Iceberg коннектор#

Примечание

Ниже приведена оригинальная документация Trino. Скоро мы ее переведем на русский язык и дополним полезными примерами.

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. The connector supports Apache Iceberg table spec versions 1 and 2.

The 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 configuration, custom properties, and snapshots of the table contents.

Iceberg data files are stored in either Parquet, ORC, or Avro format, as determined by the format property in the table definition.

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 коннектор must first call the metastore to get partition locations, then call the underlying file system 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:

General configuration#

To configure the Iceberg connector, create a catalog properties file etc/catalog/example.properties that references the iceberg connector and defines a metastore type. The Hive metastore catalog is the default implementation. To use a Hive metastore, iceberg.catalog.type must be set to hive_metastore and hive.metastore.uri must be configured:

connector.name=iceberg
iceberg.catalog.type=hive_metastore
hive.metastore.uri=thrift://example.net:9083

Other metadata catalog types as listed in the requirements section of this topic are available. Each metastore type has specific configuration properties along with general metastore configuration properties.

The following configuration properties are independent of which catalog implementation is used:

Iceberg general configuration properties#

Property name

Description

Default

iceberg.catalog.type

Define the metastore type to use. Possible values are:

  • hive_metastore

  • glue

  • jdbc

  • rest

  • nessie

iceberg.file-format

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

  • PARQUET

  • ORC

  • AVRO

PARQUET

iceberg.compression-codec

The compression codec 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 must only be used 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 are 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 оптимизация cannot make better 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

iceberg.register-table-procedure.enabled

Enable to allow user to call register_table procedure.

false

iceberg.query-partition-filter-required

Set to true to force a query to use a partition filter. You can use the query_partition_filter_required catalog session property for temporary, catalog specific use.

false

Type mapping#

The connector reads and writes data into the supported data file formats Avro, ORC, and Parquet, following the Iceberg specification.

Because Trino and Iceberg each support types that the other does not, this connector modifies some types when reading or writing data. Data types may not map the same way in both directions between Trino and the data source. Refer to the following sections for type mapping in each direction.

The Iceberg specification includes supported data types and the mapping to the formating in the Avro, ORC, or Parquet files:

Iceberg to Trino type mapping#

The connector maps Iceberg types to the corresponding Trino types according to the following table:

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

FIXED (L)

VARBINARY

STRUCT(...)

ROW(...)

LIST(e)

ARRAY(e)

MAP(k,v)

MAP(k,v)

No other types are supported.

Trino to Iceberg type mapping#

The connector maps Trino types to the corresponding Iceberg types according to the following table:

Trino to Iceberg type mapping#

Trino type

Iceberg type

BOOLEAN

BOOLEAN

INTEGER

INT

BIGINT

LONG

REAL

FLOAT

DOUBLE

DOUBLE

DECIMAL(p,s)

DECIMAL(p,s)

DATE

DATE

TIME(6)

TIME

TIMESTAMP(6)

TIMESTAMP

TIMESTAMP(6) WITH TIME ZONE

TIMESTAMPTZ

VARCHAR

STRING

UUID

UUID

VARBINARY

BINARY

ROW(...)

STRUCT(...)

ARRAY(e)

LIST(e)

MAP(k,v)

MAP(k,v)

No other types are supported.

Security#

The Iceberg connector allows you to choose one of several means of providing authorization at the catalog level.

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.

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:

Basic usage examples#

The connector supports creating schemas. You can create a schema with or without a specified location.

You can create a schema with the CREATE SCHEMA statement and the location schema property. The tables in this schema, which have no explicit location set in CREATE TABLE statement, are located in a subdirectory under the directory corresponding to the schema location.

Create a schema on S3:

CREATE SCHEMA example.example_s3_schema
WITH (location = 's3://my-bucket/a/path/');

Create a schema on an S3-compatible object storage such as MinIO:

CREATE SCHEMA example.example_s3a_schema
WITH (location = 's3a://my-bucket/a/path/');

Create a schema on HDFS:

CREATE SCHEMA example.example_hdfs_schema
WITH (location='hdfs://hadoop-master:9000/user/hive/warehouse/a/path/');

Optionally, on HDFS, the location can be omitted:

CREATE SCHEMA example.example_hdfs_schema;

The Iceberg connector supports creating tables using the CREATE TABLE syntax. Optionally, specify the table properties supported by this connector:

CREATE TABLE example_table (
    c1 INTEGER,
    c2 DATE,
    c3 DOUBLE
)
WITH (
    format = 'PARQUET',
    partitioning = ARRAY['c1', 'c2'],
    sorted_by = ARRAY['c3'],
    location = 's3://my-bucket/a/path/'
);

When the location table property is omitted, the content of the table is stored in a subdirectory under the directory corresponding to the schema location.

The Iceberg connector supports creating tables using the CREATE TABLE AS with SELECT syntax:

CREATE TABLE tiny_nation
WITH (
    format = 'PARQUET'
)
AS
    SELECT *
    FROM nation
    WHERE nationkey < 10;

Another flavor of creating tables with CREATE TABLE AS is with VALUES syntax:

CREATE TABLE yearly_clicks (
    year,
    clicks
)
WITH (
    partitioning = ARRAY['year']
)
AS VALUES
    (2021, 10000),
    (2022, 20000);

Procedures#

Use the CALL statement to perform data manipulation or administrative tasks. Procedures are available in the system schema of each catalog. The following code snippet displays how to call the example_procedure in the examplecatalog catalog:

CALL examplecatalog.system.example_procedure()

Register table#

The connector can register existing Iceberg tables with the catalog.

The procedure system.register_table allows the caller to register an existing Iceberg table in the metastore, using its existing metadata and data files:

CALL example.system.register_table(schema_name => 'testdb', table_name => 'customer_orders', table_location => 'hdfs://hadoop-master:9000/user/hive/warehouse/customer_orders-581fad8517934af6be1857a903559d44')

In addition, you can provide a file name to register a table with specific metadata. This may be used to register the table with some specific table state, or may be necessary if the connector cannot automatically figure out the metadata version to use:

CALL example.system.register_table(schema_name => 'testdb', table_name => 'customer_orders', table_location => 'hdfs://hadoop-master:9000/user/hive/warehouse/customer_orders-581fad8517934af6be1857a903559d44', metadata_file_name => '00003-409702ba-4735-4645-8f14-09537cc0b2c8.metadata.json')

To prevent unauthorized users from accessing data, this procedure is disabled by default. The procedure is enabled only when iceberg.register-table-procedure.enabled is set to true.

Unregister table#

The connector can unregister existing Iceberg tables from the catalog.

The procedure system.unregister_table allows the caller to unregister an existing Iceberg table from the metastores without deleting the data:

CALL example.system.unregister_table(schema_name => 'testdb', table_name => 'customer_orders')

Migrate table#

The connector can read from or write to Hive tables that have been migrated to Iceberg.

Use the procedure system.migrate to move a table from the Hive format to the Iceberg format, loaded with the source’s data files. Table schema, partitioning, properties, and location are copied from the source table. A bucketed Hive table will be migrated as a non-bucketed Iceberg table. The data files in the Hive table must use the Parquet, ORC, or Avro file format.

The procedure must be called for a specific catalog example with the relevant schema and table names supplied with the required parameters schema_name and table_name:

CALL example.system.migrate(
    schema_name => 'testdb',
    table_name => 'customer_orders')

Migrate fails if any table partition uses an unsupported file format.

In addition, you can provide a recursive_directory argument to migrate a Hive table that contains subdirectories:

CALL example.system.migrate(
    schema_name => 'testdb',
    table_name => 'customer_orders',
    recursive_directory => 'true')

The default value is fail, which causes the migrate procedure to throw an exception if subdirectories are found. Set the value to true to migrate nested directories, or false to ignore them.

Data management#

The DML functionality includes support for INSERT, UPDATE, DELETE, and MERGE statements.

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 from Partitioned Tables section, the following SQL statement deletes all partitions for which country is US:

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

A partition delete is performed 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.

Schema and table management#

The Схемы и таблицы functionality includes support for:

Schema evolution#

Iceberg supports 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.

ALTER TABLE EXECUTE#

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

optimize#

The optimize command is used for rewriting the content of the specified table so that it is merged into fewer but larger files. If 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 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 filter which partitions are optimized:

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 fails with a 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 a table’s data directory that 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 a 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 fails with a 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.

drop_extended_stats#

The drop_extended_stats command removes all extended statistics information from the table.

drop_extended_stats can be run as follows:

ALTER TABLE test_table EXECUTE drop_extended_stats

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

  • sorted_by

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.

Table properties#

Table properties supply or set metadata for the underlying tables. This is key for CREATE TABLE AS statements. Table properties are passed to the connector using a WITH clause.

Iceberg table properties#

Property name

Description

format

Optionally specifies the format of table data files; either PARQUET, ORC`, or ``AVRO. Defaults to the value of the iceberg.file-format catalog configuration property, which defaults to PARQUET.

partitioning

Optionally specifies table partitioning. If a table is partitioned by columns c1 and c2, the partitioning property is 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 files. 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 to use Parquet files, partitioning by columns c1 and c2, and a file system location of /var/example_tables/test_table:

CREATE TABLE test_table (
    c1 INTEGER,
    c2 DATE,
    c3 DOUBLE)
WITH (
    format = 'PARQUET',
    partitioning = ARRAY['c1', 'c2'],
    location = '/var/example_tables/test_table')

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

CREATE TABLE test_table (
    c1 INTEGER,
    c2 DATE,
    c3 DOUBLE)
WITH (
    format = 'ORC',
    location = '/var/example_tables/test_table',
    orc_bloom_filter_columns = ARRAY['c1', 'c2'],
    orc_bloom_filter_fpp = 0.05)

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$properties"
$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 are 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 that contains the mapping of the partition column names to the partition column values.

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.

$refs table#

The $refs table provides information about Iceberg references including branches and tags.

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

SELECT * FROM "test_table$refs"
name            | type   | snapshot_id | max_reference_age_in_ms | min_snapshots_to_keep | max_snapshot_age_in_ms |
----------------+--------+-------------+-------------------------+-----------------------+------------------------+
example_tag     | TAG    | 10000000000 | 10000                   | null                  | null                   |
example_branch  | BRANCH | 20000000000 | 20000                   | 2                     | 30000                  |

The output of the query has the following columns:

Refs columns#

Name

Type

Description

name

VARCHAR

Name of the reference.

type

VARCHAR

Type of the reference, either BRANCH or TAG.

snapshot_id

BIGINT

The snapshot ID of the reference.

max_reference_age_in_ms

BIGINT

The maximum age of the reference before it could be expired.

min_snapshots_to_keep

INTEGER

For branch only, the minimum number of snapshots to keep in a branch.

max_snapshot_age_in_ms

BIGINT

For branch only, the max snapshot age allowed in a branch. Older snapshots in the branch will be expired.

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 example.web.page_views;

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

SELECT *
FROM example.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 example.web.page_views
WHERE "$file_modified_time" = CAST('2022-07-01 01:02:03.456 UTC' AS TIMESTAMP WIOTH TIMEZONE)

DROP TABLE#

The Iceberg connector supports dropping a table by using the DROP TABLE syntax. When the command succeeds, both the data of the Iceberg table and also the information related to the table in the metastore service are removed. Dropping tables that have their data/metadata stored in a different location than the table’s corresponding base directory on the object store is not supported.

COMMENT#

The Iceberg connector supports setting comments on the following objects:

  • tables

  • views

  • table columns

  • materialized view columns

The COMMENT option is supported on both the table and the table columns for the CREATE TABLE operation.

The COMMENT option is supported for adding table columns through the ALTER TABLE operations.

The connector supports the command COMMENT for setting comments on existing entities.

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:

Iceberg column transforms#

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 example.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'])

Sorted tables#

The connector supports sorted files as a performance improvement. Data is sorted during writes within each file based on the specified array of one or more columns.

Sorting is particularly beneficial when the sorted columns show a high cardinality and are used as a filter for selective reads.

The sort order is configured with the sorted_by table property. Specify an array of one or more columns to use for sorting when creating the table. The following example configures the order_date column of the orders table in the customers schema in the example catalog:

CREATE TABLE example.customers.orders (
    order_id BIGINT,
    order_date DATE,
    account_number BIGINT,
    customer VARCHAR,
    country VARCHAR)
WITH (sorted_by = ARRAY['order_date'])

Sorting can be combined with partitioning on the same column. For example:

CREATE TABLE example.customers.orders (
    order_id BIGINT,
    order_date DATE,
    account_number BIGINT,
    customer VARCHAR,
    country VARCHAR)
WITH (
    partitioning = ARRAY['month(order_date)'],
    sorted_by = ARRAY['order_date']
)

You can disable sorted writing with the session property sorted_writing_enabled set to false.

Using 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 can find the snapshot IDs for the customer_orders table by running the following query:

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

Replacing tables#

The connector supports replacing a table as an atomic operation. Atomic table replacement creates a new snapshot with the new table definition (see CREATE TABLE and CREATE TABLE AS), but keeps table history.

The new table after replacement is completely new and separate from the old table. Only the name of the table remains identical. Earlier snapshots can be retrieved through Iceberg’s time travel.

For example a partitioned table my_table can be replaced by completely new definition.

CREATE TABLE my_table (
    a BIGINT,
    b DATE,
    c BIGINT)
WITH (partitioning = ARRAY['a']);

CREATE OR REPLACE TABLE my_table
WITH (sorted_by = ARRAY['a'])
AS SELECT * from another_table;

Earlier snapshots can be retrieved through Iceberg’s time travel.

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 to be retrieved:

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

You can use a date to specify a point a time in the past for using a snapshot of a table in a query. Assuming that the session time zone is Europe/Vienna the following queries are equivalent:

SELECT *
FROM example.testdb.customer_orders FOR TIMESTAMP AS OF DATE '2022-03-23'
SELECT *
FROM example.testdb.customer_orders FOR TIMESTAMP AS OF TIMESTAMP '2022-03-23 00:00:00'
SELECT *
FROM example.testdb.customer_orders FOR TIMESTAMP AS OF TIMESTAMP '2022-03-23 00:00:00.000 Europe/Vienna'

Iceberg supports named references of snapshots via branches and tags. Time travel can be performed to branches and tags in the table.

SELECT *
FROM example.testdb.customer_orders FOR VERSION AS OF 'historical-tag'

SELECT *
FROM example.testdb.customer_orders FOR VERSION AS OF 'test-branch'
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 example.testdb."customer_orders$snapshots"
ORDER BY committed_at DESC LIMIT 1

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

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

NOT NULL column constraint#

The Iceberg connector supports setting NOT NULL constraints on the table columns.

The NOT NULL constraint can be set on the columns, while creating tables by using the CREATE TABLE syntax:

CREATE TABLE example_table (
    year INTEGER NOT NULL,
    name VARCHAR NOT NULL,
    age INTEGER,
    address VARCHAR
);

When trying to insert/update data in the table, the query fails if trying to set NULL value on a column having the NOT NULL constraint.

View management#

Trino allows reading from Iceberg materialized views.

Materialized views#

The Iceberg connector supports Материализованные представления (materialized view). 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 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 is created.

Creating a materialized view does not automatically populate it with data. You must run REFRESH MATERIALIZED VIEW to populate data in the materialized view.

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. Data is replaced atomically, so users can continue to query the materialized view while it is being refreshed. Refreshing a materialized view also stores the snapshot-ids of all Iceberg 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. Detecting outdated data is possible only when the materialized view uses Iceberg tables only, or when it uses a mix of Iceberg and non-Iceberg tables but some Iceberg tables are outdated. When the materialized view is based on non-Iceberg tables, querying it can return outdated data, since the connector has no information whether the underlying non-Iceberg tables have changed.

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

Fault-tolerant execution support#

The connector supports Fault-tolerant execution of query processing. Read and write operations are both supported with any retry policy.

Производительность#

Данная секция описывает важные улучшения производительности, реализованные в Iceberg коннекторе.

Локальный дисковый кэш данных#

Коннектор позволяет сохранять часть данных из озера данных на дисках worker-узлов CedrusData для ускорения доступа к ним. Во многих случаях использование локального дискового кэша приводит к кратному ускорению запросов.

При каждом доступе к колонке CedrusData проверяет, были ли закэшированные данные изменены в удаленном источнике. Если обнаружено изменение, локальные данные будут удалены и закэшированы повторно.

CedrusData кэширует метаданные файлов, а так же диапазоны записей по мере необходимости. Гранулярность кэширования диапазонов записей зависит от формата:

  • Для формата Parquet единицей кэширования являются данные колонки из конкретной row group.

  • Для формата ORC единицей кэширования являются данные колонки из конкретной row group внутри stripe. В следующих версиях единицей кэширования станут данные колонки из конкретного stripe.

Примечание

Ограничения текущей реализации, которые будут сняты в последующих версиях CedrusData:

  • Кэширование доступно только для простых типов данных.

  • Кэширование доступно только для данных в форматах Parquet. Формат ORC поддерживается в экспериментальном режиме.

Для включения локального дискового кэша необходимо:

  • Установить параметр конфигурации каталога cedrusdata.hive.data-cache.enabled=true

  • Указать путь к файлу, в котором описаны правила кэширования в JSON формате

  • Для узлов, которые будут выполнять запросы (все worker-узлы, а так же coordinator-узел, запущенный с параметром node-scheduler.include-coordinator=true) необходимо так же указать путь к директории, в которой CedrusData будет хранить закэшированные данные. Параметр конфигурации: cedrusdata.hive.data-cache.path

Пример конфигурации для координатора, который не выполняет запросы:

cedrusdata.hive.data-cache.enabled=true
cedrusdata.hive.data-cache.rules.file=/path/to/rules.json

Пример конфигурации для worker-узла или координатора, который выполняет запросы (node-scheduler.include-coordinator=true):

cedrusdata.hive.data-cache.enabled=true
cedrusdata.hive.data-cache.rules.file=/path/to/rules.json
cedrusdata.hive.data-cache.path=file:///path/to/cache/dir

Правила кэширования необходимо задать в отдельном файле в формате:

{
  "rules": [
    <rule1>,
    <rule2>,
    ...
  ]
}

Где <rule> представляет собой отдельное правило в формате:

{
  "schema": "<regexp схемы>",
  "table": "<regexp таблицы>",
  "partition_filter": "<предикат ключа партиционирования таблицы>",
  "distribution_mode": "<способ распределения сплитов по узлам>"
  "disable_cache": <флаг отключения кэширования>
}

Подробное описание полей правила:

  • schema: обязательное поле; задает паттерн для схем в формате Java Pattern. Например, .* соответствует всем схемам, s1 соответствует схеме s1, s1|s2 соответствует схемам s1 и s2

  • table: опциональное поле; задает паттерн для таблиц в формате Java Pattern. Например, .* соответствует всем таблицам, t1 соответствует таблице t1, t1|t2 соответствует таблицам t1 и t2 Отсутствие значения эквивалентно паттерну .* (все таблицы подходят).

  • partition_filter: опциональное поле; задает фильтр партиции в виде SQL выражения. Выражение может ссылаться на колонки партиции таблицы и использовать стандартные функции. Выражение не может обращать колонки, которые не входят в ключ партиции, а так же содержать подзапросы и вызовы табличных функций. Вызов функций происходит от имени специального системного пользователя, на которого не распространяются проверки доступа к функциям. Отсутствие значение эквивалентно фильтру true (все партиции подходят). Если значение partition_filter задано, правило не может иметь disable_cache: true.

  • distribution_mode: опциональное поле, поддерживается только для Hive коннектора; задает способ сопоставления сплитов с worker-узлами. Допустимые значения: ARBITRARY (значение по умолчанию), PARTITION_KEY. Для повышения эффективности кэша CedrusData стремится обрабатывать конкретный сплит (файл или его часть) на одном и том же узле. В режиме ARBITRARY узел для обработки сплита будет определен путем вычисления хэша пути файла и ряда других свойств сплита. В режиме PARTITION_KEY узел для обработки сплита будет определен путем вычисления хэша от ключа партиции сплита. Данный режим может быть полезен, когда кэшируемая таблица является партицированной, и по крайней мере некоторые запросы используют partitioned режим выполнения запросов.

  • disable_cache: опциональный флаг; позволяет отключить кэширование заданных объектов. Значение true означает, что кэширование соответствующих правилу объектов отключено. Значения false и null означают, что кэширование соответствующих правилу объектов включено. Поле не может иметь значение true, если задан partition_filter.

Обработка правил происходит в порядке их указания в файле сверху вниз. Проверка, кэшировать ли данные из текущей таблицы или партиции, завершается, как только найдено первое подходящее правило.

Ниже приведен полный пример файла правил, который включает кэширование для всех таблиц схем s1 и s2, кроме таблицы s2.excluded_table, а так же для таблицы s3.partitioned_table, в которой закэшированы будут только партиции продаж за последний месяц:

{
  "rules": [
    {
      "schema": "s2",
      "table": "excluded_table",
      "disable_cache": true
    },
    {
      "schema": "s1|s2"
    },
    {
      "schema": "s3",
      "table": "partitioned_table",
      "partition_filter": "sales_date + interval '1' month <= current_date"
    }
  ]
}

Вы можете изменять содержимое файла без перезапуска узла. Повторное чтение содержимого файла происходит периодически в соответствии с параметром конфигурации cedrusdata.hive.data-cache.rules.refresh-period.

Конфигурация#

Название

Описание

Значение по умолчанию

cedrusdata.hive.data-cache.enabled

Использовать ли локальный кэш данных. Параметр сессии: cedrusdata_data_cache_enabled.

false

cedrusdata.hive.data-cache.rules.file

Путь к файлу с правилами кэширования в формате JSON.

cedrusdata.hive.data-cache.rules.refresh-period

Как частно повторно считывать правила кэширования из файла, путь к которому задан в cedrusdata.hive.data-cache.rules.file.

1m (одна минута)

cedrusdata.hive.data-cache.rules.partition-filter-time-zone

Имя часового пояса, в контексте которого происходит вычисление предикатов партиций.

Имя текущего часового пояса JVM

cedrusdata.hive.data-cache.rules.cache-size

Размер кэша, в котором хранится решение о кэшировании и скомпилированный предикат партиции (при наличии) для таблиц. Значение 0 отключает кэширование (не рекомендовано).

1000

cedrusdata.hive.data-cache.path

Путь к локальной директории узла, в котором будут сохранены закэшированные данные. Путь должен быть задан в формате file:///<абсолютный путь к директории>. При первом включении кэша CedrusData данная директория должна быть пустой, в противном случае попытка запуска узла завершится ошибкой. Если директория является пустой, то при первом запуске CedrusData создаст скрытый файл в директории, который указывает, что данная директория является локальным кэшем. При перезапуске узла локальный кэш будет повторно инициализирован из директории. При миграции на новую версию CedrusData необходимо вручную очистить директорию кэша, в противном случае попытка запуска узла завершится ошибкой.

cedrusdata.hive.data-cache.max-size

Максимальный размер кэша данных. При превышении размера CedrusData начнет удаление наиболее редко используемых данных. Данный размер учитывает реальный размер данных на диске с учетом возможной компрессии (см. ниже).

100GB

cedrusdata.hive.data-cache.ttl

Максимальное время хранения записи в кэше. По истечении данного времени запись будет удалена из кэша.

1d (один день)

cedrusdata.hive.data-cache.compressor

Режим компрессии закэшированных данных. При отсутствии компрессии данные занимают больше места на диске, но при этом требуют меньше ресурсов CPU для чтения. При включенной компрессии данные занимают меньше месте на диске, но каждая операция чтения потребляет больше CPU. Принимайте решение о включении компрессии на основе того, какой ресурс узла является более дефицитным. Доступные значения: NONE - не сжимать данные, LZ4 - сжимать данные с помощью алгоритма LZ4.

NONE

cedrusdata.hive.data-cache.cleanup-period

Как часто производить очистку кэша от устаревших записей. Запись считается устаревшей, если истек ее TTL, заданный параметром cedrusdata.hive.data-cache.ttl, или если предикат партиции, заданный полем partition_filter правила кэширования, стал возвращать false.

1m (одна минута)

cedrusdata.hive.data-cache.block-row-count

Сколько записей возвращать движку CedrusData при чтении данных из кэша. Используется для тонкой настройки производительности. В большинстве случаев его изменение не требуется.

8192

cedrusdata.hive.data-cache.max-pending-writes

Максимальное количество операций кэширования удаленных данных. Когда CedrusData обнаруживает, что удаленные данные соответствуют заданным правилам кэширования, но отсутствуют в кэше, происходит асинхронное кэширование данных, которое требует повторное удаленное чтение. Таким образом, при прогреве кэша могут возникать всплески сетевой и дисковой I/O активности, которые могут негативно сказаться на производительности текущих запросов. Для уменьшения негативного эффекта вы можете задать максимальное количество запросов на запись данных в кэш.

100000

cedrusdata.hive.data-cache.read-buffer-size

Размер буфера при чтении данных с диска. Увеличения размера буфера приводит к уменьшению количества IOPS, требуемых для чтения данных, но так же увеличивает потребление памяти. Значение по умолчанию должно хорошо справляться с большинством типичных нагрузок. Настройка данного параметра может быть полезна в облачных окружениях, которые зачастую ограничивают количество IOPS в секунду. Обратите внимание, что размерность «килобайт» необходимо указывать как kB (строчная буква k).

16kB

cedrusdata.hive.data-cache.write-buffer-size

Размер буфера для записи данных на диск. Увеличения размера буфера приводит к уменьшению количества IOPS, требуемых для записи данных, но так же увеличивает потребление памяти. Значение по умолчанию должно хорошо справляться с большинством типичных нагрузок. Настройка данного параметра может быть полезна в облачных окружениях, которые зачастую ограничивают количество IOPS в секунду. Обратите внимание, что размерность «килобайт» необходимо указывать как kB (строчная буква k).

16kB

Для отчистки кэша конкретного каталога воспользуйтесь встроенной процедурой system.cedrusdata.clear_data_cache. Единственным аргументом процедуры является название каталога. Следующий запрос очищает кэш каталога my_data_lake:

CALL system.cedrusdata.clear_data_cache('my_data_lake');

Статистики работы кэша доступны через таблицу JMX коннектор trino.plugin.hive.datacache:name=<имя каталога>,type=hivedatacacheservice. Следующий запрос отображает текущие статистики кэша каталога my_data_lake:

SELECT * FROM jmx."current"."trino.plugin.hive.datacache:name=my_data_lake,type=hivedatacacheservice"

Режим чтения данных «soft affinity»#

По умолчанию коннектор распределяет чтение отдельных частей файлов озера данных по узлам случайным образом в зависимости от нагрузки на узлы. Это позволяет хорошо балансировать нагрузку между узлами, но в то же время снижает эффективность работы локального кэша данных, так как части одного и того же файла могут оказаться многократно закэшированы на разных узлах.

Режим «soft affinity» обеспечивает чтение одной и той же части файла озера данных на одном и том же узле при условии, что узел не перегружен. Включение данного режима существенно повышает эффективность локального кэша данных.

Конфигурация#

Название

Описание

Значение по умолчанию

cedrusdata.iceberg.soft-affinity-scheduling.enabled

Использовать ли режим чтения данных soft affinity. Параметр сессии: cedrusdata_soft_affinity_scheduling_enabled.

false

Оптимизация запросов к partitioned таблицам#

Если таблица Iceberg была создана с параметром partitioning, то CedrusData может использовать информацию о схеме партиционирования для выбора более оптимального плана запроса.

Наибольшее ускорение ожидается для запросов, в которых присутствуют операторы Join и Aggregation. Например:

SELECT ...
FROM t1 JOIN t2
ON t1.partitioning_column = t2.other_column
SELECT a, partitioning_column, b, sum(c)
FROM t
GROUP BY a, partitioning_column, b

Ускорение запросов происходит за счет того, что оптимизатор использует информацию о ключе партиционирования для выбора более быстрого способа выполнения того или иного оператора. Например, для осуществления группировки по двум атрибутам GROUP BY a, b CedrusData обычно осуществляет предварительную группировку локально на узлах, после чего пересылает полученные данные между узлами с помощью оператора Exchange, чтобы осуществить финальную группировку:

Parent
  Aggregation[FINAL, groupBy=[a,b]]
    Exchange
      Aggregation[PARTIAL, groupBy=[a,b]]
        TableScan

Если же одна из колонок a или b является ключом партиционирования таблицы, оптимизатор CedrusData использует эту информацию, чтобы осуществить полную группировку локально, тем самым упрощая план запроса:

Parent
  Aggregation[FINAL, groupBy=[a,b]]
    TableScan
Конфигурация#

Название

Описание

Значение по умолчанию

cedrusdata.iceberg.partition-execution

Использовать ли информацию о схеме партиционирования таблиц Iceberg для оптимизации запросов. Параметр сессии: cedrusdata_partition_execution_enabled.

false

cedrusdata.iceberg.partition-execution.bucket-count

Фиксированное количество бакетов при работе с partitioned таблицами. Данный параметр используется для тонкой настройкой производительности чтений partitioned таблиц. Оптимальным является значение, которое не меньше суммарного количества физических процессорных ядер в кластере. Значение по умолчанию является оптимальным для большинства сценариев за исключением очень больших кластеров, содержащих более тысячи физических ядер.

1024

Table statistics#

The Iceberg connector can collect column statistics using ANALYZE statement. This can be disabled using iceberg.extended-statistics.enabled catalog configuration property, or the corresponding extended_statistics_enabled session property.

Updating table statistics#

If your queries are complex and include joining large data sets, running ANALYZE on tables may improve query performance by collecting statistical information about the data:

ANALYZE table_name

This query collects statistics for all columns.

On wide tables, collecting statistics for all columns can be expensive. It is also typically unnecessary - statistics are only useful on specific columns, like join keys, predicates, or grouping keys. You can specify a subset of columns to analyzed with the optional columns property:

ANALYZE table_name WITH (columns = ARRAY['col_1', 'col_2'])

This query collects statistics for columns col_1 and col_2.

Note that if statistics were previously collected for all columns, they must be dropped using the drop_extended_stats command before re-analyzing.

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 коннектор, Iceberg коннектор 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 example.example_schema;

EXPLAIN SELECT * FROM example_table;
                               Query Plan
-------------------------------------------------------------------------
Fragment 0 [SOURCE]
     ...
     Output[columnNames = [...]]
     │   ...
     └─ TableScan[table = another_catalog:example_schema:example_table]
            ...

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

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

EXPLAIN SELECT * FROM example.example_schema.example_table;
                               Query Plan
-------------------------------------------------------------------------
Fragment 0 [SOURCE]
     ...
     Output[columnNames = [...]]
     │   ...
     └─ TableScan[table = another_catalog:example_schema:example_table]
            ...

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.