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Schema

Schema is used to define the names, data types and other information for the columns of a Pinot table.

Types of columns

Columns in a Pinot table can be broadly categorized into three categories

Schema format

A Pinot schema is written in JSON format. Here's an example which shows all the fields of a schema

flights-schema.json

{
  "schemaName": "flights",
  "dimensionFieldSpecs": [
    {
      "name": "flightNumber",
      "dataType": "LONG"
    },
    {
      "name": "tags",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": "null"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "price",
      "dataType": "DOUBLE",
      "defaultNullValue": 0
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "millisSinceEpoch",
      "dataType": "LONG",
      "format": "1:MILLSECONDS:EPOCH",
      "granularity": "15:MINUTES"
    },
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "1:HOURS:EPOCH",
      "granularity": "1:HOURS"
    },
    {
      "name": "date",
      "dataType": "STRING",
      "format": "1:DAYS:SIMPLE_DATE_FORMAT:yyyy-MM-dd",
      "granularity": "1:DAYS"
    }
  ]
}

The Pinot schema is composed of

Below is a detailed description of each type of field spec.

dimensionFieldSpecs

A dimensionFieldSpec is defined for each dimension column. Here's a list of the fields in the dimensionFieldSpec

Internal default null values for dimension

metricFieldSpecs

A metricFieldSpec is defined for each metric column. Here's a list of fields in the metricFieldSpec

Internal default null values for metric

dateTimeFieldSpec

A dateTimeFieldSpec is used to define time columns of the table. Here's a list of the fields in a dateTimeFieldSpec

timeFieldSpec

This has been deprecated. Older schemas containing timeFieldSpec will be supported. But for new schemas, use DateTimeFieldSpec instead.

A timeFieldSpec is defined for the time column. A timeFieldSpec is composed of an incomingGranularitySpec and an outgoingGranularitySpec. IncomingGranularitySpec in combination with outgoingGranularitySpec can be used to transform the time column from incoming format to the outgoing format. If both of them are specified, the segment creation process will convert the time column from the incoming format to the outgoing format. If no time column transformation is required, you can specify just the incomingGranularitySpec.

The incoming and outgoing granularitySpec are defined as:

Advanced fields

Apart from these, there's some advanced fields. These are common to all field specs.

Ingestion Transform Functions

Transform functions can be defined on columns in the schema. For example:

"metricFieldSpecs": [
    {
      "name": "maxPrice",
      "dataType": "DOUBLE",
      "transformFunction": "Groovy({prices.max()}, prices)" // groovy function
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "1:HOURS:EPOCH",
      "granularity": "1:HOURS",
      "transformFunction": "toEpochHours(timestamp)" // inbuilt function
    }

Currently, we have support for 2 kinds of functions

Groovy functions
Inbuilt functions

Note

Currently, the arguments must be from the source data. They cannot be columns from the Pinot schema which have been created through transformations.

Groovy functions

Groovy functions can be defined using the syntax:

Groovy({groovy script}, argument1, argument2...argumentN)

Here's some examples of commonly needed functions. Any valid Groovy expression can be used.

String concatenation

Concat firstName and lasName to get fullName

{
      "name": "fullName",
      "dataType": "STRING",
      "transformFunction": "Groovy({firstName+' '+lastName}, firstName, lastName)"
}

Find element in an array

Find max value in array bids

{
      "name": "maxBid",
      "dataType": "INT",
      "transformFunction": "Groovy({bids.max{ it.toBigDecimal() }}, bids)"
}

Time transformation

Convert timestamp from MILLISECONDS to HOURS

"dateTimeFieldSpecs": [{
    "name": "hoursSinceEpoch",
    "dataType": "LONG",
    "format" : "1:HOURS:EPOCH",
    "granularity": "1:HOURS"
    "transformFunction": "Groovy({timestamp/(1000*60*60)}, timestamp)"
  }]

Column name change

Simply change name of the column from user_id to userId

{
      "name": "userId",
      "dataType": "LONG",
      "transformFunction": "Groovy({user_id}, user_id)"
}

Ternary operation

If eventType is IMPRESSION set impression to 1. Similar for CLICK.

{
    "name": "impressions",
    "dataType": "LONG",
    "transformFunction": "Groovy({eventType == 'IMPRESSION' ? 1: 0}, eventType)"
},
{
    "name": "clicks",
    "dataType": "LONG",
    "transformFunction": "Groovy({eventType == 'CLICK' ? 1: 0}, eventType)"
}

AVRO Map

Store an AVRO Map in Pinot as two multi-value columns. Sort the keys, to maintain the mapping. 1) The keys of the map as map_keys 2) The values of the map as map_values

{
      "name": "map2_keys",
      "dataType": "STRING",
      "singleValueField": false,
      "transformFunction": "Groovy({map2.sort()*.key}, map2)"
},
{
      "name": "map2_values",
      "dataType": "INT",
      "singleValueField": false,
      "transformFunction": "Groovy({map2.sort()*.value}, map2)"
}

Inbuilt Pinot functions

We have several inbuilt functions that can be used directly in as ingestion transform functions

DateTime functions

These are functions which enable commonly needed time transformations.

toEpochXXX

Converts from epoch milliseconds to a higher granularity.

toEpochXXXRounded

Converts from epoch milliseconds to another granularity, rounding to the nearest rounding bucket. For example, 1588469352000 (2020-05-01 42:29:12) is 26474489 minutesSinceEpoch. `toEpochMinutesRounded(1588469352000) = 26474480 (2020-05-01 42:20:00)

fromEpochXXX

Converts from an epoch granularity to milliseconds.

Simple date format

Converts simple date format strings to milliseconds and vice-a-versa, as per the provided pattern string.

Json functions

Creating a Schema

Create a schema for your data, or see examples for examples. Make sure you've setup the cluster

Note: schema can also be created as part of table creation, refer to Creating a table.

bin/pinot-admin.sh AddSchema -schemaFile transcript-schema.json -exec

curl -F schemaName=@transcript-schema.json  localhost:9000/schemas

Check out the schema in the Rest API to make sure it was successfully uploaded

Schema

Schema is used to define the names, data types and other information for the columns of a Pinot table.

Types of columns

Columns in a Pinot table can be broadly categorized into three categories

Schema format

A Pinot schema is written in JSON format. Here's an example which shows all the fields of a schema

flights-schema.json

{
  "schemaName": "flights",
  "dimensionFieldSpecs": [
    {
      "name": "flightNumber",
      "dataType": "LONG"
    },
    {
      "name": "tags",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": "null"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "price",
      "dataType": "DOUBLE",
      "defaultNullValue": 0
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "millisSinceEpoch",
      "dataType": "LONG",
      "format": "1:MILLSECONDS:EPOCH",
      "granularity": "15:MINUTES"
    },
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "1:HOURS:EPOCH",
      "granularity": "1:HOURS"
    },
    {
      "name": "date",
      "dataType": "STRING",
      "format": "1:DAYS:SIMPLE_DATE_FORMAT:yyyy-MM-dd",
      "granularity": "1:DAYS"
    }
  ]
}

The Pinot schema is composed of

Below is a detailed description of each type of field spec.

dimensionFieldSpecs

A dimensionFieldSpec is defined for each dimension column. Here's a list of the fields in the dimensionFieldSpec

Internal default null values for dimension

metricFieldSpecs

A metricFieldSpec is defined for each metric column. Here's a list of fields in the metricFieldSpec

Internal default null values for metric

dateTimeFieldSpec

A dateTimeFieldSpec is used to define time columns of the table. Here's a list of the fields in a dateTimeFieldSpec

timeFieldSpec

This has been deprecated. Older schemas containing timeFieldSpec will be supported. But for new schemas, use DateTimeFieldSpec instead.

The incoming and outgoing granularitySpec are defined as:

Advanced fields

Apart from these, there's some advanced fields. These are common to all field specs.

Ingestion Transform Functions

Transform functions can be defined on columns in the schema. For example:

"metricFieldSpecs": [
    {
      "name": "maxPrice",
      "dataType": "DOUBLE",
      "transformFunction": "Groovy({prices.max()}, prices)" // groovy function
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "1:HOURS:EPOCH",
      "granularity": "1:HOURS",
      "transformFunction": "toEpochHours(timestamp)" // inbuilt function
    }

Currently, we have support for 2 kinds of functions

Groovy functions
Inbuilt functions

Note

Currently, the arguments must be from the source data. They cannot be columns from the Pinot schema which have been created through transformations.

Groovy functions

Groovy functions can be defined using the syntax:

Groovy({groovy script}, argument1, argument2...argumentN)

Here's some examples of commonly needed functions. Any valid Groovy expression can be used.

String concatenation

Concat firstName and lasName to get fullName

{
      "name": "fullName",
      "dataType": "STRING",
      "transformFunction": "Groovy({firstName+' '+lastName}, firstName, lastName)"
}

Find element in an array

Find max value in array bids

{
      "name": "maxBid",
      "dataType": "INT",
      "transformFunction": "Groovy({bids.max{ it.toBigDecimal() }}, bids)"
}

Time transformation

Convert timestamp from MILLISECONDS to HOURS

"dateTimeFieldSpecs": [{
    "name": "hoursSinceEpoch",
    "dataType": "LONG",
    "format" : "1:HOURS:EPOCH",
    "granularity": "1:HOURS"
    "transformFunction": "Groovy({timestamp/(1000*60*60)}, timestamp)"
  }]

Column name change

Simply change name of the column from user_id to userId

{
      "name": "userId",
      "dataType": "LONG",
      "transformFunction": "Groovy({user_id}, user_id)"
}

Ternary operation

If eventType is IMPRESSION set impression to 1. Similar for CLICK.

{
    "name": "impressions",
    "dataType": "LONG",
    "transformFunction": "Groovy({eventType == 'IMPRESSION' ? 1: 0}, eventType)"
},
{
    "name": "clicks",
    "dataType": "LONG",
    "transformFunction": "Groovy({eventType == 'CLICK' ? 1: 0}, eventType)"
}

AVRO Map

Store an AVRO Map in Pinot as two multi-value columns. Sort the keys, to maintain the mapping. 1) The keys of the map as map_keys 2) The values of the map as map_values

{
      "name": "map2_keys",
      "dataType": "STRING",
      "singleValueField": false,
      "transformFunction": "Groovy({map2.sort()*.key}, map2)"
},
{
      "name": "map2_values",
      "dataType": "INT",
      "singleValueField": false,
      "transformFunction": "Groovy({map2.sort()*.value}, map2)"
}

Inbuilt Pinot functions

We have several inbuilt functions that can be used directly in as ingestion transform functions

DateTime functions

These are functions which enable commonly needed time transformations.

toEpochXXX

Converts from epoch milliseconds to a higher granularity.

toEpochXXXRounded

fromEpochXXX

Converts from an epoch granularity to milliseconds.

Simple date format

Converts simple date format strings to milliseconds and vice-a-versa, as per the provided pattern string.

Json functions

Creating a Schema

Create a schema for your data, or see examples for examples. Make sure you've setup the cluster

Note: schema can also be created as part of table creation, refer to Creating a table.

bin/pinot-admin.sh AddSchema -schemaFile transcript-schema.json -exec

curl -F schemaName=@transcript-schema.json  localhost:9000/schemas

Check out the schema in the Rest API to make sure it was successfully uploaded