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 as byte arrays (binary) with a UTF8 annotation.
This file contains the specification for all logical types.
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.
The Thrift definition of the metadata has two fields for logical types: ConvertedType
and LogicalType
. ConvertedType
is an enum of all available annotation. 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 struct of logical types, this way allowing more flexible API, logical types can have type parameters.
However, to maintain compatibility, Parquet readers should be able to read and interpret old logical type representation (in case the new one is not present, because the file was written by older writer), and write ConvertedType
field for old readers.
Compatibility considerations are mentioned for each annotation in the corresponding section.
STRING
may only be used to annotate the binary 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
annotates the binary 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
annotates a 16-byte fixed-length binary. 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.
INT
annotation can be used to specify the maximum number of bits in the stored value. The annotation has two parameter: 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.
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 parameter: 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, true)
must annotate an int64
primitive type.
The sort order used for unsigned integer types is unsigned.
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:
DECIMAL
annotation represents arbitrary-precision signed decimal numbers of the form unscaledValue * 10^(-scale)
.
The primitive type stores an unscaled integer value. For byte arrays, binary and fixed, 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 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 <= 9int64
: for 1 <= precision <= 18; precision < 10 will produce a warningfixed_len_byte_array
: precision is limited by the array size. Length n
can store <= floor(log_10(2^(8*n - 1) - 1))
base-10 digitsbinary
: 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.
DATE
is used to 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
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 precision (MILLIS
or MICROS
, NANOS
).
TIME
with precision MILLIS
is used for millisecond precision. It must annotate an int32
that stores the number of milliseconds after midnight.
TIME
with precision MICROS
is used for microsecond precision. It must annotate an int64
that stores the number of microseconds after midnight.
TIME
with precision 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.
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
.
Backward compatibility:
ConvertedType | LogicalType |
---|---|
TIME_MILLIS | TimeType (isAdjustedToUTC = true, unit = MILLIS) |
TIME_MICROS | TimeType (isAdjustedToUTC = true, unit = MICROS) |
Forward compatibility:
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 interpertations are allowed on purpose.
The TIMESTAMP
type has two type parameters:
isAdjustedToUTC
must be either true
or false
.precision
must be one of MILLIS
, MICROS
or NANOS
. This list is subject to potential expansion in the future. Upon reading, unknown precision
-s must be handled as unsupported features (rather than as errors in the data files).A TIMESTAMP
with isAdjustedToUTC=true
is defined as the number of milliseconds, microseconds or nanoseconds (depending on the precision
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, precision=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, precision=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.
A TIMESTAMP
with isAdjustedToUTC=false
represents year, month, day, hour, minute, second and subsecond fields in a local timezone, regadless 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 unambigously, 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, precision=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.
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:
int64
type, timestamps using the NANOS
precision 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
precision. (Other precisions have similar limits but those are outside of the domain for practical everyday usage.)The sort order used for TIMESTAMP
is signed.
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
.
Backward compatibility:
ConvertedType | LogicalType |
---|---|
TIMESTAMP_MILLIS | TimestampType (isAdjustedToUTC = true, unit = MILLIS) |
TIMESTAMP_MICROS | TimestampType (isAdjustedToUTC = true, unit = MICROS) |
Forward compatibility:
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 do not have type-specific orderings.
JSON
is used for an embedded JSON document. It must annotate a binary
primitive type. The binary
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
is used for an embedded BSON document. It must annotate a binary
primitive type. The binary
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.
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.
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.
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; } }
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.list
, must be a repeated group with a single field named element
.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 (UTF8); } } // List<String> (list nullable, elements non-null) optional group my_list (LIST) { repeated group list { required binary element (UTF8); } }
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; } } } }
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 (UTF8); }; }
Some existing data does not include the inner element layer. For backward-compatibility, the type of elements in LIST
-annotated structures should always be determined by the following rules:
array
or uses the LIST
-annotated group's name with _tuple
appended then the repeated type is the element type and elements are required.Examples that can be interpreted using these rules:
// List<Integer> (nullable list, non-null elements) optional group my_list (LIST) { repeated int32 element; } // List<Tuple<String, Integer>> (nullable list, non-null elements) optional group my_list (LIST) { repeated group element { required binary str (UTF8); required int32 num; }; } // List<OneTuple<String>> (nullable list, non-null elements) optional group my_list (LIST) { repeated group array { required binary str (UTF8); }; } // List<OneTuple<String>> (nullable list, non-null elements) optional group my_list (LIST) { repeated group my_list_tuple { required binary str (UTF8); }; }
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; } }
MAP
that contains a single field named key_value
. The repetition of this level must be either optional
or required
and determines whether the list is nullable.key_value
, must be a repeated group with a key
field for map keys and, optionally, a value
field for map values.key
field encodes the map's key type. This field must have repetition required
and must always be present.value
field encodes the map's value type and repetition. This field can be required
, optional
, or omitted.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 (UTF8); 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.
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.
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 (UTF8); required int32 num; } } // Map<String, Integer> (nullable map, nullable values) optional group my_map (MAP_KEY_VALUE) { repeated group map { required binary key (UTF8); optional int32 value; } }
Sometimes when discovering the schema of existing data values are always null and there's no type information. The NULL
type can be used to annotates a column that is always null. (Similar to Null type in Avro)