LogoLogo
release-1.2.0
release-1.2.0
  • 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
        • 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
    • Release notes
      • 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
        • Cardinality Estimation
        • Explain Plan (Single-Stage)
        • Explain Plan (Multi-Stage)
        • Filtering with IdSet
        • GapFill Function For Time-Series Dataset
        • Grouping Algorithm
        • JOINs
        • Lookup UDF Join
        • Querying JSON data
        • Transformation Functions
        • Window aggregate
        • Funnel Analysis
      • Query Options
      • Multi stage query
        • Operator Types
          • Aggregate
          • Filter
          • Join
          • Intersect
          • Leaf
          • Literal
          • Mailbox receive
          • Mailbox send
          • Minus
          • Sort or limit
          • Transform
          • Union
          • Window
        • Understanding Stages
        • Explain
        • Stats
      • User-Defined Functions (UDFs)
    • 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)
      • Troubleshoot issues with 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
        • Real-time
        • Routing
        • Query Routing using Adaptive Server Selection
        • Query Scheduling
      • Upgrading Pinot with confidence
      • Managing Logs
      • OOM Protection Using Automatic Query Killing
    • 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
  • Configuration Reference
    • Cluster
    • Controller
    • Broker
    • Server
    • Table
    • Ingestion
    • Schema
    • Ingestion Job Spec
    • Monitoring Metrics
    • Functions
      • ABS
      • ADD
      • ago
      • EXPR_MIN / EXPR_MAX
      • 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
      • DISTINCTCOUNTHLLMV
      • DISTINCTCOUNTHLL
      • DISTINCTCOUNTBITMAPMV
      • DISTINCTCOUNTMV
      • DISTINCTCOUNTRAWHLL
      • DISTINCTCOUNTRAWHLLMV
      • DISTINCTCOUNTRAWTHETASKETCH
      • DISTINCTCOUNTTHETASKETCH
      • 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
      • 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
      • yearOfWeek
      • Extract
    • 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
  • First Come First Serve
  • Bounded Schedulers

Was this helpful?

Edit on GitHub
Export as PDF
  1. For Operators
  2. Deployment and Monitoring
  3. Tuning

Query Scheduling

Schedule queries to prioritize them.

Pinot supports various different query scheduling mechanisms to allow for more control around which queries have priority when executing on the server instances.

Currently, there are the following options that can be configured using the pinot.query.scheduler.name configuration:

  • First Come First Serve (fcfs) (Default)

  • Bounded First Come First Serve (bounded_fcfs)

  • Token Bucket (tokenbucket)

First Come First Serve

This is the default scheduling mechanism, which simply allows all queries to execute in a first come, first serve mechanism. For most deployments, this is likely sufficient, but high QPS deployments that have skewed query workloads may struggle under this scheduling mechanism.

Bounded Schedulers

Bounded query schedulers operate under a context of a single table, and additionally respect the following configurations:

  • pinot.query.scheduler.threads_per_query_pct (default 20%) will allow individual threads to take up to this percentage of the threads allocated to a table resource group

  • pinot.query.scheduler.table_threads_soft_limit_pct (default 30%) indicates that once this percentage of the available threads are taken up by this resource group, the scheduler should prefer other resource groups but still allow queries in this group

  • pinot.query.scheduler.table_threads_hard_limit_pct (default 45%) indicates that once this percentage of the available threads are taken up, the scheduler should not schedule any more queries for this resource group

Bounded First Come First Serve

Similarly to the "first come first serve" scheduling mechanism, this option will bound the resource utilization by ensuring that only a certain number of queries are running concurrently (set using the pinot.query.scheduler.query_runner_threads configuration).

Token Bucket Scheduler

This query scheduling mechanism will periodically grant tokens to each scheduler group, and will select the group to run with the highest number of tokens assigned to it. This can be configured using the following configurations:

  • pinot.query.scheduler.tokens_per_ms will indicate how many tokens to generate per second for each group. The default value for this is the number of threads avaialble to run queries (which will essentially attempt to schedule more queries than is possible to run)

  • pinot.query.scheduler.token_lifetime_ms indicates the lifetime of every allocated token

This scheduler applies a linear decay for groups that have recently been scheduled to avoid starvation and allow groups with light workloads to be scheduled.

PreviousQuery Routing using Adaptive Server SelectionNextUpgrading Pinot with confidence

Was this helpful?