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
  • Changes that require a rebalance
  • Capacity changes
  • Replication changes
  • Segment Assignment changes
  • Table Migration to a different tenant
  • Rebalance Algorithms
  • The Default Algorithm
  • Minimal Data Movement Algorithm
  • Running a Rebalance
  • Rebalance Parameters
  • Checking status

Was this helpful?

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

Rebalance Servers

PreviousRebalanceNextRebalance Brokers

Was this helpful?

The rebalance operation is used to recompute the assignment of brokers or servers in the cluster. This is not a single command, but rather a series of steps that need to be taken.

In the case of servers, rebalance operation is used to balance the distribution of the segments amongst the servers being used by a Pinot table. This is typically done after capacity changes or config changes such as replication or segment assignment strategies or table migration to a different tenant.

Changes that require a rebalance

Below are changes that need to be followed by a rebalance.

  1. Capacity changes

  2. Increasing/decreasing replication for a table

  3. Changing segment assignment for a table

  4. Moving table from one tenant to a different tenant

Capacity changes

These are typically done when downsizing/uplifting a cluster or replacing nodes of a cluster.

Tenants and tags

Every server added to the Pinot cluster has tags associated with it. A group of servers with the same tag forms a server tenant.

By default, a server in the cluster gets added to the DefaultTenant i.e. gets tagged as DefaultTenant_OFFLINE and DefaultTenant_REALTIME.

Below is an example of how this looks in the znode, as seen in ZooInspector.

 {   
    "tableName": "myTable_OFFLINE",
    "tenants" : {
      "broker":"DefaultTenant",
      "server":"DefaultTenant"
    }
  }

Updating tags

0.6.0 onwards

In order to change the server tags, use the following API.

PUT /instances/{instanceName}/updateTags?tags=<comma separated tags>

0.5.0 and prior

UpdateTags API is not available in 0.5.0 and prior. Instead, use this API to update the Instance.

PUT /instances/{instanceName}

For example,

curl -X PUT "http://localhost:9000/instances/Server_10.1.10.51_7000" 
    -H "accept: application/json" 
    -H "Content-Type: application/json" 
    -d "{ \"host\": \"10.1.10.51\", \"port\": \"7000\", \"type\": \"SERVER\", \"tags\": [ \"newName_OFFLINE\", \"DefaultTenant_REALTIME\" ]}"

NOTE

The output of GET and input of PUT don't match for this API. Make sure to use the right payload as shown in example above. Particularly, notice that the instance name "Server_host_port" gets split up into separate fields in this PUT API.

When upsizing/downsizing a cluster, you will need to make sure that the host names of servers are consistent. You can do this by setting the following config parameter:

pinot.set.instance.id.to.hostname=true

Replication changes

In order to change the replication factor of a table, update the table config as follows:

OFFLINE table - update the replication field

REALTIME table - update the replicasPerPartition field

Segment Assignment changes

Table Migration to a different tenant

In a scenario where you need to move table across tenants, for e.g table was assigned earlier to a different Pinot tenant and now you want to move it to a separate one, then you need to call the rebalance API with reassignInstances set to true.

To move a table to other tenants, modify the following configs in both realtime and offline tables:

"REALTIME": {
  ...
  "tenants": {
    ...
    "server": "<tenant_name>",
    ...
  },
  ...
  "instanceAssignmentConfigMap": {
    ...
    "CONSUMING": {
      ...
      "tagPoolConfig": {
        ...
        "tag": "<tenant_name>_REALTIME",
        ...
      },
      ...
    },
    ...
    "COMPLETED": {
      ...
      "tagPoolConfig": {
        ...
        "tag": "<tenant_name>_REALTIME",
        ...
      },
      ...
    },
    ...
  },
  ...
}
"OFFLINE": {
  ...
  "tenants": {
    ...
    "server": "<tenant_name>",
    ...
  },
  ...
  "instanceAssignmentConfigMap": {
    ...
    "OFFLINE": {
      ...
      "tagPoolConfig": {
        ...
        "tag": "<tenant_name>_OFFLINE",
        ...
      },
      ...
    },
    ...
  },
  ...
}

Rebalance Algorithms

Currently, two rebalance algorithms are supported; one is the default algorithm and the other one is minimal data movement algorithm.

The Default Algorithm

This algorithm is used for most of the cases. When reassignInstances parameter is set to true, the final lists of instance assignment will be re-computed, and the list of instances is sorted per partition per replica group. Whenever the table rebalance is run, segment assignment will respect the sequence in the sorted list and pick up the relevant instances.

Minimal Data Movement Algorithm

This algorithm focuses more on minimizing the data movement during table rebalance. When reassignInstances parameter is set to true and this algorithm gets enabled, the position of instances which are still alive remains the same, and vacant seats are filled with newly added instances or last instances in the existing alive instance candidate. So only the instances which change the position will involve in data movement.

In order to switch to this table rebalance algorithm, just simply set the following config to the table config before triggering table rebalance:

"instanceAssignmentConfigMap": {
  ...
  "OFFLINE": {
    ...
    "replicaGroupPartitionConfig": {
      ...
      "minimizeDataMovement": true,
      ...
    },
    ...
  },
  ...
}

When instanceAssignmentConfigMap is not explicitly configured, minimizeDataMovement flag can also be set into the segmentsConfig:

"segmentsConfig": {
    ...
    "minimizeDataMovement": true,
    ...
}

Running a Rebalance

After any of the above described changes are done, a rebalance is needed to make those changes take effect.

To run a rebalance, use the following API.

POST /tables/{tableName}/rebalance?type=<OFFLINE/REALTIME>

This API has a lot of parameters to control its behavior. Make sure to go over them and change the defaults as needed.

Note

Typically, the flags that need to be changed from the default values are

includeConsuming=true for REALTIME

downtime=true if you have only 1 replica, or prefer a faster rebalance at the cost of a momentary downtime

Rebalance Parameters

Query param
Default value
Description

dryRun

false

If set to true, rebalance is run as a dry-run so that you can see the expected changes to the ideal state and instance partition assignment.

includeConsuming

false

Applicable for REALTIME tables.

CONSUMING segments are rebalanced only if this is set to true. Moving a CONSUMING segment involves dropping the data consumed so far on old server, and re-consuming on the new server. If an application is sensitive to increased memory utilization due to re-consumption or to a momentary data staleness, they may choose to not include consuming in the rebalance. Whenever the CONSUMING segment completes, the completed segment will be assigned to the right instances, and the new CONSUMING segment will also be started on the correct instances. If you choose to includeConsuming=false and let the segments move later on, any downsized nodes need to remain untagged in the cluster, until the segment completion happens.

downtime

false

This controls whether Pinot allows downtime while rebalancing. If downtime = true, all replicas of a segment can be moved around in one go, which could result in a momentary downtime for that segment (time gap between ideal state updated to new servers and new servers downloading the segments). If downtime = false, Pinot will make sure to keep certain number of replicas (config in next row) always up. The rebalance will be done in multiple iterations under the hood, in order to fulfill this constraint.

Note: If you have only 1 replica for your table, rebalance with downtime=false is not possible.

minAvailableReplicas

1

Applicable for rebalance with downtime=false.

This is the minimum number of replicas that are expected to stay alive through the rebalance.

lowDiskMode

false

Applicable for rebalance with downtime=false. When enabled, segments will first be offloaded from servers, then added to servers after offload is done. It may increase the total time of the rebalance, but can be useful when servers are low on disk space, and we want to scale up the cluster and rebalance the table to more servers.

bestEfforts

false

Applicable for rebalance with downtime=false.

If a no-downtime rebalance cannot be performed successfully, this flag controls whether to fail the rebalance or do a best-effort rebalance.

reassignInstances

false

Applicable to tables where the instance assignment has been persisted to zookeeper. Setting this to true will make the rebalance first update the instance assignment, and then rebalance the segments.

bootstrap

false

Rebalances all segments again, as if adding segments to an empty table. If this is false, then the rebalance will try to minimize segment movements.

Checking status

The following API is used to check the progress of a rebalance Job. The API takes the jobId of the rebalance job. The API to see the jobIds of rebalance Jobs for a table is shown next.

curl -X GET "https://localhost:9000/rebalanceStatus/ffb38717-81cf-40a3-8f29-9f35892b01f9" -H "accept: application/json"
{"tableRebalanceProgressStats": {
    "startTimeMs": 1679073157779,
    "status": "DONE", // IN_PROGRESS/DONE/FAILED    
    "timeToFinishInSeconds": 0, // Time it took for the rebalance job after it completes/fails 
    "completionStatusMsg": "Finished rebalancing table: airlineStats_OFFLINE with minAvailableReplicas: 1, enableStrictReplicaGroup: false, bestEfforts: false in 44 ms."
     
     // The total amount of work required for rebalance 
    "initialToTargetStateConvergence": {
      "_segmentsMissing": 0, // Number of segments missing in the current state but present in the target state
      "_segmentsToRebalance": 31, // Number of segments that needs to be assigned to hosts so that the current state can get to the target state.
      "_percentSegmentsToRebalance": 100, // Total number of replicas that needs to be assigned to hosts so that the current state can get to the target state.
      "_replicasToRebalance": 279 // Remaining work to be done in %
    },
    
    // The pending work for rebalance
    "externalViewToIdealStateConvergence": {
      "_segmentsMissing": 0,
      "_segmentsToRebalance": 0,
      "_percentSegmentsToRebalance": 0,
      "_replicasToRebalance": 0
    },
    
    // Additional work to catch up with the new ideal state, when the ideal 
    // state shifts since rebalance started. 
    "currentToTargetConvergence": {
      "_segmentsMissing": 0,
      "_segmentsToRebalance": 0,
      "_percentSegmentsToRebalance": 0,
      "_replicasToRebalance": 0
    },
  },
  "timeElapsedSinceStartInSeconds": 28 // If rebalance is IN_PROGRESS, this gives the time elapsed since it started
  }

Below is the API to get the jobIds of rebalance jobs for a given table. The API takes the table name and jobType which is TABLE_REBALANCE.

curl -X GET "https://localhost:9000/table/airlineStats_OFFLINE/jobstype=OFFLINE&jobTypes=TABLE_REBALANCE" -H "accept: application/json"
 "ffb38717-81cf-40a3-8f29-9f35892b01f9": {
    "jobId": "ffb38717-81cf-40a3-8f29-9f35892b01f9",
    "submissionTimeMs": "1679073157804",
    "jobType": "TABLE_REBALANCE",
    "REBALANCE_PROGRESS_STATS": "{\"initialToTargetStateConvergence\":{\"_segmentsMissing\":0,\"_segmentsToRebalance\":31,\"_percentSegmentsToRebalance\":100.0,\"_replicasToRebalance\":279},\"externalViewToIdealStateConvergence\":{\"_segmentsMissing\":0,\"_segmentsToRebalance\":0,\"_percentSegmentsToRebalance\":0.0,\"_replicasToRebalance\":0},\"currentToTargetConvergence\":{\"_segmentsMissing\":0,\"_segmentsToRebalance\":0,\"_percentSegmentsToRebalance\":0.0,\"_replicasToRebalance\":0},\"startTimeMs\":1679073157779,\"status\":\"DONE\",\"timeToFinishInSeconds\":0,\"completionStatusMsg\":\"Finished rebalancing table: airlineStats_OFFLINE with minAvailableReplicas: 1, enableStrictReplicaGroup: false, bestEfforts: false in 44 ms.\"}",
    "tableName": "airlineStats_OFFLINE"

A Pinot table config has a tenants section, to define the tenant to be used by the table. The Pinot table will use all the servers which belong to the tenant as described in this config. For more details about this, see the section.

The most common segment assignment change is moving from the default segment assignment to replica group segment assignment. Discussing the details of the segment assignment is beyond the scope of this page. More details can be found in and in this .

Note that rebalanceStatus API is available from this

Tenants
commit
Routing
FAQ question