iceberg

Provides a read-only table-like interface to Apache Iceberg tables in Amazon S3, Azure, HDFS or locally stored.

Syntax

icebergS3(url [, NOSIGN | access_key_id, secret_access_key, [session_token]] [,format] [,compression_method] [,extra_credentials])
icebergS3(named_collection[, option=value [,..]])

icebergAzure(connection_string|storage_account_url, container_name, blobpath, [,account_name], [,account_key] [,format] [,compression_method])
icebergAzure(named_collection[, option=value [,..]])

icebergHDFS(path_to_table, [,format] [,compression_method])
icebergHDFS(named_collection[, option=value [,..]])

icebergLocal(path_to_table, [,format] [,compression_method])
icebergLocal(named_collection[, option=value [,..]])

Arguments

Description of the arguments coincides with description of arguments in table functions s3, azureBlobStorage, HDFS and file correspondingly. format stands for the format of data files in the Iceberg table. For icebergS3, an optional extra_credentials parameter can be used to pass a role_arn for role-based access in ClickHouse Cloud. See Secure S3 for configuration steps.

Returned value

A table with the specified structure for reading data in the specified Iceberg table.

Example

SELECT * FROM icebergS3('http://test.s3.amazonaws.com/clickhouse-bucket/test_table', 'test', 'test')

ClickHouse currently supports reading v1 and v2 of the Iceberg format via the icebergS3, icebergAzure, icebergHDFS and icebergLocal table functions and IcebergS3, icebergAzure, IcebergHDFS and IcebergLocal table engines.

Defining a named collection

Here is an example of configuring a named collection for storing the URL and credentials:

<clickhouse>
    <named_collections>
        <iceberg_conf>
            <url>http://test.s3.amazonaws.com/clickhouse-bucket/</url>
            <access_key_id>test</access_key_id>
            <secret_access_key>test</secret_access_key>
            <format>auto</format>
            <structure>auto</structure>
        </iceberg_conf>
    </named_collections>
</clickhouse>

SELECT * FROM icebergS3(iceberg_conf, filename = 'test_table')
DESCRIBE icebergS3(iceberg_conf, filename = 'test_table')

Using a data catalog

Iceberg tables can also be used with various data catalogs, such as the REST Catalog, AWS Glue Data Catalog and Unity Catalog.

When using a catalog, most users will want to use the DataLakeCatalog database engine, which connects ClickHouse to your catalog to discover your tables. You can use this database engine instead of manually creating individual tables with IcebergS3 table engine.

To use them, create a table with the IcebergS3 engine and provide the necessary settings. For example, using REST Catalog with MinIO storage:

CREATE TABLE `database_name.table_name`
ENGINE = IcebergS3(
  'http://minio:9000/warehouse-rest/table_name/',
  'minio_access_key',
  'minio_secret_key'
)

Or, using AWS Glue Data Catalog with S3:

CREATE TABLE `my_database.my_table`  
ENGINE = IcebergS3(
  's3://my-data-bucket/warehouse/my_database/my_table/',
  'aws_access_key',
  'aws_secret_key'
)

Schema Evolution

At the moment, with the help of CH, you can read iceberg tables, the schema of which has changed over time. We currently support reading tables where columns have been added and removed, and their order has changed. You can also change a column where a value is required to one where NULL is allowed. Additionally, we support permitted type casting for simple types, namely:

int -> long
float -> double
decimal(P, S) -> decimal(P’, S) where P’ > P.

Currently, it is not possible to change nested structures or the types of elements within arrays and maps.

Partition Pruning

ClickHouse supports partition pruning during SELECT queries for Iceberg tables, which helps optimize query performance by skipping irrelevant data files. To enable partition pruning, set use_iceberg_partition_pruning = 1. For more information about iceberg partition pruning address https://iceberg.apache.org/spec/#partitioning

Time Travel

ClickHouse supports time travel for Iceberg tables, allowing you to query historical data with a specific timestamp or snapshot ID.

Processing of tables with deleted rows

Currently, only Iceberg tables with position deletes are supported. The following deletion methods are not supported:

Equality deletes
Deletion vectors (introduced in v3)

Basic usage

SELECT * FROM example_table ORDER BY 1 
SETTINGS iceberg_timestamp_ms = 1714636800000

SELECT * FROM example_table ORDER BY 1 
SETTINGS iceberg_snapshot_id = 3547395809148285433

Note: You cannot specify both iceberg_timestamp_ms and iceberg_snapshot_id parameters in the same query.

Important considerations

Snapshots are typically created when:
New data is written to the table
Some kind of data compaction is performed
Schema changes typically don’t create snapshots - This leads to important behaviors when using time travel with tables that have undergone schema evolution.

Example scenarios

All scenarios are written in Spark because CH doesn’t support writing to Iceberg tables yet.

Scenario 1: Schema Changes Without New Snapshots

Consider this sequence of operations:

-- Create a table with two columns
 CREATE TABLE IF NOT EXISTS spark_catalog.db.time_travel_example (
 order_number bigint, 
 product_code string
 ) 
 USING iceberg 
 OPTIONS ('format-version'='2')

- - Insert data into the table
 INSERT INTO spark_catalog.db.time_travel_example VALUES 
   (1, 'Mars')

 ts1 = now() // A piece of pseudo code

- - Alter table to add a new column
 ALTER TABLE spark_catalog.db.time_travel_example ADD COLUMN (price double)

 ts2 = now()

- - Insert data into the table
 INSERT INTO spark_catalog.db.time_travel_example VALUES (2, 'Venus', 100)

  ts3 = now()

- - Query the table at each timestamp
 SELECT * FROM spark_catalog.db.time_travel_example TIMESTAMP AS OF ts1;

+------------+------------+
|order_number|product_code|
+------------+------------+
|           1|        Mars|
+------------+------------+
 SELECT * FROM spark_catalog.db.time_travel_example TIMESTAMP AS OF ts2;

+------------+------------+
|order_number|product_code|
+------------+------------+
|           1|        Mars|
+------------+------------+

 SELECT * FROM spark_catalog.db.time_travel_example TIMESTAMP AS OF ts3;

+------------+------------+-----+
|order_number|product_code|price|
+------------+------------+-----+
|           1|        Mars| NULL|
|           2|       Venus|100.0|
+------------+------------+-----+

Query results at different timestamps:

At ts1 & ts2: Only the original two columns appear
At ts3: All three columns appear, with NULL for the price of the first row

Scenario 2: Historical vs. Current Schema Differences

A time travel query at a current moment might show a different schema than the current table:

-- Create a table
  CREATE TABLE IF NOT EXISTS spark_catalog.db.time_travel_example_2 (
  order_number bigint, 
  product_code string
  ) 
  USING iceberg 
  OPTIONS ('format-version'='2')

-- Insert initial data into the table
  INSERT INTO spark_catalog.db.time_travel_example_2 VALUES (2, 'Venus');

-- Alter table to add a new column
  ALTER TABLE spark_catalog.db.time_travel_example_2 ADD COLUMN (price double);

  ts = now();

-- Query the table at a current moment but using timestamp syntax

  SELECT * FROM spark_catalog.db.time_travel_example_2 TIMESTAMP AS OF ts;

    +------------+------------+
    |order_number|product_code|
    +------------+------------+
    |           2|       Venus|
    +------------+------------+

-- Query the table at a current moment
  SELECT * FROM spark_catalog.db.time_travel_example_2;
    +------------+------------+-----+
    |order_number|product_code|price|
    +------------+------------+-----+
    |           2|       Venus| NULL|
    +------------+------------+-----+

This happens because ALTER TABLE doesn’t create a new snapshot but for the current table Spark takes value of schema_id from the latest metadata file, not a snapshot.

Scenario 3: Historical vs. Current Schema Differences

The second one is that while doing time travel you can’t get state of table before any data was written to it:

-- Create a table
  CREATE TABLE IF NOT EXISTS spark_catalog.db.time_travel_example_3 (
  order_number bigint, 
  product_code string
  ) 
  USING iceberg 
  OPTIONS ('format-version'='2');

  ts = now();

-- Query the table at a specific timestamp
  SELECT * FROM spark_catalog.db.time_travel_example_3 TIMESTAMP AS OF ts; -- Finises with error: Cannot find a snapshot older than ts.

In Clickhouse the behavior is consistent with Spark. You can mentally replace Spark Select queries with Clickhouse Select queries and it will work the same way.

Metadata File Resolution

When using the iceberg table function in ClickHouse, the system needs to locate the correct metadata.json file that describes the Iceberg table structure. Here’s how this resolution process works:

Candidate Search (in Priority Order)

Direct Path Specification: *If you set iceberg_metadata_file_path, the system will use this exact path by combining it with the Iceberg table directory path.

When this setting is provided, all other resolution settings are ignored.

Table UUID Matching: *If iceberg_metadata_table_uuid is specified, the system will: *Look only at .metadata.json files in the metadata directory *Filter for files containing a table-uuid field matching your specified UUID (case-insensitive)
Default Search: *If neither of the above settings are provided, all .metadata.json files in the metadata directory become candidates

Selecting the Most Recent File

After identifying candidate files using the above rules, the system determines which one is the most recent:

If iceberg_recent_metadata_file_by_last_updated_ms_field is enabled:
The file with the largest last-updated-ms value is selected
Otherwise:
The file with the highest version number is selected
(Version appears as V in filenames formatted as V.metadata.json or V-uuid.metadata.json)

Note: All mentioned settings are table function settings (not global or query-level settings) and must be specified as shown below:

SELECT * FROM iceberg('s3://bucket/path/to/iceberg_table', 
    SETTINGS iceberg_metadata_table_uuid = 'a90eed4c-f74b-4e5b-b630-096fb9d09021');

Note: While Iceberg Catalogs typically handle metadata resolution, the iceberg table function in ClickHouse directly interprets files stored in S3 as Iceberg tables, which is why understanding these resolution rules is important.

Metadata cache

Iceberg table engine and table function support metadata cache storing the information of manifest files, manifest list and metadata json. The cache is stored in memory. This feature is controlled by setting use_iceberg_metadata_files_cache, which is enabled by default.

Aliases

Table function iceberg is an alias to icebergS3 now.

Virtual Columns

_path — Path to the file. Type: LowCardinality(String).
_file — Name of the file. Type: LowCardinality(String).
_size — Size of the file in bytes. Type: Nullable(UInt64). If the file size is unknown, the value is NULL.
_time — Last modified time of the file. Type: Nullable(DateTime). If the time is unknown, the value is NULL.
_etag — The etag of the file. Type: LowCardinality(String). If the etag is unknown, the value is NULL.

Writes into iceberg table

Starting from version 25.7, ClickHouse supports modifications of user’s Iceberg tables. Currently, this is an experimental feature, so you first need to enable it:

SET allow_insert_into_iceberg = 1;

Creating table

To create your own empty Iceberg table, use the same commands as for reading, but specify the schema explicitly. Writes supports all data formats from iceberg specification, such as Parquet, Avro, ORC.

Example

CREATE TABLE iceberg_writes_example
(
    x Nullable(String),
    y Nullable(Int32)
)
ENGINE = IcebergLocal('/home/scanhex12/iceberg_example/')

Note: To create a version hint file, enable the iceberg_use_version_hint setting. If you want to compress the metadata.json file, specify the codec name in the iceberg_metadata_compression_method setting.

INSERT

After creating a new table, you can insert data using the usual ClickHouse syntax.

Example

INSERT INTO iceberg_writes_example VALUES ('Pavel', 777), ('Ivanov', 993);

SELECT *
FROM iceberg_writes_example
FORMAT VERTICAL;

Row 1:
──────
x: Pavel
y: 777

Row 2:
──────
x: Ivanov
y: 993

DELETE

Deleting extra rows in the merge-on-read format is also supported in ClickHouse. This query will create a new snapshot with position delete files.

Example

ALTER TABLE iceberg_writes_example DELETE WHERE x != 'Ivanov';

SELECT *
FROM iceberg_writes_example
FORMAT VERTICAL;

Row 1:
──────
x: Ivanov
y: 993

Schema evolution

ClickHouse allows you to add, drop, modify, or rename columns with simple types (non-tuple, non-array, non-map).

Example

ALTER TABLE iceberg_writes_example MODIFY COLUMN y Nullable(Int64);
SHOW CREATE TABLE iceberg_writes_example;

   ┌─statement─────────────────────────────────────────────────┐
1. │ CREATE TABLE default.iceberg_writes_example              ↴│
   │↳(                                                        ↴│
   │↳    `x` Nullable(String),                                ↴│
   │↳    `y` Nullable(Int64)                                  ↴│
   │↳)                                                        ↴│
   │↳ENGINE = IcebergLocal('/home/scanhex12/iceberg_example/') │
   └───────────────────────────────────────────────────────────┘

ALTER TABLE iceberg_writes_example ADD COLUMN z Nullable(Int32);
SHOW CREATE TABLE iceberg_writes_example;

   ┌─statement─────────────────────────────────────────────────┐
1. │ CREATE TABLE default.iceberg_writes_example              ↴│
   │↳(                                                        ↴│
   │↳    `x` Nullable(String),                                ↴│
   │↳    `y` Nullable(Int64),                                 ↴│
   │↳    `z` Nullable(Int32)                                  ↴│
   │↳)                                                        ↴│
   │↳ENGINE = IcebergLocal('/home/scanhex12/iceberg_example/') │
   └───────────────────────────────────────────────────────────┘

SELECT *
FROM iceberg_writes_example
FORMAT VERTICAL;

Row 1:
──────
x: Ivanov
y: 993
z: ᴺᵁᴸᴸ

ALTER TABLE iceberg_writes_example DROP COLUMN z;
SHOW CREATE TABLE iceberg_writes_example;
   ┌─statement─────────────────────────────────────────────────┐
1. │ CREATE TABLE default.iceberg_writes_example              ↴│
   │↳(                                                        ↴│
   │↳    `x` Nullable(String),                                ↴│
   │↳    `y` Nullable(Int64)                                  ↴│
   │↳)                                                        ↴│
   │↳ENGINE = IcebergLocal('/home/scanhex12/iceberg_example/') │
   └───────────────────────────────────────────────────────────┘

SELECT *
FROM iceberg_writes_example
FORMAT VERTICAL;

Row 1:
──────
x: Ivanov
y: 993

ALTER TABLE iceberg_writes_example RENAME COLUMN y TO value;
SHOW CREATE TABLE iceberg_writes_example;

   ┌─statement─────────────────────────────────────────────────┐
1. │ CREATE TABLE default.iceberg_writes_example              ↴│
   │↳(                                                        ↴│
   │↳    `x` Nullable(String),                                ↴│
   │↳    `value` Nullable(Int64)                              ↴│
   │↳)                                                        ↴│
   │↳ENGINE = IcebergLocal('/home/scanhex12/iceberg_example/') │
   └───────────────────────────────────────────────────────────┘

SELECT *
FROM iceberg_writes_example
FORMAT VERTICAL;

Row 1:
──────
x: Ivanov
value: 993

Compaction

ClickHouse supports compaction iceberg table. Currently, it can merge position delete files into data files while updating metadata. Previous snapshot IDs and timestamps remain unchanged, so the time-travel feature can still be used with the same values. How to use it:

SET allow_experimental_iceberg_compaction = 1

OPTIMIZE TABLE iceberg_writes_example;

SELECT *
FROM iceberg_writes_example
FORMAT VERTICAL;

Row 1:
──────
x: Ivanov
y: 993

Expire Snapshots

Iceberg tables accumulate snapshots with each INSERT, DELETE, or UPDATE operation. Over time, this can lead to a large number of snapshots and associated data files. The expire_snapshots command removes old snapshots and cleans up data files that are no longer referenced by any retained snapshot. Syntax:

ALTER TABLE iceberg_table EXECUTE expire_snapshots(
    ['timestamp']
    [, expire_before = 'timestamp']
    [, retention_period = '3d']
    [, retain_last = 100]
    [, snapshot_ids = [1, 2, 3, 4]]
    [, dry_run = 1]
);

By default, which snapshots to keep is determined by the retention policy (table properties min-snapshots-to-keep, max-snapshot-age-ms, and per-ref overrides). When snapshot_ids is specified, the retention policy is bypassed and only the listed snapshots are considered for expiration. Arguments:

'timestamp' (positional) or expire_before = 'timestamp' — a datetime string (e.g., '2024-06-01 00:00:00') interpreted in the server’s timezone. Acts as a safety fuse: snapshots whose timestamp-ms is at or after this value are protected from expiration, even if the retention policy would otherwise expire them. Can be combined with snapshot_ids, in which case listed snapshots at or newer than the timestamp are not expired.
retention_period = '<duration>' — overrides the table-level history.expire.max-snapshot-age-ms for this invocation only. Snapshots older than this duration (measured from now) become candidates for expiration. The value is a duration string consisting of one or more {number}{unit} pairs concatenated together. Supported units: y (365 days), w (7 days), d (24 hours), h (60 minutes), m (60 seconds), s (1 second), ms (1 millisecond). Units can be combined, e.g. '3d', '12h', '1d12h30m', '500ms'.
retain_last = N — overrides the table-level history.expire.min-snapshots-to-keep for this invocation only. At least N snapshots are always retained regardless of age.
snapshot_ids = [id1, id2, ...] — expires exactly the listed snapshot IDs (except snapshots referenced by current snapshot, branches, or tags). This mode bypasses the retention policy entirely and cannot be combined with retention_period or retain_last.
dry_run = 1 — computes what would be expired and returns metrics without writing new metadata or deleting files.

retention_period and retain_last override only the table-level retention defaults. Per-ref (branch/tag) retention overrides configured in the Iceberg table properties (e.g., refs.<branch>.min-snapshots-to-keep) are never overridden — they always take effect as specified in the table metadata.

Example:

SET allow_insert_into_iceberg = 1;

-- Create some snapshots by inserting data
INSERT INTO iceberg_table VALUES (1);
INSERT INTO iceberg_table VALUES (2);
INSERT INTO iceberg_table VALUES (3);

-- Expire using retention policy only
ALTER TABLE iceberg_table EXECUTE expire_snapshots();

-- Expire with a safety fuse: protect snapshots newer than the timestamp (positional syntax)
ALTER TABLE iceberg_table EXECUTE expire_snapshots('2025-01-01 00:00:00');

-- Same using the named argument form
ALTER TABLE iceberg_table EXECUTE expire_snapshots(expire_before = '2025-01-01 00:00:00');

-- Override retention parameters for one execution
ALTER TABLE iceberg_table EXECUTE expire_snapshots(retention_period = '3d', retain_last = 10);

-- Expire explicit snapshots
ALTER TABLE iceberg_table EXECUTE expire_snapshots(snapshot_ids = [101, 102, 103]);

-- Dry-run preview (no metadata updates, no file deletes)
ALTER TABLE iceberg_table EXECUTE expire_snapshots(retention_period = '1d', dry_run = 1);

Output: The command returns a table with two columns (metric_name String, metric_value Int64) containing one row per metric. The metric names follow the Iceberg spec:

metric_name	Description
`deleted_data_files_count`	Number of data files deleted
`deleted_position_delete_files_count`	Number of position delete files deleted
`deleted_equality_delete_files_count`	Number of equality delete files deleted
`deleted_manifest_files_count`	Number of manifest files deleted
`deleted_manifest_lists_count`	Number of manifest list files deleted
`deleted_statistics_files_count`	Number of statistics files deleted (always 0 currently)
`dry_run`	`1` for dry-run mode, `0` for normal execution

The command performs the following steps:

Evaluates the retention policy (see below) to determine which snapshots must be preserved
If a timestamp argument was provided, additionally protects all snapshots at or newer than that timestamp
Expires snapshots that are neither retained by the policy nor protected by the timestamp fuse
Computes which files are exclusively associated with expired snapshots
In normal mode: generates new metadata without the expired snapshots
In normal mode: physically deletes unreachable manifest lists, manifest files, and data files
In dry_run = 1 mode: skips steps 5 and 6 and only returns the calculated metrics

Snapshot Retention Policy

The expire_snapshots command respects the Iceberg snapshot retention policy. Retention is configured via Iceberg table properties and per-reference overrides:

Property	Scope	Default	Description
`history.expire.min-snapshots-to-keep`	Table	`iceberg_expire_default_min_snapshots_to_keep` (default `1`)	Minimum number of snapshots to keep in each branch’s ancestor chain
`history.expire.max-snapshot-age-ms`	Table	`iceberg_expire_default_max_snapshot_age_ms` (default `432000000`, 5 days)	Maximum age (in ms) of snapshots to retain in a branch
`history.expire.max-ref-age-ms`	Table	`iceberg_expire_default_max_ref_age_ms` (default `∞`)	Maximum age (in ms) for a snapshot reference (branch or tag) before the reference itself is removed

Each snapshot reference (refs in the Iceberg metadata) can override these with per-ref fields: min-snapshots-to-keep, max-snapshot-age-ms, and max-ref-age-ms. Retention evaluation:

For each branch (including main): the ancestor chain is walked starting from the branch head. Snapshots are retained while either of these conditions is true:
- The snapshot is one of the first min-snapshots-to-keep in the chain
- The snapshot’s age is within max-snapshot-age-ms (i.e., now - timestamp-ms <= max-snapshot-age-ms)
For tags: the tagged snapshot is retained unless the tag has exceeded its max-ref-age-ms, in which case the tag reference is removed
Non-main references whose age exceeds max-ref-age-ms are removed entirely (the main branch is never removed)
Dangling references that point to non-existent snapshots are removed with a warning
The current snapshot is always preserved, regardless of retention settings

Required privileges: The ALTER TABLE EXECUTE privilege is required, which is a child of ALTER TABLE in the ClickHouse access control hierarchy. You can grant it specifically or via the parent:

-- Grant only EXECUTE permission
GRANT ALTER TABLE EXECUTE ON my_iceberg_table TO my_user;

-- Or grant all ALTER TABLE permissions (includes ALTER TABLE EXECUTE)
GRANT ALTER TABLE ON my_iceberg_table TO my_user;

Only Iceberg format version 2 tables are supported (v1 snapshots do not guarantee manifest-list, which is required to safely identify files for cleanup)
The current snapshot is always preserved, even if it is older than the specified timestamp
Requires the allow_insert_into_iceberg setting to be enabled
Requires the allow_experimental_expire_snapshots setting to be enabled
The catalog’s own authorization (REST catalog auth, AWS Glue IAM, etc.) is enforced independently when ClickHouse updates the metadata

Remove Orphan Files

Orphan files are files on storage that are not referenced by any snapshot in the Iceberg table metadata. They accumulate from failed writes, partial cleanup after compaction, and interrupted operations, causing unbounded storage growth. The remove_orphan_files command identifies and removes these orphan files. Syntax:

-- Positional form: single unnamed older_than argument
ALTER TABLE iceberg_table EXECUTE remove_orphan_files('timestamp')

-- Named form
ALTER TABLE iceberg_table EXECUTE remove_orphan_files(
    older_than = 'timestamp',
    location = 'path',
    dry_run = 0|1
)

-- No arguments: use all defaults (older_than = 3 days ago)
ALTER TABLE iceberg_table EXECUTE remove_orphan_files()

Parameters:

Parameter	Type	Default	Description
`older_than`	`String` (timestamp)	3 days ago (configurable via `iceberg_orphan_files_older_than_seconds`)	Only consider files with a last-modified time older than this timestamp as orphan candidates. Safety guard against deleting files from in-progress writes.
`location`	`String`	Table location	Restrict the scan to a specific subdirectory under the table location (e.g., `'data/'` or `'metadata/'`).
`dry_run`	`UInt64`	`0`	When `1`, identify orphan files and return the result summary without actually deleting anything.

Examples:

-- Remove orphan files older than a specific timestamp
ALTER TABLE iceberg_table EXECUTE remove_orphan_files('2026-03-01 00:00:00');

-- Dry run: preview which files would be deleted
ALTER TABLE iceberg_table EXECUTE remove_orphan_files(dry_run = 1);

-- Scan only the data directory
ALTER TABLE iceberg_table EXECUTE remove_orphan_files(
    older_than = '2026-03-01 00:00:00',
    location = 'data/'
);

-- Combine positional older_than with named arguments
ALTER TABLE iceberg_table EXECUTE remove_orphan_files(
    '2026-03-01 00:00:00',
    dry_run = 1
);

Output: The command returns a table with metric_name and metric_value columns showing the count of deleted (or would-be-deleted in dry_run mode) files by category. File categories are classified using best-effort heuristics based on file naming conventions; files that do not match any specific pattern default to deleted_data_files_count:

metric_name	metric_value
deleted_data_files_count	5
deleted_position_delete_files_count	2
deleted_equality_delete_files_count	0
deleted_manifest_files_count	3
deleted_manifest_lists_count	1
deleted_metadata_files_count	0
deleted_statistics_files_count	0
skipped_missing_metadata_count	0
failed_deletions_count	0

Settings:

Setting	Type	Default	Description
`allow_iceberg_remove_orphan_files`	`Bool`	`false`	Gate setting to enable the feature (experimental).
`iceberg_orphan_files_older_than_seconds`	`UInt64`	`259200` (3 days)	Default `older_than` threshold in seconds when the argument is omitted.

Requires Iceberg format version 2 (or higher). Version 1 tables are rejected because they lack manifest-list pointers in snapshots, which are needed to safely determine the reachable file set. Running the command on a v1 table returns a BAD_ARGUMENTS error.
Requires both allow_insert_into_iceberg and allow_iceberg_remove_orphan_files settings to be enabled
It is recommended to run expire_snapshots before remove_orphan_files so that files uniquely referenced by expired snapshots are cleaned up first
Use dry_run = 1 to preview orphan files before deletion
The older_than threshold protects against deleting files from in-progress writes — the default 3-day threshold provides a generous safety margin

SQL Reference

Syntax

Arguments

Returned value

Example

Defining a named collection

Using a data catalog

Schema Evolution

Partition Pruning

Time Travel

Processing of tables with deleted rows

Basic usage

Important considerations

Example scenarios

Scenario 1: Schema Changes Without New Snapshots

Scenario 2: Historical vs. Current Schema Differences

Scenario 3: Historical vs. Current Schema Differences

Metadata File Resolution

Candidate Search (in Priority Order)

Selecting the Most Recent File

Metadata cache

Aliases

Virtual Columns

Writes into iceberg table

Creating table

Example

INSERT

Example

DELETE

Example

Schema evolution

Example

Compaction

Expire Snapshots

Snapshot Retention Policy

Remove Orphan Files

See Also

​Syntax

​Arguments

​Returned value

​Example

​Defining a named collection

​Using a data catalog

​Schema Evolution

​Partition Pruning

​Time Travel

​Processing of tables with deleted rows

​Basic usage

​Important considerations

​Example scenarios

​Scenario 1: Schema Changes Without New Snapshots

​Scenario 2: Historical vs. Current Schema Differences

​Scenario 3: Historical vs. Current Schema Differences

​Metadata File Resolution

​Candidate Search (in Priority Order)

​Selecting the Most Recent File

​Metadata cache

​Aliases

​Virtual Columns

​Writes into iceberg table

​Creating table

​Example

​INSERT

​Example

​DELETE

​Example

​Schema evolution

​Example

​Compaction

​Expire Snapshots

​Snapshot Retention Policy

​Remove Orphan Files

​See Also

Syntax

Arguments

Returned value

Example

Defining a named collection

Using a data catalog

Schema Evolution

Partition Pruning

Time Travel

Processing of tables with deleted rows

Basic usage

Important considerations

Example scenarios

Scenario 1: Schema Changes Without New Snapshots

Scenario 2: Historical vs. Current Schema Differences

Scenario 3: Historical vs. Current Schema Differences

Metadata File Resolution

Candidate Search (in Priority Order)

Selecting the Most Recent File

Metadata cache

Aliases

Virtual Columns

Writes into iceberg table

Creating table

Example

INSERT

Example

DELETE

Example

Schema evolution

Example

Compaction

Expire Snapshots

Snapshot Retention Policy

Remove Orphan Files

See Also