Parquet Logical Type Definitions

Logical types are used to extend the types that parquet can be used to store, by specifying how the primitive types should be interpreted. This keeps the set of primitive types to a minimum and reuses parquet's efficient encodings. For example, strings are stored with the primitive type BYTE_ARRAY with a STRING annotation.

This file contains the specification for all logical types.

Metadata

The parquet format's LogicalType stores the type annotation. The annotation may require additional metadata fields, as well as rules for those fields.

There is an older representation of the logical type annotations called ConvertedType. To support backward compatibility with old files, readers should interpret LogicalTypes in the same way as ConvertedType, and writers should populate ConvertedType in the metadata according to well defined conversion rules.

Compatibility

The Thrift definition of the metadata has two fields for logical types: ConvertedType and LogicalType. ConvertedType is an enum of all available annotations. Since Thrift enums can't have additional type parameters, it is cumbersome to define additional type parameters, like decimal scale and precision (which are additional 32 bit integer fields on SchemaElement, and are relevant only for decimals) or time unit and UTC adjustment flag for Timestamp types. To overcome this problem, a new logical type representation was introduced into the metadata to replace ConvertedType: LogicalType. The new representation is a union of structs of logical types, this way allowing more flexible API, logical types can have type parameters.

ConvertedType is deprecated. However, to maintain compatibility with old writers, Parquet readers should be able to read and interpret ConvertedType annotations in case LogicalType annotations are not present. Parquet writers must always write LogicalType annotations where applicable, but must also write the corresponding ConvertedType annotations (if any) to maintain compatibility with old readers.

Compatibility considerations are mentioned for each annotation in the corresponding section.

String Types

STRING

STRING may only be used to annotate the BYTE_ARRAY primitive type and indicates that the byte array should be interpreted as a UTF-8 encoded character string.

The sort order used for STRING strings is unsigned byte-wise comparison.

Compatibility

STRING corresponds to UTF8 ConvertedType.

ENUM

ENUM annotates the BYTE_ARRAY primitive type and indicates that the value was converted from an enumerated type in another data model (e.g. Thrift, Avro, Protobuf). Applications using a data model lacking a native enum type should interpret ENUM annotated field as a UTF-8 encoded string.

The sort order used for ENUM values is unsigned byte-wise comparison.

UUID

UUID annotates a 16-byte FIXED_LEN_BYTE_ARRAY primitive type. The value is encoded using big-endian, so that 00112233-4455-6677-8899-aabbccddeeff is encoded as the bytes 00 11 22 33 44 55 66 77 88 99 aa bb cc dd ee ff (This example is from wikipedia's UUID page).

The sort order used for UUID values is unsigned byte-wise comparison.

Numeric Types

Signed Integers

INT annotation can be used to specify the maximum number of bits in the stored value. The annotation has two parameters: bit width and sign. Allowed bit width values are 8, 16, 32, 64, and sign can be true or false. For signed integers, the second parameter should be true, for example, a signed integer with bit width of 8 is defined as INT(8, true) Implementations may use these annotations to produce smaller in-memory representations when reading data.

If a stored value is larger than the maximum allowed by the annotation, the behavior is not defined and can be determined by the implementation. Implementations must not write values that are larger than the annotation allows.

INT(8, true), INT(16, true), and INT(32, true) must annotate an int32 primitive type and INT(64, true) must annotate an int64 primitive type. INT(32, true) and INT(64, true) are implied by the int32 and int64 primitive types if no other annotation is present and should be considered optional.

The sort order used for signed integer types is signed.

Unsigned Integers

INT annotation can be used to specify unsigned integer types, along with a maximum number of bits in the stored value. The annotation has two parameters: bit width and sign. Allowed bit width values are 8, 16, 32, 64, and sign can be true or false. In case of unsigned integers, the second parameter should be false, for example, an unsigned integer with bit width of 8 is defined as INT(8, false) Implementations may use these annotations to produce smaller in-memory representations when reading data.

If a stored value is larger than the maximum allowed by the annotation, the behavior is not defined and can be determined by the implementation. Implementations must not write values that are larger than the annotation allows.

INT(8, false), INT(16, false), and INT(32, false) must annotate an int32 primitive type and INT(64, false) must annotate an int64 primitive type.

The sort order used for unsigned integer types is unsigned.

Deprecated integer ConvertedType

INT_8, INT_16, INT_32, and INT_64 annotations can be also used to specify signed integers with 8, 16, 32, or 64 bit width.

INT_8, INT_16, and INT_32 must annotate an int32 primitive type and INT_64 must annotate an int64 primitive type. INT_32 and INT_64 are implied by the int32 and int64 primitive types if no other annotation is present and should be considered optional.

UINT_8, UINT_16, UINT_32, and UINT_64 annotations can be also used to specify unsigned integers with 8, 16, 32, or 64 bit width.

UINT_8, UINT_16, and UINT_32 must annotate an int32 primitive type and UINT_64 must annotate an int64 primitive type.

Backward compatibility:

ConvertedType	LogicalType
INT_8	IntType (bitWidth = 8, isSigned = true)
INT_16	IntType (bitWidth = 16, isSigned = true)
INT_32	IntType (bitWidth = 32, isSigned = true)
INT_64	IntType (bitWidth = 64, isSigned = true)
UINT_8	IntType (bitWidth = 8, isSigned = false)
UINT_16	IntType (bitWidth = 16, isSigned = false)
UINT_32	IntType (bitWidth = 32, isSigned = false)
UINT_64	IntType (bitWidth = 64, isSigned = false)

Forward compatibility:

LogicalType			ConvertedType
IntType	isSigned	bitWidth = 8	INT_8
		bitWidth = 16	INT_16
		bitWidth = 32	INT_32
		bitWidth = 64	INT_64
	!isSigned	bitWidth = 8	UINT_8
		bitWidth = 16	UINT_16
		bitWidth = 32	UINT_32
		bitWidth = 64	UINT_64

DECIMAL

DECIMAL annotation represents arbitrary-precision signed decimal numbers of the form unscaledValue * 10^(-scale).

The primitive type stores an unscaled integer value. For BYTE_ARRAY and FIXED_LEN_BYTE_ARRAY, the unscaled number must be encoded as two's complement using big-endian byte order (the most significant byte is the zeroth element). The scale stores the number of digits of that value that are to the right of the decimal point, and the precision stores the maximum number of digits supported in the unscaled value.

If not specified, the scale is 0. Scale must be zero or a positive integer less than or equal to the precision. Precision is required and must be a non-zero positive integer. A precision too large for the underlying type (see below) is an error.

DECIMAL can be used to annotate the following types:

int32: for 1 <= precision <= 9
int64: for 1 <= precision <= 18; precision < 10 will produce a warning
fixed_len_byte_array: precision is limited by the array size. Length n can store <= floor(log_10(2^(8*n - 1) - 1)) base-10 digits
byte_array: precision is not limited, but is required. The minimum number of bytes to store the unscaled value should be used.

The sort order used for DECIMAL values is signed comparison of the represented value.

If the column uses int32 or int64 physical types, then signed comparison of the integer values produces the correct ordering. If the physical type is fixed, then the correct ordering can be produced by flipping the most-significant bit in the first byte and then using unsigned byte-wise comparison.

Compatibility

To support compatibility with older readers, implementations of parquet-format should write DecimalType precision and scale into the corresponding SchemaElement field in metadata.

FLOAT16

The FLOAT16 annotation represents half-precision floating-point numbers in the 2-byte IEEE little-endian format.

Used in contexts where precision is traded off for smaller footprint and potentially better performance.

The primitive type is a 2-byte FIXED_LEN_BYTE_ARRAY.

The sort order for FLOAT16 is signed (with special handling of NANs and signed zeros); it uses the same logic as FLOAT and DOUBLE.

Temporal Types

DATE

DATE is used for a logical date type, without a time of day. It must annotate an int32 that stores the number of days from the Unix epoch, 1 January 1970.

The sort order used for DATE is signed.

TIME

TIME is used for a logical time type without a date with millisecond or microsecond precision. The type has two type parameters: UTC adjustment (true or false) and unit (MILLIS or MICROS, NANOS).

TIME with unit MILLIS is used for millisecond precision. It must annotate an int32 that stores the number of milliseconds after midnight.

TIME with unit MICROS is used for microsecond precision. It must annotate an int64 that stores the number of microseconds after midnight.

TIME with unit NANOS is used for nanosecond precision. It must annotate an int64 that stores the number of nanoseconds after midnight.

The sort order used for TIME is signed.

Deprecated time ConvertedType

TIME_MILLIS is the deprecated ConvertedType counterpart of a TIME logical type that is UTC normalized and has MILLIS precision. Like the logical type counterpart, it must annotate an int32.

TIME_MICROS is the deprecated ConvertedType counterpart of a TIME logical type that is UTC normalized and has MICROS precision. Like the logical type counterpart, it must annotate an int64.

Despite there is no exact corresponding ConvertedType for local time semantic, in order to support forward compatibility with those libraries, which annotated their local time with legacy TIME_MICROS and TIME_MILLIS annotation, Parquet writer implementation must annotate local time with legacy annotations too, as shown below.

Backward compatibility:

ConvertedType	LogicalType
TIME_MILLIS	TimeType (isAdjustedToUTC = true, unit = MILLIS)
TIME_MICROS	TimeType (isAdjustedToUTC = true, unit = MICROS)

Forward compatibility:

LogicalType			ConvertedType
TimeType	isAdjustedToUTC = true	unit = MILLIS	TIME_MILLIS
		unit = MICROS	TIME_MICROS
		unit = NANOS	-
	isAdjustedToUTC = false	unit = MILLIS	TIME_MILLIS
		unit = MICROS	TIME_MICROS
		unit = NANOS	-

TIMESTAMP

In data annotated with the TIMESTAMP logical type, each value is a single int64 number that can be decoded into year, month, day, hour, minute, second and subsecond fields using calculations detailed below. Please note that a value defined this way does not necessarily correspond to a single instant on the time-line and such interpretations are allowed on purpose.

The TIMESTAMP type has two type parameters:

isAdjustedToUTC must be either true or false.
unit must be one of MILLIS, MICROS or NANOS. This list is subject to potential expansion in the future. Upon reading, unknown unit-s must be handled as unsupported features (rather than as errors in the data files).

Instant semantics (timestamps normalized to UTC)

A TIMESTAMP with isAdjustedToUTC=true is defined as the number of milliseconds, microseconds or nanoseconds (depending on the unit parameter being MILLIS, MICROS or NANOS, respectively) elapsed since the Unix epoch, 1970-01-01 00:00:00 UTC. Each such value unambiguously identifies a single instant on the time-line.

For example, in a TIMESTAMP(isAdjustedToUTC=true, unit=MILLIS), the number 172800000 corresponds to 1970-01-03 00:00:00 UTC, because it is equal to 2 * 24 * 60 * 60 * 1000, therefore it is exactly two days from the reference point, the Unix epoch. In Java, this calculation can be achieved by calling Instant.ofEpochMilli(172800000).

As a slightly more complicated example, if one wants to store 1970-01-03 00:00:00 (UTC+01:00) as a TIMESTAMP(isAdjustedToUTC=true, unit=MILLIS), first the time zone offset has to be dealt with. By normalizing the timestamp to UTC, we calculate what time in UTC corresponds to the same instant: 1970-01-02 23:00:00 UTC. This is 1 day and 23 hours after the epoch, therefore it can be encoded as the number (24 + 23) * 60 * 60 * 1000 = 169200000.

Please note that time zone information gets lost in this process. Upon reading a value back, we can only reconstruct the instant, but not the original representation. In practice, such timestamps are typically displayed to users in their local time zones, therefore they may be displayed differently depending on the execution environment.

Local semantics (timestamps not normalized to UTC)

A TIMESTAMP with isAdjustedToUTC=false represents year, month, day, hour, minute, second and subsecond fields in a local timezone, regardless of what specific time zone is considered local. This means that such timestamps should always be displayed the same way, regardless of the local time zone in effect. On the other hand, without additional information such as an offset or time-zone, these values do not identify instants on the time-line unambiguously, because the corresponding instants would depend on the local time zone.

Using a single number to represent a local timestamp is a lot less intuitive than for instants. One must use a local timestamp as the reference point and calculate the elapsed time between the actual timestamp and the reference point. The problem is that the result may depend on the local time zone, for example because there may have been a daylight saving time change between the two timestamps.

The solution to this problem is to use a simplification that makes the result easy to calculate and independent of the timezone. Treating every day as consisting of exactly 86400 seconds and ignoring DST changes altogether allows us to unambiguously represent a local timestamp as a difference from a reference local timestamp. We define the reference local timestamp to be 1970-01-01 00:00:00 (note the lack of UTC at the end, as this is not an instant). This way the encoding of local timestamp values becomes very similar to the encoding of instant values. For example, in a TIMESTAMP(isAdjustedToUTC=false, unit=MILLIS), the number 172800000 corresponds to 1970-01-03 00:00:00 (note the lack of UTC at the end), because it is exactly two days from the reference point (172800000 = 2 * 24 * 60 * 60 * 1000).

Another way to get to the same definition is to treat the local timestamp value as if it were in UTC and store it as an instant. For example, if we treat the local timestamp 1970-01-03 00:00:00 as if it were the instant 1970-01-03 00:00:00 UTC, we can store it as 172800000. When reading 172800000 back, we can reconstruct the instant 1970-01-03 00:00:00 UTC and convert it to a local timestamp as if we were in the UTC time zone, resulting in 1970-01-03 00:00:00. In Java, this can be achieved by calling LocalDateTime.ofEpochSecond(172800, 0, ZoneOffset.UTC).

Please note that while from a practical point of view this second definition is equivalent to the first one, from a theoretical point of view only the first definition can be considered correct, the second one just "incidentally" leads to the same results. Nevertheless, this second definition is worth mentioning as well, because it is relatively widespread and it can lead to confusion, especially due to its usage of UTC in the calculations. One can stumble upon code, comments and specifications ambiguously stating that a timestamp "is stored in UTC". In some contexts, it means that it is normalized to UTC and acts as an instant. In some other contexts though, it means the exact opposite, namely that the timestamp is stored as if it were in UTC and acts as a local timestamp in reality.

Common considerations

Every possible int64 number represents a valid timestamp, but depending on the precision, the corresponding year may be outside of the practical everyday limits and implementations may choose to only support a limited range.

On the other hand, not every combination of year, month, day, hour, minute, second and subsecond values can be encoded into an int64. Most notably:

An arbitrary combination of timestamp fields can not be encoded as a single number if the values for some of the fields are outside of their normal range (where the "normal range" corresponds to everyday usage). For example, neither of the following can be represented in a timestamp:
- hour = -1
- hour = 25
- minute = 61
- month = 13
- day = 29, month = 2, year = any non-leap year
Due to the range of the int64 type, timestamps using the NANOS unit can only represent values between 1677-09-21 00:12:43 and 2262-04-11 23:47:16. Values outside of this range can not be represented with the NANOS unit. (Other precisions have similar limits but those are outside of the domain for practical everyday usage.)

The sort order used for TIMESTAMP is signed.

Deprecated timestamp ConvertedType

TIMESTAMP_MILLIS is the deprecated ConvertedType counterpart of a TIMESTAMP logical type that is UTC normalized and has MILLIS precision. Like the logical type counterpart, it must annotate an int64.

TIMESTAMP_MICROS is the deprecated ConvertedType counterpart of a TIMESTAMP logical type that is UTC normalized and has MICROS precision. Like the logical type counterpart, it must annotate an int64.

Despite there is no exact corresponding ConvertedType for local timestamp semantic, in order to support forward compatibility with those libraries, which annotated their local timestamps with legacy TIMESTAMP_MICROS and TIMESTAMP_MILLIS annotation, Parquet writer implementation must annotate local timestamps with legacy annotations too, as shown below.

Backward compatibility:

ConvertedType	LogicalType
TIMESTAMP_MILLIS	TimestampType (isAdjustedToUTC = true, unit = MILLIS)
TIMESTAMP_MICROS	TimestampType (isAdjustedToUTC = true, unit = MICROS)

Forward compatibility:

LogicalType			ConvertedType
TimestampType	isAdjustedToUTC = true	unit = MILLIS	TIMESTAMP_MILLIS
		unit = MICROS	TIMESTAMP_MICROS
		unit = NANOS	-
	isAdjustedToUTC = false	unit = MILLIS	TIMESTAMP_MILLIS
		unit = MICROS	TIMESTAMP_MICROS
		unit = NANOS	-

INTERVAL

INTERVAL is used for an interval of time. It must annotate a fixed_len_byte_array of length 12. This array stores three little-endian unsigned integers that represent durations at different granularities of time. The first stores a number in months, the second stores a number in days, and the third stores a number in milliseconds. This representation is independent of any particular timezone or date.

Each component in this representation is independent of the others. For example, there is no requirement that a large number of days should be expressed as a mix of months and days because there is not a constant conversion from days to months.

The sort order used for INTERVAL is undefined. When writing data, no min/max statistics should be saved for this type and if such non-compliant statistics are found during reading, they must be ignored.

Embedded Types

Embedded types do not have type-specific orderings.

JSON

JSON is used for an embedded JSON document. It must annotate a BYTE_ARRAY primitive type. The BYTE_ARRAY data is interpreted as a UTF-8 encoded character string of valid JSON as defined by the JSON specification

The sort order used for JSON is unsigned byte-wise comparison.

BSON

BSON is used for an embedded BSON document. It must annotate a BYTE_ARRAY primitive type. The BYTE_ARRAY data is interpreted as an encoded BSON document as defined by the BSON specification.

The sort order used for BSON is unsigned byte-wise comparison.

VARIANT

VARIANT is used for a Variant value. It must annotate a group. The group must contain a field named metadata and a field named value. Both fields must have type binary, which is also called BYTE_ARRAY in the Parquet thrift definition. The VARIANT annotated group can be used to store either an unshredded Variant value, or a shredded Variant value.

The Variant group must be annotated with the VARIANT logical type.
Both fields value and metadata must be of type binary (called BYTE_ARRAY in the Parquet thrift definition).
The metadata field is required and must be a valid Variant metadata component, as defined by the Variant binary encoding specification.
When present, the value field must be a valid Variant value component, as defined by the Variant binary encoding specification.
The value field is required for unshredded Variant values.
The value field is optional and may be null only when parts of the Variant value are shredded according to the Variant shredding specification.

This is the expected representation of an unshredded Variant in Parquet:

optional group variant_unshredded (VARIANT) {
  required binary metadata;
  required binary value;
}

This is an example representation of a shredded Variant in Parquet:

optional group variant_shredded (VARIANT) {
  required binary metadata;
  optional binary value;
  optional int64 typed_value;
}

GEOMETRY

GEOMETRY is used for geospatial features in the Well-Known Binary (WKB) format with linear/planar edges interpolation. It must annotate a BYTE_ARRAY primitive type. See Geospatial.md for more detail.

The type has only one type parameter:

crs: An optional string value for CRS. If unset, the CRS defaults to "OGC:CRS84", which means that the geometries must be stored in longitude, latitude based on the WGS84 datum.

The sort order used for GEOMETRY is undefined. When writing data, no min/max statistics should be saved for this type and if such non-compliant statistics are found during reading, they must be ignored.

GEOGRAPHY

GEOGRAPHY is used for geospatial features in the WKB format with an explicit (non-linear/non-planar) edges interpolation algorithm. It must annotate a BYTE_ARRAY primitive type. See Geospatial.md for more detail.

The type has two type parameters:

crs: An optional string value for CRS. It must be a geographic CRS, where longitudes are bound by [-180, 180] and latitudes are bound by [-90, 90]. If unset, the CRS defaults to "OGC:CRS84".
algorithm: An optional enum value to describes the edge interpolation algorithm. Supported values are: SPHERICAL, VINCENTY, THOMAS, ANDOYER, KARNEY. If unset, the algorithm defaults to SPHERICAL.

The sort order used for GEOGRAPHY is undefined. When writing data, no min/max statistics should be saved for this type and if such non-compliant statistics are found during reading, they must be ignored.

Nested Types

This section specifies how LIST and MAP can be used to encode nested types by adding group levels around repeated fields that are not present in the data.

This does not affect repeated fields that are not annotated: A repeated field that is neither contained by a LIST- or MAP-annotated group nor annotated by LIST or MAP should be interpreted as a required list of required elements where the element type is the type of the field.

WARNING: writers should not produce list types like these examples! They are
just for the purpose of reading existing data for backward-compatibility.

// List<Integer> (non-null list, non-null elements)
repeated int32 num;

// List<Tuple<Integer, String>> (non-null list, non-null elements)
repeated group my_list {
  required int32 num;
  optional binary str (STRING);
}

For all fields in the schema, implementations should use either LIST and MAP annotations or unannotated repeated fields, but not both. When using the annotations, no unannotated repeated types are allowed.

Lists

LIST is used to annotate types that should be interpreted as lists.

LIST must always annotate a 3-level structure:

<list-repetition> group <name> (LIST) {
  repeated group list {
    <element-repetition> <element-type> element;
  }
}

The outer-most level must be a group annotated with LIST that contains a single field named list. The repetition of this level must be either optional or required and determines whether the list is nullable.
The middle level, named list, must be a repeated group with a single field named element.
The element field encodes the list's element type and repetition. Element repetition must be required or optional.

The following examples demonstrate two of the possible lists of string values.

// List<String> (list non-null, elements nullable)
required group my_list (LIST) {
  repeated group list {
    optional binary element (STRING);
  }
}

// List<String> (list nullable, elements non-null)
optional group my_list (LIST) {
  repeated group list {
    required binary element (STRING);
  }
}

Element types can be nested structures. For example, a list of lists:

// List<List<Integer>>
optional group array_of_arrays (LIST) {
  repeated group list {
    required group element (LIST) {
      repeated group list {
        required int32 element;
      }
    }
  }
}

Backward-compatibility rules

New writer implementations should always produce the 3-level LIST structure shown above. However, historically data files have been produced that use different structures to represent list-like data, and readers may include compatibility measures to interpret them as intended.

It is required that the repeated group of elements is named list and that its element field is named element. However, these names may not be used in existing data and should not be enforced as errors when reading. For example, the following field schema should produce a nullable list of non-null strings, even though the repeated group is named element.

optional group my_list (LIST) {
  repeated group element {
    required binary str (STRING);
  };
}

Some existing data does not include the inner element layer, resulting in a LIST that annotates a 2-level structure. Unlike the 3-level structure, the repetition of a 2-level structure can be optional, required, or repeated. When it is repeated, the LIST-annotated 2-level structure can only serve as an element within another LIST-annotated 2-level structure.

For backward-compatibility, the type of elements in LIST-annotated structures should always be determined by the following rules:

If the repeated field is not a group, then its type is the element type and elements are required.
If the repeated field is a group with multiple fields, then its type is the element type and elements are required.
If the repeated field is a group with one field with repeated repetition, then its type is the element type and elements are required.
If the repeated field is a group with one field and is named either array or uses the LIST-annotated group's name with _tuple appended then the repeated type is the element type and elements are required.
Otherwise, the repeated field's type is the element type with the repeated field's repetition.

Examples that can be interpreted using these rules:

WARNING: writers should not produce list types like these examples! They are
just for the purpose of reading existing data for backward-compatibility.

// Rule 1: List<Integer> (nullable list, non-null elements)
optional group my_list (LIST) {
  repeated int32 element;
}

// Rule 2: List<Tuple<String, Integer>> (nullable list, non-null elements)
optional group my_list (LIST) {
  repeated group element {
    required binary str (STRING);
    required int32 num;
  };
}

// Rule 3: List<List<Integer>> (nullable outer list, non-null elements)
optional group my_list (LIST) {
  repeated group array (LIST) {
    repeated int32 array;
  };
}

// Rule 4: List<OneTuple<String>> (nullable list, non-null elements)
optional group my_list (LIST) {
  repeated group array {
    required binary str (STRING);
  };
}

// Rule 4: List<OneTuple<String>> (nullable list, non-null elements)
optional group my_list (LIST) {
  repeated group my_list_tuple {
    required binary str (STRING);
  };
}

// Rule 5: List<String>  (nullable list, nullable elements)
optional group my_list (LIST) {
  repeated group element {
    optional binary str (STRING);
  };
}

Maps

MAP is used to annotate types that should be interpreted as a map from keys to values. MAP must annotate a 3-level structure:

<map-repetition> group <name> (MAP) {
  repeated group key_value {
    required <key-type> key;
    <value-repetition> <value-type> value;
  }
}

The outer-most level must be a group annotated with MAP that contains a single field named key_value. The repetition of this level must be either optional or required and determines whether the map is nullable.
The middle level, named key_value, must be a repeated group with a key field for map keys and, optionally, a value field for map values. It must not contain any other values.
The key field encodes the map's key type. This field must have repetition required and must always be present. It must always be the first field of the repeated key_value group.
The value field encodes the map's value type and repetition. This field can be required, optional, or omitted. It must always be the second field of the repeated key_value group if present. In case of not present, it can be represented as a map with all null values or as a set of keys.

The following example demonstrates the type for a non-null map from strings to nullable integers:

// Map<String, Integer>
required group my_map (MAP) {
  repeated group key_value {
    required binary key (STRING);
    optional int32 value;
  }
}

If there are multiple key-value pairs for the same key, then the final value for that key must be the last value. Other values may be ignored or may be added with replacement to the map container in the order that they are encoded. The MAP annotation should not be used to encode multi-maps using duplicate keys.

Backward-compatibility rules

It is required that the repeated group of key-value pairs is named key_value and that its fields are named key and value. However, these names may not be used in existing data and should not be enforced as errors when reading. (key and value can be identified by their position in case of misnaming.)

Some existing data incorrectly used MAP_KEY_VALUE in place of MAP. For backward-compatibility, a group annotated with MAP_KEY_VALUE that is not contained by a MAP-annotated group should be handled as a MAP-annotated group.

Examples that can be interpreted using these rules:

// Map<String, Integer> (nullable map, non-null values)
optional group my_map (MAP) {
  repeated group map {
    required binary str (STRING);
    required int32 num;
  }
}

// Map<String, Integer> (nullable map, nullable values)
optional group my_map (MAP_KEY_VALUE) {
  repeated group map {
    required binary key (STRING);
    optional int32 value;
  }
}

UNKNOWN (always null)

Sometimes, when discovering the schema of existing data, values are always null and there's no type information. The UNKNOWN type can be used to annotate a column that is always null. (Similar to Null type in Avro and Arrow)

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