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
    • Binary Functions
    • DateTime Functions
    • Funnel Analysis Functions
    • GeoSpatial Functions
    • Hash Functions
    • JSON Functions
    • Math Functions
    • String Functions
    • User-Defined Functions (UDFs)
    • URL Functions
    • Unique Count and cardinality Estimation Functions
  • Window Functions
  • 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
  • Balanced Segment Assignment
  • Replica-Group Segment Assignment
  • Partitioned Replica-Group Segment Assignment
  • Configure Segment Assignment

Was this helpful?

Edit on GitHub
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 1 year ago

Was this helpful?

Segment assignment refers to 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