Iceberg connector#

Apache Iceberg is an open table format for huge analytic datasets. The Iceberg connector allows querying data stored in files written in Iceberg format, as defined in the Iceberg Table Spec. It supports Apache Iceberg table spec version 1 and 2.

The Iceberg table state is maintained in metadata files. All changes to table state create a new metadata file and replace the old metadata with an atomic swap. The table metadata file tracks the table schema, partitioning config, custom properties, and snapshots of the table contents.

Iceberg data files can be stored in either Parquet, ORC or Avro format, as determined by the format property in the table definition. The table format defaults to ORC.

Iceberg is designed to improve on the known scalability limitations of Hive, which stores table metadata in a metastore that is backed by a relational database such as MySQL. It tracks partition locations in the metastore, but not individual data files. Trino queries using the Hive connector must first call the metastore to get partition locations, then call the underlying filesystem to list all data files inside each partition, and then read metadata from each data file.

Since Iceberg stores the paths to data files in the metadata files, it only consults the underlying file system for files that must be read.

Requirements#

To use Iceberg, you need:

  • Network access from the Trino coordinator and workers to the distributed object storage.

  • Access to a Hive metastore service (HMS) or AWS Glue.

  • Network access from the Trino coordinator to the HMS. Hive metastore access with the Thrift protocol defaults to using port 9083.

Configuration#

The connector supports two Iceberg catalog types, you may use either a Hive metastore service (HMS) or AWS Glue. The catalog type is determined by the iceberg.catalog.type property, it can be set to either HIVE_METASTORE or GLUE.

Hive metastore catalog#

The Hive metastore catalog is the default implementation. When using it, the Iceberg connector supports the same metastore configuration properties as the Hive connector. At a minimum, hive.metastore.uri must be configured, see Thrift metastore configuration.

connector.name=iceberg
hive.metastore.uri=thrift://localhost:9083

Glue catalog#

When using the Glue catalog, the Iceberg connector supports the same configuration properties as the Hive connector’s Glue setup. See AWS Glue metastore configuration.

connector.name=iceberg
iceberg.catalog.type=glue

General configuration#

These configuration properties are independent of which catalog implementation is used.

Iceberg general configuration properties#

Property name

Description

Default

iceberg.file-format

Define the data storage file format for Iceberg tables. Possible values are

  • PARQUET

  • ORC

  • AVRO

ORC

iceberg.compression-codec

The compression codec to be used when writing files. Possible values are

  • NONE

  • SNAPPY

  • LZ4

  • ZSTD

  • GZIP

ZSTD

iceberg.use-file-size-from-metadata

Read file sizes from metadata instead of file system. This property should only be set as a workaround for this issue. The problem was fixed in Iceberg version 0.11.0.

true

iceberg.max-partitions-per-writer

Maximum number of partitions handled per writer.

100

iceberg.target-max-file-size

Target maximum size of written files; the actual size may be larger.

1GB

iceberg.unique-table-location

Use randomized, unique table locations.

true

iceberg.dynamic-filtering.wait-timeout

Maximum duration to wait for completion of dynamic filters during split generation.

0s

iceberg.delete-schema-locations-fallback

Whether schema locations should be deleted when Trino can’t determine whether they contain external files.

false

iceberg.minimum-assigned-split-weight

A decimal value in the range (0, 1] used as a minimum for weights assigned to each split. A low value may improve performance on tables with small files. A higher value may improve performance for queries with highly skewed aggregations or joins.

0.05

iceberg.table-statistics-enabled

Enables Table statistics. The equivalent catalog session property is statistics_enabled for session specific use. Set to false to disable statistics. Disabling statistics means that Cost based optimizations can not make smart decisions about the query plan.

true

iceberg.projection-pushdown-enabled

Enable projection pushdown

true

iceberg.hive-catalog-name

Catalog to redirect to when a Hive table is referenced.

iceberg.materialized-views.storage-schema

Schema for creating materialized views storage tables. When this property is not configured, storage tables are created in the same schema as the materialized view definition. When the storage_schema materialized view property is specified, it takes precedence over this catalog property.

Empty

ORC format configuration#

The following properties are used to configure the read and write operations with ORC files performed by the Iceberg connector.

ORC format configuration properties#

Property name

Description

Default

hive.orc.bloom-filters.enabled

Enable bloom filters for predicate pushdown.

false

Authorization checks#

You can enable authorization checks for the connector by setting the iceberg.security property in the catalog properties file. This property must be one of the following values:

Iceberg security values#

Property value

Description

ALLOW_ALL

No authorization checks are enforced.

SYSTEM

The connector relies on system-level access control.

READ_ONLY

Operations that read data or metadata, such as SELECT are permitted. No operations that write data or metadata, such as CREATE TABLE, INSERT, or DELETE are allowed.

FILE

Authorization checks are enforced using a catalog-level access control configuration file whose path is specified in the security.config-file catalog configuration property. See Catalog-level access control files for information on the authorization configuration file.

Table redirection#

Trino offers the possibility to transparently redirect operations on an existing table to the appropriate catalog based on the format of the table and catalog configuration.

In the context of connectors which depend on a metastore service (for example, Hive connector, Iceberg connector and Delta Lake connector), the metastore (Hive metastore service, AWS Glue Data Catalog) can be used to accustom tables with different table formats. Therefore, a metastore database can hold a variety of tables with different table formats.

As a concrete example, let’s use the following simple scenario which makes use of table redirection:

USE a-catalog.myschema;

EXPLAIN SELECT * FROM mytable;
                               Query Plan
-------------------------------------------------------------------------
Fragment 0 [SOURCE]
     ...
     Output[columnNames = [...]]
     │   ...
     └─ TableScan[table = another-catalog:myschema:mytable]
            ...

The output of the EXPLAIN statement points out the actual catalog which is handling the SELECT query over the table mytable.

The table redirection functionality works also when using fully qualified names for the tables:

EXPLAIN SELECT * FROM a-catalog.myschema.mytable;
                               Query Plan
-------------------------------------------------------------------------
Fragment 0 [SOURCE]
     ...
     Output[columnNames = [...]]
     │   ...
     └─ TableScan[table = another-catalog:myschema:mytable]
            ...

Trino offers table redirection support for the following operations:

Trino does not offer view redirection support.

The connector supports redirection from Iceberg tables to Hive tables with the iceberg.hive-catalog-name catalog configuration property.

SQL support#

This connector provides read access and write access to data and metadata in Iceberg. In addition to the globally available and read operation statements, the connector supports the following features:

ALTER TABLE EXECUTE#

The connector supports the following commands for use with ALTER TABLE EXECUTE.

optimize#

The optimize command is used for rewriting the active content of the specified table so that it is merged into fewer but larger files. In case that the table is partitioned, the data compaction acts separately on each partition selected for optimization. This operation improves read performance.

All files with a size below the optional file_size_threshold parameter (default value for the threshold is 100MB) are merged:

ALTER TABLE test_table EXECUTE optimize

The following statement merges the files in a table that are under 10 megabytes in size:

ALTER TABLE test_table EXECUTE optimize(file_size_threshold => '10MB')

You can use a WHERE clause with the columns used to partition the table, to apply optimize only on the partition(s) corresponding to the filter:

ALTER TABLE test_partitioned_table EXECUTE optimize
WHERE partition_key = 1

expire_snapshots#

The expire_snapshots command removes all snapshots and all related metadata and data files. Regularly expiring snapshots is recommended to delete data files that are no longer needed, and to keep the size of table metadata small. The procedure affects all snapshots that are older than the time period configured with the retention_threshold parameter.

expire_snapshots can be run as follows:

ALTER TABLE test_table EXECUTE expire_snapshots(retention_threshold => '7d')

The value for retention_threshold must be higher than or equal to iceberg.expire_snapshots.min-retention in the catalog otherwise the procedure will fail with similar message: Retention specified (1.00d) is shorter than the minimum retention configured in the system (7.00d). The default value for this property is 7d.

remove_orphan_files#

The remove_orphan_files command removes all files from table’s data directory which are not linked from metadata files and that are older than the value of retention_threshold parameter. Deleting orphan files from time to time is recommended to keep size of table’s data directory under control.

remove_orphan_files can be run as follows:

ALTER TABLE test_table EXECUTE remove_orphan_files(retention_threshold => '7d')

The value for retention_threshold must be higher than or equal to iceberg.remove_orphan_files.min-retention in the catalog otherwise the procedure will fail with similar message: Retention specified (1.00d) is shorter than the minimum retention configured in the system (7.00d). The default value for this property is 7d.

ALTER TABLE SET PROPERTIES#

The connector supports modifying the properties on existing tables using ALTER TABLE SET PROPERTIES.

The following table properties can be updated after a table is created:

  • format

  • format_version

  • partitioning

For example, to update a table from v1 of the Iceberg specification to v2:

ALTER TABLE table_name SET PROPERTIES format_version = 2;

Or to set the column my_new_partition_column as a partition column on a table:

ALTER TABLE table_name SET PROPERTIES partitioning = ARRAY[<existing partition columns>, 'my_new_partition_column'];

The current values of a table’s properties can be shown using SHOW CREATE TABLE.

Type mapping#

Both Iceberg and Trino have types that are not supported by the Iceberg connector. The following sections explain their type mapping.

Iceberg to Trino type mapping#

Trino supports selecting Iceberg data types. The following table shows the Iceberg to Trino type mapping:

Iceberg to Trino type mapping#

Iceberg type

Trino type

BOOLEAN

BOOLEAN

INT

INTEGER

LONG

BIGINT

FLOAT

REAL

DOUBLE

DOUBLE

DECIMAL(p,s)

DECIMAL(p,s)

DATE

DATE

TIME

TIME(6)

TIMESTAMP

TIMESTAMP(6)

TIMESTAMPTZ

TIMESTAMP(6) WITH TIME ZONE

STRING

VARCHAR

UUID

UUID

BINARY

VARBINARY

STRUCT(...)

ROW(...)

LIST(e)

ARRAY(e)

MAP(k,v)

MAP(k,v)

Trino to Iceberg type mapping#

Trino supports creating tables with the following types in Iceberg. The table shows the mappings from Trino to Iceberg data types:

Trino to Iceberg type mapping#

Trino type

Iceberg type

Notes

BOOLEAN

BOOLEAN

INTEGER

INT

BIGINT

LONG

REAL

FLOAT

DOUBLE

DOUBLE

DECIMAL(p,s)

DECIMAL(p,s)

DATE

DATE

TIME(6)

TIME

Other precisions not supported

TIMESTAMP(6)

TIMESTAMP

Other precisions not supported

TIMESTAMP(6) WITH TIME ZONE

TIMESTAMPTZ

Other precisions not supported

VARCHAR, VARCHAR(n)

STRING

UUID

UUID

VARBINARY

BINARY

ROW(...)

STRUCT(...)

All fields must have a name

ARRAY(e)

LIST(e)

MAP(k,v)

MAP(k,v)

Partitioned tables#

Iceberg supports partitioning by specifying transforms over the table columns. A partition is created for each unique tuple value produced by the transforms. Identity transforms are simply the column name. Other transforms are:

Transform

Description

year(ts)

A partition is created for each year. The partition value is the integer difference in years between ts and January 1 1970.

month(ts)

A partition is created for each month of each year. The partition value is the integer difference in months between ts and January 1 1970.

day(ts)

A partition is created for each day of each year. The partition value is the integer difference in days between ts and January 1 1970.

hour(ts)

A partition is created hour of each day. The partition value is a timestamp with the minutes and seconds set to zero.

bucket(x, nbuckets)

The data is hashed into the specified number of buckets. The partition value is an integer hash of x, with a value between 0 and nbuckets - 1 inclusive.

truncate(s, nchars)

The partition value is the first nchars characters of s.

In this example, the table is partitioned by the month of order_date, a hash of account_number (with 10 buckets), and country:

CREATE TABLE iceberg.testdb.customer_orders (
    order_id BIGINT,
    order_date DATE,
    account_number BIGINT,
    customer VARCHAR,
    country VARCHAR)
WITH (partitioning = ARRAY['month(order_date)', 'bucket(account_number, 10)', 'country'])

Deletion by partition#

For partitioned tables, the Iceberg connector supports the deletion of entire partitions if the WHERE clause specifies filters only on the identity-transformed partitioning columns, that can match entire partitions. Given the table definition above, this SQL will delete all partitions for which country is US:

DELETE FROM iceberg.testdb.customer_orders
WHERE country = 'US'

Tables using either v1 or v2 of the Iceberg specification will perform a partition delete if the WHERE clause meets these conditions.

Row level deletion#

Tables using v2 of the Iceberg specification support deletion of individual rows by writing position delete files.

Snapshots#

Iceberg supports a “snapshot” model of data, where table snapshots are identified by a snapshot ID.

The connector provides a system table exposing snapshot information for every Iceberg table. Snapshots are identified by BIGINT snapshot IDs. For example, you could find the snapshot IDs for the customer_orders table by running the following query:

SELECT snapshot_id
FROM iceberg.testdb."customer_orders$snapshots"
ORDER BY committed_at DESC

Time travel queries#

The connector offers the ability to query historical data. This allows you to query the table as it was when a previous snapshot of the table was taken, even if the data has since been modified or deleted.

The historical data of the table can be retrieved by specifying the snapshot identifier corresponding to the version of the table that needs to be retrieved:

SELECT *
FROM iceberg.testdb.customer_orders FOR VERSION AS OF 8954597067493422955

A different approach of retrieving historical data is to specify a point in time in the past, such as a day or week ago. The latest snapshot of the table taken before or at the specified timestamp in the query is internally used for providing the previous state of the table:

SELECT *
FROM iceberg.testdb.customer_orders FOR TIMESTAMP AS OF TIMESTAMP '2022-03-23 09:59:29.803 Europe/Vienna'

Rolling back to a previous snapshot#

Use the $snapshots metadata table to determine the latest snapshot ID of the table like in the following query:

SELECT snapshot_id
FROM iceberg.testdb."customer_orders$snapshots"
ORDER BY committed_at DESC LIMIT 1

A SQL procedure system.rollback_to_snapshot allows the caller to roll back the state of the table to a previous snapshot id:

CALL iceberg.system.rollback_to_snapshot('testdb', 'customer_orders', 8954597067493422955)

Schema evolution#

Iceberg and the Iceberg connector support schema evolution, with safe column add, drop, reorder and rename operations, including in nested structures. Table partitioning can also be changed and the connector can still query data created before the partitioning change.

Migrating existing tables#

The connector can read from or write to Hive tables that have been migrated to Iceberg. There is no Trino support for migrating Hive tables to Iceberg, so you need to either use the Iceberg API or Apache Spark.

Iceberg table properties#

Property name

Description

format

Optionally specifies the format of table data files; either PARQUET, ORC or AVRO`. Defaults to ORC.

partitioning

Optionally specifies table partitioning. If a table is partitioned by columns c1 and c2, the partitioning property would be partitioning = ARRAY['c1', 'c2']

location

Optionally specifies the file system location URI for the table.

format_version

Optionally specifies the format version of the Iceberg specification to use for new tables; either 1 or 2. Defaults to 2. Version 2 is required for row level deletes.

orc_bloom_filter_columns

Comma separated list of columns to use for ORC bloom filter. It improves the performance of queries using Equality and IN predicates when reading ORC file. Requires ORC format. Defaults to [].

orc_bloom_filter_fpp

The ORC bloom filters false positive probability. Requires ORC format. Defaults to 0.05.

The table definition below specifies format Parquet, partitioning by columns c1 and c2, and a file system location of /var/my_tables/test_table:

CREATE TABLE test_table (
    c1 integer,
    c2 date,
    c3 double)
WITH (
    format = 'PARQUET',
    partitioning = ARRAY['c1', 'c2'],
    location = '/var/my_tables/test_table')

The table definition below specifies format ORC, bloom filter index by columns c1 and c2, fpp is 0.05, and a file system location of /var/my_tables/test_table:

CREATE TABLE test_table (
    c1 integer,
    c2 date,
    c3 double)
WITH (
    format = 'ORC',
    location = '/var/my_tables/test_table',
    orc_bloom_filter_columns = ARRAY['c1', 'c2'],
    orc_bloom_filter_fpp = 0.05)

Metadata columns#

In addition to the defined columns, the Iceberg connector automatically exposes path metadata as a hidden column in each table:

  • $path: Full file system path name of the file for this row

  • $file_modified_time: Timestamp of the last modification of the file for this row

You can use these columns in your SQL statements like any other column. This can be selected directly, or used in conditional statements. For example, you can inspect the file path for each record:

SELECT *, "$path", "$file_modified_time"
FROM iceberg.web.page_views;

Retrieve all records that belong to a specific file using "$path" filter:

SELECT *
FROM iceberg.web.page_views
WHERE "$path" = '/usr/iceberg/table/web.page_views/data/file_01.parquet'

Retrieve all records that belong to a specific file using "$file_modified_time" filter:

SELECT *
FROM iceberg.web.page_views
WHERE "$file_modified_time" = CAST('2022-07-01 01:02:03.456 UTC' AS timestamp with time zone)

Metadata tables#

The connector exposes several metadata tables for each Iceberg table. These metadata tables contain information about the internal structure of the Iceberg table. You can query each metadata table by appending the metadata table name to the table name:

SELECT * FROM "test_table$data"

$data table#

The $data table is an alias for the Iceberg table itself.

The statement:

SELECT * FROM "test_table$data"

is equivalent to:

SELECT * FROM test_table

$properties table#

The $properties table provides access to general information about Iceberg table configuration and any additional metadata key/value pairs that the table is tagged with.

You can retrieve the properties of the current snapshot of the Iceberg table test_table by using the following query:

SELECT * FROM "test_table$properties"
 key                   | value    |
-----------------------+----------+
write.format.default   | PARQUET  |

$history table#

The $history table provides a log of the metadata changes performed on the Iceberg table.

You can retrieve the changelog of the Iceberg table test_table by using the following query:

SELECT * FROM "test_table$history"
 made_current_at                  | snapshot_id          | parent_id            | is_current_ancestor
----------------------------------+----------------------+----------------------+--------------------
2022-01-10 08:11:20 Europe/Vienna | 8667764846443717831  |  <null>              |  true
2022-01-10 08:11:34 Europe/Vienna | 7860805980949777961  | 8667764846443717831  |  true

The output of the query has the following columns:

History columns#

Name

Type

Description

made_current_at

timestamp(3) with time zone

The time when the snapshot became active

snapshot_id

bigint

The identifier of the snapshot

parent_id

bigint

The identifier of the parent snapshot

is_current_ancestor

boolean

Whether or not this snapshot is an ancestor of the current snapshot

$snapshots table#

The $snapshots table provides a detailed view of snapshots of the Iceberg table. A snapshot consists of one or more file manifests, and the complete table contents is represented by the union of all the data files in those manifests.

You can retrieve the information about the snapshots of the Iceberg table test_table by using the following query:

SELECT * FROM "test_table$snapshots"
 committed_at                      | snapshot_id          | parent_id            | operation          |  manifest_list                                                                                                                           |   summary
----------------------------------+----------------------+----------------------+--------------------+------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
2022-01-10 08:11:20 Europe/Vienna | 8667764846443717831  |  <null>              |  append            |   hdfs://hadoop-master:9000/user/hive/warehouse/test_table/metadata/snap-8667764846443717831-1-100cf97e-6d56-446e-8961-afdaded63bc4.avro | {changed-partition-count=0, total-equality-deletes=0, total-position-deletes=0, total-delete-files=0, total-files-size=0, total-records=0, total-data-files=0}
2022-01-10 08:11:34 Europe/Vienna | 7860805980949777961  | 8667764846443717831  |  append            |   hdfs://hadoop-master:9000/user/hive/warehouse/test_table/metadata/snap-7860805980949777961-1-faa19903-1455-4bb8-855a-61a1bbafbaa7.avro | {changed-partition-count=1, added-data-files=1, total-equality-deletes=0, added-records=1, total-position-deletes=0, added-files-size=442, total-delete-files=0, total-files-size=442, total-records=1, total-data-files=1}

The output of the query has the following columns:

Snapshots columns#

Name

Type

Description

committed_at

timestamp(3) with time zone

The time when the snapshot became active

snapshot_id

bigint

The identifier for the snapshot

parent_id

bigint

The identifier for the parent snapshot

operation

varchar

The type of operation performed on the Iceberg table. The supported operation types in Iceberg are:

  • append when new data is appended

  • replace when files are removed and replaced without changing the data in the table

  • overwrite when new data is added to overwrite existing data

  • delete when data is deleted from the table and no new data is added

manifest_list

varchar

The list of avro manifest files containing the detailed information about the snapshot changes.

summary

map(varchar, varchar)

A summary of the changes made from the previous snapshot to the current snapshot

$manifests table#

The $manifests table provides a detailed overview of the manifests corresponding to the snapshots performed in the log of the Iceberg table.

You can retrieve the information about the manifests of the Iceberg table test_table by using the following query:

SELECT * FROM "test_table$manifests"
 path                                                                                                           | length          | partition_spec_id    | added_snapshot_id     | added_data_files_count  | added_rows_count | existing_data_files_count   | existing_rows_count | deleted_data_files_count    | deleted_rows_count | partitions
----------------------------------------------------------------------------------------------------------------+-----------------+----------------------+-----------------------+-------------------------+------------------+-----------------------------+---------------------+-----------------------------+--------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------
 hdfs://hadoop-master:9000/user/hive/warehouse/test_table/metadata/faa19903-1455-4bb8-855a-61a1bbafbaa7-m0.avro |  6277           |   0                  | 7860805980949777961   | 1                       | 100              | 0                           | 0                   | 0                           | 0                  | {{contains_null=false, contains_nan= false, lower_bound=1, upper_bound=1},{contains_null=false, contains_nan= false, lower_bound=2021-01-12, upper_bound=2021-01-12}}

The output of the query has the following columns:

Manifests columns#

Name

Type

Description

path

varchar

The manifest file location

length

bigint

The manifest file length

partition_spec_id

integer

The identifier for the partition specification used to write the manifest file

added_snapshot_id

bigint

The identifier of the snapshot during which this manifest entry has been added

added_data_files_count

integer

The number of data files with status ADDED in the manifest file

added_rows_count

bigint

The total number of rows in all data files with status ADDED in the manifest file.

existing_data_files_count

integer

The number of data files with status EXISTING in the manifest file

existing_rows_count

bigint

The total number of rows in all data files with status EXISTING in the manifest file.

deleted_data_files_count

integer

The number of data files with status DELETED in the manifest file

deleted_rows_count

bigint

The total number of rows in all data files with status DELETED in the manifest file.

partitions

array(row(contains_null boolean, contains_nan boolean, lower_bound varchar, upper_bound varchar))

Partition range metadata

$partitions table#

The $partitions table provides a detailed overview of the partitions of the Iceberg table.

You can retrieve the information about the partitions of the Iceberg table test_table by using the following query:

SELECT * FROM "test_table$partitions"
 partition             | record_count  | file_count    | total_size    |  data
-----------------------+---------------+---------------+---------------+------------------------------------------------------
{c1=1, c2=2021-01-12}  |  2            | 2             |  884          | {c3={min=1.0, max=2.0, null_count=0, nan_count=NULL}}
{c1=1, c2=2021-01-13}  |  1            | 1             |  442          | {c3={min=1.0, max=1.0, null_count=0, nan_count=NULL}}

The output of the query has the following columns:

Partitions columns#

Name

Type

Description

partition

row(...)

A row which contains the mapping of the partition column name(s) to the partition column value(s)

record_count

bigint

The number of records in the partition

file_count

bigint

The number of files mapped in the partition

total_size

bigint

The size of all the files in the partition

data

row(... row (min ..., max ... , null_count bigint, nan_count bigint))

Partition range metadata

$files table#

The $files table provides a detailed overview of the data files in current snapshot of the Iceberg table.

To retrieve the information about the data files of the Iceberg table test_table use the following query:

SELECT * FROM "test_table$files"
 content  | file_path                                                                                                                     | record_count    | file_format   | file_size_in_bytes   |  column_sizes        |  value_counts     |  null_value_counts | nan_value_counts  | lower_bounds                |  upper_bounds               |  key_metadata  | split_offsets  |  equality_ids
----------+-------------------------------------------------------------------------------------------------------------------------------+-----------------+---------------+----------------------+----------------------+-------------------+--------------------+-------------------+-----------------------------+-----------------------------+----------------+----------------+---------------
 0        | hdfs://hadoop-master:9000/user/hive/warehouse/test_table/data/c1=3/c2=2021-01-14/af9872b2-40f3-428f-9c87-186d2750d84e.parquet |  1              |  PARQUET      |  442                 | {1=40, 2=40, 3=44}   |  {1=1, 2=1, 3=1}  |  {1=0, 2=0, 3=0}   | <null>            |  {1=3, 2=2021-01-14, 3=1.3} |  {1=3, 2=2021-01-14, 3=1.3} |  <null>        | <null>         |   <null>

The output of the query has the following columns:

Files columns#

Name

Type

Description

content

integer

Type of content stored in the file. The supported content types in Iceberg are:

  • DATA(0)

  • POSITION_DELETES(1)

  • EQUALITY_DELETES(2)

file_path

varchar

The data file location

file_format

varchar

The format of the data file

record_count

bigint

The number of entries contained in the data file

file_size_in_bytes

bigint

The data file size

column_sizes

map(integer, bigint)

Mapping between the Iceberg column ID and its corresponding size in the file

value_counts

map(integer, bigint)

Mapping between the Iceberg column ID and its corresponding count of entries in the file

null_value_counts

map(integer, bigint)

Mapping between the Iceberg column ID and its corresponding count of NULL values in the file

nan_value_counts

map(integer, bigint)

Mapping between the Iceberg column ID and its corresponding count of non numerical values in the file

lower_bounds

map(integer, bigint)

Mapping between the Iceberg column ID and its corresponding lower bound in the file

upper_bounds

map(integer, bigint)

Mapping between the Iceberg column ID and its corresponding upper bound in the file

key_metadata

varbinary

Metadata about the encryption key used to encrypt this file, if applicable

split_offsets

array(bigint)

List of recommended split locations

equality_ids

array(integer)

The set of field IDs used for equality comparison in equality delete files

Materialized views#

The Iceberg connector supports Materialized view management. In the underlying system each materialized view consists of a view definition and an Iceberg storage table. The storage table name is stored as a materialized view property. The data is stored in that storage table.

You can use the Iceberg table properties to control the created storage table and therefore the layout and performance. For example, you can use the following clause with CREATE MATERIALIZED VIEW to use the ORC format for the data files and partition the storage per day using the column _date:

WITH ( format = 'ORC', partitioning = ARRAY['event_date'] )

By default, the storage table is created in the same schema as the materialized view definition. The iceberg.materialized-views.storage-schema catalog configuration property or storage_schema materialized view property can be used to specify the schema where the storage table will be created.

Updating the data in the materialized view with REFRESH MATERIALIZED VIEW deletes the data from the storage table, and inserts the data that is the result of executing the materialized view query into the existing table. Refreshing a materialized view also stores the snapshot-ids of all tables that are part of the materialized view’s query in the materialized view metadata. When the materialized view is queried, the snapshot-ids are used to check if the data in the storage table is up to date. If the data is outdated, the materialized view behaves like a normal view, and the data is queried directly from the base tables.

Dropping a materialized view with DROP MATERIALIZED VIEW removes the definition and the storage table.