LogoLogo
release-0.10.0
release-0.10.0
  • Introduction
  • Basics
    • Concepts
    • Architecture
    • Components
      • Cluster
      • Controller
      • Broker
      • Server
      • Minion
      • Tenant
      • Schema
      • Table
      • Segment
      • Deep Store
      • 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
      • 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
    • Import Data
      • Batch Ingestion
        • Spark
        • Hadoop
        • Backfill Data
        • Dimension Table
      • Stream ingestion
        • Apache Kafka
        • Amazon Kinesis
        • Apache Pulsar
      • Stream Ingestion with Upsert
      • File Systems
        • Amazon S3
        • Azure Data Lake Storage
        • HDFS
        • Google Cloud Storage
      • Input formats
      • Complex Type (Array, Map) Handling
    • Indexing
      • Forward Index
      • Inverted Index
      • Star-Tree Index
      • Bloom Filter
      • Range Index
      • Text search support
      • JSON Index
      • Geospatial
      • Timestamp Index
    • Releases
      • 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
      • GitHub Events Stream
  • For Users
    • Query
      • Querying Pinot
      • Filtering with IdSet
      • Transformation Functions
      • Aggregation Functions
      • User-Defined Functions (UDFs)
      • Cardinality Estimation
      • Lookup UDF Join
      • Querying JSON data
      • Explain Plan
      • Grouping Algorithm
    • APIs
      • Broker Query API
        • Query Response Format
      • Controller Admin API
    • 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
      • Update Documentation
    • Advanced
      • Data Ingestion Overview
      • Ingestion Transformations
      • Null Value Support
      • 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
      • Setup cluster
      • Setup table
      • Setup ingestion
      • Decoupling Controller from the Data Path
      • Segment Assignment
      • Instance Assignment
      • Rebalance
        • Rebalance Servers
        • Rebalance Brokers
      • Tiered Storage
      • Pinot managed Offline flows
      • Minion merge rollup task
      • Access Control
      • Monitoring
      • Tuning
        • Realtime
        • Routing
      • Upgrading Pinot with confidence
    • Command-Line Interface (CLI)
    • Configuration Recommendation Engine
    • Tutorials
      • Authentication, Authorization, and ACLs
      • Configuring TLS/SSL
      • Build Docker Images
      • Running Pinot in Production
      • Kubernetes Deployment
      • Amazon EKS (Kafka)
      • Amazon MSK (Kafka)
      • Monitor Pinot using Prometheus and Grafana
  • Configuration Reference
    • Cluster
    • Controller
    • Broker
    • Server
    • Table
    • Schema
    • Ingestion Job Spec
    • Functions
      • ABS
      • ADD
      • arrayConcatInt
      • arrayConcatString
      • arrayContainsInt
      • arrayContainsString
      • arrayDistinctString
      • arrayDistinctInt
      • arrayIndexOfInt
      • arrayIndexOfString
      • ARRAYLENGTH
      • arrayRemoveInt
      • arrayRemoveString
      • arrayReverseInt
      • arrayReverseString
      • arraySliceInt
      • arraySliceString
      • arraySortInt
      • arraySortString
      • arrayUnionInt
      • arrayUnionString
      • AVGMV
      • ceil
      • CHR
      • codepoint
      • concat
      • count
      • COUNTMV
      • day
      • dayOfWeek
      • dayOfYear
      • DISTINCT
      • DISTINCTCOUNT
      • DISTINCTCOUNTBITMAP
      • DISTINCTCOUNTBITMAPMV
      • DISTINCTCOUNTHLL
      • DISTINCTCOUNTHLLMV
      • DISTINCTCOUNTMV
      • DISTINCTCOUNTRAWHLL
      • DISTINCTCOUNTRAWHLLMV
      • DISTINCTCOUNTRAWTHETASKETCH
      • DISTINCTCOUNTTHETASKETCH
      • DIV
      • DATETIMECONVERT
      • DATETRUNC
      • exp
      • FLOOR
      • FromDateTime
      • FromEpoch
      • FromEpochBucket
      • hour
      • JSONFORMAT
      • JSONPATH
      • JSONPATHARRAY
      • JSONPATHARRAYDEFAULTEMPTY
      • JSONPATHDOUBLE
      • JSONPATHLONG
      • JSONPATHSTRING
      • jsonextractkey
      • jsonextractscalar
      • 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
      • quarter
      • regexpExtract
      • remove
      • replace
      • reverse
      • round
      • rpad
      • rtrim
      • second
      • SEGMENTPARTITIONEDDISTINCTCOUNT
      • 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
      • VALUEIN
      • week
      • year
      • yearOfWeek
  • RESOURCES
    • Community
    • Team
    • Blogs
    • Presentations
    • Videos
  • Integrations
    • Tableau
    • Trino
    • ThirdEye
    • Superset
    • Presto
Powered by GitBook
On this page
  • Balanced Segment Assignment
  • Replica-Group Segment Assignment
  • Partitioned Replica-Group Segment Assignment
  • Configure Segment Assignment

Was this helpful?

Export as PDF
  1. For Operators
  2. Deployment and Monitoring

Segment Assignment

This page introduces all the segment assignment strategies, when to use them, and how to configure them

PreviousDecoupling Controller from the Data PathNextInstance Assignment

Last updated 3 years ago

Was this helpful?

Segment assignment means the strategy of assigning each segment from a table to the servers hosting the table. Picking the best segment assignment strategy can help reduce the overhead of the query routing, thus providing better performance.

Balanced Segment Assignment

Balanced Segment Assignment is the default assignment strategy, where each segment is assigned to the server with the least segments already assigned. With this strategy, each server will have balanced query load, and each query will be routed to all the servers. It requires minimum configuration, and works well for small use cases.

Replica-Group Segment Assignment

Balanced Segment Assignment is ideal for small use cases with a small number of servers, but as the number of servers increases, routing each query to all the servers could harm the query performance due to the overhead of the increased fanout.

Replica-Group Segment Assignment is introduced to solve the horizontal scalability problem of the large use cases, which makes Pinot linearly scalable. This strategy breaks the servers into multiple replica-groups, where each replica-group contains a full copy of all the segments.

When executing queries, each query will only be routed to the servers within the same replica-group. In order to scale up the cluster, more replica-groups can be added without affecting the fanout of the query, thus not impacting the query performance but increasing the overall throughput linearly.

Partitioned Replica-Group Segment Assignment

In order to further increase the query performance, we can reduce the number of segments processed for each query by partitioning the data and use the Partitioned Replica-Group Segment Assignment.

Partitioned Replica-Group Segment Assignment extends the Replica-Group Segment Assignment by assigning the segments from the same partition to the same set of servers. To solve a query which hits only one partition (e.g. SELECT * FROM myTable WHERE memberId = 123 where myTable is partitioned with memberId column), the query only needs to be routed to the servers for the targeting partition, which can significantly reduce the number of segments to be processed. This strategy is especially useful to achieve high throughput and low latency for use cases that filter on an id field.

Configure Segment Assignment

Segment assignment is configured along with the instance assignment, check for details.

Instance Assignment