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Quick Start

info

In this guide you'll launch an Apache Pinot Quick Start example. This guide assumes that you have already installed Apache Pinot.

Apache Pinot ships with a set of Quick Start examples that spin up a single instance of the various Pinot components in the same process. For a full list of all the Quick Starts, see Quick Start Examples in the Apache Pinot Documentation.

In this guide we'll go through the Hybrid Quick Start, which ingests flight data.

This example starts Apache Kafka, Apache Zookeeper, Pinot Controller, Pinot Broker, and Pinot Server. It then creates a airlineStats schema, an airlineStats offline table, and an airlineStats real-time table that ingests messages from a Kafka topic.

Launch Quick Start

To launch the Quick Start, run one of the commands below:

docker run \
-p 9000:9000 \
apachepinot/pinot:0.9.3 QuickStart \
-type hybrid

Let's now go through this Quick Start's schema, table config, and provide some sample queries that you can run against it.

Data Source

The data that gets ingested by this QuickStart contains flight information from the Bureau of Transportation Statistics. It includes the origin airport, destination airport, departure time, delays incurred, plane details, as well as many other bits of information.

The diagram below shows how the data gets into Pinot:

QuickStart ArchitectureQuickStart Architecture

Schema

A schema defines what fields are present in the table along with their data types in JSON format. The airlineStats schema is described below:

airlineStats_schema.json
{
"metricFieldSpecs": [
],
"dimensionFieldSpecs": [
{
"dataType": "INT",
"name": "ActualElapsedTime"
},
{
"dataType": "INT",
"name": "AirTime"
},
{
"dataType": "INT",
"name": "AirlineID"
},
{
"dataType": "INT",
"name": "ArrDel15"
},
{
"dataType": "INT",
"name": "ArrDelay"
},
{
"dataType": "INT",
"name": "ArrDelayMinutes"
},
{
"dataType": "INT",
"name": "ArrTime"
},
{
"dataType": "STRING",
"name": "ArrTimeBlk"
},
{
"dataType": "INT",
"name": "ArrivalDelayGroups"
},
{
"dataType": "INT",
"name": "CRSArrTime"
},
{
"dataType": "INT",
"name": "CRSDepTime"
},
{
"dataType": "INT",
"name": "CRSElapsedTime"
},
{
"dataType": "STRING",
"name": "CancellationCode"
},
{
"dataType": "INT",
"name": "Cancelled"
},
{
"dataType": "STRING",
"name": "Carrier"
},
{
"dataType": "INT",
"name": "CarrierDelay"
},
{
"dataType": "INT",
"name": "DayOfWeek"
},
{
"dataType": "INT",
"name": "DayofMonth"
},
{
"dataType": "INT",
"name": "DepDel15"
},
{
"dataType": "INT",
"name": "DepDelay"
},
{
"dataType": "INT",
"name": "DepDelayMinutes"
},
{
"dataType": "INT",
"name": "DepTime"
},
{
"dataType": "STRING",
"name": "DepTimeBlk"
},
{
"dataType": "INT",
"name": "DepartureDelayGroups"
},
{
"dataType": "STRING",
"name": "Dest"
},
{
"dataType": "INT",
"name": "DestAirportID"
},
{
"dataType": "INT",
"name": "DestAirportSeqID"
},
{
"dataType": "INT",
"name": "DestCityMarketID"
},
{
"dataType": "STRING",
"name": "DestCityName"
},
{
"dataType": "STRING",
"name": "DestState"
},
{
"dataType": "INT",
"name": "DestStateFips"
},
{
"dataType": "STRING",
"name": "DestStateName"
},
{
"dataType": "INT",
"name": "DestWac"
},
{
"dataType": "INT",
"name": "Distance"
},
{
"dataType": "INT",
"name": "DistanceGroup"
},
{
"dataType": "INT",
"name": "DivActualElapsedTime"
},
{
"dataType": "INT",
"name": "DivAirportIDs",
"singleValueField": false
},
{
"dataType": "INT",
"name": "DivAirportLandings"
},
{
"dataType": "INT",
"name": "DivAirportSeqIDs",
"singleValueField": false
},
{
"dataType": "STRING",
"name": "DivAirports",
"singleValueField": false
},
{
"dataType": "INT",
"name": "DivArrDelay"
},
{
"dataType": "INT",
"name": "DivDistance"
},
{
"dataType": "INT",
"name": "DivLongestGTimes",
"singleValueField": false
},
{
"dataType": "INT",
"name": "DivReachedDest"
},
{
"dataType": "STRING",
"name": "DivTailNums",
"singleValueField": false
},
{
"dataType": "INT",
"name": "DivTotalGTimes",
"singleValueField": false
},
{
"dataType": "INT",
"name": "DivWheelsOffs",
"singleValueField": false
},
{
"dataType": "INT",
"name": "DivWheelsOns",
"singleValueField": false
},
{
"dataType": "INT",
"name": "Diverted"
},
{
"dataType": "INT",
"name": "FirstDepTime"
},
{
"dataType": "STRING",
"name": "FlightDate"
},
{
"dataType": "INT",
"name": "FlightNum"
},
{
"dataType": "INT",
"name": "Flights"
},
{
"dataType": "INT",
"name": "LateAircraftDelay"
},
{
"dataType": "INT",
"name": "LongestAddGTime"
},
{
"dataType": "INT",
"name": "Month"
},
{
"dataType": "INT",
"name": "NASDelay"
},
{
"dataType": "STRING",
"name": "Origin"
},
{
"dataType": "INT",
"name": "OriginAirportID"
},
{
"dataType": "INT",
"name": "OriginAirportSeqID"
},
{
"dataType": "INT",
"name": "OriginCityMarketID"
},
{
"dataType": "STRING",
"name": "OriginCityName"
},
{
"dataType": "STRING",
"name": "OriginState"
},
{
"dataType": "INT",
"name": "OriginStateFips"
},
{
"dataType": "STRING",
"name": "OriginStateName"
},
{
"dataType": "INT",
"name": "OriginWac"
},
{
"dataType": "INT",
"name": "Quarter"
},
{
"dataType": "STRING",
"name": "RandomAirports",
"singleValueField": false
},
{
"dataType": "INT",
"name": "SecurityDelay"
},
{
"dataType": "STRING",
"name": "TailNum"
},
{
"dataType": "INT",
"name": "TaxiIn"
},
{
"dataType": "INT",
"name": "TaxiOut"
},
{
"dataType": "INT",
"name": "Year"
},
{
"dataType": "INT",
"name": "WheelsOn"
},
{
"dataType": "INT",
"name": "WheelsOff"
},
{
"dataType": "INT",
"name": "WeatherDelay"
},
{
"dataType": "STRING",
"name": "UniqueCarrier"
},
{
"dataType": "INT",
"name": "TotalAddGTime"
}
],
"dateTimeFieldSpecs": [
{
"name": "DaysSinceEpoch",
"dataType": "INT",
"format": "1:DAYS:EPOCH",
"granularity": "1:DAYS"
}
],
"schemaName": "airlineStats"
}

Download schema

Table Config

A table is a logical abstraction that represents a collection of related data. It is composed of columns and rows (known as documents in Pinot). The table config defines the table's properties in JSON format.

tip

The Hybrid QuickStart contains both offline and real-time tables. Offline tables ingest data from batch data sources, such as CSV or Parquet files. Real-time tables ingest data from streaming data sources like Kafka or Kinesis.

The offline table config is described below:

airlineStats_offline_table_config.json
{
"tableName": "airlineStats",
"tableType": "OFFLINE",
"segmentsConfig": {
"timeColumnName": "DaysSinceEpoch",
"timeType": "DAYS",
"segmentPushType": "APPEND",
"segmentAssignmentStrategy": "BalanceNumSegmentAssignmentStrategy",
"replication": "1"
},
"tenants": {},
"tableIndexConfig": {
"loadMode": "MMAP"
},
"metadata": {
"customConfigs": {}
}
}

Download table

Data is ingested into this table using an ingestion job.

The real-time table config is described below:

airlineStats_realtime_table_config.json
{
"tableName": "airlineStats",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "DaysSinceEpoch",
"timeType": "DAYS",
"retentionTimeUnit": "DAYS",
"retentionTimeValue": "5",
"segmentPushType": "APPEND",
"segmentAssignmentStrategy": "BalanceNumSegmentAssignmentStrategy",
"schemaName": "airlineStats",
"replication": "1",
"replicasPerPartition": "1"
},
"tenants": {},
"tableIndexConfig": {
"loadMode": "MMAP",
"streamConfigs": {
"streamType": "kafka",
"stream.kafka.consumer.type": "simple",
"stream.kafka.topic.name": "flights-realtime",
"stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
"stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
"stream.kafka.hlc.zk.connect.string": "localhost:2191/kafka",
"stream.kafka.zk.broker.url": "localhost:2191/kafka",
"stream.kafka.broker.list": "localhost:19092",
"realtime.segment.flush.threshold.time": "3600000",
"realtime.segment.flush.threshold.size": "50000",
"stream.kafka.consumer.prop.auto.offset.reset": "smallest"
}
},
"metadata": {
"customConfigs": {}
}
}

Download table

The data ingested into this table comes from the flights-realtime topic in Kafka.

Sample Queries

You can now navigate to localhost:9000/#/query to query the dataset. If you click on the airlineStats table, it will run a query that returns the first 10 records in this table.

Below are some other queries to try out:

Airports with the most incoming flights
select Dest, count(*) AS count
from airlineStats
GROUP BY Dest
ORDER BY count DESC
LIMIT 10
Flights from 'ORD' to 'ATL'
select DayOfWeek, DayofMonth, DepTime, ArrTime
from airlineStats
WHERE Dest = 'ATL' and Origin = 'ORD'
LIMIT 10
Origins and Destinations for the plane with tail number 'N416SW'
select Origin, Dest, count(*)
from airlineStats
where TailNum = 'N416SW'
group by Origin, Dest
order by count(*) DESC

Next Steps

Now that you've seen an example of Pinot in action, it's time to setup our own local cluster.