LogoLogo
release-1.0.0
release-1.0.0
  • Introduction
  • Basics
    • Concepts
    • Architecture
    • Components
      • Cluster
        • Tenant
        • Server
        • Controller
        • Broker
        • Minion
      • Table
        • Segment
          • Deep Store
        • Schema
      • 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
      • 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
        • Apache Kafka
        • Amazon Kinesis
        • Apache Pulsar
      • Stream Ingestion with Upsert
      • 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
      • Reload a table segment
      • Upload a table segment
    • Indexing
      • Forward Index
      • Inverted Index
      • Star-Tree Index
      • Bloom Filter
      • Range Index
      • Native Text Index
      • Text search support
      • JSON Index
      • Geospatial
      • Timestamp Index
    • Releases
      • Apache Pinot™ 1.0.0 release notes
      • 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
      • GitHub Events Stream
  • For Users
    • Query
      • Querying Pinot
      • Querying JSON data
      • Query Options
      • Aggregation Functions
      • Cardinality Estimation
      • Explain Plan
      • Filtering with IdSet
      • GapFill Function For Time-Series Dataset
      • Grouping Algorithm
      • JOINs
      • Lookup UDF Join
      • Transformation Functions
      • User-Defined Functions (UDFs)
      • Window functions
    • 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
      • 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
      • 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
    • Schema
    • Ingestion Job Spec
    • Monitoring Metrics
    • Functions
      • ABS
      • ADD
      • ago
      • ARG_MIN / ARG_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
      • FLOOR
      • FromDateTime
      • FromEpoch
      • FromEpochBucket
      • FUNNELCOUNT
      • Histogram
      • hour
      • isSubnetOf
      • JSONFORMAT
      • JSONPATH
      • JSONPATHARRAY
      • JSONPATHARRAYDEFAULTEMPTY
      • JSONPATHDOUBLE
      • JSONPATHLONG
      • JSONPATHSTRING
      • jsonextractkey
      • jsonextractscalar
      • 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
  • Reference
    • Single-stage query engine (v1)
    • Multi-stage query engine (v2)
  • 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
  • Example
  • Flink application
  • Table Config

Was this helpful?

Export as PDF
  1. Basics
  2. Import Data
  3. Batch Ingestion

Flink

Batch ingestion of data into Apache Pinot using Apache Flink.

PreviousSparkNextHadoop

Was this helpful?

Pinot supports Apache Flink as a processing framework to push segment files to the database.

Pinot distribution contains an Apache Flink that can be used as part of the Apache Flink application (Streaming or Batch) to directly write into a designated Pinot database.

Example

Flink application

Here is an example code snippet to show how to utilize the in a Flink streaming application:

// some environmental setup
StreamExecutionEnvironment execEnv = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<Row> srcRows = execEnv.addSource(new FlinkKafkaConsumer<Row>(...));
RowTypeInfo typeInfo = new RowTypeInfo(
  new TypeInformation[]{Types.FLOAT, Types.FLOAT, Types.STRING, Types.STRING},
  new String[]{"lon", "lat", "address", "name"});


// add processing logic for the data stream for example:
DataStream<Row> processedRows = srcRow.keyBy(r -> r.getField(0));
...

// configurations for PinotSinkFunction
Schema pinotSchema = ...
TableConfig pinotTableConfig = ...
processedRows.addSink(new PinotSinkFunction<>(
  new FlinkRowGenericRowConverter(typeInfo), 
  pinotTableConfig,
  pinotSchema);

// execute the program
execEnv.execute();

Table Config

PinotSinkFunction uses mostly the TableConfig object to infer the batch ingestion configuration to start a SegmentWriter and SegmentUploader to communicate with the Pinot cluster.

Note that even though in the above example Flink application is running in streaming mode, the data is still batch together and flush/upload to Pinot once the flush threshold is reached. It is not a direct streaming write into Pinot.

Here is an example table config

{
  "tableName" : "tbl_OFFLINE",
  "tableType" : "OFFLINE",
  "segmentsConfig" : {
    // ...
  },
  "tenants" : {
    // ...
  },
  "tableIndexConfig" : {
    // ....
  },
  "ingestionConfig": {
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "HOURLY", 
      "batchConfigMaps": [
        {
          "outputDirURI": "file://path/to/flink/segmentwriter/output/dir",
          "overwriteOutput": "false",
          "push.controllerUri": "https://target.pinot.cluster.controller.url"
        }
      ]
    }
  }
}

the only required configurations are:

  • "outputDirURI": where PinotSinkFunction should write the constructed segment file to

  • "push.controllerUri": which Pinot cluster (controller) URL PinotSinkFunction should communicate with.

The rest of the configurations are standard for any Pinot table.

As in the example shown above, the only required information from the Pinot side is the table and the table .

For a more detailed executable, refer to the .

SinkFunction
PinotSinkFunction
schema
config
quick start example