Schema

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

The Pinot schema is composed of:

Field
Release Version
Default
Description

schemaName

-

required

Name of the schema. This must be the same as the table name without the REALTIME or OFFLINE suffix. Therefore, the offline and the real-time table of a hybrid table should use the same schema.

enableColumnBasedNullHandling

1.1.0

false

When set to true, enables column-based null handling. The default value false means to use table-based null handling. See Null value support for more information about this.

dimensionFieldSpec

-

[]

A dimensionFieldSpec is defined for each dimension column. For more details, see DimensionFieldSpec.

metricFieldSpec

-

[]

A metricFieldSpec is defined for each metric column. For more details, see MetricFieldSpec.

dateTimeFieldSpec

-

[]

A dateTimeFieldSpec is defined for the time columns. There can be multiple time columns. For more details, see DateTimeFieldSpec.

complexFieldSpec

-

[]

A complexFieldSpec is defined for complex data types Map. For more details, see ComplexFieldSpec

flights-schema.json
{
  "schemaName": "flights",
  "enableColumnBasedNullHandling": false,
  "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": "EPOCH",
      "granularity": "15:MINUTES"
    },
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "EPOCH|HOURS",
      "granularity": "1:HOURS"
    },
    {
      "name": "dateString",
      "dataType": "STRING",
      "format": "SIMPLE_DATE_FORMAT|yyyy-MM-dd",
      "granularity": "1:DAYS"
    }
  ]
}

The above json configuration is the example of Pinot schema derived from the flight data. As seen in the example, the schema is composed of 4 parts: schemaName, dimensionFieldSpec, metricFieldSpec, and dateTimeFieldSpec. Below is a detailed description of each type of field spec.

flights-schema-map.json
{
  "schemaName": "flights",
  "enableColumnBasedNullHandling": false,
  "dimensionFieldSpecs": [
    {
      "name": "flightNumber",
      "dataType": "LONG"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "price",
      "dataType": "DOUBLE",
      "defaultNullValue": 0
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "millisSinceEpoch",
      "dataType": "LONG",
      "format": "EPOCH",
      "granularity": "15:MINUTES"
    },
    {
      "name": "hoursSinceEpoch",
      "dataType": "INT",
      "format": "EPOCH|HOURS",
      "granularity": "1:HOURS"
    },
    {
      "name": "dateString",
      "dataType": "STRING",
      "format": "SIMPLE_DATE_FORMAT|yyyy-MM-dd",
      "granularity": "1:DAYS"
    }
  ],
  "complexFieldSpecs": [
    {
      "name": "tags",
      "dataType": "MAP",
      "fieldType": "COMPLEX",
      "notNull": false,
      "childFieldSpecs": {
        "key": {
          "name": "key",
          "dataType": "STRING",
          "fieldType": "DIMENSION",
          "notNull": false
        },
        "value": {
          "name": "value",
          "dataType": "STRING",
          "fieldType": "DIMENSION",
          "notNull": false
        }
      }
    }
  ]
}

The above JSON configuration is an example of a Pinot schema derived from flight data. As seen in the example, the schema is composed of 5 parts: schemaName, dimensionFieldSpecs, metricFieldSpecs, dateTimeFieldSpecs, and complexFieldSpecs.

Data Types

Data types determine the operations that can be performed on a column. Pinot supports the following data types:

Data Type
Default Dimension Value
Default Metric Value

INT

Integer.MIN_VALUE

0

LONG

Long.MIN_VALUE

0

FLOAT

Float.NEGATIVE_INFINITY

0.0

DOUBLE

Double.NEGATIVE_INFINITY

0.0

BIG_DECIMAL

Not supported

0.0

BOOLEAN

0 (false)

N/A

TIMESTAMP

0 (1970-01-01 00:00:00 UTC)

N/A

STRING

"null"

N/A

JSON

"null"

N/A

BYTES

byte array of length 0

byte array of length 0

The lowest granularity TIMESTAMP type supports is milliseconds epoch, nanoseconds is not supported.

All the data types are comparable, and the ordering of the values must be consistent with equals (i.e. v1.compareTo(v2) == 0 always has the same value as v1.equals(v2)). Also any value should equal to itself. In order to achieve so, the following conversions are applied:

For FLOATand DOUBLE:

  • Negative zero (-0.0) is converted to 0.0

  • NaN is converted to null (default value)

For BIG_DECIMAL:

  • Trailing zeros are stripped off (can be turned off)

Read the following sections for details on how data types are used in various parts of a schema.

DimensionFieldSpec

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

Property
Description

name

Name of the dimension column.

dataType

Data type of the dimension column. Can be INT, LONG, FLOAT, DOUBLE, BOOLEAN, TIMESTAMP, STRING, BYTES,JSON.

defaultNullValue

Represents null values in the data, since Pinot doesn't support storing null column values natively (as part of its on-disk storage format). If not specified, an internal default null value is used as listed here.

singleValueField

Boolean indicating if this is a single-valued or a multi-valued column. Multi-valued column is modeled as a list, where the order of the values are preserved and duplicate values are allowed. Individual rows don’t necessarily have the same number of values. Typical use case for this would be a column such as skillSet for a person (one row in the table) that can have multiple values such as Real Estate, Mortgages. The default null value for a multi-valued column is a single defaultNullValue, e.g. [Integer.MIN_VALUE].

Internal default null values for dimension

Data Type
Internal Default Null Value

INT

​Integer.MIN_VALUE​

LONG

​Long.MIN_VALUE​

FLOAT

​Float.NEGATIVE_INFINITY​

DOUBLE

​Double.NEGATIVE_INFINITY​

BOOLEAN

0 (false)

TIMESTAMP

0 (1970-01-01 00:00:00 UTC)

STRING

"null"

BYTES

byte array of length 0

JSON

"null"

MetricFieldSpec

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

Property
Description

name

Name of the metric column

dataType

Data type of the column. Can be INT, LONG, FLOAT, DOUBLE, BIG_DECIMAL, BYTES (for specialized representations such as HLL, TDigest, etc, where the column stores byte serialized version of the value)

defaultNullValue

Represents null values in the data. If not specified, an internal default null value is used, as listed here.

Internal default null values for metric

Data Type
Internal Default Null Value

INT

0

LONG

0

FLOAT

0.0

DOUBLE

0.0

BIG_DECIMAL

0.0

BYTES

byte array of length 0

ComplexFieldSpec

A complexFieldSpec is defined for complex data types Map. The following fields can be configured in the complex field spec -

Property
Description

Name

Name of the complex column

dataType

Data type of the complex column.Currently supports MAP

fieldType

Should be set to COMPLEX

notNull

Boolean indicating if this column can contain null values

childFieldSpecs

Specification for the key and value fields of the Map. See the details below

childFieldSpecs

The `childFieldSpecs` property defines the structure of the key and value fields within the Map. It contains two sub-specifications: `key` and `value`.

key childFieldSpec

The key of a Map in Pinot is always a String. The key childFieldSpec has the following properties:

Property
Description

Name

Should be set to key.

dataType

Should be set to String

fieldType

Should be set to Dimension

notNull

Boolean indicating if the key can be null (typically set to false)

value childFieldSpec

The value childFieldSpec defines the type of values stored in the Map. It has the following properties:

Property
Description

Name

Should be set to "value"

dataType

Data type of the value ("STRING", "INT", "LONG", "FLOAT", "DOUBLE")

fieldType

Should be set to "DIMENSION" for non-numeric types

notNull

Boolean indicating if the value can be null

DateTimeFieldSpec

A dateTimeFieldSpec is used to define time columns of the table. The following fields can be configured in the date time field spec -

Property
Description

name

Name of the date time column

dataType

Data type of the date time column. Can be STRING, INT, LONG, or TIMESTAMP. Note:Internally TIMESTAMP is stored as LONG (milliseconds since epoch). To use theTIMESTAMP format, ensure your source data is either inLONG values or STRING values of JDBC timestamp format (2021-01-01 01:01:01.123).

format

The format in which the datetime is present in the column. Refer to Date time formats for supported formats.

granularity

The granularity in which the column is bucketed. The syntax of granularity is bucket size:bucket unit For example, the format can be milliseconds 1:MILLISECONDS:EPOCH, but bucketed to 15 minutes i.e. we only have one value for every 15 minute interval, in which case granularity can be specified as 15:MINUTES. Currently it is just for documentation purpose, and Pinot won't automatically round the time value to the specified granularity.

defaultNullValue

Represents null values in the data. If not specified, an internal default null value is used. If date time is in String format, the default value will be null or if timestamp then it is epoch 0 (i.e. 1970-01-01 00:00:00).

For the main time column of the table (time column specified in the segmentsConfig

in the table config), the main time column value must be in the range of 1971-01-01 UTC to 2071-01-01 UTC for segment management purpose (e.g. retention, time boundary). If the specified default null value is not within this range, segment creation time is used.

New DateTime Formats

In the pinot master (0.12.0-SNAPSHOT), We have simplified date time formats for the users. The formats now follow the pattern - timeFormat|pattern/timeUnit|[timeZone/timeSize] . The fields present in [] are completely optional. timeFormat can be one of EPOCH , SIMPLE_DATE_FORMAT or TIMESTAMP .

  • TIMESTAMP - This represents timestamp in milliseconds. It is equivalent to specifying EPOCH|MILLISECONDS|1 Examples -

    • TIMESTAMP

  • EPOCH - This represents time in timeUnit since 00:00:00 UTC on 1 January 1970, where timeUnit is one of TimeUnit enum values, e.g. HOURS , MINUTES etc. You can also specify the timeSize parameter. This size is multiplied to the value present in the time column to get an actual timestamp. e.g. if timesize is 5 and value in time column is 4996308 minutes. The value that will be converted to epoch timestamp will be 4996308 * 5 * 60 * 1000 = 1498892400000 milliseconds. In simplest terms, EPOCH|SECONDS|5 denotes the count of intervals of length 5 seconds from epoch 0 to now. Examples -

    • EPOCH - Defaults to MILLISECONDS (only in master branch)

    • EPOCH|SECONDS

    • EPOCH|SECONDS|5

  • SIMPLE_DATE_FORMAT - This represents time in the string format. The pattern should be specified using the Joda's DateTimeFormat representation. In the master branch build, if no pattern is specified, we use ISO 8601 DateTimeFormat to parse the date times. Optionals are supported with ISO format so users can specify date time string in yyyy or yyyy-MM or yyyy-MM-dd and so on You can also specify optional timeZone parameter which is the ID for a TimeZone, either an abbreviation such as PST, a full name such as America/Los_Angeles, or a custom ID such as GMT-8:00. Examples -

    • SIMPLE_DATE_FORMAT (only in master branch)

    • SIMPLE_DATE_FORMAT|yyyy-MM-dd HH:mm:ss

    • SIMPLE_DATE_FORMAT|yyyy-MM-dd|IST

Only datetime timeformats in lexicographical order are support in Pinot. so yyyy-MM-dd ,MM-dd and yyyy-dd are valid while MM-dd-yyyy is not. The order is decided as year > month > day > hour > minutes > second.

Old date time formats

These date-time formats are still supported in Pinot for backward compatibility. However, new users should prefer to use the formats mentioned in the previous sections.

You will need to provide the format of the date along with the data type in the schema. The format is described using the following syntax: timeSize:timeUnit:timeFormat:pattern .

  • time size - the size of the time unit. This size is multiplied to the value present in the time column to get an actual timestamp. e.g. if timesize is 5 and value in time column is 4996308 minutes. The value that will be converted to epoch timestamp will be 4996308 * 5 * 60 * 1000 = 1498892400000 milliseconds. If your date is not in EPOCH format, this value is not used and can be set to 1 or any other integer.

  • time unit - one of TimeUnit enum values. e.g. HOURS , MINUTES etc. If your date is not in EPOCH format, this value is not used and can be set to MILLISECONDS or any other unit.

  • timeFormat - can be either EPOCH or SIMPLE_DATE_FORMAT. If it is SIMPLE_DATE_FORMAT, the pattern string is also specified.

  • pattern - This is optional and is only specified when the date is in SIMPLE_DATE_FORMAT . The pattern should be specified using Joda's DateTimeFormat representation. e.g. 2020-08-21 can be represented as yyyy-MM-dd.

Here are some sample date-time formats you can use in the schema:

  • 1:MILLISECONDS:EPOCH - used when timestamp is in the epoch milliseconds and stored in LONG format

  • 1:HOURS:EPOCH - used when timestamp is in the epoch hours and stored in LONG or INT format

  • 1:DAYS:SIMPLE_DATE_FORMAT:yyyy-MM-dd - when the date is in STRING format and has the pattern year-month-date. e.g. 2020-08-21

  • 1:HOURS:SIMPLE_DATE_FORMAT:EEE MMM dd HH:mm:ss ZZZ yyyy - when date is in STRING format. e.g. Mon Aug 24 12:36:50 America/Los_Angeles 2019

Built-in virtual columns

There are several built-in virtual columns inside the schema the can be used for debugging purposes:

Column Name
Column Type
Data Type
Description

$hostName

Dimension

STRING

Name of the server hosting the data

$segmentName

Dimension

STRING

Name of the segment containing the record

$docId

Dimension

INT

Document id of the record within the segment

These virtual columns can be used in queries in a similar way to regular columns.

Advanced fields

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

Property
Description

maxLength

Max length of this column. Applicable for dataType: STRING, JSON and BYTES

maxLengthExceedStrategy

Takes in 4 values: TRIM_LENGTH, SUBSTITUTE_DEFAULT_VALUE, NO_ACTION, ERROR. Default is TRIM_LENGTH for STRING; NO_ACTION for JSON and BYTES. TRIM_LENGTH: Only maxLength characters are ingested for this field in incoming record. SUBSTITUTE_DEFAULT_VALUE: If the length of value in incoming record exceeds maxLength, substitute it with default value specified for the field. NO_ACTION: Ingest the record as is. ERROR: Throws an error if length of incoming record exceeds maxLength.

allowTrailingZeros

Whether to allow trailing zeros for BIG_DECIMAL column. By default false, where trailing zeros are stripped off.

virtualColumnProvider

Column value provider

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