Spark Queries🔗

To use Iceberg in Spark, first configure Spark catalogs. Iceberg uses Apache Spark's DataSourceV2 API for data source and catalog implementations.

Querying with SQL🔗

In Spark 3, tables use identifiers that include a catalog name.

SELECT * FROM prod.db.table; -- catalog: prod, namespace: db, table: table

Metadata tables, like history and snapshots, can use the Iceberg table name as a namespace.

For example, to read from the files metadata table for prod.db.table:

SELECT * FROM prod.db.table.files;

file_path	file_format	partition	record_count	file_size_in_bytes	column_sizes	value_counts	null_value_counts	nan_value_counts	lower_bounds	upper_bounds	key_metadata	split_offsets	equality_ids	sort_order_id
s3:/.../table/data/00000-3-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet	PARQUET	{1999-01-01, 01}	1	597	[1 -> 90, 2 -> 62]	[1 -> 1, 2 -> 1]	[1 -> 0, 2 -> 0]	[]	[1 -> , 2 -> c]	[1 -> , 2 -> c]	null	[4]	null	null
s3:/.../table/data/00001-4-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet	PARQUET	{1999-01-01, 02}	1	597	[1 -> 90, 2 -> 62]	[1 -> 1, 2 -> 1]	[1 -> 0, 2 -> 0]	[]	[1 -> , 2 -> b]	[1 -> , 2 -> b]	null	[4]	null	null
s3:/.../table/data/00002-5-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet	PARQUET	{1999-01-01, 03}	1	597	[1 -> 90, 2 -> 62]	[1 -> 1, 2 -> 1]	[1 -> 0, 2 -> 0]	[]	[1 -> , 2 -> a]	[1 -> , 2 -> a]	null	[4]	null	null

Querying with DataFrames🔗

To load a table as a DataFrame, use table:

val df = spark.table("prod.db.table")

Catalogs with DataFrameReader🔗

Paths and table names can be loaded with Spark's DataFrameReader interface. How tables are loaded depends on how the identifier is specified. When using spark.read.format("iceberg").load(table) or spark.table(table) the table variable can take a number of forms as listed below:

file:///path/to/table: loads a HadoopTable at given path
tablename: loads currentCatalog.currentNamespace.tablename
catalog.tablename: loads tablename from the specified catalog.
namespace.tablename: loads namespace.tablename from current catalog
catalog.namespace.tablename: loads namespace.tablename from the specified catalog.
namespace1.namespace2.tablename: loads namespace1.namespace2.tablename from current catalog

The above list is in order of priority. For example: a matching catalog will take priority over any namespace resolution.

Time travel🔗

SQL🔗

Spark 3.3 and later supports time travel in SQL queries using TIMESTAMP AS OF or VERSION AS OF clauses. The VERSION AS OF clause can contain a long snapshot ID or a string branch or tag name.

Info

Note: If the name of a branch or tag is the same as a snapshot ID, then the snapshot which is selected for time travel is the snapshot with the given snapshot ID. For example, consider the case where there is a tag named '1' and it references snapshot with ID 2. If the version travel clause is VERSION AS OF '1', time travel will be done to the snapshot with ID 1. If this is not desired, rename the tag or branch with a well-defined prefix such as 'snapshot-1'.

-- time travel to October 26, 1986 at 01:21:00
SELECT * FROM prod.db.table TIMESTAMP AS OF '1986-10-26 01:21:00';

-- time travel to snapshot with id 10963874102873L
SELECT * FROM prod.db.table VERSION AS OF 10963874102873;

-- time travel to the head snapshot of audit-branch
SELECT * FROM prod.db.table VERSION AS OF 'audit-branch';

-- time travel to the snapshot referenced by the tag historical-snapshot
SELECT * FROM prod.db.table VERSION AS OF 'historical-snapshot';

In addition, FOR SYSTEM_TIME AS OF and FOR SYSTEM_VERSION AS OF clauses are also supported:

SELECT * FROM prod.db.table FOR SYSTEM_TIME AS OF '1986-10-26 01:21:00';
SELECT * FROM prod.db.table FOR SYSTEM_VERSION AS OF 10963874102873;
SELECT * FROM prod.db.table FOR SYSTEM_VERSION AS OF 'audit-branch';
SELECT * FROM prod.db.table FOR SYSTEM_VERSION AS OF 'historical-snapshot';

Timestamps may also be supplied as a Unix timestamp, in seconds:

-- timestamp in seconds
SELECT * FROM prod.db.table TIMESTAMP AS OF 499162860;
SELECT * FROM prod.db.table FOR SYSTEM_TIME AS OF 499162860;

DataFrame🔗

To select a specific table snapshot or the snapshot at some time in the DataFrame API, Iceberg supports four Spark read options:

snapshot-id selects a specific table snapshot
as-of-timestamp selects the current snapshot at a timestamp, in milliseconds
branch selects the head snapshot of the specified branch. Note that currently branch cannot be combined with as-of-timestamp.
tag selects the snapshot associated with the specified tag. Tags cannot be combined with as-of-timestamp.

// time travel to October 26, 1986 at 01:21:00
spark.read
    .option("as-of-timestamp", "499162860000")
    .format("iceberg")
    .load("path/to/table")

// time travel to snapshot with ID 10963874102873L
spark.read
    .option("snapshot-id", 10963874102873L)
    .format("iceberg")
    .load("path/to/table")

// time travel to tag historical-snapshot
spark.read
    .option(SparkReadOptions.TAG, "historical-snapshot")
    .format("iceberg")
    .load("path/to/table")

// time travel to the head snapshot of audit-branch
spark.read
    .option(SparkReadOptions.BRANCH, "audit-branch")
    .format("iceberg")
    .load("path/to/table")

Info

Spark 3.0 and earlier versions do not support using option with table in DataFrameReader commands. All options will be silently ignored. Do not use table when attempting to time-travel or use other options. See SPARK-32592.

Incremental read🔗

To read appended data incrementally, use:

start-snapshot-id Start snapshot ID used in incremental scans (exclusive).
end-snapshot-id End snapshot ID used in incremental scans (inclusive). This is optional. Omitting it will default to the current snapshot.

// get the data added after start-snapshot-id (10963874102873L) until end-snapshot-id (63874143573109L)
spark.read()
  .format("iceberg")
  .option("start-snapshot-id", "10963874102873")
  .option("end-snapshot-id", "63874143573109")
  .load("path/to/table")

Info

Currently gets only the data from append operation. Cannot support replace, overwrite, delete operations. Incremental read works with both V1 and V2 format-version. Incremental read is not supported by Spark's SQL syntax.

Inspecting tables🔗

To inspect a table's history, snapshots, and other metadata, Iceberg supports metadata tables.

Metadata tables are identified by adding the metadata table name after the original table name. For example, history for db.table is read using db.table.history.

Info

For Spark 3, prior to 3.2, the Spark session catalog does not support table names with multipart identifiers such as catalog.database.table.metadata. As a workaround, configure an org.apache.iceberg.spark.SparkCatalog, or use the Spark DataFrameReader API.

History🔗

To show table history:

SELECT * FROM prod.db.table.history;

made_current_at	snapshot_id	parent_id	is_current_ancestor
2019-02-08 03:29:51.215	5781947118336215154	NULL	true
2019-02-08 03:47:55.948	5179299526185056830	5781947118336215154	true
2019-02-09 16:24:30.13	296410040247533544	5179299526185056830	false
2019-02-09 16:32:47.336	2999875608062437330	5179299526185056830	true
2019-02-09 19:42:03.919	8924558786060583479	2999875608062437330	true
2019-02-09 19:49:16.343	6536733823181975045	8924558786060583479	true

Info

This shows a commit that was rolled back. The example has two snapshots with the same parent, and one is not an ancestor of the current table state.

Metadata Log Entries🔗

To show table metadata log entries:

SELECT * from prod.db.table.metadata_log_entries;

timestamp	file	latest_snapshot_id	latest_schema_id	latest_sequence_number
2022-07-28 10:43:52.93	s3://.../table/metadata/00000-9441e604-b3c2-498a-a45a-6320e8ab9006.metadata.json	null	null	null
2022-07-28 10:43:57.487	s3://.../table/metadata/00001-f30823df-b745-4a0a-b293-7532e0c99986.metadata.json	170260833677645300	0	1
2022-07-28 10:43:58.25	s3://.../table/metadata/00002-2cc2837a-02dc-4687-acc1-b4d86ea486f4.metadata.json	958906493976709774	0	2

Snapshots🔗

To show the valid snapshots for a table:

SELECT * FROM prod.db.table.snapshots;

committed_at	snapshot_id	parent_id	operation	manifest_list	summary
2019-02-08 03:29:51.215	57897183625154	null	append	s3://.../table/metadata/snap-57897183625154-1.avro	{ added-records -> 2478404, total-records -> 2478404, added-data-files -> 438, total-data-files -> 438, spark.app.id -> application_1520379288616_155055 }

You can also join snapshots to table history. For example, this query will show table history, with the application ID that wrote each snapshot:

select
    h.made_current_at,
    s.operation,
    h.snapshot_id,
    h.is_current_ancestor,
    s.summary['spark.app.id']
from prod.db.table.history h
join prod.db.table.snapshots s
  on h.snapshot_id = s.snapshot_id
order by made_current_at;

made_current_at	operation	snapshot_id	is_current_ancestor	summary[spark.app.id]
2019-02-08 03:29:51.215	append	57897183625154	true	application_1520379288616_155055
2019-02-09 16:24:30.13	delete	29641004024753	false	application_1520379288616_151109
2019-02-09 16:32:47.336	append	57897183625154	true	application_1520379288616_155055
2019-02-08 03:47:55.948	overwrite	51792995261850	true	application_1520379288616_152431

Entries🔗

To show all the table's current manifest entries for both data and delete files.

SELECT * FROM prod.db.table.entries;

status	snapshot_id	sequence_number	file_sequence_number	data_file	readable_metrics
2	57897183625154	0	0	{"content":0,"file_path":"s3:/.../table/data/00047-25-833044d0-127b-415c-b874-038a4f978c29-00612.parquet","file_format":"PARQUET","spec_id":0,"record_count":15,"file_size_in_bytes":473,"column_sizes":{1:103},"value_counts":{1:15},"null_value_counts":{1:0},"nan_value_counts":{},"lower_bounds":{1:},"upper_bounds":{1:},"key_metadata":null,"split_offsets":[4],"equality_ids":null,"sort_order_id":0}	{"c1":{"column_size":103,"value_count":15,"null_value_count":0,"nan_value_count":null,"lower_bound":1,"upper_bound":3}}

Files🔗

To show a table's current files:

SELECT * FROM prod.db.table.files;

content	file_path	file_format	record_count	file_size_in_bytes	column_sizes	value_counts	null_value_counts	nan_value_counts	lower_bounds	upper_bounds	key_metadata	split_offsets	equality_ids	sort_order_id	readable_metrics
0	s3:/.../table/data/00042-3-a9aa8b24-20bc-4d56-93b0-6b7675782bb5-00001.parquet	PARQUET	1	652	{1:52,2:48}	{1:1,2:1}	{1:0,2:0}	{}	{1:,2:d}	{1:,2:d}	NULL	[4]	NULL	0	{"data":{"column_size":48,"value_count":1,"null_value_count":0,"nan_value_count":null,"lower_bound":"d","upper_bound":"d"},"id":{"column_size":52,"value_count":1,"null_value_count":0,"nan_value_count":null,"lower_bound":1,"upper_bound":1}}
0	s3:/.../table/data/00000-0-f9709213-22ca-4196-8733-5cb15d2afeb9-00001.parquet	PARQUET	1	643	{1:46,2:48}	{1:1,2:1}	{1:0,2:0}	{}	{1:,2:a}	{1:,2:a}	NULL	[4]	NULL	0	{"data":{"column_size":48,"value_count":1,"null_value_count":0,"nan_value_count":null,"lower_bound":"a","upper_bound":"a"},"id":{"column_size":46,"value_count":1,"null_value_count":0,"nan_value_count":null,"lower_bound":1,"upper_bound":1}}
0	s3:/.../table/data/00001-1-f9709213-22ca-4196-8733-5cb15d2afeb9-00001.parquet	PARQUET	2	644	{1:49,2:51}	{1:2,2:2}	{1:0,2:0}	{}	{1:,2:b}	{1:,2:c}	NULL	[4]	NULL	0	{"data":{"column_size":51,"value_count":2,"null_value_count":0,"nan_value_count":null,"lower_bound":"b","upper_bound":"c"},"id":{"column_size":49,"value_count":2,"null_value_count":0,"nan_value_count":null,"lower_bound":2,"upper_bound":3}}
1	s3:/.../table/data/00081-4-a9aa8b24-20bc-4d56-93b0-6b7675782bb5-00001-deletes.parquet	PARQUET	1	1560	{2147483545:46,2147483546:152}	{2147483545:1,2147483546:1}	{2147483545:0,2147483546:0}	{}	{2147483545:,2147483546:s3:/.../table/data/00000-0-f9709213-22ca-4196-8733-5cb15d2afeb9-00001.parquet}	{2147483545:,2147483546:s3:/.../table/data/00000-0-f9709213-22ca-4196-8733-5cb15d2afeb9-00001.parquet}	NULL	[4]	NULL	NULL	{"data":{"column_size":null,"value_count":null,"null_value_count":null,"nan_value_count":null,"lower_bound":null,"upper_bound":null},"id":{"column_size":null,"value_count":null,"null_value_count":null,"nan_value_count":null,"lower_bound":null,"upper_bound":null}}
2	s3:/.../table/data/00047-25-833044d0-127b-415c-b874-038a4f978c29-00612.parquet	PARQUET	126506	28613985	{100:135377,101:11314}	{100:126506,101:126506}	{100:105434,101:11}	{}	{100:0,101:17}	{100:404455227527,101:23}	NULL	NULL	[1]	0	{"id":{"column_size":135377,"value_count":126506,"null_value_count":105434,"nan_value_count":null,"lower_bound":0,"upper_bound":404455227527},"data":{"column_size":11314,"value_count":126506,"null_value_count": 11,"nan_value_count":null,"lower_bound":17,"upper_bound":23}}

Info

Content refers to type of content stored by the data file: 0 Data 1 Position Deletes 2 Equality Deletes

To show only data files or delete files, query prod.db.table.data_files and prod.db.table.delete_files respectively. To show all files, data files and delete files across all tracked snapshots, query prod.db.table.all_files, prod.db.table.all_data_files and prod.db.table.all_delete_files respectively.

Manifests🔗

To show a table's current file manifests:

SELECT * FROM prod.db.table.manifests;

path	length	partition_spec_id	added_snapshot_id	added_data_files_count	existing_data_files_count	deleted_data_files_count	partition_summaries
s3://.../table/metadata/45b5290b-ee61-4788-b324-b1e2735c0e10-m0.avro	4479	0	6668963634911763636	8	0	0	[[false,null,2019-05-13,2019-05-15]]

Note: 1. Fields within partition_summaries column of the manifests table correspond to field_summary structs within manifest list, with the following order: - contains_null - contains_nan - lower_bound - upper_bound 2. contains_nan could return null, which indicates that this information is not available from the file's metadata. This usually occurs when reading from V1 table, where contains_nan is not populated.

Partitions🔗

To show a table's current partitions:

SELECT * FROM prod.db.table.partitions;

partition	record_count	file_count	total_data_file_size_in_bytes	position_delete_record_count	position_delete_file_count	equality_delete_record_count	equality_delete_file_count	last_updated_at(μs)	last_updated_snapshot_id
{20211001, 11}	1	1	100	2	1	0	0	1633086034192000	9205185327307503337
{20211002, 11}	4	3	500	1	1	0	0	1633172537358000	867027598972211003
{20211001, 10}	7	4	700	0	0	0	0	1633082598716000	3280122546965981531
{20211002, 10}	3	2	400	0	0	1	1	1633169159489000	6941468797545315876

Note: 1. For unpartitioned tables, the partitions table will not contain the partition and spec_id fields.

The partitions metadata table shows partitions with data files or delete files in the current snapshot. However, delete files are not applied, and so in some cases partitions may be shown even though all their data rows are marked deleted by delete files.

Positional Delete Files🔗

To show all positional delete files from the current snapshot of table:

SELECT * from prod.db.table.position_deletes;

file_path	pos	row	spec_id	delete_file_path
s3:/.../table/data/00042-3-a9aa8b24-20bc-4d56-93b0-6b7675782bb5-00001.parquet	1	0	0	s3:/.../table/data/00191-1933-25e9f2f3-d863-4a69-a5e1-f9aeeebe60bb-00001-deletes.parquet

All Metadata Tables🔗

These tables are unions of the metadata tables specific to the current snapshot, and return metadata across all snapshots.

Danger

The "all" metadata tables may produce more than one row per data file or manifest file because metadata files may be part of more than one table snapshot.

All Data Files🔗

To show all of the table's data files and each file's metadata:

SELECT * FROM prod.db.table.all_data_files;

file_path	file_format	partition	record_count	file_size_in_bytes	column_sizes	value_counts	null_value_counts	nan_value_counts	lower_bounds	upper_bounds	key_metadata	split_offsets	equality_ids
s3://.../dt=20210102/00000-0-756e2512-49ae-45bb-aae3-c0ca475e7879-00001.parquet	PARQUET	{20210102}	14	2444	{1 -> 94, 2 -> 17}	{1 -> 14, 2 -> 14}	{1 -> 0, 2 -> 0}	{}	{1 -> 1, 2 -> 20210102}	{1 -> 2, 2 -> 20210102}	null	[4]	null
s3://.../dt=20210103/00000-0-26222098-032f-472b-8ea5-651a55b21210-00001.parquet	PARQUET	{20210103}	14	2444	{1 -> 94, 2 -> 17}	{1 -> 14, 2 -> 14}	{1 -> 0, 2 -> 0}	{}	{1 -> 1, 2 -> 20210103}	{1 -> 3, 2 -> 20210103}	null	[4]	null
s3://.../dt=20210104/00000-0-a3bb1927-88eb-4f1c-bc6e-19076b0d952e-00001.parquet	PARQUET	{20210104}	14	2444	{1 -> 94, 2 -> 17}	{1 -> 14, 2 -> 14}	{1 -> 0, 2 -> 0}	{}	{1 -> 1, 2 -> 20210104}	{1 -> 3, 2 -> 20210104}	null	[4]	null

All Delete Files🔗

To show the table's delete files and each file's metadata from all the snapshots:

SELECT * FROM prod.db.table.all_delete_files;

content	file_path	file_format	spec_id	record_count	file_size_in_bytes	column_sizes	value_counts	null_value_counts	nan_value_counts	lower_bounds	upper_bounds	key_metadata	split_offsets	equality_ids	sort_order_id	readable_metrics
1	s3:/.../table/data/00081-4-a9aa8b24-20bc-4d56-93b0-6b7675782bb5-00001-deletes.parquet	PARQUET	0	1	1560	{2147483545:46,2147483546:152}	{2147483545:1,2147483546:1}	{2147483545:0,2147483546:0}	{}	{2147483545:,2147483546:s3:/.../table/data/00000-0-f9709213-22ca-4196-8733-5cb15d2afeb9-00001.parquet}	{2147483545:,2147483546:s3:/.../table/data/00000-0-f9709213-22ca-4196-8733-5cb15d2afeb9-00001.parquet}	NULL	[4]	NULL	NULL	{"data":{"column_size":null,"value_count":null,"null_value_count":null,"nan_value_count":null,"lower_bound":null,"upper_bound":null},"id":{"column_size":null,"value_count":null,"null_value_count":null,"nan_value_count":null,"lower_bound":null,"upper_bound":null}}
2	s3:/.../table/data/00047-25-833044d0-127b-415c-b874-038a4f978c29-00612.parquet	PARQUET	0	126506	28613985	{100:135377,101:11314}	{100:126506,101:126506}	{100:105434,101:11}	{}	{100:0,101:17}	{100:404455227527,101:23}	NULL	NULL	[1]	0	{"id":{"column_size":135377,"value_count":126506,"null_value_count":105434,"nan_value_count":null,"lower_bound":0,"upper_bound":404455227527},"data":{"column_size":11314,"value_count":126506,"null_value_count": 11,"nan_value_count":null,"lower_bound":17,"upper_bound":23}}

All Entries🔗

To show the table's manifest entries from all the snapshots for both data and delete files:

SELECT * FROM prod.db.table.all_entries;

status	snapshot_id	sequence_number	file_sequence_number	data_file	readable_metrics
2	57897183625154	0	0	{"content":0,"file_path":"s3:/.../table/data/00047-25-833044d0-127b-415c-b874-038a4f978c29-00612.parquet","file_format":"PARQUET","spec_id":0,"record_count":15,"file_size_in_bytes":473,"column_sizes":{1:103},"value_counts":{1:15},"null_value_counts":{1:0},"nan_value_counts":{},"lower_bounds":{1:},"upper_bounds":{1:},"key_metadata":null,"split_offsets":[4],"equality_ids":null,"sort_order_id":0}	{"c1":{"column_size":103,"value_count":15,"null_value_count":0,"nan_value_count":null,"lower_bound":1,"upper_bound":3}}
#### All Manifests

To show all of the table's manifest files:

SELECT * FROM prod.db.table.all_manifests;

path	length	partition_spec_id	added_snapshot_id	added_data_files_count	existing_data_files_count	deleted_data_files_count	partition_summaries
s3://.../metadata/a85f78c5-3222-4b37-b7e4-faf944425d48-m0.avro	6376	0	6272782676904868561	2	0	0	[{false, false, 20210101, 20210101}]

Note: 1. Fields within partition_summaries column of the manifests table correspond to field_summary structs within manifest list, with the following order: - contains_null - contains_nan - lower_bound - upper_bound 2. contains_nan could return null, which indicates that this information is not available from the file's metadata. This usually occurs when reading from V1 table, where contains_nan is not populated.

References🔗

To show a table's known snapshot references:

SELECT * FROM prod.db.table.refs;

name	type	snapshot_id	max_reference_age_in_ms	min_snapshots_to_keep	max_snapshot_age_in_ms
main	BRANCH	4686954189838128572	10	20	30
testTag	TAG	4686954189838128572	10	null	null

Inspecting with DataFrames🔗

Metadata tables can be loaded using the DataFrameReader API:

// named metastore table
spark.read.format("iceberg").load("db.table.files")
// Hadoop path table
spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#files")

Time Travel with Metadata Tables🔗

To inspect a tables's metadata with the time travel feature:

-- get the table's file manifests at timestamp Sep 20, 2021 08:00:00
SELECT * FROM prod.db.table.manifests TIMESTAMP AS OF '2021-09-20 08:00:00';

-- get the table's partitions with snapshot id 10963874102873L
SELECT * FROM prod.db.table.partitions VERSION AS OF 10963874102873;

Metadata tables can also be inspected with time travel using the DataFrameReader API:

// load the table's file metadata at snapshot-id 10963874102873 as DataFrame
spark.read.format("iceberg").option("snapshot-id", 10963874102873L).load("db.table.files")