Spec - Apache Iceberg™

Spec - Apache Iceberg™
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Iceberg Table Spec
This is a specification for the Iceberg table format that is designed to manage a large, slow-changing collection of files in a distributed file system or key-value store as a table.
Format Versioning
Versions 1, 2 and 3 of the Iceberg spec are complete and adopted by the community.
Version 4 is under active development and has not been formally adopted.
The format version number is incremented when new features are added that will break forward-compatibility---that is, when older readers would not read newer table features correctly. Tables may continue to be written with an older version of the spec to ensure compatibility by not using features that are not yet implemented by processing engines.
Version 1: Analytic Data Tables
Version 1 of the Iceberg spec defines how to manage large analytic tables using immutable file formats: Parquet, Avro, and ORC.
All version 1 data and metadata files are valid after upgrading a table to version 2.
Appendix E
documents how to default version 2 fields when reading version 1 metadata.
Version 2: Row-level Deletes
Version 2 of the Iceberg spec adds row-level updates and deletes for analytic tables with immutable files.
The primary change in version 2 adds delete files to encode rows that are deleted in existing data files. This version can be used to delete or replace individual rows in immutable data files without rewriting the files.
In addition to row-level deletes, version 2 makes some requirements stricter for writers. The full set of changes are listed in
Appendix E
Version 3: Extended Types and Capabilities
Version 3 of the Iceberg spec extends data types and existing metadata structures to add new capabilities:
New data types: nanosecond timestamp(tz), unknown, variant, geometry, geography
Default value support for columns
Multi-argument transforms for partitioning and sorting
Row Lineage tracking
Binary deletion vectors
Table encryption keys
The full set of changes are listed in
Appendix E
Goals
Serializable isolation
-- Reads will be isolated from concurrent writes and always use a committed snapshot of a table’s data. Writes will support removing and adding files in a single operation and are never partially visible. Readers will not acquire locks.
Speed
-- Operations will use O(1) remote calls to plan the files for a scan and not O(n) where n grows with the size of the table, like the number of partitions or files.
Scale
-- Job planning will be handled primarily by clients and not bottleneck on a central metadata store. Metadata will include information needed for cost-based optimization.
Evolution
-- Tables will support full schema and partition spec evolution. Schema evolution supports safe column add, drop, reorder and rename, including in nested structures.
Dependable types
-- Tables will provide well-defined and dependable support for a core set of types.
Storage separation
-- Partitioning will be table configuration. Reads will be planned using predicates on data values, not partition values. Tables will support evolving partition schemes.
Formats
-- Underlying data file formats will support identical schema evolution rules and types. Both read-optimized and write-optimized formats will be available.
Overview
This table format tracks individual data files in a table instead of directories. This allows writers to create data files in-place and only adds files to the table in an explicit commit.
Table state is maintained in metadata files. All changes to table state create a new metadata file and replace the old metadata with an atomic swap. The table metadata file tracks the table schema, partitioning config, custom properties, and snapshots of the table contents. A snapshot represents the state of a table at some time and is used to access the complete set of data files in the table.
Data files in snapshots are tracked by one or more manifest files that contain a row for each data file in the table, the file's partition data, and its metrics. The data in a snapshot is the union of all files in its manifests. Manifest files are reused across snapshots to avoid rewriting metadata that is slow-changing. Manifests can track data files with any subset of a table and are not associated with partitions.
The manifests that make up a snapshot are stored in a manifest list file. Each manifest list stores metadata about manifests, including partition stats and data file counts. These stats are used to avoid reading manifests that are not required for an operation.
Optimistic Concurrency
An atomic swap of one table metadata file for another provides the basis for serializable isolation. Readers use the snapshot that was current when they load the table metadata and are not affected by changes until they refresh and pick up a new metadata location.
Writers create table metadata files optimistically, assuming that the current version will not be changed before the writer's commit. Once a writer has created an update, it commits by swapping the table’s metadata file pointer from the base version to the new version.
If the snapshot on which an update is based is no longer current, the writer must retry the update based on the new current version. Some operations support retry by re-applying metadata changes and committing, under well-defined conditions. For example, a change that rewrites files can be applied to a new table snapshot if all of the rewritten files are still in the table.
The conditions required by a write to successfully commit determines the isolation level. Writers can select what to validate and can make different isolation guarantees.
Sequence Numbers
The relative age of data and delete files relies on a sequence number that is assigned to every successful commit. When a snapshot is created for a commit, it is optimistically assigned the next sequence number, and it is written into the snapshot's metadata. If the commit fails and must be retried, the sequence number is reassigned and written into new snapshot metadata.
All manifests, data files, and delete files created for a snapshot inherit the snapshot's sequence number. Manifest file metadata in the manifest list stores a manifest's sequence number. New data and metadata file entries are written with
null
in place of a sequence number, which is replaced with the manifest's sequence number at read time. When a data or delete file is written to a new manifest (as "existing"), the inherited sequence number is written to ensure it does not change after it is first inherited.
Inheriting the sequence number from manifest metadata allows writing a new manifest once and reusing it in commit retries. To change a sequence number for a retry, only the manifest list must be rewritten -- which would be rewritten anyway with the latest set of manifests.
Row-level Deletes
Row-level deletes are stored in delete files.
There are two types of row-level deletes:
Position deletes
-- Mark a row deleted by data file path and the row position in the data file. Position deletes are encoded in a
position delete file
(V2) or
deletion vector
(V3 or above).
Equality deletes
-- Mark a row deleted by one or more column values, like id = 5. Equality deletes are encoded in
equality delete file
Like data files, delete files are tracked by partition. In general, a delete file must be applied to older data files with the same partition; see
Scan Planning
for details. Column metrics can be used to determine whether a delete file's rows overlap the contents of a data file or a scan range.
File System Operations
Iceberg only requires that file systems support the following operations:
In-place write
-- Files are not moved or altered once they are written.
Seekable reads
-- Data file formats require seek support.
Deletes
-- Tables delete files that are no longer used.
These requirements are compatible with object stores, like S3.
Tables do not require random-access writes. Once written, data and metadata files are immutable until they are deleted.
Tables do not require rename, except for tables that use atomic rename to implement the commit operation for new metadata files.
Specification
Schema
-- Names and types of fields in a table.
Partition spec
-- A definition of how partition values are derived from data fields.
Snapshot
-- The state of a table at some point in time, including the set of all data files.
Manifest list
-- A file that lists manifest files; one per snapshot.
Manifest
-- A file that lists data or delete files; a subset of a snapshot.
Data file
-- A file that contains rows of a table.
Delete file
-- A file that encodes rows of a table that are deleted by position or data values.
Writer requirements
Some tables in this spec have columns that specify requirements for tables by version. These requirements are intended for writers when adding metadata files (including manifests files and manifest lists) to a table with the given version.
Requirement
Write behavior
(blank)
The field should be omitted
optional
The field can be written or omitted
required
The field must be written
Readers should be more permissive because v1 metadata files are allowed in v2 tables (or later) so that tables can be upgraded to without rewriting the metadata tree. For manifest list and manifest files, this table shows the expected read behavior for later versions:
v1
v2
v2+ read behavior
optional
Read the field as
optional
required
Read the field as
optional
; it may be missing in v1 files
optional
Ignore the field
optional
optional
Read the field as
optional
optional
required
Read the field as
optional
; it may be missing in v1 files
required
Ignore the field
required
optional
Read the field as
optional
required
required
Fill in a default or throw an exception if the field is missing
If a later version is not shown, the requirement for a version is not changed from the most recent version shown. For example, v3 uses the same requirements as v2 if a table shows only v1 and v2 requirements.
Readers may be more strict for metadata JSON files because the JSON files are not reused and will always match the table version. Required fields that were not present in or were optional in prior versions may be handled as required fields. For example, a v2 table that is missing
last-sequence-number
can throw an exception.
Writing data files
All columns must be written to data files even if they introduce redundancy with metadata stored in manifest files (e.g. columns with identity partition transforms). Writing all columns provides a backup in case of corruption or bugs in the metadata layer.
Writers are not allowed to commit files with a partition spec that contains a field with an unknown transform.
Schemas and Data Types
A table's
schema
is a list of named columns. All data types are either primitives or nested types, which are maps, lists, or structs. A table schema is also a struct type.
For the representations of these types in Avro, ORC, and Parquet file formats, see Appendix A.
Nested Types
struct
is a tuple of typed values. Each field in the tuple is named and has an integer id that is unique in the table schema. Each field can be either optional or required, meaning that values can (or cannot) be null. Fields may be any type. Fields may have an optional comment or doc string. Fields can have
default values
list
is a collection of values with some element type. The element field has an integer id that is unique in the table schema. Elements can be either optional or required. Element types may be any type.
map
is a collection of key-value pairs with a key type and a value type. Both the key field and value field each have an integer id that is unique in the table schema. Map keys are required and map values can be either optional or required. Both map keys and map values may be any type, including nested types.
Semi-structured Types
variant
is a value that stores semi-structured data. The structure and data types in a variant are not necessarily consistent across rows in a table or data file. The variant type and binary encoding are defined in the
Parquet project
, with support currently available for V1. Support for Variant is added in Iceberg v3.
Variants are similar to JSON with a wider set of primitive values including date, timestamp, timestamptz, binary, and decimals.
Variant values may contain nested types:
An array is an ordered collection of variant values.
An object is a collection of fields that are a string key and a variant value.
As a semi-structured type, there are important differences between variant and Iceberg's other types:
Variant arrays are similar to lists, but may contain any variant value rather than a fixed element type.
Variant objects are similar to structs, but may contain variable fields identified by name and field values may be any variant value rather than a fixed field type.
Primitive Types
Supported primitive types are defined in the table below. Primitive types added after v1 have an "added by" version that is the first spec version in which the type is allowed. For example, nanosecond-precision timestamps are part of the v3 spec; using v3 types in v1 or v2 tables can break forward compatibility.
Added by version
Primitive type
Description
Requirements
v3
unknown
Default / null column type used when a more specific type is not known
Must be optional with
null
defaults; not stored in data files
boolean
True or false
int
32-bit signed integers
Can promote to
long
long
64-bit signed integers
float
32-bit IEEE 754
floating point
Can promote to double
double
64-bit IEEE 754
floating point
decimal(P,S)
Fixed-point decimal; precision P, scale S
Scale is fixed, precision must be 38 or less
date
Calendar date without timezone or time
time
Time of day, microsecond precision, without date, timezone
timestamp
Timestamp, microsecond precision, without timezone
[1]
timestamptz
Timestamp, microsecond precision, with timezone
[2]
v3
timestamp_ns
Timestamp, nanosecond precision, without timezone
[1]
v3
timestamptz_ns
Timestamp, nanosecond precision, with timezone
[2]
string
Arbitrary-length character sequences
Encoded with UTF-8 [3]
uuid
Universally unique identifiers
Should use 16-byte fixed
fixed(L)
Fixed-length byte array of length L
binary
Arbitrary-length byte array
v3
geometry(C)
Geospatial features from
OGC – Simple feature access
. Edge-interpolation is always linear/planar. See
Appendix G
. Parameterized by CRS C. If not specified, C is
OGC:CRS84
v3
geography(C, A)
Geospatial features from
OGC – Simple feature access
. See
Appendix G
. Parameterized by CRS C and edge-interpolation algorithm A. If not specified, C is
OGC:CRS84
and A is
spherical
Notes:
Timestamp values
without time zone
represent a date and time of day regardless of zone: the time value is independent of zone adjustments (
2017-11-16 17:10:34
is always retrieved as
2017-11-16 17:10:34
).
Timestamp values
with time zone
represent a point in time: values are stored as UTC and do not retain a source time zone (
2017-11-16 17:10:34 PST
is stored/retrieved as
2017-11-17 01:10:34 UTC
and these values are considered identical).
Character strings must be stored as UTF-8 encoded byte arrays.
For details on how to serialize a schema to JSON, see Appendix C.
CRS
For
geometry
and
geography
types, the parameter C refers to the CRS (coordinate reference system), a mapping of how coordinates refer to locations on Earth.
For
geometry
type, the CRS does not affect geometric calculations, which are always Cartesian.
The default CRS value
OGC:CRS84
means that the objects must be stored in longitude, latitude based on the WGS84 datum.
Custom CRS values can be specified by a string of the format
type:identifier
, where
type
is one of the following values:
srid
Spatial reference identifier
identifier
is the SRID itself.
projjson
PROJJSON
identifier
is the name of a table property where the projjson string is stored.
For
geography
types, the custom CRS must be geographic, with longitudes bound by [-180, 180] and latitudes bound by [-90, 90].
Edge-Interpolation Algorithm
For
geography
types, an additional parameter A specifies an algorithm for interpolating edges, and is one of the following values:
spherical
: edges are interpolated as geodesics on a sphere.
vincenty
thomas
: Thomas, Paul D. Spheroidal geodesics, reference systems, & local geometry. US Naval Oceanographic Office, 1970.
andoyer
: Thomas, Paul D. Mathematical models for navigation systems. US Naval Oceanographic Office, 1965.
karney
Karney, Charles FF. "Algorithms for geodesics." Journal of Geodesy 87 (2013): 43-55
, and
GeographicLib
Default values
Default values can be tracked for struct fields (both nested structs and the top-level schema's struct). There can be two defaults with a field:
initial-default
is used to populate the field's value for all records that were written before the field was added to the schema
write-default
is used to populate the field's value for any records written after the field was added to the schema, if the writer does not supply the field's value
The
initial-default
is set only when a field is added to an existing schema. The
write-default
is initially set to the same value as
initial-default
and can be changed through schema evolution. If either default is not set for an optional field, then the default value is null for compatibility with older spec versions.
The
initial-default
and
write-default
produce SQL default value behavior, without rewriting data files. SQL default value behavior when a field is added handles all existing rows as though the rows were written with the new field's default value. Default value changes may only affect future records and all known fields are written into data files. Omitting a known field when writing a data file is never allowed. The write default for a field must be written if a field is not supplied to a write. If the write default for a required field is not set, the writer must fail.
All columns of
unknown
variant
geometry
, and
geography
types must default to null. Non-null values for
initial-default
or
write-default
are invalid.
Default values for the fields of a struct are tracked as
initial-default
and
write-default
at the field level. Default values for fields that are nested structs must not contain default values for the struct's fields (sub-fields). Sub-field defaults are tracked in sub-field's metadata. As a result, the default stored for a nested struct may be either null or a non-null struct with no field values. The effective default value is produced by setting each fields' default in a new struct.
For example, a struct column
point
with fields
(default 0) and
(default 0) can be defaulted to
{"x": 0, "y": 0}
or
null
. A non-null default is stored by setting
initial-default
or
write-default
to an empty struct (
{}
) that will use field values set from each field's
initial-default
or
write-default
, respectively.
point
default
point.x
default
point.y
default
Data value
Result value
null
(missing)
null
null
{"x": 3}
{"x": 3, "y": 0}
{}
(missing)
{"x": 0, "y": 0}
{}
{"y": -1}
{"x": 0, "y": -1}
Default values are attributes of fields in schemas and serialized with fields in the JSON format. See
Appendix C
Schema Evolution
Schemas may be evolved by type promotion or adding, deleting, renaming, or reordering fields in structs (both nested structs and the top-level schema’s struct).
Evolution applies changes to the table's current schema to produce a new schema that is identified by a unique schema ID, is added to the table's list of schemas, and is set as the table's current schema.
Valid primitive type promotions are:
Primitive type
v1, v2 valid type promotions
v3+ valid type promotions
Requirements
unknown
any type
int
long
long
date
timestamp
timestamp_ns
Promotion to
timestamptz
or
timestamptz_ns
is
not
allowed; values outside the promoted type's range must result in a runtime failure
float
double
double
decimal(P, S)
decimal(P', S)
if
P' > P
decimal(P', S)
if
P' > P
Widen precision only
Iceberg's Avro manifest format does not store the type of lower and upper bounds, and type promotion does not rewrite existing bounds. For example, when a
float
is promoted to
double
, existing data file bounds are encoded as 4 little-endian bytes rather than 8 little-endian bytes for
double
. To correctly decode the value, the original type at the time the file was written must be inferred according to the following table:
Current type
Length of bounds
Inferred type at write time
long
4 bytes
int
long
8 bytes
long
double
4 bytes
float
double
8 bytes
double
timestamp
4 bytes
date
timestamp
8 bytes
timestamp
timestamp_ns
4 bytes
date
timestamp_ns
8 bytes
timestamp_ns
decimal(P, S)
any
decimal(P', S)
P' <= P
Type promotion is not allowed for a field that is referenced by
source-id
or
source-ids
of a partition field if the partition transform would produce a different value after promoting the type. For example,
bucket[N]
produces different hash values for
34
and
"34"
(2017239379 != -427558391) but the same value for
34
and
34L
; when an
int
field is the source for a bucket partition field, it may be promoted to
long
but not to
string
. This may happen for the following type promotion cases:
date
to
timestamp
or
timestamp_ns
Any struct, including a top-level schema, can evolve through deleting fields, adding new fields, renaming existing fields, reordering existing fields, or promoting a primitive using the valid type promotions. Adding a new field assigns a new ID for that field and for any nested fields. Renaming an existing field must change the name, but not the field ID. Deleting a field removes it from the current schema. Field deletion cannot be rolled back unless the field was nullable or if the current snapshot has not changed.
Grouping a subset of a struct’s fields into a nested struct is
not
allowed, nor is moving fields from a nested struct into its immediate parent struct (
struct ↔ struct>
). Evolving primitive types to structs is
not
allowed, nor is evolving a single-field struct to a primitive (
map ↔ map>
).
Struct evolution requires the following rules for default values:
The
initial-default
must be set when a field is added and cannot change
The
write-default
must be set when a field is added and may change
When a required field is added, both defaults must be set to a non-null value
When an optional field is added, the defaults may be null and should be explicitly set
When a field that is a struct type is added, its default may only be null or a non-null struct with no field values. Default values for fields must be stored in field metadata.
If a field value is missing from a struct's
initial-default
, the field's
initial-default
must be used for the field
If a field value is missing from a struct's
write-default
, the field's
write-default
must be used for the field
Column Projection
Columns in Iceberg data files are selected by field id. The table schema's column names and order may change after a data file is written, and projection must be done using field ids.
Values for field ids which are not present in a data file must be resolved according the following rules:
Return the value from partition metadata if an
Identity Transform
exists for the field and the partition value is present in the
partition
struct on
data_file
object in the manifest. This allows for metadata only migrations of Hive tables.
Use
schema.name-mapping.default
metadata to map field id to columns without field id as described below and use the column if it is present.
Return the default value if it has a defined
initial-default
(See
Default values
section for more details).
Return
null
in all other cases.
For example, a file may be written with schema
1: a int, 2: b string, 3: c double
and read using projection schema
3: measurement, 2: name, 4: a
. This must select file columns
(renamed to
measurement
),
(now called
name
), and a column of
null
values called
; in that order.
Tables may also define a property
schema.name-mapping.default
with a JSON name mapping containing a list of field mapping objects. These mappings provide fallback field ids to be used when a data file does not contain field id information. Each object should contain
names
: A required list of 0 or more names for a field.
field-id
: An optional Iceberg field ID used when a field's name is present in
names
fields
: An optional list of field mappings for child field of structs, maps, and lists.
Field mapping fields are constrained by the following rules:
A name may contain
but this refers to a literal name, not a nested field. For example,
a.b
refers to a field named
a.b
, not child field
of field
Each child field should be defined with their own field mapping under
fields
Multiple values for
names
may be mapped to a single field ID to support cases where a field may have different names in different data files. For example, all Avro field aliases should be listed in
names
Fields which exist only in the Iceberg schema and not in imported data files may use an empty
names
list.
Fields that exist in imported files but not in the Iceberg schema may omit
field-id
List types should contain a mapping in
fields
for
element
Map types should contain mappings in
fields
for
key
and
value
Struct types should contain mappings in
fields
for their child fields.
For details on serialization, see
Appendix C
Identifier Field IDs
A schema can optionally track the set of primitive fields that identify rows in a table, using the property
identifier-field-ids
(see JSON encoding in Appendix C).
Two rows are the "same"---that is, the rows represent the same entity---if the identifier fields are equal. However, uniqueness of rows by this identifier is not guaranteed or required by Iceberg and it is the responsibility of processing engines or data providers to enforce.
Identifier fields may be nested in structs but cannot be nested within maps or lists. Float, double, and optional fields cannot be used as identifier fields and a nested field cannot be used as an identifier field if it is nested in an optional struct, to avoid null values in identifiers.
Reserved Field IDs
Iceberg tables must not use field ids greater than 2147483447 (
Integer.MAX_VALUE - 200
). This id range is reserved for metadata columns that can be used in user data schemas, like the
_file
column that holds the file path in which a row was stored.
The set of metadata columns is:
Field id, name
Type
Description
2147483646 _file
string
Path of the file in which a row is stored
2147483645 _pos
long
Ordinal position of a row in the source data file, starting at
2147483644 _deleted
boolean
Whether the row has been deleted
2147483643 _spec_id
int
Spec ID used to track the file containing a row
2147483642 _partition
struct
Partition to which a row belongs
2147483546 file_path
string
Path of a file, used in position-based delete files
2147483545 pos
long
Ordinal position of a row, used in position-based delete files
2147483544 row
struct<...>
Deleted row values, used in position-based delete files
2147483543 _change_type
string
The record type in the changelog (INSERT, DELETE, UPDATE_BEFORE, or UPDATE_AFTER)
2147483542 _change_ordinal
int
The order of the change
2147483541 _commit_snapshot_id
long
The snapshot ID in which the change occurred
2147483540 _row_id
long
A unique long assigned for row lineage, see
Row Lineage
2147483539 _last_updated_sequence_number
long
The sequence number which last updated this row, see
Row Lineage
Row Lineage
In v3 and later, an Iceberg table must track row lineage fields for all newly created rows. Engines must maintain the
next-row-id
table field and the following row-level fields when writing data files:
_row_id
a unique long identifier for every row within the table. The value is assigned via inheritance when a row is first added to the table.
_last_updated_sequence_number
the sequence number of the commit that last updated a row. The value is inherited when a row is first added or modified.
These fields are assigned and updated by inheritance because the commit sequence number and starting row ID are not assigned until the snapshot is successfully committed. Inheritance is used to allow writing data and manifest files before values are known so that it is not necessary to rewrite data and manifest files when an optimistic commit is retried.
Row lineage does not track lineage for rows updated via
Equality Deletes
, because engines using equality deletes avoid reading existing data before writing changes and can't provide the original row ID for the new rows. These updates are always treated as if the existing row was completely removed and a unique new row was added.
Row lineage assignment
When a row is added or modified, the
_last_updated_sequence_number
field is set to
null
so that it is inherited when reading. Similarly, the
_row_id
field for an added row is set to
null
and assigned when reading.
A data file with only new rows for the table may omit the
_last_updated_sequence_number
and
_row_id
. If the columns are missing, readers should treat both columns as if they exist and are set to null for all rows.
On read, if
_last_updated_sequence_number
is
null
it is assigned the
sequence_number
of the data file's manifest entry. The data sequence number of a data file is documented in
Sequence Number Inheritance
When
null
, a row's
_row_id
field is assigned to the
first_row_id
from its containing data file plus the row position in that data file (
_pos
). A data file's
first_row_id
field is assigned using inheritance and is documented in
First Row ID Inheritance
. A manifest's
first_row_id
is assigned when writing the manifest list for a snapshot and is documented in
First Row ID Assignment
. A snapshot's
first-row-id
is set to the table's
next-row-id
and is documented in
Snapshot Row IDs
When an existing row is moved to a different data file for any reason, writers should write
_row_id
and
_last_updated_sequence_number
according to the following rules:
The row's existing non-null
_row_id
must be copied into the new data file
If the write has modified the row, the
_last_updated_sequence_number
field must be set to
null
(so that the modification's sequence number replaces the current value)
If the write has not modified the row, the existing non-null
_last_updated_sequence_number
value must be copied to the new data file
Engines may model operations as deleting/inserting rows or as modifications to rows that preserve row ids.
Row lineage example
This example demonstrates how
_row_id
and
_last_updated_sequence_number
are assigned for a snapshot. This starts with a table with a
next-row-id
of 1000.
Writing a new append snapshot creates snapshot metadata with
first-row-id
assigned to the table's
next-row-id
"operation"
"append"
"first-row-id"
1000
...
The snapshot's manifest list will contain existing manifests, plus new manifests that are each assigned a
first_row_id
based on the
added_rows_count
and
existing_rows_count
of preceding new manifests:
manifest_path
added_rows_count
existing_rows_count
first_row_id
...
...
...
...
existing
75
925
added1
100
25
1000
added2
100
1125
added3
125
25
1225
The existing manifests are written with the
first_row_id
assigned when the manifests were added to the table.
The first added manifest,
added1
, is assigned the same
first_row_id
as the snapshot and each of the remaining added manifests are assigned a
first_row_id
based on the number of rows in preceding manifests that were assigned a
first_row_id
Note that the second file,
added2
, changes the
first_row_id
of the next manifest even though it contains no added data files because any data file without a
first_row_id
could be assigned one, even if it has existing status. This is optional if the writer knows that existing data files in the manifest have assigned
first_row_id
values.
Within
added1
, the first added manifest, each data file's
first_row_id
follows a similar pattern:
status
file_path
record_count
first_row_id
EXISTING
data1
25
800
ADDED
data2
50
null (1000)
ADDED
data3
50
null (1050)
The
first_row_id
of the EXISTING file
data1
was already assigned, so the file metadata was copied into manifest
added1
Files
data2
and
data3
are written with
null
for
first_row_id
and are assigned
first_row_id
at read time based on the manifest's
first_row_id
and the
record_count
of previous files without
first_row_id
in this manifest: (1,000 + 0) and (1,000 + 50).
The snapshot then populates the total number of
added-rows
based on the sum of all added rows in the manifests: 100 (50 + 50)
When the new snapshot is committed, the table's
next-row-id
must also be updated (even if the new snapshot is not in the main branch). Because 375 rows were in data files in manifests that were assigned a
first_row_id
added1
100+25,
added2
0+100,
added3
125+25) the new value is 1,000 + 375 = 1,375.
Row Lineage for Upgraded Tables
When a table is upgraded to v3, its
next-row-id
is initialized to 0 and existing snapshots are not modified (that is,
first-row-id
remains unset or null). For such snapshots without
first-row-id
first_row_id
values for data files and data manifests are null, and values for
_row_id
are read as null for all rows. When
first_row_id
is null, inherited row ID values are also null.
Snapshots that are created after upgrading to v3 must set the snapshot's
first-row-id
and assign row IDs to existing and added files in the snapshot. When writing the manifest list, all data manifests must be assigned a
first_row_id
, which assigns a
first_row_id
to all data files via inheritance.
Note that:
Snapshots created before upgrading to v3 do not have row IDs.
After upgrading, new snapshots in different branches will assign disjoint ID ranges to existing data files, based on the table's
next-row-id
when the snapshot is committed. For a data file in multiple branches, a writer may write the
first_row_id
from another branch or may assign a new
first_row_id
to the data file (to avoid large metadata rewrites).
Existing rows will inherit
_last_updated_sequence_number
from their containing data file.
Partitioning
Data files are stored in manifests with a tuple of partition values that are used in scans to filter out files that cannot contain records that match the scan’s filter predicate. Partition values for a data file must be the same for all records stored in the data file. (Manifests store data files from any partition, as long as the partition spec is the same for the data files.)
Tables are configured with a
partition spec
that defines how to produce a tuple of partition values from a record. A partition spec has a list of fields that consist of:
source column id
or a list of
source column ids
from the table’s schema
partition field id
that is used to identify a partition field and is unique within a partition spec. In v2 table metadata, it is unique across all partition specs.
transform
that is applied to the source column(s) to produce a partition value
partition name
The source columns, selected by ids, must be a primitive type and cannot be contained in a map or list, but may be nested in a struct. For details on how to serialize a partition spec to JSON, see Appendix C.
Partition specs capture the transform from table data to partition values. This is used to transform predicates to partition predicates, in addition to transforming data values. Deriving partition predicates from column predicates on the table data is used to separate the logical queries from physical storage: the partitioning can change and the correct partition filters are always derived from column predicates. This simplifies queries because users don’t have to supply both logical predicates and partition predicates. For more information, see Scan Planning below.
Partition fields that use an unknown transform can be read by ignoring the partition field for the purpose of filtering data files during scan planning. In v1 and v2, readers should ignore fields with unknown transforms while reading; this behavior is required in v3. Writers are not allowed to commit data using a partition spec that contains a field with an unknown transform.
Two partition specs are considered equivalent with each other if they have the same number of fields and for each corresponding field, the fields have the same source column IDs, transform definition and partition name. Writers must not create a new partition spec if there already exists a compatible partition spec defined in the table.
Partition field IDs must be reused if an existing partition spec contains an equivalent field.
Partition Transforms
Transform name
Description
Source types
Result type
identity
Source value, unmodified
Any except for
geometry
geography
, and
variant
Source type
bucket[N]
Hash of value, mod
(see below)
int
long
decimal
date
time
timestamp
timestamptz
timestamp_ns
timestamptz_ns
string
uuid
fixed
binary
int
truncate[W]
Value truncated to width
(see below)
int
long
decimal
string
binary
Source type
year
Extract a date or timestamp year, as years from 1970
date
timestamp
timestamptz
timestamp_ns
timestamptz_ns
int
month
Extract a date or timestamp month, as months from 1970-01-01
date
timestamp
timestamptz
timestamp_ns
timestamptz_ns
int
day
Extract a date or timestamp day, as days from 1970-01-01
date
timestamp
timestamptz
timestamp_ns
timestamptz_ns
int
hour
Extract a timestamp hour, as hours from 1970-01-01 00:00:00
timestamp
timestamptz
timestamp_ns
timestamptz_ns
int
void
Always produces
null
Any
Source type or
int
All transforms must return
null
for a
null
input value.
The
void
transform may be used to replace the transform in an existing partition field so that the field is effectively dropped in v1 tables. See partition evolution below.
Bucket Transform Details
Bucket partition transforms use a 32-bit hash of the source value. The 32-bit hash implementation is the 32-bit Murmur3 hash, x86 variant, seeded with 0.
Transforms are parameterized by a number of buckets [1],
. The hash mod
must produce a positive value by first discarding the sign bit of the hash value. In pseudo-code, the function is:
def bucket_N(x) = (murmur3_x86_32_hash(x) & Integer.MAX_VALUE) % N
Notes:
Changing the number of buckets as a table grows is possible by evolving the partition spec.
For hash function details by type, see Appendix B.
Truncate Transform Details
Type
Config
Truncate specification
Examples
int
, width
v - (v % W)
remainders must be positive [1]
W=10
-1
-10
long
, width
v - (v % W)
remainders must be positive [1]
W=10
-1
-10
decimal
, width (no scale)
scaled_W = decimal(W, scale(v))
v - (v % scaled_W)
[1, 2]
W=50
s=2
10.65
10.50
string
, length
Substring of length
v.substring(0, L)
[3]
L=3
iceberg
ice
binary
, length
Sub array of length
v.subarray(0, L)
[4]
L=3
\x01\x02\x03\x04\x05
\x01\x02\x03
Notes:
The remainder,
v % W
, must be positive. For languages where
can produce negative values, the correct truncate function is:
v - (((v % W) + W) % W)
The width,
, used to truncate decimal values is applied using the scale of the decimal column to avoid additional (and potentially conflicting) parameters.
Strings are truncated to a valid UTF-8 string with no more than
code points.
In contrast to strings, binary values do not have an assumed encoding and are truncated to
bytes.
Partition Evolution
Table partitioning can be evolved by adding, removing, renaming, or reordering partition spec fields.
Changing a partition spec produces a new spec identified by a unique spec ID that is added to the table's list of partition specs and may be set as the table's default spec.
When evolving a spec, changes should not cause partition field IDs to change because the partition field IDs are used as the partition tuple field IDs in manifest files.
In v2, partition field IDs must be explicitly tracked for each partition field. New IDs are assigned based on the last assigned partition ID in table metadata.
In v1, partition field IDs were not tracked, but were assigned sequentially starting at 1000 in the reference implementation. This assignment caused problems when reading metadata tables based on manifest files from multiple specs because partition fields with the same ID may contain different data types. For compatibility with old versions, the following rules are recommended for partition evolution in v1 tables:
Do not reorder partition fields
Do not drop partition fields; instead replace the field's transform with the
void
transform
Only add partition fields at the end of the previous partition spec
Sorting
Users can sort their data within partitions by columns to gain performance. The information on how the data is sorted can be declared per data or delete file, by a
sort order
A sort order is defined by a sort order id and a list of sort fields. The order of the sort fields within the list defines the order in which the sort is applied to the data. Each sort field consists of:
source column id
or a list of
source column ids
from the table's schema
transform
that is used to produce values to be sorted on from the source column(s). This is the same transform as described in
partition transforms
sort direction
, that can only be either
asc
or
desc
null order
that describes the order of null values when sorted. Can only be either
nulls-first
or
nulls-last
For details on how to serialize a sort order to JSON, see Appendix C.
Order id
is reserved for the unsorted order.
Sorting floating-point numbers should produce the following behavior:
-NaN
-Infinity
-value
-0
value
Infinity
NaN
. This aligns with the implementation of Java floating-point types comparisons.
A data or delete file is associated with a sort order by the sort order's id within
a manifest
. Therefore, the table must declare all the sort orders for lookup. A table could also be configured with a default sort order id, indicating how the new data should be sorted by default. Writers should use this default sort order to sort the data on write, but are not required to if the default order is prohibitively expensive, as it would be for streaming writes.
Manifests
A manifest is an immutable Avro file that lists data files or delete files, along with each file’s partition data tuple, metrics, and tracking information. One or more manifest files are used to store a
snapshot
, which tracks all of the files in a table at some point in time. Manifests are tracked by a
manifest list
for each table snapshot.
A manifest is a valid Iceberg data file: files must use valid Iceberg formats, schemas, and column projection.
A manifest may store either data files or delete files, but not both because manifests that contain delete files are scanned first during job planning. Whether a manifest is a data manifest or a delete manifest is stored in manifest metadata.
A manifest stores files for a single partition spec. When a table’s partition spec changes, old files remain in the older manifest and newer files are written to a new manifest. This is required because a manifest file’s schema is based on its partition spec (see below). The partition spec of each manifest is also used to transform predicates on the table's data rows into predicates on partition values that are used during job planning to select files from a manifest.
A manifest file must store the partition spec and other metadata as properties in the Avro file's key-value metadata:
v1
v2
Key
Value
required
required
schema
JSON representation of the table schema at the time the manifest was written
optional
required
schema-id
ID of the schema used to write the manifest as a string
required
required
partition-spec
JSON representation of only the partition fields array of the partition spec used to write the manifest. See
Appendix C
optional
required
partition-spec-id
ID of the partition spec used to write the manifest as a string
optional
required
format-version
Table format version number of the manifest as a string
required
content
Type of content files tracked by the manifest: "data" or "deletes"
The schema of a manifest file is defined by the
manifest_entry
struct, described in the following section.
Manifest Entry Fields
The
manifest_entry
struct consists of the following fields:
v1
v2
Field id, name
Type
Description
required
required
0 status
int
with meaning:
0: EXISTING
1: ADDED
2: DELETED
Used to track additions and deletions. Deletes are informational only and not used in scans.
required
optional
1 snapshot_id
long
Snapshot id where the file was added, or deleted if status is 2. Inherited when null.
optional
3 sequence_number
long
Data sequence number of the file. Inherited when null and status is 1 (added).
optional
4 file_sequence_number
long
File sequence number indicating when the file was added. Inherited when null and status is 1 (added).
required
required
2 data_file
data_file
struct
(see below)
File path, partition tuple, metrics, ...
The manifest entry fields are used to keep track of the snapshot in which files were added or logically deleted. The
data_file
struct, defined below, is nested inside the manifest entry so that it can be easily passed to job planning without the manifest entry fields.
When a file is added to the dataset, its manifest entry should store the snapshot ID in which the file was added and set status to 1 (added).
When a file is replaced or deleted from the dataset, its manifest entry fields store the snapshot ID in which the file was deleted and status 2 (deleted). The file may be deleted from the file system when the snapshot in which it was deleted is garbage collected, assuming that older snapshots have also been garbage collected [1].
Iceberg v2 adds data and file sequence numbers to the entry and makes the snapshot ID optional. Values for these fields are inherited from manifest metadata when
null
. That is, if the field is
null
for an entry, then the entry must inherit its value from the manifest file's metadata, stored in the manifest list.
The
sequence_number
field represents the data sequence number and must never change after a file is added to the dataset. The data sequence number represents a relative age of the file content and should be used for planning which delete files apply to a data file.
The
file_sequence_number
field represents the sequence number of the snapshot that added the file and must also remain unchanged upon assigning at commit. The file sequence number can't be used for pruning delete files as the data within the file may have an older data sequence number.
The data and file sequence numbers are inherited only if the entry status is 1 (added). If the entry status is 0 (existing) or 2 (deleted), the entry must include both sequence numbers explicitly.
Notes:
Technically, data files can be deleted when the last snapshot that contains the file as “live” data is garbage collected. But this is harder to detect and requires finding the diff of multiple snapshots. It is easier to track what files are deleted in a snapshot and delete them when that snapshot expires. It is not recommended to add a deleted file back to a table. Adding a deleted file can lead to edge cases where incremental deletes can break table snapshots.
Manifest list files are required in v2, so that the
sequence_number
and
snapshot_id
to inherit are always available.
Data File Fields
The
data_file
struct consists of the following fields:
v1
v2
v3
Field id, name
Type
Description
required
required
134 content
int
with meaning:
0: DATA
1: POSITION DELETES
2: EQUALITY DELETES
Type of content stored by the data file: data, equality deletes, or position deletes (all v1 files are data files)
required
required
required
100 file_path
string
Full URI for the file with FS scheme
required
required
required
101 file_format
string
String file format name,
avro
orc
parquet
, or
puffin
required
required
required
102 partition
struct<...>
Partition data tuple, schema based on the partition spec output using partition field ids for the struct field ids
required
required
required
103 record_count
long
Number of records in this file, or the cardinality of a deletion vector
required
required
required
104 file_size_in_bytes
long
Total file size in bytes
required
105 block_size_in_bytes
long
Deprecated. Always write a default in v1. Do not write in v2 or v3.
optional
106 file_ordinal
int
Deprecated. Do not write.
optional
107 sort_columns
list<112: int>
Deprecated. Do not write.
optional
optional
optional
108 column_sizes
map<117: int, 118: long>
Map from column id to the total size on disk of all regions that store the column. Does not include bytes necessary to read other columns, like footers. Leave null for row-oriented formats (Avro)
optional
optional
optional
109 value_counts
map<119: int, 120: long>
Map from column id to number of values in the column (including null and NaN values)
optional
optional
optional
110 null_value_counts
map<121: int, 122: long>
Map from column id to number of null values in the column
optional
optional
optional
137 nan_value_counts
map<138: int, 139: long>
Map from column id to number of NaN values in the column
optional
optional
111 distinct_counts
map<123: int, 124: long>
Deprecated. Do not write.
optional
optional
optional
125 lower_bounds
map<126: int, 127: binary>
Map from column id to lower bound in the column serialized as binary [1]. Each value must be less than or equal to all non-null, non-NaN values in the column for the file [2]
optional
optional
optional
128 upper_bounds
map<129: int, 130: binary>
Map from column id to upper bound in the column serialized as binary [1]. Each value must be greater than or equal to all non-null, non-Nan values in the column for the file [2]
optional
optional
optional
131 key_metadata
binary
Implementation-specific key metadata for encryption
optional
optional
optional
132 split_offsets
list<133: long>
Split offsets for the data file. For example, all row group offsets in a Parquet file. Must be sorted ascending
optional
optional
135 equality_ids
list<136: int>
Field ids used to determine row equality in equality delete files. Required when
content=2
and should be null otherwise. Fields with ids listed in this column must be present in the delete file
optional
optional
optional
140 sort_order_id
int
ID representing sort order for this file [3].
optional
142 first_row_id
long
The
_row_id
for the first row in the data file. See
First Row ID Inheritance
optional
optional
143 referenced_data_file
string
Fully qualified location (URI with FS scheme) of a data file that all deletes reference [4]
optional
144 content_offset
long
The offset in the file where the content starts [5]
optional
145 content_size_in_bytes
long
The length of a referenced content stored in the file; required if
content_offset
is present [5]
The
partition
struct stores the tuple of partition values for each file. Its type is derived from the partition fields of the partition spec used to write the manifest file. In v2, the partition struct's field ids must match the ids from the partition spec.
The column metrics maps are used when filtering to select both data and delete files. For delete files, the metrics must store bounds and counts for all deleted rows, or must be omitted. Storing metrics for deleted rows ensures that the values can be used during job planning to find delete files that must be merged during a scan.
Notes:
Single-value serialization for lower and upper bounds is detailed in Appendix D.
For
float
and
double
, the value
-0.0
must precede
+0.0
, as in the IEEE 754
totalOrder
predicate. NaNs are not permitted as lower or upper bounds.
If sort order ID is missing or unknown, then the order is assumed to be unsorted. Only data files and equality delete files should be written with a non-null order id.
Position deletes
are required to be sorted by file and position, not a table order, and should set sort order id to null. Readers must ignore sort order id for position delete files.
Position delete metadata can use
referenced_data_file
when all deletes tracked by the entry are in a single data file. Setting the referenced file is required for deletion vectors.
The
content_offset
and
content_size_in_bytes
fields are used to reference a specific blob for direct access to a deletion vector. For deletion vectors, these values are required and must exactly match the
offset
and
length
stored in the Puffin footer for the deletion vector blob.
The following field ids are reserved on
data_file
: 141.
Bounds for Variant, Geometry, and Geography
For Variant, values in the
lower_bounds
and
upper_bounds
maps store serialized Variant objects that contain lower or upper bounds respectively. The object keys for the bound-variants are normalized JSON path expressions that uniquely identify a field. The object values are primitive Variant representations of the lower or upper bound for that field. Including bounds for any field is optional and upper and lower bounds must have the same Variant type.
Bounds for a field must be accurate for all non-null values of the field in a data file. Bounds for values within arrays must be accurate for all values in the array. Bounds must not be written to describe values with mixed Variant types (other than null). For example, a
measurement
field that contains int64 and null values may have bounds, but if the field also contained a string value such as
n/a
or
then the field may not have bounds.
The Variant bounds objects are serialized by concatenating the
Variant encoding
of the metadata (containing the normalized field paths) and the bounds object.
Field paths follow the JSON path format to use normalized path, such as
$['location']['latitude']
or
$['user.name']
. The special path
represents bounds for the variant root, indicating that the variant data consists of uniform primitive types, such as strings.
Examples of valid field paths using normalized JSON path format are:
-- the root of the Variant value
$['event_type']
-- the
event_type
field in a Variant object
$['user.name']
-- the
"user.name"
field in a Variant object
$['location']['latitude']
-- the
latitude
field nested within a
location
object
$['tags']
-- the
tags
array
$['addresses']['zip']
-- the
zip
field in an
addresses
array that contains objects
For
geometry
and
geography
types,
lower_bounds
and
upper_bounds
are both points of the following coordinates X, Y, Z, and M (see Appendix G) which are the lower / upper bound of all objects in the file.
For
geography
only, xmin (X value of
lower_bounds
) may be greater than xmax (X value of
upper_bounds
), in which case an object in this bounding box may match if it contains an X such that x >= xmin OR x <= xmax. In geographic terminology, the concepts of xmin, xmax, ymin, and ymax are also known as westernmost, easternmost, southernmost and northernmost, respectively. These points are further restricted to the canonical ranges of [-180..180] for X and [-90..90] for Y.
When calculating upper and lower bounds for
geometry
and
geography
, null or NaN values in a coordinate dimension are skipped; for example, POINT (1 NaN) contributes a value to X but no values to Y, Z, or M dimension bounds. If a dimension has only null or NaN values, that dimension is omitted from the bounding box. If either the X or Y dimension is missing then the bounding box itself is not produced.
Sequence Number Inheritance
Manifests track the sequence number when a data or delete file was added to the table.
When adding a new file, its data and file sequence numbers are set to
null
because the snapshot's sequence number is not assigned until the snapshot is successfully committed. When reading, sequence numbers are inherited by replacing
null
with the manifest's sequence number from the manifest list.
It is also possible to add a new file with data that logically belongs to an older sequence number. In that case, the data sequence number must be provided explicitly and not inherited. However, the file sequence number must be always assigned when the snapshot is successfully committed.
When writing an existing file to a new manifest or marking an existing file as deleted, the data and file sequence numbers must be non-null and set to the original values that were either inherited or provided at the commit time.
Inheriting sequence numbers through the metadata tree allows writing a new manifest without a known sequence number, so that a manifest can be written once and reused in commit retries. To change a sequence number for a retry, only the manifest list must be rewritten.
When reading v1 manifests with no sequence number column, sequence numbers for all files must default to 0.
First Row ID Inheritance
When adding a new data file, its
first_row_id
field is set to
null
because it is not assigned until the snapshot is successfully committed.
When reading, the
first_row_id
is assigned by replacing
null
with the manifest's
first_row_id
plus the sum of
record_count
for all data files that preceded the file in the manifest that also had a null
first_row_id
The inherited value of
first_row_id
must be written into data file metadata when creating existing and deleted entries. The value of
first_row_id
for delete files is always
null
Any null (unassigned)
first_row_id
must be assigned via inheritance, even if the data file is existing. This ensures that row IDs are assigned to existing data files in upgraded tables in the first commit after upgrading to v3.
Snapshots
A snapshot consists of the following fields:
v1
v2
v3
Field
Description
required
required
required
snapshot-id
A unique long ID
optional
optional
optional
parent-snapshot-id
The snapshot ID of the snapshot's parent. Omitted for any snapshot with no parent
required
required
sequence-number
A monotonically increasing long that tracks the order of changes to a table
required
required
required
timestamp-ms
A timestamp when the snapshot was created, used for garbage collection and table inspection
optional
required
required
manifest-list
The location of a manifest list for this snapshot that tracks manifest files with additional metadata
optional
manifests
A list of manifest file locations. Must be omitted if
manifest-list
is present
optional
required
required
summary
A string map that summarizes the snapshot changes, including
operation
as a
required
field (see below)
optional
optional
optional
schema-id
ID of the table's current schema when the snapshot was created
required
first-row-id
The first
_row_id
assigned to the first row in the first data file in the first manifest, see
Row Lineage
required
added-rows
The upper bound of the number of rows with assigned row IDs, see
Row Lineage
optional
key-id
ID of the encryption key that encrypts the manifest list key metadata
The snapshot summary's
operation
field is used by some operations, like snapshot expiration, to skip processing certain snapshots. Possible
operation
values are:
append
-- Only data files were added and no files were removed.
replace
-- Data and delete files were added and removed without changing table data; i.e., compaction, changing the data file format, or relocating data files.
overwrite
-- Data and delete files were added and removed in a logical overwrite operation.
delete
-- Data files were removed and their contents logically deleted and/or delete files were added to delete rows.
For other optional snapshot summary fields, see
Appendix F
Data and delete files for a snapshot can be stored in more than one manifest. This enables:
Appends can add a new manifest to minimize the amount of data written, instead of adding new records by rewriting and appending to an existing manifest. (This is called a “fast append”.)
Tables can use multiple partition specs. A table’s partition configuration can evolve if, for example, its data volume changes. Each manifest uses a single partition spec, and queries do not need to change because partition filters are derived from data predicates.
Large tables can be split across multiple manifests so that implementations can parallelize job planning or reduce the cost of rewriting a manifest.
Manifests for a snapshot are tracked by a manifest list.
Valid snapshots are stored as a list in table metadata. For serialization, see Appendix C.
Snapshot Row IDs
A snapshot's
first-row-id
is assigned to the table's current
next-row-id
on each commit attempt. If a commit is retried, the
first-row-id
must be reassigned based on the table's current
next-row-id
. The
first-row-id
field is required even if a commit does not assign any ID space.
The snapshot's
first-row-id
is the starting
first_row_id
assigned to manifests in the snapshot's manifest list.
The snapshot's
added-rows
captures the upper bound of the number of rows with assigned row IDs.
It can be used safely to increment the table's
next-row-id
during a commit.
It can be more than the number of rows added in this snapshot and include some existing rows,
see
Row Lineage Example
Manifest Lists
Snapshots are embedded in table metadata, but the list of manifests for a snapshot are stored in a separate manifest list file.
A new manifest list is written for each attempt to commit a snapshot because the list of manifests always changes to produce a new snapshot. When a manifest list is written, the (optimistic) sequence number of the snapshot is written for all new manifest files tracked by the list.
A manifest list includes summary metadata that can be used to avoid scanning all of the manifests in a snapshot when planning a table scan. This includes the number of added, existing, and deleted files, and a summary of values for each field of the partition spec used to write the manifest.
A manifest list is a valid Iceberg data file: files must use valid Iceberg formats, schemas, and column projection.
Manifest list files store
manifest_file
, a struct with the following fields:
v1
v2
v3
Field id, name
Type
Description
required
required
required
500 manifest_path
string
Location of the manifest file
required
required
required
501 manifest_length
long
Length of the manifest file in bytes
required
required
required
502 partition_spec_id
int
ID of a partition spec used to write the manifest; must be listed in table metadata
partition-specs
required
required
517 content
int
with meaning:
0: data
1: deletes
The type of files tracked by the manifest, either data or delete files; 0 for all v1 manifests
required
required
515 sequence_number
long
The sequence number when the manifest was added to the table; use 0 when reading v1 manifest lists
required
required
516 min_sequence_number
long
The minimum data sequence number of all live data or delete files in the manifest; use 0 when reading v1 manifest lists
required
required
required
503 added_snapshot_id
long
ID of the snapshot where the manifest file was added
optional
required
required
504 added_files_count
int
Number of entries in the manifest that have status
ADDED
(1), when
null
this is assumed to be non-zero
optional
required
required
505 existing_files_count
int
Number of entries in the manifest that have status
EXISTING
(0), when
null
this is assumed to be non-zero
optional
required
required
506 deleted_files_count
int
Number of entries in the manifest that have status
DELETED
(2), when
null
this is assumed to be non-zero
optional
required
required
512 added_rows_count
long
Number of rows in all of files in the manifest that have status
ADDED
, when
null
this is assumed to be non-zero
optional
required
required
513 existing_rows_count
long
Number of rows in all of files in the manifest that have status
EXISTING
, when
null
this is assumed to be non-zero
optional
required
required
514 deleted_rows_count
long
Number of rows in all of files in the manifest that have status
DELETED
, when
null
this is assumed to be non-zero
optional
optional
optional
507 partitions
list<508: field_summary>
(see below)
A list of field summaries for each partition field in the spec. Each field in the list corresponds to a field in the manifest file’s partition spec.
optional
optional
optional
519 key_metadata
binary
Implementation-specific key metadata for encryption
optional
520 first_row_id
long
The starting
_row_id
to assign to rows added by
ADDED
data files
First Row ID Assignment
field_summary
is a struct with the following fields:
v1
v2
Field id, name
Type
Description
required
required
509 contains_null
boolean
Whether the manifest contains at least one partition with a null value for the field
optional
optional
518 contains_nan
boolean
Whether the manifest contains at least one partition with a NaN value for the field
optional
optional
510 lower_bound
bytes
[1]
Lower bound for the non-null, non-NaN values in the partition field, or null if all values are null or NaN [2]
optional
optional
511 upper_bound
bytes
[1]
Upper bound for the non-null, non-NaN values in the partition field, or null if all values are null or NaN [2]
Notes:
Lower and upper bounds are serialized to bytes using the single-object serialization in Appendix D. The type of used to encode the value is the type of the partition field data.
If -0.0 is a value of the partition field, the
lower_bound
must not be +0.0, and if +0.0 is a value of the partition field, the
upper_bound
must not be -0.0.
First Row ID Assignment
The
first_row_id
for existing manifests must be preserved when writing a new manifest list. The value of
first_row_id
for delete manifests is always
null
. The
first_row_id
is only assigned for data manifests that do not have a
first_row_id
. Assignment must account for data files that will be assigned
first_row_id
values when the manifest is read.
The first manifest without a
first_row_id
is assigned a value that is greater than or equal to the
first_row_id
of the snapshot. Subsequent manifests without a
first_row_id
are assigned one based on the previous manifest to be assigned a
first_row_id
. Each assigned
first_row_id
must increase by the row count of all files that will be assigned a
first_row_id
via inheritance in the last assigned manifest. That is, each
first_row_id
must be greater than or equal to the last assigned
first_row_id
plus the total record count of data files with a null
first_row_id
in the last assigned manifest.
A simple and valid approach is to estimate the number of rows in data files that will be assigned a
first_row_id
using the manifest's
added_rows_count
and
existing_rows_count
first_row_id = last_assigned.first_row_id + last_assigned.added_rows_count + last_assigned.existing_rows_count
Scan Planning
Scans are planned by reading the manifest files for the current snapshot. Deleted entries in data and delete manifests (those marked with status "DELETED") are not used in a scan.
Manifests that contain no matching files, determined using either file counts or partition summaries, may be skipped.
For each manifest, scan predicates, which filter data rows, are converted to partition predicates, which filter partition tuples. These partition predicates are used to select relevant data files, delete files, and deletion vector metadata. Conversion uses the partition spec that was used to write the manifest file regardless of the current partition spec.
Scan predicates are converted to partition predicates using an
inclusive projection
: if a scan predicate matches a row, then the partition predicate must match that row’s partition. This is called
inclusive
[1] because rows that do not match the scan predicate may be included in the scan by the partition predicate.
For example, an
events
table with a timestamp column named
ts
that is partitioned by
ts_day=day(ts)
is queried by users with ranges over the timestamp column:
ts > X
. The inclusive projection is
ts_day >= day(X)
, which is used to select files that may have matching rows. Note that, in most cases, timestamps just before
will be included in the scan because the file contains rows that match the predicate and rows that do not match the predicate.
The inclusive projection for an unknown partition transform is
true
because the partition field is ignored and not used in filtering.
Scan predicates are also used to filter data and delete files using column bounds and counts that are stored by field id in manifests. The same filter logic can be used for both data and delete files because both store metrics of the rows either inserted or deleted. If metrics show that a delete file has no rows that match a scan predicate, it may be ignored just as a data file would be ignored [2].
Data files that match the query filter must be read by the scan.
Note that for any snapshot, all file paths marked with "ADDED" or "EXISTING" may appear at most once across all manifest files in the snapshot. If a file path appears more than once, the results of the scan are undefined. Reader implementations may raise an error in this case, but are not required to do so.
Delete files and deletion vector metadata that match the filters must be applied to data files at read time, limited by the following scope rules.
A deletion vector must be applied to a data file when all of the following are true:
The data file's
file_path
is equal to the deletion vector's
referenced_data_file
The data file's data sequence number is
less than or equal to
the deletion vector's data sequence number
The data file's partition (both spec and partition values) is equal [4] to the deletion vector's partition
position
delete file must be applied to a data file when all of the following are true:
The data file's
file_path
is equal to the delete file's
referenced_data_file
if it is non-null
The data file's data sequence number is
less than or equal to
the delete file's data sequence number
The data file's partition (both spec and partition values) is equal [4] to the delete file's partition
There is no deletion vector that must be applied to the data file (when added, such a vector must contain all deletes from existing position delete files)
An
equality
delete file must be applied to a data file when all of the following are true:
The data file's data sequence number is
strictly less than
the delete's data sequence number
The data file's partition (both spec id and partition values) is equal [4] to the delete file's partition
or
the delete file's partition spec is unpartitioned
In general, deletes are applied only to data files that are older and in the same partition, except for two special cases:
Equality delete files stored with an unpartitioned spec are applied as global deletes. Otherwise, delete files do not apply to files in other partitions.
Position deletes (vectors and files) must be applied to data files from the same commit, when the data and delete file data sequence numbers are equal. This allows deleting rows that were added in the same commit.
Notes:
An alternative,
strict projection
, creates a partition predicate that will match a file if all of the rows in the file must match the scan predicate. These projections are used to calculate the residual predicates for each file in a scan.
For example, if
file_a
has rows with
id
between 1 and 10 and a delete file contains rows with
id
between 1 and 4, a scan for
id = 9
may ignore the delete file because none of the deletes can match a row that will be selected.
Floating point partition values are considered equal if their IEEE 754 floating-point "single format" bit layout are equal with NaNs normalized to have only the most significant mantissa bit set (the equivalent of calling
Float.floatToIntBits
or
Double.doubleToLongBits
in Java). The Avro specification requires all floating point values to be encoded in this format.
Unknown partition transforms do not affect partition equality. Although partition fields with unknown transforms are ignored for filtering, the result of an unknown transform is still used when testing whether partition values are equal.
Snapshot References
Iceberg tables keep track of branches and tags using snapshot references.
Tags are labels for individual snapshots. Branches are mutable named references that can be updated by committing a new snapshot as the branch's referenced snapshot using the
Commit Conflict Resolution and Retry
procedures.
The snapshot reference object records all the information of a reference including snapshot ID, reference type and
Snapshot Retention Policy
v1
v2
Field name
Type
Description
required
required
snapshot-id
long
A reference's snapshot ID. The tagged snapshot or latest snapshot of a branch.
required
required
type
string
Type of the reference,
tag
or
branch
optional
optional
min-snapshots-to-keep
int
For
branch
type only, a positive number for the minimum number of snapshots to keep in a branch while expiring snapshots. Defaults to table property
history.expire.min-snapshots-to-keep
optional
optional
max-snapshot-age-ms
long
For
branch
type only, a positive number for the max age of snapshots to keep when expiring, including the latest snapshot. Defaults to table property
history.expire.max-snapshot-age-ms
optional
optional
max-ref-age-ms
long
For snapshot references except the
main
branch, a positive number for the max age of the snapshot reference to keep while expiring snapshots. Defaults to table property
history.expire.max-ref-age-ms
. The
main
branch never expires.
Valid snapshot references are stored as the values of the
refs
map in table metadata. For serialization, see Appendix C.
Snapshot Retention Policy
Table snapshots expire and are removed from metadata to allow removed or replaced data files to be physically deleted.
The snapshot expiration procedure removes snapshots from table metadata and applies the table's retention policy.
Retention policy can be configured both globally and on snapshot reference through properties
min-snapshots-to-keep
max-snapshot-age-ms
and
max-ref-age-ms
When expiring snapshots, retention policies in table and snapshot references are evaluated in the following way:
Start with an empty set of snapshots to retain
Remove any refs (other than main) where the referenced snapshot is older than
max-ref-age-ms
For each branch and tag, add the referenced snapshot to the retained set
For each branch, add its ancestors to the retained set until:
The snapshot is older than
max-snapshot-age-ms
, AND
The snapshot is not one of the first
min-snapshots-to-keep
in the branch (including the branch's referenced snapshot)
Expire any snapshot not in the set of snapshots to retain.
Table Metadata
Table metadata is stored as JSON. Each table metadata change creates a new table metadata file that is committed by an atomic operation. This operation is used to ensure that a new version of table metadata replaces the version on which it was based. This produces a linear history of table versions and ensures that concurrent writes are not lost.
The atomic operation used to commit metadata depends on how tables are tracked and is not standardized by this spec. See the sections below for examples.
Table Metadata Fields
Table metadata consists of the following fields:
v1
v2
v3
Field
Description
required
required
required
format-version
An integer version number for the format. Implementations must throw an exception if a table's version is higher than the supported version.
optional
required
required
table-uuid
A UUID that identifies the table, generated when the table is created. Implementations must throw an exception if a table's UUID does not match the expected UUID after refreshing metadata.
required
required
required
location
The table's base location. This is used by writers to determine where to store data files, manifest files, and table metadata files.
required
required
last-sequence-number
The table's highest assigned sequence number, a monotonically increasing long that tracks the order of snapshots in a table.
required
required
required
last-updated-ms
Timestamp in milliseconds from the unix epoch when the table was last updated. Each table metadata file should update this field just before writing.
required
required
required
last-column-id
An integer; the highest assigned column ID for the table. This is used to ensure columns are always assigned an unused ID when evolving schemas.
required
schema
The table’s current schema. (
Deprecated
: use
schemas
and
current-schema-id
instead)
optional
required
required
schemas
A list of schemas, stored as objects with
schema-id
optional
required
required
current-schema-id
ID of the table's current schema.
required
partition-spec
The table’s current partition spec, stored as only fields. Note that this is used by writers to partition data, but is not used when reading because reads use the specs stored in manifest files. (
Deprecated
: use
partition-specs
and
default-spec-id
instead)
optional
required
required
partition-specs
A list of partition specs, stored as full partition spec objects.
optional
required
required
default-spec-id
ID of the "current" spec that writers should use by default.
optional
required
required
last-partition-id
An integer; the highest assigned partition field ID across all partition specs for the table. This is used to ensure partition fields are always assigned an unused ID when evolving specs.
optional
optional
optional
properties
A string to string map of table properties. This is used to control settings that affect reading and writing and is not intended to be used for arbitrary metadata. For example,
commit.retry.num-retries
is used to control the number of commit retries.
optional
optional
optional
current-snapshot-id
long
ID of the current table snapshot; must be the same as the current ID of the
main
branch in
refs
optional
optional
optional
snapshots
A list of valid snapshots. Valid snapshots are snapshots for which all data files exist in the file system. A data file must not be deleted from the file system until the last snapshot in which it was listed is garbage collected.
optional
optional
optional
snapshot-log
A list (optional) of timestamp and snapshot ID pairs that encodes changes to the current snapshot for the table. Each time the current-snapshot-id is changed, a new entry should be added with the last-updated-ms and the new current-snapshot-id. When snapshots are expired from the list of valid snapshots, all entries before a snapshot that has expired should be removed.
optional
optional
optional
metadata-log
A list (optional) of timestamp and metadata file location pairs that encodes changes to the previous metadata files for the table. Each time a new metadata file is created, a new entry of the previous metadata file location should be added to the list. Tables can be configured to remove oldest metadata log entries and keep a fixed-size log of the most recent entries after a commit.
optional
required
required
sort-orders
A list of sort orders, stored as full sort order objects.
optional
required
required
default-sort-order-id
Default sort order id of the table. Note that this could be used by writers, but is not used when reading because reads use the specs stored in manifest files.
optional
optional
refs
A map of snapshot references. The map keys are the unique snapshot reference names in the table, and the map values are snapshot reference objects. There is always a
main
branch reference pointing to the
current-snapshot-id
even if the
refs
map is null.
optional
optional
optional
statistics
A list (optional) of
table statistics
optional
optional
optional
partition-statistics
A list (optional) of
partition statistics
required
next-row-id
long
higher than all assigned row IDs; the next snapshot's
first-row-id
. See
Row Lineage
optional
encryption-keys
A list (optional) of
encryption keys
used for table encryption.
For serialization details, see Appendix C.
When a new snapshot is added, the table's
next-row-id
should be increased by the sum of
record_count
for all data files that will be assigned a
first_row_id
via inheritance in the snapshot. The
next-row-id
must always be higher than any assigned row ID in the table.
A simple and valid approach is estimate of the number of rows in data files that will be assigned a
first_row_id
using the manifests'
added_rows_count
and
existing_rows_count
. Using the last assigned manifest, this is
next-row-id = last_assigned.first_row_id + last_assigned.added_rows_count + last_assigned.existing_rows_count
Table Statistics
Table statistics files are valid
Puffin files
. Statistics are informational. A reader can choose to
ignore statistics information. Statistics support is not required to read the table correctly. A table can contain
many statistics files associated with different table snapshots.
Statistics files metadata within
statistics
table metadata field is a struct with the following fields:
v1
v2
Field name
Type
Description
required
required
snapshot-id
long
ID of the Iceberg table's snapshot the statistics file is associated with.
required
required
statistics-path
string
Path of the statistics file. See
Puffin file format
required
required
file-size-in-bytes
long
Size of the statistics file.
required
required
file-footer-size-in-bytes
long
Total size of the statistics file's footer (not the footer payload size). See
Puffin file format
for footer definition.
optional
optional
key-metadata
Base64-encoded implementation-specific key metadata for encryption.
required
required
blob-metadata
list
(see below)
A list of the blob metadata for statistics contained in the file with structure described below.
Blob metadata is a struct with the following fields:
v1
v2
Field name
Type
Description
required
required
type
string
Type of the blob. Matches Blob type in the Puffin file.
required
required
snapshot-id
long
ID of the Iceberg table's snapshot the blob was computed from.
required
required
sequence-number
long
Sequence number of the Iceberg table's snapshot the blob was computed from.
required
required
fields
list
Ordered list of fields, given by field ID, on which the statistic was calculated.
optional
optional
properties
map
Additional properties associated with the statistic. Subset of Blob properties in the Puffin file.
Partition Statistics
Partition statistics files are based on
partition statistics file spec
Partition statistics are not required for reading or planning and readers may ignore them.
Each table snapshot may be associated with at most one partition statistics file.
A writer can optionally write the partition statistics file during each write operation, or it can also be computed on demand.
Partition statistics file must be registered in the table metadata file to be considered as a valid statistics file for the reader.
partition-statistics
field of table metadata is an optional list of structs with the following fields:
v1
v2
v3
Field name
Type
Description
required
required
required
snapshot-id
long
ID of the Iceberg table's snapshot the partition statistics file is associated with.
required
required
required
statistics-path
string
Path of the partition statistics file. See
Partition statistics file
required
required
required
file-size-in-bytes
long
Size of the partition statistics file.
Partition Statistics File
Statistics information for each unique partition tuple is stored as a row in any of the data file format of the table (for example, Parquet or ORC).
These rows must be sorted (in ascending manner with NULL FIRST) by
partition
field to optimize filtering rows while scanning.
The schema of the partition statistics file is as follows:
v1
v2
v3
Field id, name
Type
Description
required
required
required
1 partition
struct<..>
Partition data tuple, schema based on the unified partition type considering all specs in a table
required
required
required
2 spec_id
int
Partition spec id
required
required
required
3 data_record_count
long
Count of records in data files
required
required
required
4 data_file_count
int
Count of data files
required
required
required
5 total_data_file_size_in_bytes
long
Total size of data files in bytes
optional
optional
required
6 position_delete_record_count
long
Count of position deletes across position delete files and deletion vectors
optional
optional
required
7 position_delete_file_count
int
Count of position delete files ignoring deletion vectors
required
13 dv_count
int
Count of deletion vectors
optional
optional
required
8 equality_delete_record_count
long
Count of records in equality delete files
optional
optional
required
9 equality_delete_file_count
int
Count of equality delete files
optional
optional
optional
10 total_record_count
long
Accurate count of records in a partition after applying deletes if any
optional
optional
optional
11 last_updated_at
long
Timestamp in milliseconds from the unix epoch when the partition was last updated
optional
optional
optional
12 last_updated_snapshot_id
long
ID of snapshot that last updated this partition
Note that partition data tuple's schema is based on the partition spec output using partition field ids for the struct field ids.
The unified partition type is a struct containing all fields that have ever been a part of any spec in the table
and sorted by the field ids in ascending order.
In other words, the struct fields represent a union of all known partition fields sorted in ascending order by the field ids.
For example,
spec#0
has two fields
{field#1, field#2}
and then the table has evolved into
spec#1
which has three fields
{field#1, field#2, field#3}
The unified partition type looks like
Struct
spec#0
has two fields
{field#1, field#2}
and then the table has evolved into
spec#1
which has just one field
{field#2}
The unified partition type looks like
Struct
When a v2 table is upgraded to v3 or later, the
position_delete_record_count
field must account for all position deletes, including those from remaining v2 position delete files and any deletion vectors added after the upgrade.
Calculating
total_record_count
for a table with equality deletes or v2 position delete files requires reading data. In such cases, implementations may omit this field and must write
NULL
, indicating that the exact record count in a partition is unknown.
If a table has no deletes or only deletion vectors, implementations are encouraged to populate
total_record_count
using metadata in manifests.
Encryption Keys
Keys used for table encryption can be tracked in table metadata as a list named
encryption-keys
. The schema of each key is a struct with the following fields:
v1
v2
v3
Field name
Type.
Description
required
key-id
string
ID of the encryption key
required
encrypted-key-metadata
string
Encrypted key and metadata, base64 encoded [1]
optional
encrypted-by-id
string
Optional ID of the key used to encrypt or wrap
key-metadata
optional
properties
map
A string to string map of additional metadata used by the table's encryption scheme
Notes:
The format of encrypted key metadata is determined by the table's encryption scheme and can be a wrapped format specific to the table's KMS provider.
Commit Conflict Resolution and Retry
When two commits happen at the same time and are based on the same version, only one commit will succeed. In most cases, the failed commit can be applied to the new current version of table metadata and retried. Updates verify the conditions under which they can be applied to a new version and retry if those conditions are met.
Append operations have no requirements and can always be applied.
Replace operations must verify that the files that will be deleted are still in the table. Examples of replace operations include format changes (replace an Avro file with a Parquet file) and compactions (several files are replaced with a single file that contains the same rows).
Delete operations must verify that specific files to delete are still in the table. Delete operations based on expressions can always be applied (e.g., where timestamp < X).
Table schema updates and partition spec changes must validate that the schema has not changed between the base version and the current version.
File System Tables
Note: This file system based scheme to commit a metadata file is
deprecated
and will be removed in version 4 of this spec. The scheme is
unsafe
in object stores and local file systems.
An atomic swap can be implemented using atomic rename in file systems that support it, like HDFS [1].
Each version of table metadata is stored in a metadata folder under the table’s base location using a file naming scheme that includes a version number,
v.metadata.json
. To commit a new metadata version,
V+1
, the writer performs the following steps:
Read the current table metadata version
Create new table metadata based on version
Write the new table metadata to a unique file:
.metadata.json
Rename the unique file to the well-known file for version
v.metadata.json
If the rename succeeds, the commit succeeded and
V+1
is the table’s current version
If the rename fails, go back to step 1.
Notes:
The file system table scheme is implemented in
HadoopTableOperations
Metastore Tables
The atomic swap needed to commit new versions of table metadata can be implemented by storing a pointer in a metastore or database that is updated with a check-and-put operation [1]. The check-and-put validates that the version of the table that a write is based on is still current and then makes the new metadata from the write the current version.
Each version of table metadata is stored in a metadata folder under the table’s base location using a naming scheme that includes a version and UUID:
-.metadata.json
. To commit a new metadata version,
V+1
, the writer performs the following steps:
Create a new table metadata file based on the current metadata.
Write the new table metadata to a unique file:
-.metadata.json
Request that the metastore swap the table’s metadata pointer from the location of
to the location of
V+1
If the swap succeeds, the commit succeeded.
was still the latest metadata version and the metadata file for
V+1
is now the current metadata.
If the swap fails, another writer has already created
V+1
. The current writer goes back to step 1.
Notes:
The metastore table scheme is partly implemented in
BaseMetastoreTableOperations
Delete Formats
This section details how to encode row-level deletes in Iceberg delete files. Row-level deletes are added by v2 and are not supported in v1. Deletion vectors are added in v3 and are not supported in v2 or earlier. Position delete files must not be added to v3 tables, but existing position delete files are valid.
There are different formats for encoding row-level deletes:
Deletion vectors (DVs) identify deleted rows within a single referenced data file by position in a bitmap
Position delete files identify deleted rows by file location and row position (
deprecated
in v3)
Equality delete files identify deleted rows by the value of one or more columns
Deletion vectors are a binary representation of deletes for a single data file that is more efficient at execution time than position delete files. Unlike equality or position delete files, there can be at most one deletion vector for a given data file in a snapshot. Writers must ensure that there is at most one deletion vector per data file and must merge new deletes with existing vectors or position delete files.
When removing a data file, writers must also remove any deletion vector that applies to that data file from delete manifests. Writers are not required to rewrite Puffin files that contain the removed deletion vectors.
Row-level delete files (both equality and position delete files) are valid Iceberg data files: files must use valid Iceberg formats, schemas, and column projection. It is recommended that these delete files are written using the table's default file format.
Row-level delete files and deletion vectors are tracked by manifests. A separate set of manifests is used for delete files and DVs, but the same manifest schema is used for both data and delete manifests. Deletion vectors are tracked individually by file location, offset, and length within the containing file. Deletion vector metadata must include the referenced data file.
Both position and equality delete files allow encoding deleted row values with a delete. This can be used to reconstruct a stream of changes to a table.
Deletion Vectors
Deletion vectors identify deleted rows of a file by encoding deleted positions in a bitmap. A set bit at position P indicates that the row at position P is deleted.
These vectors are stored using the
deletion-vector-v1
blob definition from the
Puffin spec
Deletion vectors support positive 64-bit positions, but are optimized for cases where most positions fit in 32 bits by using a collection of 32-bit Roaring bitmaps. 64-bit positions are divided into a 32-bit "key" using the most significant 4 bytes and a 32-bit sub-position using the least significant 4 bytes. For each key in the set of positions, a 32-bit Roaring bitmap is maintained to store a set of 32-bit sub-positions for that key.
To test whether a certain position is set, its most significant 4 bytes (the key) are used to find a 32-bit bitmap and the least significant 4 bytes (the sub-position) are tested for inclusion in the bitmap. If a bitmap is not found for the key, then it is not set.
Delete manifests track deletion vectors individually by the containing file location (
file_path
), starting offset of the DV blob (
content_offset
), and total length of the blob (
content_size_in_bytes
). Multiple deletion vectors can be stored in the same file. There are no restrictions on the data files that can be referenced by deletion vectors in the same Puffin file.
At most one deletion vector is allowed per data file in a snapshot. If a DV is written for a data file, it must replace all previously written position delete files so that when a DV is present, readers can safely ignore matching position delete files.
Position Delete Files
Position-based delete files identify deleted rows by file and position in one or more data files, and may optionally contain the deleted row.
Note: Position delete files are
deprecated
in v3. Existing position deletes must be written to delete vectors when updating the position deletes for a data file.
A data row is deleted if there is an entry in a position delete file for the row's file and position in the data file, starting at 0.
Position-based delete files store
file_position_delete
, a struct with the following fields:
Field id, name
Type
Description
2147483546 file_path
string
Full URI of a data file with FS scheme. This must match the
file_path
of the target data file in a manifest entry
2147483545 pos
long
Ordinal position of a deleted row in the target data file identified by
file_path
, starting at
2147483544 row
required struct<...>
[1]
Deleted row values. Omit the column when not storing deleted rows.
When present in the delete file,
row
is required because all delete entries must include the row values.
When the deleted row column is present, its schema may be any subset of the table schema and must use field ids matching the table.
To ensure the accuracy of statistics, all delete entries must include row values, or the column must be omitted (this is why the column type is
required
).
The rows in the delete file must be sorted by
file_path
then
pos
to optimize filtering rows while scanning.
Sorting by
file_path
allows filter pushdown by file in columnar storage formats.
Sorting by
pos
allows filtering rows while scanning, to avoid keeping deletes in memory.
Equality Delete Files
Equality delete files identify deleted rows in a collection of data files by one or more column values, and may optionally contain additional columns of the deleted row.
Equality delete files store any subset of a table's columns and use the table's field ids. The
delete columns
are the columns of the delete file used to match data rows. Delete columns are identified by id in the delete file
metadata column
equality_ids
. The column restrictions for columns used in equality delete files are the same as those for
identifier fields
with the exception that optional columns and columns nested under optional structs are allowed (if a parent struct column is null it implies the leaf column is null).
A data row is deleted if its values are equal to all delete columns for any row in an equality delete file that applies to the row's data file (see
Scan Planning
).
Each row of the delete file produces one equality predicate that matches any row where the delete columns are equal. Multiple columns can be thought of as an
AND
of equality predicates. A
null
value in a delete column matches a row if the row's value is
null
, equivalent to
col IS NULL
For example, a table with the following data:
1: id | 2: category | 3: name
-------|-------------|---------
1 | marsupial | Koala
2 | toy | Teddy
3 | NULL | Grizzly
4 | NULL | Polar
The delete
id = 3
could be written as either of the following equality delete files:
equality_ids=[1]
1: id
-------
equality_ids=[1]
1: id | 2: category | 3: name
-------|-------------|---------
3 | NULL | Grizzly
The delete
id = 4 AND category IS NULL
could be written as the following equality delete file:
equality_ids=[1, 2]
1: id | 2: category | 3: name
-------|-------------|---------
4 | NULL | Polar
If a delete column in an equality delete file is later dropped from the table, it must still be used when applying the equality deletes. If a column was added to a table and later used as a delete column in an equality delete file, the column value is read for older data files using normal projection rules (defaults to
null
).
Delete File Stats
Manifests hold the same statistics for delete files and data files. For delete files, the metrics describe the values that were deleted.
Appendix A: Format-specific Requirements
Avro
Data Type Mappings
Values should be stored in Avro using the Avro types and logical type annotations in the table below.
Optional fields, array elements, and map values must be wrapped in an Avro
union
with
null
. This is the only union type allowed in Iceberg data files.
Optional fields without an Iceberg default must set the Avro field default value to null. Fields with a non-null Iceberg default must convert the default to an equivalent Avro default.
Maps with non-string keys must use an array representation with the
map
logical type. The array representation or Avro’s map type may be used for maps with string keys.
Type
Avro type
Notes
unknown
null
or omitted
boolean
boolean
int
int
long
long
float
float
double
double
decimal(P,S)
{ "type": "fixed",
"size": minBytesRequired(P),
"logicalType": "decimal",
"precision": P,
"scale": S }
Stored as fixed using the minimum number of bytes for the given precision.
date
{ "type": "int",
"logicalType": "date" }
Stores days from 1970-01-01.
time
{ "type": "long",
"logicalType": "time-micros" }
Stores microseconds from midnight.
timestamp
{ "type": "long",
"logicalType": "timestamp-micros",
"adjust-to-utc": false }
Stores microseconds from 1970-01-01 00:00:00.000000. [1]
timestamptz
{ "type": "long",
"logicalType": "timestamp-micros",
"adjust-to-utc": true }
Stores microseconds from 1970-01-01 00:00:00.000000 UTC. [1]
timestamp_ns
{ "type": "long",
"logicalType": "timestamp-nanos",
"adjust-to-utc": false }
Stores nanoseconds from 1970-01-01 00:00:00.000000000. [1], [2]
timestamptz_ns
{ "type": "long",
"logicalType": "timestamp-nanos",
"adjust-to-utc": true }
Stores nanoseconds from 1970-01-01 00:00:00.000000000 UTC. [1], [2]
string
string
uuid
{ "type": "fixed",
"size": 16,
"logicalType": "uuid" }
fixed(L)
{ "type": "fixed",
"size": L }
binary
bytes
struct
record
list
array
map
array
of key-value records, or
map
when keys are strings (optional).
Array storage must use logical type name
map
and must store elements that are 2-field records. The first field is a non-null key and the second field is the value.
variant
record
with
metadata
and
value
fields.
metadata
and
value
must not be assigned field IDs and the fields are accessed through names.
Shredding is not supported in Avro.
geometry
bytes
WKB format, see
Appendix G
geography
bytes
WKB format, see
Appendix G
Notes:
Avro type annotation
adjust-to-utc
is an Iceberg convention; default value is
false
if not present.
Avro logical type
timestamp-nanos
is an Iceberg convention; the Avro specification does not define this type.
Field IDs
Iceberg struct, list, and map types identify nested types by ID. When writing data to Avro files, these IDs must be stored in the Avro schema to support ID-based column pruning.
IDs are stored as JSON integers in the following locations:
ID
Avro schema location
Property
Example
Struct field
Record field object
field-id
{ "type": "record", ...
"fields": [
{ "name": "l",
"type": ["null", "long"],
"default": null,
"field-id": 8 }
] }
List element
Array schema object
element-id
{ "type": "array",
"items": "int",
"element-id": 9 }
String map key
Map schema object
key-id
{ "type": "map",
"values": "int",
"key-id": 10,
"value-id": 11 }
String map value
Map schema object
value-id
Map key, value
Key, value fields in the element record.
field-id
{ "type": "array",
"logicalType": "map",
"items": {
"type": "record",
"name": "k12_v13",
"fields": [
{ "name": "key",
"type": "int",
"field-id": 12 },
{ "name": "value",
"type": "string",
"field-id": 13 }
] } }
Note that the string map case is for maps where the key type is a string. Using Avro’s map type in this case is optional. Maps with string keys may be stored as arrays.
Parquet
Data Type Mappings
Values should be stored in Parquet using the types and logical type annotations in the table below. Column IDs are required to be stored as
field IDs
on the parquet schema.
Lists must use the
3-level representation
Type
Parquet physical type
Logical type
Notes
unknown
None
Omit from data files
boolean
boolean
int
int32
long
int64
float
float
double
double
decimal(P,S)
P <= 9
int32
P <= 18
int64
fixed
otherwise
DECIMAL(P,S)
Fixed must use the minimum number of bytes that can store
date
int32
DATE
Stores days from 1970-01-01.
time
int64
TIME_MICROS
with
adjustToUtc=false
Stores microseconds from midnight.
timestamp
int64
TIMESTAMP_MICROS
with
adjustToUtc=false
Stores microseconds from 1970-01-01 00:00:00.000000.
timestamptz
int64
TIMESTAMP_MICROS
with
adjustToUtc=true
Stores microseconds from 1970-01-01 00:00:00.000000 UTC.
timestamp_ns
int64
TIMESTAMP_NANOS
with
adjustToUtc=false
Stores nanoseconds from 1970-01-01 00:00:00.000000000.
timestamptz_ns
int64
TIMESTAMP_NANOS
with
adjustToUtc=true
Stores nanoseconds from 1970-01-01 00:00:00.000000000 UTC.
string
binary
UTF8
Encoding must be UTF-8.
uuid
fixed_len_byte_array[16]
UUID
fixed(L)
fixed_len_byte_array[L]
binary
binary
struct
group
list
3-level list
LIST
See Parquet docs for 3-level representation.
map
3-level map
MAP
See Parquet docs for 3-level representation.
variant
group
with
metadata
and
value
fields.
metadata
and
value
must not be assigned field IDs and the fields are accessed through names.
VARIANT
See Parquet docs for
Variant encoding
and
Variant shredding encoding
geometry
binary
GEOMETRY
WKB format, see
Appendix G
geography
binary
GEOGRAPHY
WKB format, see
Appendix G
When reading an
unknown
column, any corresponding column must be ignored and replaced with
null
values.
ORC
Data Type Mappings
Type
ORC type
ORC type attributes
Notes
unknown
None
Omit from data files
boolean
boolean
int
int
ORC
tinyint
and
smallint
would also map to
int
long
long
float
float
double
double
decimal(P,S)
decimal
date
date
time
long
iceberg.long-type
TIME
Stores microseconds from midnight.
timestamp
timestamp
iceberg.timestamp-unit
MICROS
Stores microseconds from 2015-01-01 00:00:00.000000. [1], [2]
timestamptz
timestamp_instant
iceberg.timestamp-unit
MICROS
Stores microseconds from 2015-01-01 00:00:00.000000 UTC. [1], [2]
timestamp_ns
timestamp
iceberg.timestamp-unit
NANOS
Stores nanoseconds from 2015-01-01 00:00:00.000000000. [1]
timestamptz_ns
timestamp_instant
iceberg.timestamp-unit
NANOS
Stores nanoseconds from 2015-01-01 00:00:00.000000000 UTC. [1]
string
string
ORC
varchar
and
char
would also map to
string
uuid
binary
iceberg.binary-type
UUID
fixed(L)
binary
iceberg.binary-type
FIXED
iceberg.length
The length would not be checked by the ORC reader and should be checked by the adapter.
binary
binary
struct
struct
list
array
map
map
variant
struct
with
metadata
and
value
fields.
metadata
and
value
must not be assigned field IDs.
iceberg.struct-type
VARIANT
Shredding is not supported in ORC.
geometry
binary
iceberg.binary-type
GEOMETRY
WKB format, see
Appendix G
geography
binary
iceberg.binary-type
GEOGRAPHY
WKB format, see
Appendix G
Notes:
ORC's
TimestampColumnVector
consists of a time field (milliseconds since epoch) and a nanos field (nanoseconds within the second). Hence the milliseconds within the second are reported twice; once in the time field and again in the nanos field. The read adapter should only use milliseconds within the second from one of these fields. The write adapter should also report milliseconds within the second twice; once in the time field and again in the nanos field. ORC writer is expected to correctly consider millis information from one of the fields. More details at https://issues.apache.org/jira/browse/ORC-546
ORC
timestamp
and
timestamp_instant
values store nanosecond precision. Iceberg ORC writers for Iceberg types
timestamp
and
timestamptz
must
truncate nanoseconds to microseconds.
iceberg.timestamp-unit
is assumed to be
MICROS
if not present.
One of the interesting challenges with this is how to map Iceberg’s schema evolution (id based) on to ORC’s (name based). In theory, we could use Iceberg’s column ids as the column and field names, but that would be inconvenient.
The column IDs must be stored in ORC type attributes using the key
iceberg.id
, and
iceberg.required
to store
"true"
if the Iceberg column is required, otherwise it will be optional.
Iceberg would build the desired reader schema with their schema evolution rules and pass that down to the ORC reader, which would then use its schema evolution to map that to the writer’s schema. Basically, Iceberg would need to change the names of columns and fields to get the desired mapping.
Iceberg writer
ORC writer
Iceberg reader
ORC reader
struct
struct
struct
struct
struct>
struct>
struct>
struct>
Appendix B: 32-bit Hash Requirements
The 32-bit hash implementation is 32-bit Murmur3 hash, x86 variant, seeded with 0.
Primitive type
Hash specification
Test value
int
hashLong(long(v))
[1]
34
2017239379
long
hashBytes(littleEndianBytes(v))
34L
2017239379
decimal(P,S)
hashBytes(minBigEndian(unscaled(v)))
[2]
14.20
-500754589
date
hashInt(daysFromUnixEpoch(v))
2017-11-16
-653330422
time
hashLong(microsecsFromMidnight(v))
22:31:08
-662762989
timestamp
hashLong(microsecsFromUnixEpoch(v))
2017-11-16T22:31:08
-2047944441
2017-11-16T22:31:08.000001
-1207196810
timestamptz
hashLong(microsecsFromUnixEpoch(v))
2017-11-16T14:31:08-08:00
-2047944441
2017-11-16T14:31:08.000001-08:00
-1207196810
timestamp_ns
hashLong(microsecsFromUnixEpoch(v))
[3]
2017-11-16T22:31:08
-2047944441
2017-11-16T22:31:08.000001001
-1207196810
timestamptz_ns
hashLong(microsecsFromUnixEpoch(v))
[3]
2017-11-16T14:31:08-08:00
-2047944441
2017-11-16T14:31:08.000001001-08:00
-1207196810
string
hashBytes(utf8Bytes(v))
iceberg
1210000089
uuid
hashBytes(uuidBytes(v))
[4]
f79c3e09-677c-4bbd-a479-3f349cb785e7
1488055340
fixed(L)
hashBytes(v)
00 01 02 03
-188683207
binary
hashBytes(v)
00 01 02 03
-188683207
The types below are not currently valid for bucketing, and so are not hashed. However, if that changes and a hash value is needed, the following table shall apply:
Primitive type
Hash specification
Test value
unknown
always
null
boolean
false: hashInt(0)
true: hashInt(1)
true
1392991556
float
hashLong(doubleToLongBits(double(v))
[5]
1.0F
-142385009
0.0F
1669671676
-0.0F
1669671676
double
hashLong(doubleToLongBits(v))
[5]
1.0D
-142385009
0.0D
1669671676
-0.0D
1669671676
A 32-bit hash is not defined for
variant
because there are multiple representations for equivalent values.
Notes:
Integer and long hash results must be identical for all integer values. This ensures that schema evolution does not change bucket partition values if integer types are promoted.
Decimal values are hashed using the minimum number of bytes required to hold the unscaled value as a two’s complement big-endian; this representation does not include padding bytes required for storage in a fixed-length array.
Hash results are not dependent on decimal scale, which is part of the type, not the data value.
Nanosecond timestamps must be converted to microsecond precision before hashing to ensure timestamps have the same hash value.
UUIDs are encoded using big endian. The test UUID for the example above is:
f79c3e09-677c-4bbd-a479-3f349cb785e7
. This UUID encoded as a byte array is:
F7 9C 3E 09 67 7C 4B BD A4 79 3F 34 9C B7 85 E7
doubleToLongBits
must give the IEEE 754 compliant bit representation of the double value. All
NaN
bit patterns must be canonicalized to
0x7ff8000000000000L
. Negative zero (
-0.0
) must be canonicalized to positive zero (
0.0
). Float hash values are the result of hashing the float cast to double to ensure that schema evolution does not change hash values if float types are promoted.
Appendix C: JSON serialization
Schemas
Schemas are serialized as a JSON object with the same fields as a struct in the table below, and the following additional fields:
v1
v2
Field
JSON representation
Example
optional
required
schema-id
JSON int
optional
optional
identifier-field-ids
JSON list of ints
[1, 2]
Types are serialized according to this table:
Type
JSON representation
Example
unknown
JSON string: "unknown"
"unknown"
boolean
JSON string: "boolean"
"boolean"
int
JSON string: "int"
"int"
long
JSON string: "long"
"long"
float
JSON string: "float"
"float"
double
JSON string: "double"
"double"
date
JSON string: "date"
"date"
time
JSON string: "time"
"time"
timestamp, microseconds, without zone
JSON string: "timestamp"
"timestamp"
timestamp, microseconds, with zone
JSON string: "timestamptz"
"timestamptz"
timestamp, nanoseconds, without zone
JSON string: "timestamp_ns"
"timestamp_ns"
timestamp, nanoseconds, with zone
JSON string: "timestamptz_ns"
"timestamptz_ns"
string
JSON string: "string"
"string"
uuid
JSON string: "uuid"
"uuid"
fixed(L)
JSON string: "fixed[]"
"fixed[16]"
binary
JSON string: "binary"
"binary"
decimal(P, S)
JSON string: "decimal(

,)"
"decimal(9,2)"
"decimal(9, 2)"
struct
JSON object: {
"type": "struct",
"fields": [ {
"id": ,
"name": ,
"required": ,
"type": ,
"doc": ,
"initial-default": ,
"write-default":
}, ...
] }
"type": "struct",
"fields": [ {
"id": 1,
"name": "id",
"required": true,
"type": "uuid",
"initial-default": "0db3e2a8-9d1d-42b9-aa7b-74ebe558dceb",
"write-default": "ec5911be-b0a7-458c-8438-c9a3e53cffae"
}, {
"id": 2,
"name": "data",
"required": false,
"type": {
"type": "list",
...
} ]
list
JSON object: {
"type": "list",
"element-id": ,
"element-required":
"element":
"type": "list",
"element-id": 3,
"element-required": true,
"element": "string"
map
JSON object: {
"type": "map",
"key-id": ,
"key": ,
"value-id": ,
"value-required":
"value":
"type": "map",
"key-id": 4,
"key": "string",
"value-id": 5,
"value-required": false,
"value": "double"
variant
JSON string: "variant"
"variant"
geometry(C)
JSON string: "geometry()"
"geometry(srid:4326)"
geography(C, A)
JSON string: "geography(,)"
"geography(srid:4326,spherical)"
Note that default values are serialized using the JSON single-value serialization in
Appendix D
Partition Specs
Partition specs are serialized as a JSON object with the following fields:
Field
JSON representation
Example
spec-id
JSON int
fields
JSON list: [
,
...
[ {
"source-id": 4,
"field-id": 1000,
"name": "ts_day",
"transform": "day"
}, {
"source-id": 1,
"field-id": 1001,
"name": "id_bucket",
"transform": "bucket[16]"
} ]
Each partition field in
fields
is stored as a JSON object with the following properties.
V1
V2
V3
Field
JSON representation
Example
required
required
optional
source-id
JSON int
optional
source-ids
JSON list of ints
[1,2]
required
required
field-id
JSON int
1000
required
required
required
name
JSON string
id_bucket
required
required
required
transform
JSON string
bucket[16]
Notes:
For partition fields with a transform with a single argument, only
source-id
is written. In case of a multi-argument transform, only
source-ids
is written.
Supported partition transforms are listed below.
Transform or Field
JSON representation
Example
identity
JSON string: "identity"
"identity"
bucket[N]
JSON string: "bucket[]"
"bucket[16]"
truncate[W]
JSON string: "truncate[]"
"truncate[20]"
year
JSON string: "year"
"year"
month
JSON string: "month"
"month"
day
JSON string: "day"
"day"
hour
JSON string: "hour"
"hour"
In some cases partition specs are stored using only the field list instead of the object format that includes the spec ID, like the deprecated
partition-spec
field in table metadata. The object format should be used unless otherwise noted in this spec.
The
field-id
property was added for each partition field in v2. In v1, the reference implementation assigned field ids sequentially in each spec starting at 1,000. See Partition Evolution for more details.
Older versions of the reference implementation can read tables with transforms unknown to it, ignoring them. But other implementations may break if they encounter unknown transforms. All v3 readers are required to read tables with unknown transforms, ignoring them. Writers should not write using partition specs that use unknown transforms.
Sort Orders
Sort orders are serialized as a list of JSON objects, each of which contains the following fields:
Field
JSON representation
Example
order-id
JSON int
fields
JSON list: [
,
...
[ {
"transform": "identity",
"source-id": 2,
"direction": "asc",
"null-order": "nulls-first"
}, {
"transform": "bucket[4]",
"source-id": 3,
"direction": "desc",
"null-order": "nulls-last"
} ]
Each sort field in the fields list is stored as an object with the following properties:
V1
V2
V3
Field
JSON representation
Example
required
required
required
transform
JSON string
bucket[4]
required
required
optional
source-id
JSON int
optional
source-ids
JSON list of ints
[1,2]
required
required
required
direction
JSON string
asc
required
required
required
null-order
JSON string
nulls-last
Notes:
For sort fields with a transform with a single argument, only
source-id
is written. In case of a multi-argument transform, only
source-ids
is written.
Older versions of the reference implementation can read tables with transforms unknown to it, ignoring them. But other implementations may break if they encounter unknown transforms. All v3 readers are required to read tables with unknown transforms, ignoring them.
The following table describes the possible values for the some of the field within sort field:
Field
JSON representation
Possible values
direction
JSON string
"asc", "desc"
null-order
JSON string
"nulls-first", "nulls-last"
Table Metadata and Snapshots
Table metadata is serialized as a JSON object according to the following table. Snapshots are not serialized separately. Instead, they are stored in the table metadata JSON.
A metadata JSON file may be compressed with
GZIP
Metadata field
JSON representation
Example
format-version
JSON int
table-uuid
JSON string
"fb072c92-a02b-11e9-ae9c-1bb7bc9eca94"
location
JSON string
"s3://b/wh/data.db/table"
last-updated-ms
JSON long
1515100955770
last-column-id
JSON int
22
schema
JSON schema (object)
See above, read schemas instead
schemas
JSON schemas (list of objects)
See above
current-schema-id
JSON int
partition-spec
JSON partition fields (list)
See above, read partition-specs instead
partition-specs
JSON partition specs (list of objects)
See above
default-spec-id
JSON int
last-partition-id
JSON int
1000
properties
JSON object: {
"": "",
...
"write.format.default": "avro",
"commit.retry.num-retries": "4"
current-snapshot-id
JSON long
3051729675574597004
snapshots
JSON list of objects: [ {
"snapshot-id": ,
"timestamp-ms": ,
"summary": {
"operation": ,
... },
"manifest-list": "",
"schema-id": ""
},
...
[ {
"snapshot-id": 3051729675574597004,
"timestamp-ms": 1515100955770,
"summary": {
"operation": "append"
},
"manifest-list": "s3://b/wh/.../s1.avro"
"schema-id": 0
} ]
snapshot-log
JSON list of objects: [
"snapshot-id": ,
"timestamp-ms":
},
...
[ {
"snapshot-id": 30517296...,
"timestamp-ms": 1515100...
} ]
metadata-log
JSON list of objects: [
"metadata-file": ,
"timestamp-ms":
},
...
[ {
"metadata-file": "s3://bucket/.../v1.json",
"timestamp-ms": 1515100...
} ]
sort-orders
JSON sort orders (list of sort field object)
See above
default-sort-order-id
JSON int
refs
JSON map with string key and object value:
"": {
"snapshot-id": ,
"type": ,
"max-ref-age-ms": ,
...
...
"test": {
"snapshot-id": 123456789000,
"type": "tag",
"max-ref-age-ms": 10000000
encryption-keys
JSON list of encryption key objects
[ {"key-id": "5f819b", "key-metadata": "aWNlYmVyZwo="} ]
Name Mapping Serialization
Name mapping is serialized as a list of field mapping JSON Objects which are serialized as follows
Field mapping field
JSON representation
Example
names
JSON list of strings
["latitude", "lat"]
field-id
JSON int
fields
JSON field mappings (list of objects)
[{
"field-id": 4,
"names": ["latitude", "lat"]
}, {
"field-id": 5,
"names": ["longitude", "long"]
}]
Example
"field-id"
"names"
"id"
"record_id"
},
"field-id"
"names"
"data"
},
"field-id"
"names"
"location"
],
"fields"
"field-id"
"names"
"latitude"
"lat"
},
"field-id"
"names"
"longitude"
"long"
Appendix D: Single-value serialization
Binary single-value serialization
This serialization scheme is for storing single values as individual binary values.
Type
Binary serialization
unknown
Not supported
boolean
0x00
for false, non-zero byte for true
int
Stored as 4-byte little-endian
long
Stored as 8-byte little-endian
float
Stored as 4-byte little-endian
double
Stored as 8-byte little-endian
date
Stores days from the 1970-01-01 in an 4-byte little-endian int
time
Stores microseconds from midnight in an 8-byte little-endian long
timestamp
Stores microseconds from 1970-01-01 00:00:00.000000 in an 8-byte little-endian long
timestamptz
Stores microseconds from 1970-01-01 00:00:00.000000 UTC in an 8-byte little-endian long
timestamp_ns
Stores nanoseconds from 1970-01-01 00:00:00.000000000 in an 8-byte little-endian long
timestamptz_ns
Stores nanoseconds from 1970-01-01 00:00:00.000000000 UTC in an 8-byte little-endian long
string
UTF-8 bytes (without length)
uuid
16-byte big-endian value, see example in Appendix B
fixed(L)
Binary value
binary
Binary value (without length)
decimal(P, S)
Stores unscaled value as two’s-complement big-endian binary, using the minimum number of bytes for the value
struct
Not supported
list
Not supported
map
Not supported
variant
Not supported
geometry
WKB format, see
Appendix G
geography
WKB format, see
Appendix G
Bound serialization
The binary single-value serialization can be used to store the lower and upper bounds maps of manifest files, except as specified by the following table.
Type
Binary serialization
geometry
A single point, encoded as a x:y:z:m concatenation of its 8-byte little-endian IEEE 754 coordinate values. x and y are mandatory. This becomes x:y if z and m are both unset, x:y:z if only m is unset, and x:y:NaN:m if only z is unset.
geography
A single point, encoded as a x:y:z:m concatenation of its 8-byte little-endian IEEE 754 coordinate values. x and y are mandatory. This becomes x:y if z and m are both unset, x:y:z if only m is unset, and x:y:NaN:m if only z is unset.
variant
A serialized Variant of encoded v1 metadata concatenated with an encoded variant object. Object keys are normalized JSON paths to identify fields; values are lower or upper bound values.
JSON single-value serialization
Single values are serialized as JSON by type according to the following table:
Type
JSON representation
Example
Description
boolean
JSON boolean
true
int
JSON int
34
long
JSON long
34
float
JSON number
1.0
double
JSON number
1.0
decimal(P,S)
JSON string
"14.20"
"2E+20"
Stores the string representation of the decimal value, specifically, for values with a positive scale, the number of digits to the right of the decimal point is used to indicate scale, for values with a negative scale, the scientific notation is used and the exponent must equal the negated scale
date
JSON string
"2017-11-16"
Stores ISO-8601 standard date
time
JSON string
"22:31:08.123456"
Stores ISO-8601 standard time with microsecond precision
timestamp
JSON string
"2017-11-16T22:31:08.123456"
Stores ISO-8601 standard timestamp with microsecond precision; must not include a zone offset
timestamptz
JSON string
"2017-11-16T22:31:08.123456+00:00"
Stores ISO-8601 standard timestamp with microsecond precision; must include a zone offset and it must be '+00:00'
timestamp_ns
JSON string
"2017-11-16T22:31:08.123456789"
Stores ISO-8601 standard timestamp with nanosecond precision; must not include a zone offset
timestamptz_ns
JSON string
"2017-11-16T22:31:08.123456789+00:00"
Stores ISO-8601 standard timestamp with nanosecond precision; must include a zone offset and it must be '+00:00'
string
JSON string
"iceberg"
uuid
JSON string
"f79c3e09-677c-4bbd-a479-3f349cb785e7"
Stores the lowercase uuid string
fixed(L)
JSON string
"000102ff"
Stored as a hexadecimal string
binary
JSON string
"000102ff"
Stored as a hexadecimal string
struct
JSON object by field ID
{"1": 1, "2": "bar"}
Stores struct fields using the field ID as the JSON field name; field values are stored using this JSON single-value format
list
JSON array of values
[1, 2, 3]
Stores a JSON array of values that are serialized using this JSON single-value format
map
JSON object of key and value arrays
{ "keys": ["a", "b"], "values": [1, 2] }
Stores arrays of keys and values; individual keys and values are serialized using this JSON single-value format
geometry
JSON string
POINT (30 10)
Stored using WKT representation, see
Appendix G
geography
JSON string
POINT (30 10)
Stored using WKT representation, see
Appendix G
Appendix E: Format version changes
Version 3
Default values are added to struct fields in v3.
The
write-default
is a forward-compatible change because it is only used at write time. Old writers will fail because the field is missing.
Tables with
initial-default
will be read correctly by older readers if
initial-default
is always null for optional fields. Otherwise, old readers will default optional columns with null. Old readers will fail to read required fields which are populated by
initial-default
because that default is not supported.
Types
variant
geometry
geography
unknown
timestamp_ns
, and
timestamptz_ns
are added in v3.
All readers are required to read tables with unknown partition transforms, ignoring the unsupported partition fields when filtering.
Writing v3 metadata:
Partition Field and Sort Field JSON:
source-ids
was added and must be written in the case of a multi-argument transform.
source-id
must be written in the case of single-argument transforms.
Row-level delete changes:
Deletion vectors are added in v3, stored using the Puffin
deletion-vector-v1
blob type
Manifests are updated to track deletion vectors:
referenced_data_file
was added and can be used for both deletion vectors (required) and v2 position delete files that contain deletes for only one data file (optional)
content_offset
was added and must match the deletion vector blob's offset in a Puffin file
content_size_in_bytes
was added and must match the deletion vector blob's length in a Puffin file
Deletion vectors are maintained synchronously: Writers must merge DVs (and older position delete files) to ensure there is at most one DV per data file
Readers can safely ignore position delete files if there is a DV for a data file
Writers are not allowed to add new position delete files to v3 tables
Existing position delete files are valid in tables that have been upgraded from v2
These position delete files must be merged into the DV for a data file when one is created
Position delete files that contain deletes for more than one data file need to be kept in table metadata until all deletes are replaced by DVs
Row lineage changes:
Writers must set the table's
next-row-id
and use the existing
next-row-id
as the
first-row-id
when creating new snapshots
When a table is upgraded to v3,
next_row_id
should be initialized to 0
When committing a new snapshot
next-row-id
must be incremented by at least the number of newly assigned row ids in the snapshot
It is recommended to increment
next-row-id
by the total
added_rows_count
and
existing_rows_count
of all manifests assigned a
first_row_id
Writers must assign a
first_row_id
to new data manifests when writing a manifest list
When writing a new manifest list each
first_row_id
must be incremented by at least the number of newly assigned row ids in the manifest
It is recommended to increment
first_row_id
by a manifest's
added_rows_count
and
existing_rows_count
When writing a manifest, new data files must be written with a null
first_row_id
so that the value is assigned at read time based on the manifest's
first_row_id
When a manifest has a non-null
first_row_id
, readers must assign a
first_row_id
to any data file that has a missing or null value in that manifest
Readers must increment
first_row_id
by the data file's
record_count
When writing an existing data file into a new manifest, its
first_row_id
must be written into the manifest
When a data file has a non-null
first_row_id
, readers must:
Replace any null or missing
_row_id
with the data file's
first_row_id
plus the row's
_pos
Replace any null or missing
_last_updated_sequence_number
to the data file's
data_sequence_number
Read any non-null
_row_id
or
_last_updated_sequence_number
without modification
When a data file has a null
first_row_id
, readers must produce null for
_row_id
and
_last_updated_sequence_number
When writing an existing row into a new data file, writers must write
_row_id
and
_last_updated_sequence_number
if they are non-null
Encryption changes:
Encryption keys are tracked by table metadata
encryption-keys
The encryption key used for a snapshot is specified by
key-id
Version 2
Writing v1 metadata:
Table metadata field
last-sequence-number
should not be written
Snapshot field
sequence-number
should not be written
Manifest list field
sequence-number
should not be written
Manifest list field
min-sequence-number
should not be written
Manifest list field
content
must be 0 (data) or omitted
Manifest entry field
sequence_number
should not be written
Manifest entry field
file_sequence_number
should not be written
Data file field
content
must be 0 (data) or omitted
Reading v1 metadata for v2:
Table metadata field
last-sequence-number
must default to 0
Snapshot field
sequence-number
must default to 0
Manifest list field
sequence-number
must default to 0
Manifest list field
min-sequence-number
must default to 0
Manifest list field
content
must default to 0 (data)
Manifest entry field
sequence_number
must default to 0
Manifest entry field
file_sequence_number
must default to 0
Data file field
content
must default to 0 (data)
Writing v2 metadata:
Table metadata JSON:
last-sequence-number
was added and is required; default to 0 when reading v1 metadata
table-uuid
is now required
current-schema-id
is now required
schemas
is now required
partition-specs
is now required
default-spec-id
is now required
last-partition-id
is now required
sort-orders
is now required
default-sort-order-id
is now required
schema
is no longer required and should be omitted; use
schemas
and
current-schema-id
instead
partition-spec
is no longer required and should be omitted; use
partition-specs
and
default-spec-id
instead
Snapshot JSON:
sequence-number
was added and is required; default to 0 when reading v1 metadata
manifest-list
is now required
manifests
is no longer required and should be omitted; always use
manifest-list
instead
Manifest list
manifest_file
content
was added and is required; 0=data, 1=deletes; default to 0 when reading v1 manifest lists
sequence_number
was added and is required
min_sequence_number
was added and is required
added_files_count
is now required
existing_files_count
is now required
deleted_files_count
is now required
added_rows_count
is now required
existing_rows_count
is now required
deleted_rows_count
is now required
Manifest key-value metadata:
schema-id
is now required
partition-spec-id
is now required
format-version
is now required
content
was added and is required (must be "data" or "deletes")
Manifest
manifest_entry
snapshot_id
is now optional to support inheritance
sequence_number
was added and is optional, to support inheritance
file_sequence_number
was added and is optional, to support inheritance
Manifest
data_file
content
was added and is required; 0=data, 1=position deletes, 2=equality deletes; default to 0 when reading v1 manifests
equality_ids
was added, to be used for equality deletes only
block_size_in_bytes
was removed (breaks v1 reader compatibility)
file_ordinal
was removed
sort_columns
was removed
Note that these requirements apply when writing data to a v2 table. Tables that are upgraded from v1 may contain metadata that does not follow these requirements. Implementations should remain backward-compatible with v1 metadata requirements.
Appendix F: Implementation Notes
This section covers topics not required by the specification but recommendations for systems implementing the Iceberg specification to help maintain a uniform experience.
Point in Time Reads (Time Travel)
Iceberg supports two types of histories for tables. A history of previous "current snapshots" stored in
"snapshot-log" table metadata
and
parent-child lineage stored in "snapshots"
. These two histories
might indicate different snapshot IDs for a specific timestamp. The discrepancies can be caused by a variety of table operations (e.g. updating the
current-snapshot-id
can be used to set the snapshot of a table to any arbitrary snapshot, which might have a lineage derived from a table branch or no lineage at all).
When processing point in time queries implementations should use "snapshot-log" metadata to lookup the table state at the given point in time. This ensures time-travel queries reflect the state of the table at the provided timestamp. For example a SQL query like
SELECT * FROM prod.db.table TIMESTAMP AS OF '1986-10-26 01:21:00Z';
would find the snapshot of the Iceberg table just prior to '1986-10-26 01:21:00 UTC' in the snapshot logs and use the metadata from that snapshot to perform the scan of the table. If no snapshot exists prior to the timestamp given or "snapshot-log" is not populated (it is an optional field), then systems should raise an informative error message about the missing metadata.
Optional Snapshot Summary Fields
Snapshot summary can include metrics fields to track numeric stats of the snapshot (see
Metrics
) and operational details (see
Other Fields
). The value of these fields should be of string type (e.g.,
"120"
).
Metrics
Field
Description
added-data-files
Number of data files added in the snapshot
deleted-data-files
Number of data files deleted in the snapshot
total-data-files
Total number of live data files in the snapshot
added-delete-files
Number of positional/equality delete files and deletion vectors added in the snapshot
added-equality-delete-files
Number of equality delete files added in the snapshot
removed-equality-delete-files
Number of equality delete files removed in the snapshot
added-position-delete-files
Number of position delete files added in the snapshot
removed-position-delete-files
Number of position delete files removed in the snapshot
added-dvs
Number of deletion vectors added in the snapshot
removed-dvs
Number of deletion vectors removed in the snapshot
removed-delete-files
Number of positional/equality delete files and deletion vectors removed in the snapshot
total-delete-files
Total number of live positional/equality delete files and deletion vectors in the snapshot
added-records
Number of records added in the snapshot
deleted-records
Number of records deleted in the snapshot
total-records
Total number of records in the snapshot
added-files-size
The size of files added in the snapshot
removed-files-size
The size of files removed in the snapshot
total-files-size
Total size of live files in the snapshot
added-position-deletes
Number of position delete records added in the snapshot
removed-position-deletes
Number of position delete records removed in the snapshot
total-position-deletes
Total number of position delete records in the snapshot
added-equality-deletes
Number of equality delete records added in the snapshot
removed-equality-deletes
Number of equality delete records removed in the snapshot
total-equality-deletes
Total number of equality delete records in the snapshot
deleted-duplicate-files
Number of duplicate files deleted (duplicates are files recorded more than once in the manifest)
changed-partition-count
Number of partitions with files added or removed in the snapshot
manifests-created
Number of manifest files created in the snapshot
manifests-kept
Number of manifest files kept in the snapshot
manifests-replaced
Number of manifest files replaced in the snapshot
entries-processed
Number of manifest entries processed in the snapshot
Other Fields
Field
Example
Description
wap.id
"12345678"
The Write-Audit-Publish id of a staged snapshot
published-wap-id
"12345678"
The Write-Audit-Publish id of a snapshot already been published
source-snapshot-id
"12345678"
The original id of a cherry-picked snapshot
engine-name
"spark"
Name of the engine that created the snapshot
engine-version
"3.5.4"
Version of the engine that created the snapshot
Assignment of Snapshot IDs and
current-snapshot-id
Writers should produce positive values for snapshot ids in a manner that minimizes the probability of id collisions and should verify the id does not conflict with existing snapshots. Producing snapshot ids based on timestamps alone is not recommended as it increases the potential for collisions.
The reference Java implementation uses a type 4 uuid and XORs the 8 most significant bytes with the 8 least significant bytes then ANDs with the maximum long value to arrive at a pseudo-random snapshot id with a low probability of collision.
Java writes
-1
for "no current snapshot" with V1 and V2 tables and considers this equivalent to omitted or
null
. This has never been formalized in the spec, but for compatibility, other implementations can accept
-1
as
null
. Java will no longer write
-1
and will use
null
for "no current snapshot" for all tables with a version greater than or equal to V3.
Naming for GZIP compressed Metadata JSON files
Some implementations require that GZIP compressed files have the suffix
.gz.metadata.json
to be read correctly. The Java reference implementation can additionally read GZIP compressed files with the suffix
metadata.json.gz
Position Delete Files with Row Data
Although the spec allows for including the deleted row itself (in addition to the path and position of the row in the data file) in v2 position delete files, writing the row is optional and no implementation currently writes it. The ability to write and read the row is supported in the Java implementation but is deprecated in version 1.11.0.
Appendix G: Geospatial Notes
The Geometry and Geography class hierarchy and its Well-known text (WKT) and Well-known binary (WKB) serializations (ISO supporting XY, XYZ, XYM, XYZM) are defined by
OpenGIS Implementation Specification for Geographic information – Simple feature access – Part 1: Common architecture
, from
OGC (Open Geospatial Consortium)
Points are always defined by the coordinates X, Y, Z (optional), and M (optional), in this order. X is the longitude/easting, Y is the latitude/northing, and Z is usually the height, or elevation. M is a fourth optional dimension, for example a linear reference value (e.g., highway milepost value), a timestamp, or some other value as defined by the CRS.
The version of the OGC standard first used here is 1.2.1, but future versions may also be used if the WKB representation remains wire-compatible.