At most one replica is allowed to be unavailable during rebalance.

SegmentStatusChecker

This task manages segment status metrics such as realtimeTableCount, offlineTableCount, disableTableCount, numberOfReplicas, percentOfReplicas, percentOfSegments, idealStateZnodeSize, idealStateZnodeByteSize, segmentCount, segmentsInErrorState, tableCompressedSize.

TaskMetricsEmitter

TBD

Running the periodic task manually

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",
  "MinionInstancesCleanupTask",
  "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 setup 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 will introduce 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 be introduced to the two different types of tables used to ingest and query data in offline (batch) or real-time (stream) mode.

Guiding design principles

Pinot was designed by engineers at LinkedIn and Uber to scale query performance based on the number of nodes in a cluster. As you add more nodes, query performance will always improve 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, the following guiding design principles were established.

• 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: Ability to scale 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 be performed without impacting 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

All Pinot servers and brokers are managed by Helix. Helix is a generic cluster management framework to manage partitions and replicas in a distributed system. 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. Query load is optimized as Helix updates routing configurations between nodes based on where data is stored in the cluster.

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

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

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

3. 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 that are distributed in a Pinot cluster will 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 is the first component that is typically started after Zookeeper. Two parameters are required for starting a controller: Zookeeper address and cluster name. The controller will automatically create a cluster via Helix if it does not yet exist.

Fault tolerance

To achieve fault tolerance, one can 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 re-balancing 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).

Broker

The responsibility of the broker is to route a given query to an appropriate server instance. A broker will collect and merge the responses from all servers into a final result and send 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

At the start, a broker registers as a Helix Participant and awaits notifications from other Helix agents. These notifications will be handled 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

Irrespective 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.

• 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 <1MB in size per query, one can run a broker and servers inside the same instance container. This lowers the overall footprint of a cluster deployment for use cases that do not 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 does not really know if it's going to be a real-time server or an offline server. The responsibility of a server 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. Servers are notified via Helix about the new segments. Servers fetch the segments from deep store and load them before being ready to serve query requests. At this point, the cluster's broker detects that new segments are available and starts including them in query responses.

Real-time servers

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

Minion

Minion is an optional component and is not required to get started with Pinot. Minion is used for purging data from a Pinot cluster (for reasons such as 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. The reason for having two types of tables is because each one follows a different state model.

A real-time and offline table provide different configuration options for indexing and, in the case of real-time, the connector properties for the stream data source (i.e. Kafka). 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 where real-time servers might need higher system memory and/or more CPU cores.

The two types of tables also scale differently.

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

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

There are a few things to keep in mind when configuring the different types of tables for your workloads. 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.

Batch data flow

In batch mode, data is ingested into Pinot via an ingestion job. An ingestion job transforms a raw data source (such as a CSV file) into segments. Once segments are generated for the imported data, an ingestion job stores them into the cluster's segment store (a.k.a deep store) and notifies the controller. The notification is processed and the result is that the Helix agent on the controller updates the ideal state configuration in Zookeeper. Helix will then notify the offline server that there are new segments available. In response to the notification from the controller, the offline server downloads the newly created segments directly from the cluster's segment store. 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 (i.e. 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 checks the request against the segment-to-server routing table—scattering the request between real-time and offline servers.

The two tables then 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.

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 realtime. 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 setup 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

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, 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

USAGE

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 Config

This section contains 2 main fields broker and server , which decide the tenants used for the broker and server components of this table.

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.

Creating a tenant

Broker tenant

Here's a sample broker tenant config. This will create a broker tenant sampleBrokerTenant by tagging 3 untagged broker nodes as sampleBrokerTenant_BROKER.

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

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.

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

Pinot has the concept of a , 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.

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

Columns 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 .

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 page.

Load Data in Batch

Prerequisites

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

Job Spec YAML

Create and push segment

To create and push the segment in one go, use

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.

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

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

Load Data in Streaming

Prerequisites

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

Kafka

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 the 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.

Data Types

Data types determine the operations that can be performed on a column. Pinot supports the following data types:

Date Time Fields

Built-in Virtual Columns

There are several built-in virtual columns inside the schema the can be used for debugging purposes:

These virtual columns can be used in queries in a similar way to regular columns.

Creating a Schema

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

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

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 .

Pinot breaks a table into multiple and stores these segments in a deep-store such as HDFS as well as Pinot servers.

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

Pinot supports the following types of table:

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

You can use the following properties to make your tables faster or leaner:

• Segment

• Indexing

• Tenants

Segments

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 X no. 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 so that the persisted segment is approximately the size. The learning phase starts by setting the number of rows to 100,000 (this value can be changed) and adjusts it to reach the desired segment size. The segment size may go significantly over the desired size during the learning phase. Pinot corrects the estimation as it goes along, so it is not guaranteed that the resulting completed segments are of the exact size as configured. 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

However, in certain scenarios, the segment build can get very memory intensive. It might be desirable 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

Download Scheme

A Pinot server may 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 can be added in the future.

Indexing

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

• • Dictionary-encoded forward index with bit compression

  • Raw value forward index

  • Sorted forward index with run-length encoding

• • Bitmap inverted index

  • Sorted inverted index

Pre-aggregation

You can aggregate the real-time stream data as it is consumed to reduce segment sizes. We sum 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 on which pre-aggregation is to be done need to satisfy the following requirements:

• All metrics should be listed in noDictionaryColumns .

• There should not be any 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.

Tenants

You can also override if a table should move to a server with different tenant based on segment status.

A tagOverrideConfig can be added under the tenants section for realtime tables, to override tags for consuming and completed segments. For example:

Hybrid Table

A hybrid table is a table composed of 2 tables, one offline and one real-time that share the same name. In such a table, offline segments may be pushed periodically. The retention on the offline table can be set to a high value since 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.

A typical scenario is pushing a deduped cleaned up data into an offline table every day while consuming real-time data as and when it arrives. The data can be kept in offline tables for even a few years while the real-time data would be cleaned every few days.

Examples

Prerequisites

Offline Table Creation

Streaming Table Creation

Hybrid Table creation

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

ConvertToRawIndexTask

To be added

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 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, one can see 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.

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

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

Batch

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

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

• Creates the baseballStats table

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

• Issues sample queries to Pinot

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

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

Batch JSON

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

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

• Creates the githubEvents table

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

• Issues sample queries to Pinot

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

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

Batch with complex data types

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

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

• Creates the githubEvents table

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

• Issues sample queries to Pinot

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

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

Streaming

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

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

• Creates meetupRsvp table

• Launches a meetup stream

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

• Issues sample queries to Pinot

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

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

Streaming JSON

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

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

• Creates meetupRsvp table

• Launches a meetup stream

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

• Issues sample queries to Pinot

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

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

Streaming with minion cleanup

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

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

• Creates githubEvents table

• Launches a GitHub events stream

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

• Issues sample queries to Pinot

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

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

Streaming with complex data types

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

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

• Creates meetupRsvp table

• Launches a meetup stream

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

• Issues sample queries to Pinot

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

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

Upsert

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

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

• Creates meetupRsvp table

• Launches a meetup stream

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

• Issues sample queries to Pinot

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

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

Upsert JSON

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

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

• Creates meetupRsvp table

• Launches a meetup stream

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

• Issues sample queries to Pinot

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

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

Hybrid

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

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

2. Creates airlineStats table

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

4. Launches a stream of flights stats

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

6. Issues sample queries to Pinot

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

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

Join

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

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

• Creates the baseballStats table

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

• Creates the dimBaseballTeams table

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

• Issues sample queries to Pinot

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

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

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

Download Apache Pinot

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

You can build from source or download the distribution:

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

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

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

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

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

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

# navigate to directory containing the setup scripts
cd build

M1 Mac Support

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

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

Also make sure to install rosetta

softwareupdate --install-rosetta

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

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

Quick Start

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

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

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

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

Manual Cluster

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

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

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

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

export JAVA_OPTS="-Xms4G -Xmx8G"

Start Zookeeper

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

You can use Zooinspector to browse the Zookeeper instance.

Start Pinot Controller

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

Start Pinot Broker

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

Start Pinot Server

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

Start Kafka

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

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

Start Pinot Component in Debug Mode with IntelliJ

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

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