Metadata: Logical types, schemas, data headers

This is documentation for the Arrow metadata specification, which enables systems to communicate the

Canonical implementation

We are using Flatbuffers for low-overhead reading and writing of the Arrow metadata. See Message.fbs.


The Schema type describes a table-like structure consisting of any number of Arrow arrays, each of which can be interpreted as a column in the table. A schema by itself does not describe the physical structure of any particular set of data.

A schema consists of a sequence of fields, which are metadata describing the columns. The Flatbuffers IDL for a field is:

table Field {
  // Name is not required, in i.e. a List
  name: string;
  nullable: bool;
  type: Type;
  // present only if the field is dictionary encoded
  // will point to a dictionary provided by a DictionaryBatch message
  dictionary: long;
  // children apply only to Nested data types like Struct, List and Union
  children: [Field];
  /// layout of buffers produced for this type (as derived from the Type)
  /// does not include children
  /// each recordbatch will return instances of those Buffers.
  layout: [ VectorLayout ];
  // User-defined metadata
  custom_metadata: [ KeyValue ];

The type is the logical type of the field. Nested types, such as List, Struct, and Union, have a sequence of child fields.

a JSON representation of the schema is also provided: Field:

  "name" : "name_of_the_field",
  "nullable" : false,
  "type" : /* Type */,
  "children" : [ /* Field */ ],
  "typeLayout" : {
    "vectors" : [ /* VectorLayout */ ]


  "typeBitWidth" : /* int */


  "name" : "null|struct|list|union|int|floatingpoint|utf8|binary|fixedsizebinary|bool|decimal|date|time|timestamp|interval"
  // fields as defined in the Flatbuffer depending on the type name


  "name" : "union",
  "mode" : "Sparse|Dense",
  "typeIds" : [ /* integer */ ]

The typeIds field in the Union are the codes used to denote each type, which may be different from the index of the child array. This is so that the union type ids do not have to be enumerated from 0.


  "name" : "int",
  "bitWidth" : /* integer */,
  "isSigned" : /* boolean */


  "name" : "floatingpoint",
  "precision" : "HALF|SINGLE|DOUBLE"


  "name" : "decimal",
  "precision" : /* integer */,
  "scale" : /* integer */


  "name" : "timestamp",


  "name" : "date",
  "unit" : "DAY|MILLISECOND"


  "name" : "time",
  "bitWidth": /* integer: 32 or 64 */


  "name" : "interval",
  "unit" : "YEAR_MONTH|DAY_TIME"


  "fields" : [
    /* Field */

Record data headers

A record batch is a collection of top-level named, equal length Arrow arrays (or vectors). If one of the arrays contains nested data, its child arrays are not required to be the same length as the top-level arrays.

One can be thought of as a realization of a particular schema. The metadata describing a particular record batch is called a “data header”. Here is the Flatbuffers IDL for a record batch data header

table RecordBatch {
  length: long;
  nodes: [FieldNode];
  buffers: [Buffer];

The RecordBatch metadata provides for record batches with length exceeding 2^31 - 1, but Arrow implementations are not required to implement support beyond this size.

The nodes and buffers fields are produced by a depth-first traversal / flattening of a schema (possibly containing nested types) for a given in-memory data set.


A buffer is metadata describing a contiguous memory region relative to some virtual address space. This may include:

The key form of the Buffer type is:

struct Buffer {
  offset: long;
  length: long;

In the context of a record batch, each field has some number of buffers associated with it, which are derived from their physical memory layout.

Each logical type (separate from its children, if it is a nested type) has a deterministic number of buffers associated with it. These will be specified in the logical types section.

Field metadata

The FieldNode values contain metadata about each level in a nested type hierarchy.

struct FieldNode {
  /// The number of value slots in the Arrow array at this level of a nested
  /// tree
  length: long;

  /// The number of observed nulls.
  null_count: lohng;

The FieldNode metadata provides for fields with length exceeding 2^31 - 1, but Arrow implementations are not required to implement support for large arrays.

Flattening of nested data

Nested types are flattened in the record batch in depth-first order. When visiting each field in the nested type tree, the metadata is appended to the top-level fields array and the buffers associated with that field (but not its children) are appended to the buffers array.

For example, let’s consider the schema

col1: Struct<a: Int32, b: List<Int64>, c: Float64>
col2: Utf8

The flattened version of this is:

FieldNode 0: Struct name='col1'
FieldNode 1: Int32 name=a'
FieldNode 2: List name='b'
FieldNode 3: Int64 name='item'  # arbitrary
FieldNode 4: Float64 name='c'
FieldNode 5: Utf8 name='col2'

For the buffers produced, we would have the following (as described in more detail for each type below):

buffer 0: field 0 validity bitmap

buffer 1: field 1 validity bitmap
buffer 2: field 1 values <int32_t*>

buffer 3: field 2 validity bitmap
buffer 4: field 2 list offsets <int32_t*>

buffer 5: field 3 validity bitmap
buffer 6: field 3 values <int64_t*>

buffer 7: field 4 validity bitmap
buffer 8: field 4 values <double*>

buffer 9: field 5 validity bitmap
buffer 10: field 5 offsets <int32_t*>
buffer 11: field 5 data <uint8_t*>

Logical types

A logical type consists of a type name and metadata along with an explicit mapping to a physical memory representation. These may fall into some different categories:


In the first version of Arrow we provide the standard 8-bit through 64-bit size standard C integer types, both signed and unsigned:

The IDL looks like:

table Int {
  bitWidth: int;
  is_signed: bool;

The integer endianness is currently set globally at the schema level. If a schema is set to be little-endian, then all integer types occurring within must be little-endian. Integers that are part of other data representations, such as list offsets and union types, must have the same endianness as the entire record batch.

Floating point numbers

We provide 3 types of floating point numbers as fixed bit-width primitive array

The IDL looks like:

enum Precision:int {HALF, SINGLE, DOUBLE}

table FloatingPoint {
  precision: Precision;


The Boolean logical type is represented as a 1-bit wide primitive physical type. The bits are numbered using least-significant bit (LSB) ordering.

Like other fixed bit-width primitive types, boolean data appears as 2 buffers in the data header (one bitmap for the validity vector and one for the values).


The List logical type is the logical (and identically-named) counterpart to the List physical type.

In data header form, the list field node contains 2 buffers:

The buffers associated with a list’s child field are handled recursively according to the child logical type (e.g. List<Utf8> vs. List<Boolean>).

Utf8 and Binary

We specify two logical types for variable length bytes:

These types both have the same memory layout as the nested type List<UInt8>, with the constraint that the inner bytes can contain no null values. From a logical type perspective they are primitive, not nested types.

In data header form, while List<UInt8> would appear as 2 field nodes (List and UInt8) and 4 buffers (2 for each of the nodes, as per above), these types have a simplified representation single field node (of Utf8 or Binary logical type, which have no children) and 3 buffers:




All timestamps are stored as a 64-bit integer, with one of four unit resolutions: second, millisecond, microsecond, and nanosecond.


We support two different date types:


Time supports the same unit resolutions: second, millisecond, microsecond, and nanosecond. We represent time as the smallest integer accommodating the indicated unit. For second and millisecond: 32-bit, for the others 64-bit.

Dictionary encoding