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release-1.3.0
release-1.3.0
  • Introduction
  • Basics
    • Concepts
      • Pinot storage model
      • Architecture
      • Components
        • Cluster
          • Tenant
          • Server
          • Controller
          • Broker
          • Minion
        • Table
          • Segment
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        • 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
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      • 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
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    • Release notes
      • 1.3.0
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    • Recipes
      • Connect to Streamlit
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      • 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
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      • Query Options
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      • Multi-stage query
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          • Random + broadcast join strategy
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    • Tutorials
      • Use OSS as Deep Storage for Pinot
      • Ingest Parquet Files from S3 Using Spark
      • Creating Pinot Segments
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      • 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
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      • Update documentation
    • Advanced
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      • Use the multi-stage query engine (v2)
      • Advanced Pinot Setup
    • Plugins
      • Write Custom Plugins
        • Input Format Plugin
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        • Batch Segment Fetcher Plugin
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  • For Operators
    • Deployment and Monitoring
      • Set up cluster
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      • Decoupling Controller from the Data Path
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        • Tuning Default MMAP Advice
        • Real-time
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      • Upgrading Pinot with confidence
      • Managing Logs
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      • Pause ingestion based on resource utilization
    • Command-Line Interface (CLI)
    • Configuration Recommendation Engine
    • Tutorials
      • Authentication
        • Basic auth access control
        • ZkBasicAuthAccessControl
      • Configuring TLS/SSL
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      • Performance Optimization Configurations
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  • 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
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      • arrayDistinctInt
      • arrayDistinctString
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      • arrayIndexOfString
      • ARRAYLENGTH
      • arrayRemoveInt
      • arrayRemoveString
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      • 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
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  • Integrations
    • Tableau
    • Trino
    • ThirdEye
    • Superset
    • Presto
    • Spark-Pinot Connector
  • Contributing
    • Contribute Pinot documentation
    • Style guide
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On this page
  • Batch Record Reader Plugin
  • Generic Row
  • Contracts for Record Reader
  • Stream Decoder Plugin

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  1. For Developers
  2. Plugins
  3. Write Custom Plugins

Input Format Plugin

PreviousWrite Custom PluginsNextFilesystem Plugin

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Pinot out of the box for batch ingestion. For real-time ingestion, currently only JSON is supported. However, due to pluggable architecture of pinot you can easily use any format by implementing standard interfaces.

Batch Record Reader Plugin

All the Batch Input formats supported by Pinot utilise to deserialize the data. You can also implement the RecordReader and interface to add support for your own file formats.

To index the file into Pinot segment, simply implement the interface and plug it into the index engine - . We use a 2-passes algorithm to index the file into Pinot segment, hence the rewind() method is required for the record reader.

Generic Row

is the record abstraction which the index engine can read and index with. It is a map from column name (String) to column value (Object). For multi-valued column, the value should be an object array (Object[]).

Contracts for Record Reader

There are several contracts for record readers that developers should follow when implementing their own record readers:

  • The output GenericRow should follow the table schema provided, in the sense that:

    • All the columns in the schema should be preserved (if column does not exist in the original record, put default value instead)

    • Columns not in the schema should not be included

    • Values for the column should follow the field spec from the schema (data type, single-valued/multi-valued)

  • For the time column (refer to ), record reader should be able to read both incoming and outgoing time (we allow incoming time - time value from the original data to outgoing time - time value stored in Pinot conversion during index creation).

    • If incoming and outgoing time column name are the same, use incoming time field spec

    • If incoming and outgoing time column name are different, put both of them as time field spec

    • We keep both incoming and outgoing time column to handle cases where the input file contains time values that are already converted

Stream Decoder Plugin

Pinot uses decoders to parse data available in real-time streams. Decoders are responsible for converting binary data in the streams to a GenericRow object.

You can write your own decoder by implementing the interface. You can also use the from the batch input formats to extract fields to GenericRow from the parsed object.

supports multiple input formats
RecordReader
RecordExtractor
SegmentCreationDriverImpl
GenericRow
TimeFieldSpec
StreamMessageDecoder
RecordExtractor