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
    • 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
  • Bitmap inverted index
  • Sorted inverted index

Was this helpful?

Edit on GitHub
Export as PDF
  1. Basics
  2. Indexing

Inverted index

This page describes configuring the inverted index for Apache Pinot

PreviousGeospatialNextJSON index

Last updated 1 year ago

Was this helpful?

We can define the as a mapping from document IDs (also known as rows) to values. Similarly, an inverted index establishes a mapping from values to a set of document IDs, making it the "inverted" version of the forward index. When you frequently use a column for filtering operations like EQ (equal), IN (membership check), GT (greater than), etc., incorporating an inverted index can significantly enhance query performance.

Pinot supports two distinct types of inverted indexes: bitmap inverted indexes and sorted inverted indexes. Bitmap inverted indexes represent the actual inverted index type, whereas the sorted type is automatically available when the column is sorted. Both types of indexes necessitate the enabling of a for the respective column.

Bitmap inverted index

When a column is not sorted, and an inverted index is enabled for that column, Pinot maintains a mapping from each value to a bitmap of rows. This design ensures that value lookup operations take constant time, providing efficient querying capabilities.

When an inverted index is enabled for a column, Pinot maintains a map from each value to a bitmap of rows, which makes value lookup take constant time. If you have a column that is frequently used for filtering, adding an inverted index will improve performance greatly. You can create an inverted index on a multi-value column.

Inverted indexes are disabled by default and can be enabled for a column by specifying the configuration within the :

inverted index defined in tableConfig
{
  "fieldConfigList": [
    {
      "name": "theColumnName",
      "indexes": {
        "inverted": {}
      }
    }
  ],
  ...
}

The older way to configure inverted indexes can also be used, although it is not actually recommended:

old way to define inverted index in tableConfig
{
    "tableIndexConfig": {
        "invertedIndexColumns": [
            "theColumnName",
            ...
        ],
        ...
    }
}

When the index is created

By default, bitmap inverted indexes are not generated when the segment is initially created; instead, they are created when the segment is loaded by Pinot. This behavior is governed by the table configuration option indexingConfig.createInvertedIndexDuringSegmentGeneration, which is set to false by default.

Sorted inverted index

For instance, consider the following example: if a query includes a filter on the memberId column, Pinot will perform a binary search on memberId values to find the range pair of docIds for corresponding filtering value. If the query needs to scan values for other columns after filtering, values within the range docId pair will be located together, which means we can benefit from data locality.

A sorted inverted index indeed offers superior performance compared to a bitmap inverted index, but it's important to note that it can only be applied to sorted columns. In cases where query performance with a regular inverted index is unsatisfactory, especially when a large portion of queries involve filtering on the same column (e.g., _memberId_), using a sorted index can substantially enhance query performance.

As explained in the section, a column that is both sorted and equipped with a dictionary is encoded in a specialized manner that serves the purpose of implementing both forward and inverted indexes. Consequently, when these conditions are met, an inverted index is effectively created without additional configuration, even if the configuration suggests otherwise. This sorted version of the forward index offers a lookup time complexity of log(n) and leverages data locality.

forward index
dictionary
table configuration
forward index
_images/sorted-inverted.png