Spark

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:

SQL feature support Spark 3.0 Spark 2.4 Notes
CREATE TABLE ✔️
CREATE TABLE AS ✔️
REPLACE TABLE AS ✔️
ALTER TABLE ✔️ ⚠ Requires SQL extensions enabled to update partition field and sort order
DROP TABLE ✔️
SELECT ✔️
INSERT INTO ✔️
INSERT OVERWRITE ✔️
DataFrame feature support Spark 3.0 Spark 2.4 Notes
DataFrame reads ✔️ ✔️
DataFrame append ✔️ ✔️
DataFrame overwrite ✔️ ✔️ ⚠ Behavior changed in Spark 3.0
DataFrame CTAS and RTAS ✔️
Metadata tables ✔️ ✔️

Configuring catalogs

Spark 3.0 adds an API to plug in table catalogs that are used to load, create, and manage Iceberg tables. Spark catalogs are configured by setting Spark properties under spark.sql.catalog.

This creates an Iceberg catalog named hive_prod that loads tables from a Hive metastore:

spark.sql.catalog.hive_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.hive_prod.type = hive
spark.sql.catalog.hive_prod.uri = thrift://metastore-host:port
# omit uri to use the same URI as Spark: hive.metastore.uris in hive-site.xml

Iceberg also supports a directory-based catalog in HDFS that can be configured using type=hadoop:

spark.sql.catalog.hadoop_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.hadoop_prod.type = hadoop
spark.sql.catalog.hadoop_prod.warehouse = hdfs://nn:8020/warehouse/path

Note

The Hive-based catalog only loads Iceberg tables. To load non-Iceberg tables in the same Hive metastore, use a session catalog.

Using catalogs

Catalog names are used in SQL queries to identify a table. In the examples above, hive_prod and hadoop_prod can be used to prefix database and table names that will be loaded from those catalogs.

SELECT * FROM hive_prod.db.table -- load db.table from catalog hive_prod

Spark 3 keeps track of the current catalog and namespace, which can be omitted from table names.

USE hive_prod.db;
SELECT * FROM table -- load db.table from catalog hive_prod

To see the current catalog and namespace, run SHOW CURRENT NAMESPACE.

Replacing the session catalog

To add Iceberg table support to Spark’s built-in catalog, configure spark_catalog to use Iceberg’s SparkSessionCatalog.

spark.sql.catalog.spark_catalog = org.apache.iceberg.spark.SparkSessionCatalog
spark.sql.catalog.spark_catalog.type = hive

Spark’s built-in catalog supports existing v1 and v2 tables tracked in a Hive Metastore. This configures Spark to use Iceberg’s SparkSessionCatalog as a wrapper around that session catalog. When a table is not an Iceberg table, the built-in catalog will be used to load it instead.

This configuration can use same Hive Metastore for both Iceberg and non-Iceberg tables.

Loading a custom catalog

Spark supports loading a custom Iceberg Catalog implementation by specifying the catalog-impl property. When catalog-impl is set, the value of type is ignored. Here is an example:

spark.sql.catalog.custom_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.custom_prod.catalog-impl = com.my.custom.CatalogImpl
spark.sql.catalog.custom_prod.my-additional-catalog-config = my-value

DDL commands

Note

Spark 2.4 can’t create Iceberg tables with DDL, instead use the Iceberg API.

CREATE TABLE

Spark 3.0 can create tables in any Iceberg catalog with the clause USING iceberg:

CREATE TABLE prod.db.sample (
    id bigint COMMENT 'unique id',
    data string)
USING iceberg

Iceberg will convert the column type in Spark to corresponding Iceberg type. Please check the section of type compatibility on creating table for details.

Table create commands, including CTAS and RTAS, support the full range of Spark create clauses, including:

Create commands may also set the default format with the USING clause. This is only supported for SparkCatalog because Spark handles the USING clause differently for the built-in catalog.

PARTITIONED BY

To create a partitioned table, use PARTITIONED BY:

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string)
USING iceberg
PARTITIONED BY (category)

The PARTITIONED BY clause supports transform expressions to create hidden partitions.

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string,
    ts timestamp)
USING iceberg
PARTITIONED BY (bucket(16, id), days(ts), category)

Supported partition transforms are:

CREATE TABLE ... AS SELECT

Iceberg supports CTAS as an atomic operation when using a SparkCatalog. CTAS is supported, but is not atomic when using SparkSessionCatalog.

CREATE TABLE prod.db.sample
USING iceberg
AS SELECT ...

REPLACE TABLE ... AS SELECT

Iceberg supports RTAS as an atomic operation when using a SparkCatalog. RTAS is supported, but is not atomic when using SparkSessionCatalog.

Atomic table replacement creates a new snapshot with the results of the SELECT query, but keeps table history.

REPLACE TABLE prod.db.sample
USING iceberg
AS SELECT ...
CREATE OR REPLACE TABLE prod.db.sample
USING iceberg
AS SELECT ...

The schema and partition spec will be replaced if changed. To avoid modifying the table’s schema and partitioning, use INSERT OVERWRITE instead of REPLACE TABLE. The new table properties in the REPLACE TABLE command will be merged with any existing table properties. The existing table properties will be updated if changed else they are preserved.

ALTER TABLE

Iceberg has full ALTER TABLE support in Spark 3, including:

ALTER TABLE ... RENAME TO

ALTER TABLE prod.db.sample RENAME TO prod.db.new_name

ALTER TABLE ... SET TBLPROPERTIES

ALTER TABLE prod.db.sample SET TBLPROPERTIES (
    'read.split.target-size'='268435456'
)

Iceberg uses table properties to control table behavior. For a list of available properties, see Table configuration.

UNSET is used to remove properties:

ALTER TABLE prod.db.sample UNSET TBLPROPERTIES ('read.split.target-size')

ALTER TABLE ... ADD COLUMN

ALTER TABLE prod.db.sample ADD COLUMN point struct<x: double NOT NULL, y: double NOT NULL> AFTER data
ALTER TABLE prod.db.sample ADD COLUMN point.z double FIRST

ALTER TABLE ... RENAME COLUMN

ALTER TABLE prod.db.sample RENAME COLUMN data TO payload
ALTER TABLE prod.db.sample RENAME COLUMN location.lat TO latitude

Note that nested rename commands only rename the leaf field. The above command renames location.lat to location.latitude

ALTER TABLE ... ALTER COLUMN

Alter column is used to widen types, make a field optional, set comments, and reorder fields.

ALTER TABLE prod.db.sample ALTER COLUMN id DROP NOT NULL
ALTER TABLE prod.db.sample ALTER COLUMN location.lat TYPE double
ALTER TABLE prod.db.sample ALTER COLUMN point.z AFTER y
ALTER TABLE prod.db.sample ALTER COLUMN id COMMENT 'unique id'

ALTER TABLE ... DROP COLUMN

ALTER TABLE prod.db.sample DROP COLUMN id
ALTER TABLE prod.db.sample DROP COLUMN point.z

ALTER TABLE ... ADD PARTITION FIELD

ALTER TABLE prod.db.sample ADD PARTITION FIELD catalog -- identity transform
ALTER TABLE prod.db.sample ADD PARTITION FIELD bucket(16, id)
ALTER TABLE prod.db.sample ADD PARTITION FIELD truncate(data, 4)
ALTER TABLE prod.db.sample ADD PARTITION FIELD years(ts)
-- use optional AS keyword to specify a custom name for the partition field 
ALTER TABLE prod.db.sample ADD PARTITION FIELD bucket(16, id) AS shard

Warning

Changing partitioning will change the behavior of dynamic writes, which overwrite any partition that is written to. For example, if you partition by days and move to partitioning by hours, overwrites will overwrite hourly partitions but not days anymore.

ALTER TABLE ... DROP PARTITION FIELD

ALTER TABLE prod.db.sample DROP PARTITION FIELD catalog
ALTER TABLE prod.db.sample DROP PARTITION FIELD bucket(16, id)
ALTER TABLE prod.db.sample DROP PARTITION FIELD truncate(data, 4)
ALTER TABLE prod.db.sample DROP PARTITION FIELD years(ts)
ALTER TABLE prod.db.sample DROP PARTITION FIELD shard

Warning

Changing partitioning will change the behavior of dynamic writes, which overwrite any partition that is written to. For example, if you partition by days and move to partitioning by hours, overwrites will overwrite hourly partitions but not days anymore.

ALTER TABLE ... WRITE ORDERED BY

ALTER TABLE prod.db.sample WRITE ORDERED BY category, id
-- use optional ASC/DEC keyword to specify sort order of each field (default ASC)
ALTER TABLE prod.db.sample WRITE ORDERED BY category ASC, id DESC
-- use optional NULLS FIRST/NULLS LAST keyword to specify null order of each field (default FIRST)
ALTER TABLE prod.db.sample WRITE ORDERED BY category ASC NULLS LAST, id DESC NULLS FIRST

DROP TABLE

To delete a table, run:

DROP TABLE prod.db.sample

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")

Warning

When reading with DataFrames in Spark 3, use table to load a table by name from a catalog unless option is also required. Using format("iceberg") loads an isolated table reference that is not refreshed when other queries update the table.

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.

Table names and paths

Paths and table names can be loaded from the Spark3 dataframe interface. How paths/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.

Spark 2.4

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

// named metastore table
spark.read.format("iceberg").load("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()

Writing with SQL

Spark 3 supports SQL INSERT INTO and INSERT OVERWRITE, as well as the new DataFrameWriterV2 API.

INSERT INTO

To append new data to a table, use INSERT INTO.

INSERT INTO prod.db.table VALUES (1, 'a'), (2, 'b')
INSERT INTO prod.db.table SELECT ...

INSERT OVERWRITE

To replace data in the table with the result of a query, use INSERT OVERWRITE. Overwrites are atomic operations for Iceberg tables.

The partitions that will be replaced by INSERT OVERWRITE depends on Spark’s partition overwrite mode and the partitioning of a table.

Warning

Spark 3.0.0 has a correctness bug that affects dynamic INSERT OVERWRITE with hidden partitioning, SPARK-32168. For tables with hidden partitions, wait for Spark 3.0.1.

Overwrite behavior

Spark’s default overwrite mode is static, but dynamic overwrite mode is recommended when writing to Iceberg tables. Static overwrite mode determines which partitions to overwrite in a table by converting the PARTITION clause to a filter, but the PARTITION clause can only reference table columns.

Dynamic overwrite mode is configured by setting spark.sql.sources.partitionOverwriteMode=dynamic.

To demonstrate the behavior of dynamic and static overwrites, consider a logs table defined by the following DDL:

CREATE TABLE prod.my_app.logs (
    uuid string NOT NULL,
    level string NOT NULL,
    ts timestamp NOT NULL,
    message string)
USING iceberg
PARTITIONED BY (level, hours(ts))

Dynamic overwrite

When Spark’s overwrite mode is dynamic, partitions that have rows produced by the SELECT query will be replaced.

For example, this query removes duplicate log events from the example logs table.

INSERT OVERWRITE prod.my_app.logs
SELECT uuid, first(level), first(ts), first(message)
FROM prod.my_app.logs
WHERE cast(ts as date) = '2020-07-01'
GROUP BY uuid

In dynamic mode, this will replace any partition with rows in the SELECT result. Because the date of all rows is restricted to 1 July, only hours of that day will be replaced.

Static overwrite

When Spark’s overwrite mode is static, the PARTITION clause is converted to a filter that is used to delete from the table. If the PARTITION clause is omitted, all partitions will be replaced.

Because there is no PARTITION clause in the query above, it will drop all existing rows in the table when run in static mode, but will only write the logs from 1 July.

To overwrite just the partitions that were loaded, add a PARTITION clause that aligns with the SELECT query filter:

INSERT OVERWRITE prod.my_app.logs
PARTITION (level = 'INFO')
SELECT uuid, first(level), first(ts), first(message)
FROM prod.my_app.logs
WHERE level = 'INFO'
GROUP BY uuid

Note that this mode cannot replace hourly partitions like the dynamic example query because the PARTITION clause can only reference table columns, not hidden partitions.

DELETE FROM

Spark 3 added support for DELETE FROM queries to remove data from tables.

Delete queries accept a filter to match rows to delete. Iceberg can delete data as long as the filter matches entire partitions of the table, or it can determine that all rows of a file match. If a file contains some rows that should be deleted and some that should not, Iceberg will throw an exception.

DELETE FROM prod.db.table
WHERE ts >= '2020-05-01 00:00:00' and ts < '2020-06-01 00:00:00'

Writing with DataFrames

Spark 3 introduced the new DataFrameWriterV2 API for writing to tables using data frames. The v2 API is recommended for several reasons:

The v1 DataFrame write API is still supported, but is not recommended.

Warning

When writing with the v1 DataFrame API in Spark 3, use saveAsTable or insertInto to load tables with a catalog. Using format("iceberg") loads an isolated table reference that will not automatically refresh tables used by queries.

Appending data

To append a dataframe to an Iceberg table, use append:

val data: DataFrame = ...
data.writeTo("prod.db.table").append()

Spark 2.4

In Spark 2.4, use the v1 API with append mode and iceberg format:

data.write
    .format("iceberg")
    .mode("append")
    .save("db.table")

Overwriting data

To overwrite partitions dynamically, use overwritePartitions():

val data: DataFrame = ...
data.writeTo("prod.db.table").overwritePartitions()

To explicitly overwrite partitions, use overwrite to supply a filter:

data.writeTo("prod.db.table").overwrite($"level" === "INFO")

Spark 2.4

In Spark 2.4, overwrite values in an Iceberg table with overwrite mode and iceberg format:

data.write
    .format("iceberg")
    .mode("overwrite")
    .save("db.table")

Warning

The behavior of overwrite mode changed between Spark 2.4 and Spark 3.

The behavior of DataFrameWriter overwrite mode was undefined in Spark 2.4, but is required to overwrite the entire table in Spark 3. Because of this new requirement, the Iceberg source’s behavior changed in Spark 3. In Spark 2.4, the behavior was to dynamically overwrite partitions. To use the Spark 2.4 behavior, add option overwrite-mode=dynamic.

Creating tables

To run a CTAS or RTAS, use create, replace, or createOrReplace operations:

val data: DataFrame = ...
data.writeTo("prod.db.table").create()

Create and replace operations support table configuration methods, like partitionedBy and tableProperty:

data.writeTo("prod.db.table")
    .tableProperty("write.format.default", "orc")
    .partitionBy($"level", days($"ts"))
    .createOrReplace()

Writing against partitioned table

Iceberg requires the data to be sorted according to the partition spec per task (Spark partition) in prior to write against partitioned table. This applies both Writing with SQL and Writing with DataFrames.

Note

Explicit sort is necessary because Spark doesn’t allow Iceberg to request a sort before writing as of Spark 3.0. SPARK-23889 is filed to enable Iceberg to require specific distribution & sort order to Spark.

Note

Both global sort (orderBy/sort) and local sort (sortWithinPartitions) work for the requirement.

Let’s go through writing the data against below sample table:

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string,
    ts timestamp)
USING iceberg
PARTITIONED BY (days(ts), category)

To write data to the sample table, your data needs to be sorted by days(ts), category.

If you’re inserting data with SQL statement, you can use ORDER BY to achieve it, like below:

INSERT INTO prod.db.sample
SELECT id, data, category, ts FROM another_table
ORDER BY ts, category

If you’re inserting data with DataFrame, you can use either orderBy/sort to trigger global sort, or sortWithinPartitions to trigger local sort. Local sort for example:

data.sortWithinPartitions("ts", "category")
    .writeTo("prod.db.sample")
    .append()

You can simply add the original column to the sort condition for the most partition transformations, except bucket.

For bucket partition transformation, you need to register the Iceberg transform function in Spark to specify it during sort.

Let’s go through another sample table having bucket partition:

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string,
    ts timestamp)
USING iceberg
PARTITIONED BY (bucket(16, id))

You need to register the function to deal with bucket, like below:

import org.apache.iceberg.spark.IcebergSpark
import org.apache.spark.sql.types.DataTypes

IcebergSpark.registerBucketUDF(spark, "iceberg_bucket16", DataTypes.LongType, 16)

Note

Explicit registration of the function is necessary because Spark doesn’t allow Iceberg to provide functions. SPARK-27658 is filed to enable Iceberg to provide functions which can be used in query.

Here we just registered the bucket function as iceberg_bucket16, which can be used in sort clause.

If you’re inserting data with SQL statement, you can use the function like below:

INSERT INTO prod.db.sample
SELECT id, data, category, ts FROM another_table
ORDER BY iceberg_bucket16(id)

If you’re inserting data with DataFrame, you can use the function like below:

data.sortWithinPartitions(expr("iceberg_bucket16(id)"))
    .writeTo("prod.db.sample")
    .append()

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

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 | partitions                      |
+----------------------------------------------------------------------+--------+-------------------+---------------------+------------------------+---------------------------+--------------------------+---------------------------------+
| s3://.../table/metadata/45b5290b-ee61-4788-b324-b1e2735c0e10-m0.avro | 4479   | 0                 | 6668963634911763636 | 8                      | 0                         | 0                        | [[false,2019-05-13,2019-05-15]] |
+----------------------------------------------------------------------+--------+-------------------+---------------------+------------------------+---------------------------+--------------------------+---------------------------------+

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 | 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]           |
+-------------------------------------------------------------------------+-------------+--------------+--------------------+--------------------+------------------+-------------------+-----------------+-----------------+--------------+---------------+

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)