LogoLogo
release-0.9.0
release-0.9.0
  • 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
      • 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
      • 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
    • Videos
  • Integrations
    • Tableau
    • Trino
    • ThirdEye
    • Superset
    • Presto
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. For Users
  2. Query

Lookup UDF Join

PreviousCardinality EstimationNextQuerying JSON data

Last updated 3 years ago

Was this helpful?

Lookup UDF is used to get dimension data via primary key from a dimension table allowing a decoration join functionality. Lookup UDF can only be used with in Pinot. The UDF signature is as below:

lookUp('dimTableName', 'dimColToLookUp', 'dimJoinKey1', factJoinKeyVal1, 'dimJoinKey2', factJoinKeyVal2 ... )

  • dimTableName Name of the dim table to perform the lookup on.

  • dimColToLookUp The column name of the dim table to be retrieved to decorate our result.

  • dimJoinKey The column name on which we want to perform the lookup i.e. the join column name for dim table.

  • factJoinKeyVal The value of the dim table join column for which we will retrieve the dimColToLookUp for the scope and invocation.

Return type of the UDF will be that of the dimColToLookUp column type. There can also be multiple primary keys and corresponding values.

Note: If the dimension table uses a composite primary key i.e multiple primary keys, then ensure that the order of keys appearing in the lookup() UDF is same as the order defined for "primaryKeyColumns" in the dimension table schema.

a dimension table