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:
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
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.
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:
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.
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:
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
INT
LONG
FLOAT
DOUBLE
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
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
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 -
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:
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:
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 -
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 specifyingEPOCH|MILLISECONDS|1
Examples -TIMESTAMP
EPOCH
- This represents time intimeUnit
since00:00:00 UTC on 1 January 1970
, wheretimeUnit
is one of TimeUnit enum values, e.g.HOURS
,MINUTES
etc. You can also specify thetimeSize
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 inmaster
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 inyyyy
oryyyy-MM
oryyyy-MM-dd
and so on You can also specify optionaltimeZone
parameter which is the ID for a TimeZone, either an abbreviation such asPST
, a full name such asAmerica/Los_Angeles
, or a custom ID such asGMT-8:00
. Examples -SIMPLE_DATE_FORMAT
(only inmaster
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 inEPOCH
format, this value is not used and can be set toMILLISECONDS
or any other unit.timeFormat - can be either
EPOCH
orSIMPLE_DATE_FORMAT
. If it isSIMPLE_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 asyyyy-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 inLONG
format1:HOURS:EPOCH
- used when timestamp is in the epoch hours and stored inLONG
orINT
format1:DAYS:SIMPLE_DATE_FORMAT:yyyy-MM-dd
- when the date is inSTRING
format and has the pattern year-month-date. e.g. 2020-08-211:HOURS:SIMPLE_DATE_FORMAT:EEE MMM dd HH:mm:ss ZZZ yyyy
- when date is inSTRING
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:
$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.
maxLength
Max length of this column mainly applicable for dataTypes - STRING, JSON and BYTES
virtualColumnProvider
Column value provider
maxLengthExceedStrategy
Takes in 4 values: TRIM_LENGTH, ERROR, SUBSTITUTE_DEFAULT_VALUE, NO_ACTION. Default for STRING dataType is TRIM_LENGTH and for JSON and bytes field is NO_ACTION.
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, then substitute default value specified for the field.
NO_ACTION: Ingest the record as is.
ERROR: Throws an error if length of incoming record exceeds maxLength
.
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