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.

Version 1: Analytic Data Tables

Iceberg format version 1 is the current version. It defines how to manage large analytic tables using immutable file formats, like Parquet, Avro, and ORC.

Version 2: Row-level Deletes

The Iceberg community is currently working on version 2 of the Iceberg format that supports encoding row-level deletes. The v2 specification is incomplete and may change until it is finished and adopted. This document includes tentative v2 format requirements, but there are currently no compatibility guarantees with the unfinished v2 spec.

The goal of version 2 is to provide a way to encode row-level deletes. This update can be used to delete or replace individual rows in an immutable data file without rewriting the file.

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- and write-optimized formats will be available.

Overview

Iceberg snapshot structure {.floating}

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 ways to encode a row-level delete:

Position deletes mark a row deleted by data file path and the row position in the data file
Equality deletes mark a row deleted by one or more column values, like id = 5

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

Terms

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.

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

A 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.

A 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.

A 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.

Primitive Types

Primitive type	Description	Requirements
`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 [1], precision must be 38 or less
`date`	Calendar date without timezone or time
`time`	Time of day without date, timezone	Microsecond precision [2]
`timestamp`	Timestamp without timezone	Microsecond precision [2]
`timestamptz`	Timestamp with timezone	Stored as UTC [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

Notes:

Decimal scale is fixed and cannot be changed by schema evolution. Precision can only be widened.
All time and timestamp values are stored with microsecond precision.
- Timestamps 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).
- Timestamps 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 are stored as a long that encodes microseconds from the unix epoch.
Character strings must be stored as UTF-8 encoded byte arrays.

For details on how to serialize a schema to JSON, see Appendix C.

Schema Evolution

Schema evolution is limited to type promotion and adding, deleting, and renaming fields in structs (both nested structs and the top-level schema’s struct).

Valid type promotions are:

int to long
float to double
decimal(P, S) to decimal(P', S) if P' > P -- widen the precision of decimal types.

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<a, b, c> ↔ struct<a, struct<b, c>>). Evolving primitive types to structs is not allowed, nor is evolving a single-field struct to a primitive (map<string, int> ↔ map<string, struct<int>>).

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. If a field id is missing from a data file, its value for each row should be null.

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 c (renamed to measurement), b (now called name), and a column of null values called a; in that order.

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
`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

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:

A source column id from the table’s schema
A 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.
A transform that is applied to the source column to produce a partition value
A partition name

The source column, selected by id, 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 Transforms

Transform	Description	Source types	Result type
`identity`	Source value, unmodified	Any	Source type
`bucket[N]`	Hash of value, mod `N` (see below)	`int`, `long`, `decimal`, `date`, `time`, `timestamp`, `timestamptz`, `string`, `uuid`, `fixed`, `binary`	`int`
`truncate[W]`	Value truncated to width `W` (see below)	`int`, `long`, `decimal`, `string`	Source type
`year`	Extract a date or timestamp year, as years from 1970	`date`, `timestamp(tz)`	`int`
`month`	Extract a date or timestamp month, as months from 1970-01-01	`date`, `timestamp(tz)`	`int`
`day`	Extract a date or timestamp day, as days from 1970-01-01	`date`, `timestamp(tz)`	`date`
`hour`	Extract a timestamp hour, as hours from 1970-01-01 00:00:00	`timestamp(tz)`	`int`

All transforms must return null for a null input value.

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], N. The hash mod N 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`	`W`, width	`v - (v % W)` remainders must be positive [1]	`W=10`: `1` ￫ `0`, `-1` ￫ `-10`
`long`	`W`, width	`v - (v % W)` remainders must be positive [1]	`W=10`: `1` ￫ `0`, `-1` ￫ `-10`
`decimal`	`W`, width (no scale)	`scaled_W = decimal(W, scale(v))` `v - (v % scaled_W)` [1, 2]	`W=50`, `s=2`: `10.65` ￫ `10.50`
`string`	`L`, length	Substring of length `L`: `v.substring(0, L)`	`L=3`: `iceberg` ￫ `ice`

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, W, used to truncate decimal values is applied using the scale of the decimal column to avoid additional (and potentially conflicting) parameters.

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 an 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:

A source column id from the table's schema
A transform that is used to produce values to be sorted on from the source column. This is the same transform as described in partition transforms.
A sort direction, that can only be either asc or desc
A null order that describes the order of null values when sorted. Can only be either nulls-first or nulls-last

Order id 0 is reserved for the unsorted order.

Sorting floating-point numbers should produce the following behavior: -NaN < -Infinity < -value < -0 < 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
required	required	`partition-spec`	JSON fields representation of the partition spec used to write the manifest
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 a struct called manifest_entry with 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
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`	Sequence number when the file was added. Inherited when null.
required	required	`2 data_file`	`data_file` `struct` (see below)	File path, partition tuple, metrics, ...

data_file is a struct with the following fields:

v1	v2	Field id, name	Type	Description
	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	`100 file_path`	`string`	Full URI for the file with FS scheme
required	required	`101 file_format`	`string`	String file format name, avro, orc or parquet
required	required	`102 partition`	`struct<...>`	Partition data tuple, schema based on the partition spec
required	required	`103 record_count`	`long`	Number of records in this file
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.
optional		~~`106 file_ordinal`~~	`int`	Deprecated. Do not write.
optional		~~`107 sort_columns`~~	`list<112: int>`	Deprecated. Do not write.
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	`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	`110 null_value_counts`	`map<121: int, 122: long>`	Map from column id to number of null values in the column
optional	optional	`137 nan_value_counts`	`map<138: int, 139: long>`	Map from column id to number of NaN values in the column
optional		~~`111 distinct_counts`~~	`map<123: int, 124: long>`	Deprecated. Do not write.
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	`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	`131 key_metadata`	`binary`	Implementation-specific key metadata for encryption
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	`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	`140 sort_order_id`	`int`	ID representing sort order for this file [3].

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.
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.

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.

Manifest Entry Fields

The manifest entry fields are used to keep track of the snapshot in which files were added or logically deleted. The data_file struct is nested inside of 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, it’s 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, it’s 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 a sequence number to the entry and makes the snapshot id optional. Both fields, sequence_number and snapshot_id, 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 [2].

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.
Manifest list files are required in v2, so that the sequence_number and snapshot_id to inherit are always available.

Sequence Number Inheritance

Manifests track the sequence number when a data or delete file was added to the table.

When adding new file, its sequence number is 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.

When writing an existing file to a new manifest, the sequence number must be non-null and set to the sequence number that was inherited.

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.

Snapshots

A snapshot consists of the following fields:

v1	v2	Field	Description
required	required	`snapshot-id`	A unique long ID
optional	optional	`parent-snapshot-id`	The snapshot ID of the snapshot's parent. Omitted for any snapshot with no parent
	required	`sequence-number`	A monotonically increasing long that tracks the order of changes to a table
required	required	`timestamp-ms`	A timestamp when the snapshot was created, used for garbage collection and table inspection
optional	required	`manifest-list`	The location of a manifest list for this snapshot that tracks manifest files with additional meadata
optional		`manifests`	A list of manifest file locations. Must be omitted if `manifest-list` is present
optional	required	`summary`	A string map that summarizes the snapshot changes, including `operation` (see below)

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.

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.

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	Field id, name	Type	Description
required	required	`500 manifest_path`	`string`	Location of the manifest file
required	required	`501 manifest_length`	`long`	Length of the manifest file
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	`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	`515 sequence_number`	`long`	The sequence number when the manifest was added to the table; use 0 when reading v1 manifest lists
	required	`516 min_sequence_number`	`long`	The minimum sequence number of all data or delete files in the manifest; use 0 when reading v1 manifest lists
required	required	`503 added_snapshot_id`	`long`	ID of the snapshot where the manifest file was added
optional	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	`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	`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	`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	`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	`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	`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.

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	`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.

Scan Planning

Scans are planned by reading the manifest files for the current snapshot. Deleted entries in data and delete manifests 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 data and delete files. These partition predicates are used to select the data and delete files in the manifest. This conversion uses the partition spec used to write the manifest file.

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 X will be included in the scan because the file contains rows that match the predicate and rows that do not match the predicate.

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.

Delete files that match the query filter must be applied to data files at read time, limited by the scope of the delete file using the following rules.

A position delete file must be applied to a data file when all of the following are true:
- The data file‘s sequence number is less than or equal to the delete file’s sequence number
- The data file‘s partition (both spec and partition values) is equal to the delete file’s partition
An equality delete file must be applied to a data file when all of the following are true:
- The data file‘s sequence number is strictly less than the delete’s sequence number
- The data file‘s partition (both spec and partition values) is equal 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 delete files must be applied to data files from the same commit, when the data and delete file 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.

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	Field	Description
required	required	`format-version`	An integer version number for the format. Currently, this is always 1. Implementations must throw an exception if a table's version is higher than the supported version.
optional	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	`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	`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	`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	`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	required	`schema`	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	`partition-specs`	A list of partition specs, stored as full partition spec objects.
optional	required	`default-spec-id`	ID of the “current” spec that writers should use by default.
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	`current-snapshot-id`	`long` ID of the current table snapshot.
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	`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	required	`sort-orders`	A list of sort orders, stored as full sort order objects.
optional	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.

For serialization details, see Appendix C.

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

An atomic swap can be implemented using atomic rename in file systems that support it, like HDFS or most local file systems [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: v<V>.metadata.json. To commit a new metadata version, V+1, the writer performs the following steps:

Read the current table metadata version V.
Create new table metadata based on version V.
Write the new table metadata to a unique file: <random-uuid>.metadata.json.
Rename the unique file to the well-known file for version V: v<V+1>.metadata.json.
1. If the rename succeeds, the commit succeeded and V+1 is the table’s current version
2. 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: <V>-<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: <V+1>-<uuid>.metadata.json.
Request that the metastore swap the table’s metadata pointer from the location of V to the location of V+1.
1. If the swap succeeds, the commit succeeded. V was still the latest metadata version and the metadata file for V+1 is now the current metadata.
2. 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 not supported in v1.

Row-level delete files are valid Iceberg data files: files must use valid Iceberg formats, schemas, and column projection. It is recommended that delete files are written using the table's default file format.

Row-level delete files are tracked by manifests, like data files. A separate set of manifests is used for delete files, but the manifest schemas are identical.

Both position and equality deletes allow encoding deleted row values with a delete. This can be used to reconstruct a stream of changes to a table.

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.

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 `0`
`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 position to optimize filtering rows while scanning.

Sorting by file_path allows filter pushdown by file in columnar storage formats.
Sorting by position 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.

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
-------
 3

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 must always set the Avro field default value to null.

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
`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 the 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.
`timestamptz`	`{ "type": "long",` `"logicalType": "timestamp-micros",` `"adjust-to-utc": true }`	Stores microseconds from 1970-01-01 00:00:00.000000 UTC.
`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.

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.

Lists must use the 3-level representation.

Type	Parquet physical type	Logical type	Notes
`boolean`	`boolean`
`int`	`int`
`long`	`long`
`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 `P`.
`date`	`int32`	`DATE`	Stores days from the 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.
`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.

ORC

Data Type Mappings

Type	ORC type	ORC type attributes	Notes
`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`		[1]
`timestamptz`	`timestamp_instant`		[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`=`L`	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`

Notes:

ORC's TimestampColumnVector comprises 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

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 suck from a user’s point of view.

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<a (1): int, b (2): string>`	`struct<a: int, b: string>`	`struct<a (2): string, c (3): date>`	`struct<b: string, c: date>`
`struct<a (1): struct<b (2): string, c (3): date>>`	`struct<a: struct<b:string, c:date>>`	`struct<aa (1): struct<cc (3): date, bb (2): string>>`	`struct<a: struct<c:date, b:string>>`

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`
`timestamptz`	`hashLong(microsecsFromUnixEpoch(v))`	`2017-11-16T14:31:08-08:00`￫ `-2047944441`
`string`	`hashBytes(utf8Bytes(v))`	`iceberg` ￫ `1210000089`
`uuid`	`hashBytes(uuidBytes(v))` [3]	`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
`boolean`	`false: hashInt(0)`, `true: hashInt(1)`	`true` ￫ `1392991556`
`float`	`hashDouble(double(v))` [4]	`1.0F` ￫ `-142385009`
`double`	`hashLong(doubleToLongBits(v))`	`1.0D` ￫ `-142385009`

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.
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
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 to JSON as a struct. Types are serialized according to this table:

Type	JSON representation	Example
`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 without zone`	`JSON string: "timestamp"`	`"timestamp"`
`timestamp with zone`	`JSON string: "timestamptz"`	`"timestamptz"`
`string`	`JSON string: "string"`	`"string"`
`uuid`	`JSON string: "uuid"`	`"uuid"`
`fixed(L)`	`JSON string: "fixed[<L>]"`	`"fixed[16]"`
`binary`	`JSON string: "binary"`	`"binary"`
`decimal(P, S)`	`JSON string: "decimal(<P>,<S>)"`	`"decimal(9,2)"`, `"decimal(9, 2)"`
`struct`	`JSON object: {` `"type": "struct",` `"fields": [ {` `"id": <field id int>,` `"name": <name string>,` `"required": <boolean>,` `"type": <type JSON>,` `"doc": <comment string>` `}, ...` `] }`	`{` `"type": "struct",` `"fields": [ {` `"id": 1,` `"name": "id",` `"required": true,` `"type": "uuid"` `}, {` `"id": 2,` `"name": "data",` `"required": false,` `"type": {` `"type": "list",` `...` `}` `} ]` `}`
`list`	`JSON object: {` `"type": "list",` `"element-id": <id int>,` `"element-required": <bool>` `"element": <type JSON>` `}`	`{` `"type": "list",` `"element-id": 3,` `"element-required": true,` `"element": "string"` `}`
`map`	`JSON object: {` `"type": "map",` `"key-id": <key id int>,` `"key": <type JSON>,` `"value-id": <val id int>,` `"value-required": <bool>` `"value": <type JSON>` `}`	`{` `"type": "map",` `"key-id": 4,` `"key": "string",` `"value-id": 5,` `"value-required": false,` `"value": "double"` `}`

Partition Specs

Partition specs are serialized as a JSON object with the following fields:

Field	JSON representation	Example
`spec-id`	`JSON int`	`0`
`fields`	`JSON list: [` `<partition field JSON>,` `...` `]`	`[ {` `"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 the fields list is stored as an object. See the table for more detail:

Transform or Field	JSON representation	Example
`identity`	`JSON string: "identity"`	`"identity"`
`bucket[N]`	`JSON string: "bucket<N>]"`	`"bucket[16]"`
`truncate[W]`	`JSON string: "truncate[<W>]"`	`"truncate[20]"`
`year`	`JSON string: "year"`	`"year"`
`month`	`JSON string: "month"`	`"month"`
`day`	`JSON string: "day"`	`"day"`
`hour`	`JSON string: "hour"`	`"hour"`
`Partition Field`	`JSON object: {` `"source-id": <id int>,` `"field-id": <field id int>,` `"name": <name string>,` `"transform": <transform JSON>` `}`	`{` `"source-id": 1,` `"field-id": 1000,` `"name": "id_bucket",` `"transform": "bucket[16]"` `}`

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.

Sort Orders

Sort orders are serialized as a list of JSON object, each of which contains the following fields:

Field	JSON representation	Example
`order-id`	`JSON int`	`1`
`fields`	`JSON list: [` `<sort field JSON>,` `...` `]`	`[ {` `"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:

Field	JSON representation	Example
`Sort Field`	`JSON object: {` `"transform": <transform JSON>,` `"source-id": <source id int>,` `"direction": <direction string>,` `"null-order": <null-order string>` `}`	`{` `"transform": "bucket[4]",` `"source-id": 3,` `"direction": "desc",` `"null-order": "nulls-last"` `}`

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.

Metadata field	JSON representation	Example
`format-version`	`JSON int`	`1`
`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`
`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`	`0`
`properties`	`JSON object: {` `"<key>": "<val>",` `...` `}`	`{` `"write.format.default": "avro",` `"commit.retry.num-retries": "4"` `}`
`current-snapshot-id`	`JSON long`	`3051729675574597004`
`snapshots`	`JSON list of objects: [ {` `"snapshot-id": <id>,` `"timestamp-ms": <timestamp-in-ms>,` `"summary": {` `"operation": <operation>,` `... },` `"manifest-list": "<location>"` `},` `...` `]`	`[ {` `"snapshot-id": 3051729675574597004,` `"timestamp-ms": 1515100955770,` `"summary": {` `"operation": "append"` `},` `"manifest-list": "s3://b/wh/.../s1.avro"` `} ]`
`snapshot-log`	`JSON list of objects: [` `{` `"snapshot-id": ,` `"timestamp-ms":` `},` `...` `]`	`[ {` `"snapshot-id": 30517296...,` `"timestamp-ms": 1515100...` `} ]`
`sort-orders`	`JSON sort orders (list of sort field object)`	`See above`
`default-sort-order-id`	`JSON int`	`0`

Appendix D: Single-value serialization

This serialization scheme is for storing single values as individual binary values in the lower and upper bounds maps of manifest files.

Type	Binary serialization
`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 without zone`	Stores microseconds from 1970-01-01 00:00:00.000000 in an 8-byte little-endian long
`timestamp with zone`	Stores microseconds from 1970-01-01 00:00:00.000000 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

Format version changes

Version 2

Writing v1 metadata:

Table metadata field last-sequence-number should not be written.
Snapshot field sequence-number should not be written.

Reading v1 metadata:

Table metadata field last-sequence-number must default to 0.
Snapshot field sequence-number must default to 0.

Writing v2 metadata:

Table metadata added required field last-sequence-number.
Table metadata now requires field table-uuid.
Table metadata now requires field partition-specs.
Table metadata now requires field default-spec-id.
Table metadata field partition-spec is no longer required and may be omitted.
Snapshot added required field field sequence-number.
Snapshot now requires field manifest-list.
Snapshot field manifests is no longer allowed.
Table metadata now requires field sort-orders.
Table metadata now requires field default-sort-order-id.

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.