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 Notes
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

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:

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:

// 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")

Note

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.

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.

Note

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

Note

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 | record_count | file_size_in_bytes | column_sizes       | value_counts     | null_value_counts | nan_value_counts | lower_bounds    | upper_bounds    | key_metadata | split_offsets |
+-------------------------------------------------------------------------+-------------+--------------+--------------------+--------------------+------------------+-------------------+------------------+-----------------+-----------------+--------------+---------------+
| s3:/.../table/data/00000-3-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet | PARQUET     | 1            | 597                | [1 -> 90, 2 -> 62] | [1 -> 1, 2 -> 1] | [1 -> 0, 2 -> 0]  | []               | [1 -> , 2 -> c] | [1 -> , 2 -> c] | null         | [4]           |
| s3:/.../table/data/00001-4-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet | PARQUET     | 1            | 597                | [1 -> 90, 2 -> 62] | [1 -> 1, 2 -> 1] | [1 -> 0, 2 -> 0]  | []               | [1 -> , 2 -> b] | [1 -> , 2 -> b] | null         | [4]           |
| s3:/.../table/data/00002-5-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet | PARQUET     | 1            | 597                | [1 -> 90, 2 -> 62] | [1 -> 1, 2 -> 1] | [1 -> 0, 2 -> 0]  | []               | [1 -> , 2 -> a] | [1 -> , 2 -> a] | null         | [4]           |
+-------------------------------------------------------------------------+-------------+--------------+--------------------+--------------------+------------------+-------------------+------------------+-----------------+-----------------+--------------+---------------+

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: 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 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)