LogoLogo
release-1.3.0
release-1.3.0
  • 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
    • Import Data
      • From Query Console
      • 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 Upsert
      • Segment compaction on upserts
      • Stream ingestion with Dedup
      • Stream ingestion with CLP
      • File Systems
        • Amazon S3
        • Azure Data Lake Storage
        • HDFS
        • Google Cloud Storage
      • Input formats
        • Complex Type (Array, Map) Handling
        • Complex Type Examples
        • Ingest records with dynamic schemas
      • Reload a table segment
      • Upload a table segment
    • 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
        • Aggregation Functions
        • Array Functions
        • Cardinality Estimation
        • Explain Plan (Single-Stage)
        • Filtering with IdSet
        • Funnel Analysis
        • GapFill Function For Time-Series Dataset
        • Grouping Algorithm
        • Hash Functions
        • JOINs
        • Lookup UDF Join
        • Querying JSON data
        • Transformation Functions
        • URL Functions
        • Window Functions
      • 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
      • User-Defined Functions (UDFs)
      • Explain plan
    • APIs
      • Broker Query API
        • Query Response Format
      • 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
        • 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
  • Configuration Reference
    • Cluster
    • Controller
    • Broker
    • Server
    • Table
    • Ingestion
    • Schema
    • Ingestion Job Spec
    • Monitoring Metrics
    • Functions
      • 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
      • DISTINCTAVG
      • DISTINCTAVGMV
      • DISTINCTCOUNT
      • DISTINCTCOUNTBITMAP
      • DISTINCTCOUNTBITMAPMV
      • DISTINCTCOUNTHLL
      • DISTINCTCOUNTSMARTHLL
      • DISTINCTCOUNTHLLPLUS
      • DISTINCTCOUNTHLLMV
      • DISTINCTCOUNTMV
      • DISTINCTCOUNTRAWHLL
      • DISTINCTCOUNTRAWHLLMV
      • DISTINCTCOUNTRAWTHETASKETCH
      • DISTINCTCOUNTTHETASKETCH
      • DISTINCTCOUNTULL
      • DISTINCTSUM
      • DISTINCTSUMMV
      • DIV
      • DATETIMECONVERT
      • DATETRUNC
      • exp
      • FIRSTWITHTIME
      • FLOOR
      • FrequentLongsSketch
      • FrequentStringsSketch
      • FromDateTime
      • FromEpoch
      • FromEpochBucket
      • FUNNELCOUNT
      • FunnelCompleteCount
      • FunnelMaxStep
      • FunnelMatchStep
      • 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
      • 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
      • Url
      • UTF8
      • VALUEIN
      • week
      • year
      • Extract
      • yearOfWeek
      • FIRST_VALUE
      • LAST_VALUE
      • ST_GeomFromGeoJSON
      • ST_GeogFromGeoJSON
      • ST_AsGeoJSON
    • Plugin Reference
      • Stream Ingestion Connectors
      • VAR_POP
      • VAR_SAMP
      • STDDEV_POP
      • STDDEV_SAMP
    • Dynamic Environment
  • 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
  • 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
  • Details
  • Configuration
  • Example

Was this helpful?

Export as PDF
  1. Basics
  2. Indexing

Bloom filter

This page describes configuring the Bloom filter for Apache Pinot

When a column is configured to use this filter, Pinot creates one Bloom filter per segment. The Bloom filter help to prune segments that do not contain any record matching an EQUALITY or IN predicate.

Note: Support for IN clause is limited to <= 10 values in the predicate, this is to ensure pruning overhead is minimal.

This is useful for query patterns like below where Bloom Filter is defined on playerID column in the table:

SELECT COUNT(*) 
FROM baseballStats 
WHERE playerID = 12345

OR

SELECT COUNT(*) 
FROM baseballStats 
WHERE playerID IN(12345, 45668, 56789)

Details

A Bloom filter is a probabilistic data structure used to definitively determine if an element is not present in a dataset, but it cannot be employed to determine if an element is present in the dataset. This limitation arises because Bloom filters may produce false positives but never yield false negatives.

An intriguing aspect of these filters is the existence of a mathematical formula that establishes a relationship between their size, the cardinality of the dataset they index, and the rate of false positives.

In Pinot, this cardinality corresponds to the number of unique values expected within each segment. If necessary, the false positive rate and the index size can be configured.

Configuration

There are 3 optional parameters to configure the Bloom filter:

Parameter
Default
Description

fpp

0.05

False positive probability of the Bloom filter (from 0 to 1).

maxSizeInBytes

0 (unlimited)

Maximum size of the Bloom filter.

loadOnHeap

false

Whether to load the Bloom filter using heap memory or off-heap memory.

The lower the fpp (false positive probability), the greater the accuracy of the Bloom filter, but this reduction in fpp will also lead to an increase in the index size. It's important to note that maxSizeInBytes takes precedence over fpp. If maxSizeInBytes is set to a value greater than 0 and the calculated size of the Bloom filter, based on the specified fpp, exceeds this size limit, Pinot will adjust the fpp to ensure that the Bloom filter size remains within the specified limit.

Similar to other indexes, a Bloom filter can be explicitly deactivated by setting the special parameter disabled to true.

Example

For example the following table config enables the Bloom filter in the playerId column using the default values:

Configured in tableConfig fieldConfigList
{
  "tableName": "somePinotTable",
  "fieldConfigList": [
    {
      "name": "playerID",
      "encodingType": "RAW",
      "indexes": {
        "bloom": {}
      }
    },
    ...
  ],
  ...
}

In case some parameter needs to be customized, they can be included in fieldConfigList.indexes.bloom. Remember that even the example customizes all parameters, you can just modify the ones you need.

Configured in tableConfig fieldConfigList
{
  "tableName": "somePinotTable",
  "fieldConfigList": [
    {
      "name": "playerID",
      "encodingType": "RAW",
      "indexes": {
        "bloom": {
          "fpp": 0.01,
          "maxSizeInBytes": 1000000,
          "loadOnHeap": true
        }
      }
    },
    ...
  ],
  ...
}
Older configuration

Use default settings

To use default values, include the name of the column in tableIndexConfig.bloomFilterColumns.

For example:

Part of a tableConfig
{
  "tableName": "somePinotTable",
  "tableIndexConfig": {
    "bloomFilterColumns": [
      "playerID",
      ...
    ],
    ...
  },
  ...
}

Customized parameters

To specify custom parameters, add a new entry in tableIndexConfig.bloomFilterConfig object. The key should be the name of the column and the value should be an object similar to the one that can be used in the Bloom section of fieldConfigList.

For example:

Part of a tableConfig
{
  "tableIndexConfig": {
    "bloomFilterConfigs": {
      "playerID": {
        "fpp": 0.01,
        "maxSizeInBytes": 1000000,
        "loadOnHeap": true
      },
      ...
    },
    ...
  },
  ...
}
PreviousIndexingNextDictionary index

Was this helpful?

Bloom filters are deactivated by default, implying that columns will not be indexed unless they are explicitly configured within the .

table configuration