LogoLogo
latest
latest
  • Introduction
  • Basics
    • Concepts
      • Pinot storage model
      • Architecture
      • Components
        • Cluster
          • Tenant
          • Server
          • Controller
          • Broker
          • Minion
        • Table
          • Segment
            • Deep Store
            • Segment threshold
            • Segment retention
          • Schema
          • Time boundary
        • Pinot Data Explorer
    • Getting Started
      • Running Pinot locally
      • Running Pinot in Docker
      • Quick Start Examples
      • Running in Kubernetes
      • Running on public clouds
        • Running on Azure
        • Running on GCP
        • Running on AWS
      • Create and update a table configuration
      • Batch import example
      • Stream ingestion example
      • HDFS as Deep Storage
      • Troubleshooting Pinot
      • Frequently Asked Questions (FAQs)
        • General
        • Pinot On Kubernetes FAQ
        • Ingestion FAQ
        • Query FAQ
        • Operations FAQ
    • Indexing
      • Bloom filter
      • Dictionary index
      • Forward index
      • FST index
      • Geospatial
      • Inverted index
      • JSON index
      • Native text index
      • Range index
      • Star-tree index
      • Text search support
      • Timestamp index
      • Vector index
    • Release notes
      • 1.3.0
      • 1.2.0
      • 1.1.0
      • 1.0.0
      • 0.12.1
      • 0.12.0
      • 0.11.0
      • 0.10.0
      • 0.9.3
      • 0.9.2
      • 0.9.1
      • 0.9.0
      • 0.8.0
      • 0.7.1
      • 0.6.0
      • 0.5.0
      • 0.4.0
      • 0.3.0
      • 0.2.0
      • 0.1.0
    • Recipes
      • Connect to Streamlit
      • Connect to Dash
      • Visualize data with Redash
      • GitHub Events Stream
  • For Users
    • Query
      • Querying Pinot
      • Query Syntax
        • Explain Plan (Single-Stage)
        • Filtering with IdSet
        • GapFill Function For Time-Series Dataset
        • Grouping Algorithm
        • JOINs
        • Lookup UDF Join
      • Query Options
      • Query Quotas
      • Query using Cursors
      • Multi-stage query
        • Understanding Stages
        • Stats
        • Optimizing joins
        • Join strategies
          • Random + broadcast join strategy
          • Query time partition join strategy
          • Colocated join strategy
          • Lookup join strategy
        • Hints
        • Operator Types
          • Aggregate
          • Filter
          • Join
          • Intersect
          • Leaf
          • Literal
          • Mailbox receive
          • Mailbox send
          • Minus
          • Sort or limit
          • Transform
          • Union
          • Window
        • Stage-Level Spooling
      • Explain plan
    • APIs
      • Broker Query API
        • Query Response Format
      • Broker GRPC API
      • Controller Admin API
      • Controller API Reference
    • External Clients
      • JDBC
      • Java
      • Python
      • Golang
    • Tutorials
      • Use OSS as Deep Storage for Pinot
      • Ingest Parquet Files from S3 Using Spark
      • Creating Pinot Segments
      • Use S3 as Deep Storage for Pinot
      • Use S3 and Pinot in Docker
      • Batch Data Ingestion In Practice
      • Schema Evolution
  • For Developers
    • Basics
      • Extending Pinot
        • Writing Custom Aggregation Function
        • Segment Fetchers
      • Contribution Guidelines
      • Code Setup
      • Code Modules and Organization
      • Dependency Management
      • Update documentation
    • Advanced
      • Data Ingestion Overview
      • Ingestion Aggregations
      • Ingestion Transformations
      • Null value support
      • Use the multi-stage query engine (v2)
      • Advanced Pinot Setup
    • Plugins
      • Write Custom Plugins
        • Input Format Plugin
        • Filesystem Plugin
        • Batch Segment Fetcher Plugin
        • Stream Ingestion Plugin
    • Design Documents
      • Segment Writer API
  • For Operators
    • Deployment and Monitoring
      • Set up cluster
      • Server Startup Status Checkers
      • Set up table
      • Set up ingestion
      • Decoupling Controller from the Data Path
      • Segment Assignment
      • Instance Assignment
      • Rebalance
        • Rebalance Servers
          • Examples and Scenarios
        • Rebalance Brokers
        • Rebalance Tenant
      • Separating data storage by age
        • Using multiple tenants
        • Using multiple directories
      • Pinot managed Offline flows
      • Minion merge rollup task
      • Consistent Push and Rollback
      • Access Control
      • Monitoring
      • Tuning
        • Tuning Default MMAP Advice
        • Real-time
        • Routing
        • Query Routing using Adaptive Server Selection
        • Query Scheduling
      • Upgrading Pinot with confidence
      • Managing Logs
      • OOM Protection Using Automatic Query Killing
      • Pause ingestion based on resource utilization
    • Command-Line Interface (CLI)
    • Configuration Recommendation Engine
    • Tutorials
      • Authentication
        • Basic auth access control
        • ZkBasicAuthAccessControl
      • Configuring TLS/SSL
      • Build Docker Images
      • Running Pinot in Production
      • Kubernetes Deployment
      • Amazon EKS (Kafka)
      • Amazon MSK (Kafka)
      • Monitor Pinot using Prometheus and Grafana
      • Performance Optimization Configurations
      • Segment Operations Throttling
      • Reload a table segment
  • Configuration Reference
    • Cluster
    • Controller
    • Broker
    • Server
    • Table
    • Ingestion
    • Schema
    • Database
    • Ingestion Job Spec
    • Monitoring Metrics
    • Plugin Reference
      • Stream Ingestion Connectors
      • VAR_POP
      • VAR_SAMP
      • STDDEV_POP
      • STDDEV_SAMP
    • Dynamic Environment
  • Manage Data
    • Import Data
      • SQL Insert Into From Files
      • Upload Pinot segment Using CommandLine
      • Batch Ingestion
        • Spark
        • Flink
        • Hadoop
        • Backfill Data
        • Dimension table
      • Stream Ingestion
        • Ingest streaming data from Apache Kafka
        • Ingest streaming data from Amazon Kinesis
        • Ingest streaming data from Apache Pulsar
        • Configure indexes
        • Stream ingestion with CLP
      • Upsert and Dedup
        • Stream ingestion with Upsert
        • Segment compaction on upserts
        • Stream ingestion with Dedup
      • Supported Data Formats
      • File Systems
        • Amazon S3
        • Azure Data Lake Storage
        • HDFS
        • Google Cloud Storage
      • Complex Type (Array, Map) Handling
        • Complex Type Examples (Unnest)
      • Ingest records with dynamic schemas
  • Functions
    • Aggregation Functions
    • Transformation Functions
    • Array Functions
    • DateTime Functions
    • Funnel Analysis Functions
    • Hash Functions
    • JSON Functions
    • User-Defined Functions (UDFs)
    • URL Functions
    • Unique Count and cardinality Estimation Functions
  • Window Functions
  • (Deprecating) Function List
    • ABS
    • ADD
    • ago
    • EXPR_MIN / EXPR_MAX
    • ARRAY_AGG
    • arrayConcatDouble
    • arrayConcatFloat
    • arrayConcatInt
    • arrayConcatLong
    • arrayConcatString
    • arrayContainsInt
    • arrayContainsString
    • arrayDistinctInt
    • arrayDistinctString
    • arrayIndexOfInt
    • arrayIndexOfString
    • ARRAYLENGTH
    • arrayRemoveInt
    • arrayRemoveString
    • arrayReverseInt
    • arrayReverseString
    • arraySliceInt
    • arraySliceString
    • arraySortInt
    • arraySortString
    • arrayUnionInt
    • arrayUnionString
    • AVGMV
    • Base64
    • caseWhen
    • ceil
    • CHR
    • codepoint
    • concat
    • count
    • COUNTMV
    • COVAR_POP
    • COVAR_SAMP
    • day
    • dayOfWeek
    • dayOfYear
    • DISTINCT
    • DISTINCTCOUNT
    • DISTINCTCOUNTMV
    • DISTINCT_COUNT_OFF_HEAP
    • SEGMENTPARTITIONEDDISTINCTCOUNT
    • DISTINCTCOUNTBITMAP
    • DISTINCTCOUNTBITMAPMV
    • DISTINCTCOUNTHLL
    • DISTINCTCOUNTHLLMV
    • DISTINCTCOUNTRAWHLL
    • DISTINCTCOUNTRAWHLLMV
    • DISTINCTCOUNTSMARTHLL
    • DISTINCTCOUNTHLLPLUS
    • DISTINCTCOUNTULL
    • DISTINCTCOUNTTHETASKETCH
    • DISTINCTCOUNTRAWTHETASKETCH
    • DISTINCTSUM
    • DISTINCTSUMMV
    • DISTINCTAVG
    • DISTINCTAVGMV
    • DIV
    • DATETIMECONVERT
    • DATETRUNC
    • exp
    • FIRSTWITHTIME
    • FLOOR
    • FrequentLongsSketch
    • FrequentStringsSketch
    • FromDateTime
    • FromEpoch
    • FromEpochBucket
    • FUNNELCOUNT
    • FunnelCompleteCount
    • FunnelMaxStep
    • FunnelMatchStep
    • GridDistance
    • Histogram
    • hour
    • isSubnetOf
    • JSONFORMAT
    • JSONPATH
    • JSONPATHARRAY
    • JSONPATHARRAYDEFAULTEMPTY
    • JSONPATHDOUBLE
    • JSONPATHLONG
    • JSONPATHSTRING
    • jsonextractkey
    • jsonextractscalar
    • LAG
    • LASTWITHTIME
    • LEAD
    • length
    • ln
    • lower
    • lpad
    • ltrim
    • max
    • MAXMV
    • MD5
    • millisecond
    • min
    • minmaxrange
    • MINMAXRANGEMV
    • MINMV
    • minute
    • MOD
    • mode
    • month
    • mult
    • now
    • percentile
    • percentileest
    • percentileestmv
    • percentilemv
    • percentiletdigest
    • percentiletdigestmv
    • percentilekll
    • percentilerawkll
    • percentilekllmv
    • percentilerawkllmv
    • quarter
    • regexpExtract
    • regexpReplace
    • remove
    • replace
    • reverse
    • round
    • roundDecimal
    • ROW_NUMBER
    • rpad
    • rtrim
    • second
    • sha
    • sha256
    • sha512
    • sqrt
    • startswith
    • ST_AsBinary
    • ST_AsText
    • ST_Contains
    • ST_Distance
    • ST_GeogFromText
    • ST_GeogFromWKB
    • ST_GeometryType
    • ST_GeomFromText
    • ST_GeomFromWKB
    • STPOINT
    • ST_Polygon
    • strpos
    • ST_Union
    • SUB
    • substr
    • sum
    • summv
    • TIMECONVERT
    • timezoneHour
    • timezoneMinute
    • ToDateTime
    • ToEpoch
    • ToEpochBucket
    • ToEpochRounded
    • TOJSONMAPSTR
    • toGeometry
    • toSphericalGeography
    • trim
    • upper
    • Url
    • UTF8
    • VALUEIN
    • week
    • year
    • Extract
    • yearOfWeek
    • FIRST_VALUE
    • LAST_VALUE
    • ST_GeomFromGeoJSON
    • ST_GeogFromGeoJSON
    • ST_AsGeoJSON
  • Reference
    • Single-stage query engine (v1)
    • Multi-stage query engine (v2)
    • Troubleshooting
      • Troubleshoot issues with the multi-stage query engine (v2)
      • Troubleshoot issues with ZooKeeper znodes
      • Realtime Ingestion Stopped
  • RESOURCES
    • Community
    • Team
    • Blogs
    • Presentations
    • Videos
  • Integrations
    • Tableau
    • Trino
    • ThirdEye
    • Superset
    • Presto
    • Spark-Pinot Connector
  • Contributing
    • Contribute Pinot documentation
    • Style guide
Powered by GitBook
On this page
  • Cluster configuration
  • Cluster components
  • Participant
  • Spectator
  • Controller
  • Logical view
  • Set up a Pinot cluster

Was this helpful?

Edit on GitHub
Export as PDF
  1. Basics
  2. Concepts
  3. Components

Cluster

Learn to build and manage Apache Pinot clusters, uncovering key components for efficient data processing and optimized analysis.

PreviousComponentsNextTenant

Last updated 1 year ago

Was this helpful?

A Pinot cluster is a collection of the software processes and hardware resources required to ingest, store, and process data. For detail about Pinot cluster components, see .

A Pinot cluster consists of the following processes, which are typically deployed on separate hardware resources in production. In development, they can fit comfortably into Docker containers on a typical laptop:

  • Controller: Maintains cluster metadata and manages cluster resources.

  • Zookeeper: Manages the Pinot cluster on behalf of the controller. Provides fault-tolerant, persistent storage of metadata, including table configurations, schemas, segment metadata, and cluster state.

  • Broker: Accepts queries from client processes and forwards them to servers for processing.

  • Server: Provides storage for segment files and compute for query processing.

  • (Optional) Minion: Computes background tasks other than query processing, minimizing impact on query latency. Optimizes segments, and builds additional indexes to ensure performance (even if data is deleted).

The simplest possible Pinot cluster consists of four components: a server, a broker, a controller, and a Zookeeper node. In production environments, these components typically run on separate server instances, and scale out as needed for data volume, load, availability, and latency. Pinot clusters in production range from fewer than ten total instances to more than 1,000.

Pinot uses as a distributed metadata store and for cluster management.

Helix is a cluster management solution that maintains a persistent, fault-tolerant map of the intended state of the Pinot cluster. Helix constantly monitors the cluster to ensure that the right hardware resources are allocated for the present configuration. When the configuration changes, Helix schedules or decommissions hardware resources to reflect the new configuration. When elements of the cluster change state catastrophically, Helix schedules hardware resources to keep the actual cluster consistent with the ideal represented in the metadata. From a physical perspective, Helix takes the form of a controller process plus agents running on servers and brokers.

Cluster configuration

For details of cluster configuration settings, see .

Cluster components

Helix divides nodes into logical components based on their responsibilities:

Participant

Participants are the nodes that host distributed, partitioned resources

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).

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.

Logical view

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

Set up a Pinot cluster

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

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

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

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

A cluster contains

Tenants contain

Tables contain

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

Server
Broker
Controller
tenants
tables
segments
tenants
Running Pinot in Docker
Running Pinot locally
Apache Zookeeper
Apache Helix
Cluster configuration reference
Physical architecture