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
  • Signature
  • Usage Examples

Was this helpful?

Edit on GitHub
Export as PDF
  1. (Deprecating) Function List

percentilekll

This section contains reference documentation for the PERCENTILEKLL function.

PreviouspercentiletdigestmvNextpercentilerawkll

Last updated 1 year ago

Was this helpful?

KLL Sketch is an approxiamate quantiles algorithm which targets optimal space for a given accuracy. PERCENTILEKLL is a percentile calculation aggregation function based on Apache Datasketches implementation.

Pinot also offers a 'raw' variant, PERCENTILEKLLRAW, which returns the serialized sketch that can be used for calculating 'rank' or 'histogram'.

All of the variants of PercentileKLL also support raw sketches in Pinot columns. This means you can create KLL Doubles sketches outside of Pinot and ingest them into columns as binary strings. PercentileKLL will identify these columns merge them to produce aggregate results.

Signature

PercentileKLL(column, percentile, kValue) -> Double

  • column (required): Name of the column to aggregate on. If the column is a multi value column, use PERCENTILEKLLMV variant.

  • percentile (required): Percentile value to be calculated [0..100]

  • kValue: Integer value which determines the size of the sketch. Default value is 200 which corresponds to a normalized rank error of about 1.65%. For details, see the .

Usage Examples

select percentileKLL(ArrDelayMinutes, 90) as DelayP90
from airlineStats
DelayP90

40

select Carrier, percentileKll(ArrDelay, 50, 600) as MedianDelay
from airlineStats
where ArrDelay > 0
group by Carrier
order by 2 desc
limit 3
Carrier
MedianDelay

MQ

28

B6

28

EV

24

KLL Doubles Sketch
accuracy vs size chart