Spark Queries #

To use Iceberg in Spark, first configure Spark catalogs.

Iceberg uses Apache Spark’s DataSourceV2 API for data source and catalog implementations. Spark DSv2 is an evolving API with different levels of support in Spark versions:

Feature support	Spark 3.0	Spark 2.4
`SELECT`	✔️
DataFrame reads	✔️	✔️
Metadata table `SELECT`	✔️
History metadata table	✔️	✔️
Snapshots metadata table	✔️	✔️
Files metadata table	✔️	✔️
Manifests metadata table	✔️	✔️

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, run:

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 #

Iceberg 0.11.0 adds multi-catalog support to DataFrameReader in both Spark 3.x and 2.4.

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").path(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 #

To select a specific table snapshot or the snapshot at some time, Iceberg supports two Spark read options:

snapshot-id selects a specific table snapshot
as-of-timestamp selects the current snapshot at a timestamp, in milliseconds

// 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")

Spark does not currently 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. Options will be supported with table in Spark 3.1 - SPARK-32592.

Time travel is not yet supported by Spark’s SQL syntax.

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")

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.

Spark 2.4 #

Spark 2.4 requires using the DataFrame reader with iceberg as a format, because 2.4 does not support direct SQL queries:

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

Spark 2.4 with SQL #

To run SQL SELECT statements on Iceberg tables in 2.4, register the DataFrame as a temporary table:

val df = spark.read.format("iceberg").load("db.table")
df.createOrReplaceTempView("table")

spark.sql("""select count(1) from table""").show()

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.

As of Spark 3.0, the format of the table name for inspection (catalog.database.table.metadata) doesn’t work with Spark’s default catalog (spark_catalog). If you’ve replaced the default catalog, you may want to use DataFrameReader API to inspect the table.

History #

To show table history, run:

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                |
+-------------------------+---------------------+---------------------+---------------------+

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.

Snapshots #

To show the valid snapshots for a table, run:

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 |
+-------------------------+-----------+----------------+---------------------+----------------------------------+

Files #

To show a table’s data files and each file’s metadata, run:

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

Manifests #

To show a table’s file manifests and each file’s metadata, run:

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:

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
contains_nan could return null, which indicates that this information is not available from files' metadata. This usually occurs when reading from V1 table, where contains_nan is not populated.

Inspecting with DataFrames #

Metadata tables can be loaded in Spark 2.4 or Spark 3 using the DataFrameReader API:

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