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  • Introduction
  • Basics
    • Concepts
    • Architecture
    • Components
      • Cluster
      • Controller
      • Broker
      • Server
      • Minion
      • Tenant
      • Schema
      • Table
      • Segment
      • Pinot Data Explorer
    • Getting Started
      • Running Pinot locally
      • Running Pinot in Docker
      • Running Pinot in Kubernetes
      • Public cloud examples
        • Running on Azure
        • Running on GCP
        • Running on AWS
      • Hdfs as Deep Storage
      • Manual cluster setup
      • Batch import example
      • Stream ingestion example
      • 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
      • 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
    • Releases
      • 0.9.0
      • 0.8.0
      • 0.7.1
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      • 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
      • Supported Transformations
      • Supported Aggregations
      • User-Defined Functions (UDFs)
      • Cardinality Estimation
      • Lookup UDF Join
      • Querying JSON data
    • 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
  • RESOURCES
    • Community
    • Team
    • Blogs
    • Presentations
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  • Integrations
    • Tableau
    • Trino
    • ThirdEye
    • Superset
    • Presto
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  1. Basics
  2. Indexing

Range Index

Range indexing allows user to get better performance for queries which involve filtering over a range. e.g.

SELECT COUNT(*) from baseballStats where hits > 11

Range index is just a variant of inverted index. Instead of creating mapping from values to columns, we create mapping of a range of values to columns. You can use the range index by setting the following config in the table config json.

{
    "tableIndexConfig": {
        "rangeIndexColumns": [
            "column_name",
            ...
        ],
        ...
    }
}

Currently, range indexing is only supported for dictionary columns. Range indexing support for raw value columns is WIP.

When to use Range Index?

A good thumb rule is to use range index when you want to apply range predicates on metric columns which have very large number of unique values. Using inverted index for such columns will create a very large index that is inefficient in terms of storage and performance.

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Last updated 3 years ago

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