Delta Lake connector#

Примечание

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

The Delta Lake connector allows querying data stored in the Delta Lake format, including Databricks Delta Lake. The connector can natively read the Delta Lake transaction log and thus detect when external systems change data.

Requirements#

To connect to Databricks Delta Lake, you need:

  • Tables written by Databricks Runtime 7.3 LTS, 9.1 LTS, 10.4 LTS, 11.3 LTS, 12.2 LTS and 13.3 LTS are supported.

  • Deployments using AWS, HDFS, Azure Storage, and Google Cloud Storage (GCS) are fully supported.

  • Network access from the coordinator and workers to the Delta Lake storage.

  • Access to the Hive metastore service (HMS) of Delta Lake or a separate HMS, or a Glue metastore.

  • Network access to the HMS from the coordinator and workers. Port 9083 is the default port for the Thrift protocol used by the HMS.

  • Data files stored in the Parquet file format. These can be configured using file format configuration properties per catalog.

General configuration#

To configure the Delta Lake connector, create a catalog properties file etc/catalog/example.properties that references the delta_lake connector and defines a metastore. You must configure a metastore for table metadata. If you are using a Hive metastore, hive.metastore.uri must be configured:

connector.name=delta_lake
hive.metastore.uri=thrift://example.net:9083

If you are using AWS Glue as your metastore, you must instead set hive.metastore to glue:

connector.name=delta_lake
hive.metastore=glue

Each metastore type has specific configuration properties along with general metastore configuration properties.

The connector recognizes Delta Lake tables created in the metastore by the Databricks runtime. If non-Delta Lake tables are present in the metastore as well, they are not visible to the connector.

To configure access to S3 and S3-compatible storage, Azure storage, and others, consult the appropriate section of the Hive documentation:

Delta Lake general configuration properties#

The following configuration properties are all using reasonable, tested default values. Typical usage does not require you to configure them.

Delta Lake configuration properties#

Property name

Description

Default

delta.metadata.cache-ttl

Frequency of checks for metadata updates equivalent to transactions to update the metadata cache specified in duration.

5m

delta.metadata.cache-size

The maximum number of Delta table metadata entries to cache.

1000

delta.metadata.live-files.cache-size

Amount of memory allocated for caching information about files. Must be specified in data size values such as 64MB. Default is calculated to 10% of the maximum memory allocated to the JVM.

delta.metadata.live-files.cache-ttl

Caching duration for active files that correspond to the Delta Lake tables.

30m

delta.compression-codec

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

  • NONE

  • SNAPPY

  • ZSTD

  • GZIP

The equivalent catalog session property is compression_codec.

SNAPPY

delta.max-partitions-per-writer

Maximum number of partitions per writer.

100

delta.hide-non-delta-lake-tables

Hide information about tables that are not managed by Delta Lake. Hiding only applies to tables with the metadata managed in a Glue catalog, and does not apply to usage with a Hive metastore service.

false

delta.enable-non-concurrent-writes

Enable write support for all supported file systems. Specifically, take note of the warning about concurrency and checkpoints.

false

delta.default-checkpoint-writing-interval

Default integer count to write transaction log checkpoint entries. If the value is set to N, then checkpoints are written after every Nth statement performing table writes. The value can be overridden for a specific table with the checkpoint_interval table property.

10

delta.hive-catalog-name

Name of the catalog to which SELECT queries are redirected when a Hive table is detected.

delta.checkpoint-row-statistics-writing.enabled

Enable writing row statistics to checkpoint files.

true

delta.dynamic-filtering.wait-timeout

Duration to wait for completion of dynamic filtering during split generation. The equivalent catalog session property is dynamic_filtering_wait_timeout.

delta.table-statistics-enabled

Enables Table statistics for performance improvements. The equivalent catalog session property is statistics_enabled.

true

delta.extended-statistics.enabled

Enable statistics collection with ANALYZE and use of extended statistics. The equivalent catalog session property is extended_statistics_enabled.

true

delta.extended-statistics.collect-on-write

Enable collection of extended statistics for write operations. The equivalent catalog session property is extended_statistics_collect_on_write.

true

delta.per-transaction-metastore-cache-maximum-size

Maximum number of metastore data objects per transaction in the Hive metastore cache.

1000

delta.delete-schema-locations-fallback

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

false

delta.parquet.time-zone

Time zone for Parquet read and write.

JVM default

delta.target-max-file-size

Target maximum size of written files; the actual size could be larger. The equivalent catalog session property is target_max_file_size.

1GB

delta.unique-table-location

Use randomized, unique table locations.

true

delta.register-table-procedure.enabled

Enable to allow users to call the register_table procedure.

false

delta.vacuum.min-retention

Minimum retention threshold for the files taken into account for removal by the VACUUM procedure. The equivalent catalog session property is vacuum_min_retention.

7 DAYS

Catalog session properties#

The following table describes catalog session properties supported by the Delta Lake connector:

Catalog session properties#

Property name

Description

Default

parquet_max_read_block_size

The maximum block size used when reading Parquet files.

16MB

parquet_writer_block_size

The maximum block size created by the Parquet writer.

128MB

parquet_writer_page_size

The maximum page size created by the Parquet writer.

1MB

parquet_writer_batch_size

Maximum number of rows processed by the Parquet writer in a batch.

10000

projection_pushdown_enabled

Read only projected fields from row columns while performing SELECT queries

true

Type mapping#

Because Trino and Delta Lake each support types that the other does not, this connector modifies some types when reading or writing data. Data types might 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.

See the Delta Transaction Log specification for more information about supported data types in the Delta Lake table format specification.

Delta Lake to Trino type mapping#

The connector maps Delta Lake types to the corresponding Trino types following this table:

Delta Lake to Trino type mapping#

Delta Lake type

Trino type

BOOLEAN

BOOLEAN

INTEGER

INTEGER

BYTE

TINYINT

SHORT

SMALLINT

LONG

BIGINT

FLOAT

REAL

DOUBLE

DOUBLE

DECIMAL(p,s)

DECIMAL(p,s)

STRING

VARCHAR

BINARY

VARBINARY

DATE

DATE

TIMESTAMPNTZ (TIMESTAMP_NTZ)

TIMESTAMP(6)

TIMESTAMP

TIMESTAMP(3) WITH TIME ZONE

ARRAY

ARRAY

MAP

MAP

STRUCT(...)

ROW(...)

No other types are supported.

Trino to Delta Lake type mapping#

The connector maps Trino types to the corresponding Delta Lake types following this table:

Trino to Delta Lake type mapping#

Trino type

Delta Lake type

BOOLEAN

BOOLEAN

INTEGER

INTEGER

TINYINT

BYTE

SMALLINT

SHORT

BIGINT

LONG

REAL

FLOAT

DOUBLE

DOUBLE

DECIMAL(p,s)

DECIMAL(p,s)

VARCHAR

STRING

VARBINARY

BINARY

DATE

DATE

TIMESTAMP

TIMESTAMPNTZ (TIMESTAMP_NTZ)

TIMESTAMP(3) WITH TIME ZONE

TIMESTAMP

ARRAY

ARRAY

MAP

MAP

ROW(...)

STRUCT(...)

No other types are supported.

Security#

The Delta Lake connector allows you to choose one of several means of providing authorization at the catalog level. You can select a different type of authorization check in different Delta Lake catalog files.

Authorization checks#

Enable authorization checks for the connector by setting the delta.security property in the catalog properties file. This property must be one of the security values in the following table:

Delta Lake security values#

Property value

Description

ALLOW_ALL (default value)

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#

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

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 table into the metastore with existing transaction logs and data files.

The system.register_table procedure allows the caller to register an existing Delta Lake table in the metastore, using its existing transaction logs and data files:

CALL example.system.register_table(schema_name => 'testdb', table_name => 'customer_orders', table_location => 's3://my-bucket/a/path')

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

Unregister table#

The connector can unregister existing Delta Lake tables from the metastore.

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

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

Flush metadata cache#

  • system.flush_metadata_cache()

    Flushes all metadata caches.

  • system.flush_metadata_cache(schema_name => ..., table_name => ...)

    Flushes metadata cache entries of a specific table. Procedure requires passing named parameters.

VACUUM#

The VACUUM procedure removes all old files that are not in the transaction log, as well as files that are not needed to read table snapshots newer than the current time minus the retention period defined by the retention period parameter.

Users with INSERT and DELETE permissions on a table can run VACUUM as follows:

CALL example.system.vacuum('exampleschemaname', 'exampletablename', '7d');

All parameters are required and must be presented in the following order:

  • Schema name

  • Table name

  • Retention period

The delta.vacuum.min-retention configuration property provides a safety measure to ensure that files are retained as expected. The minimum value for this property is 0s. There is a minimum retention session property as well, vacuum_min_retention.

Data management#

You can use the connector to INSERT, DELETE, UPDATE, and MERGE data in Delta Lake tables.

Write operations are supported for tables stored on the following systems:

  • Azure ADLS Gen2, Google Cloud Storage

    Writes to the Azure ADLS Gen2 and Google Cloud Storage are enabled by default. Trino detects write collisions on these storage systems when writing from multiple Trino clusters, or from other query engines.

  • S3 and S3-compatible storage

    Writes to Amazon S3 and S3-compatible storage must be enabled with the delta.enable-non-concurrent-writes property. Writes to S3 can safely be made from multiple Trino clusters; however, write collisions are not detected when writing concurrently from other Delta Lake engines. You must make sure that no concurrent data modifications are run to avoid data corruption.

Schema and table management#

The Схемы и таблицы functionality includes support for:

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. Tables in this schema are located in a subdirectory under the schema location. Data files for tables in this schema using the default location are cleaned up if the table is dropped:

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

Optionally, the location can be omitted. Tables in this schema must have a location included when you create them. The data files for these tables are not removed if the table is dropped:

CREATE SCHEMA example.example_schema;

When Delta Lake tables exist in storage but not in the metastore, Trino can be used to register the tables:

CREATE TABLE example.default.example_table (
  dummy BIGINT
)
WITH (
  location = '...'
)

Columns listed in the DDL, such as dummy in the preceding example, are ignored. The table schema is read from the transaction log instead. If the schema is changed by an external system, Trino automatically uses the new schema.

Предупреждение

Using CREATE TABLE with an existing table content is deprecated, instead use the system.register_table procedure. The CREATE TABLE ... WITH (location=...) syntax can be temporarily re-enabled using the delta.legacy-create-table-with-existing-location.enabled catalog configuration property or legacy_create_table_with_existing_location_enabled catalog session property.

If the specified location does not already contain a Delta table, the connector automatically writes the initial transaction log entries and registers the table in the metastore. As a result, any Databricks engine can write to the table:

CREATE TABLE example.default.new_table (id BIGINT, address VARCHAR);

The Delta Lake connector also supports creating tables using the CREATE TABLE AS syntax.

The connector supports the following ALTER TABLE statements.

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

ALTER TABLE RENAME TO#

The connector only supports the ALTER TABLE RENAME TO statement when met with one of the following conditions:

  • The table type is external.

  • The table is backed by a metastore that does not perform object storage operations, for example, AWS Glue or Thrift.

Table properties#

The following table properties are available for use:

Delta Lake table properties#

Property name

Description

location

File system location URI for the table.

partitioned_by

Set partition columns.

checkpoint_interval

Set the checkpoint interval in number of table writes.

change_data_feed_enabled

Enables storing change data feed entries.

column_mapping_mode

Column mapping mode. Possible values are:

  • ID

  • NAME

  • NONE

Defaults to NONE.

The following example uses all available table properties:

CREATE TABLE example.default.example_partitioned_table
WITH (
  location = 's3://my-bucket/a/path',
  partitioned_by = ARRAY['regionkey'],
  checkpoint_interval = 5,
  change_data_feed_enabled = false,
  column_mapping_mode = 'name'
)
AS SELECT name, comment, regionkey FROM tpch.tiny.nation;

Metadata tables#

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

SELECT * FROM "test_table$history"
$history table#

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

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

SELECT * FROM "test_table$history"
 version |               timestamp               | user_id | user_name |  operation   |         operation_parameters          |                 cluster_id      | read_version |  isolation_level  | is_blind_append
---------+---------------------------------------+---------+-----------+--------------+---------------------------------------+---------------------------------+--------------+-------------------+----------------
       2 | 2023-01-19 07:40:54.684 Europe/Vienna | trino   | trino     | WRITE        | {queryId=20230119_064054_00008_4vq5t} | trino-406-trino-coordinator     |            2 | WriteSerializable | true
       1 | 2023-01-19 07:40:41.373 Europe/Vienna | trino   | trino     | ADD COLUMNS  | {queryId=20230119_064041_00007_4vq5t} | trino-406-trino-coordinator     |            0 | WriteSerializable | true
       0 | 2023-01-19 07:40:10.497 Europe/Vienna | trino   | trino     | CREATE TABLE | {queryId=20230119_064010_00005_4vq5t} | trino-406-trino-coordinator     |            0 | WriteSerializable | true

The output of the query has the following history columns:

History columns#

Name

Type

Description

version

BIGINT

The version of the table corresponding to the operation

timestamp

TIMESTAMP(3) WITH TIME ZONE

The time when the table version became active

user_id

VARCHAR

The identifier for the user which performed the operation

user_name

VARCHAR

The username for the user which performed the operation

operation

VARCHAR

The name of the operation performed on the table

operation_parameters

map(VARCHAR, VARCHAR)

Parameters of the operation

cluster_id

VARCHAR

The ID of the cluster which ran the operation

read_version

BIGINT

The version of the table which was read in order to perform the operation

isolation_level

VARCHAR

The level of isolation used to perform the operation

is_blind_append

BOOLEAN

Whether or not the operation appended data

$properties table#

The $properties table provides access to Delta Lake table configuration, table features and table properties. The table rows are key/value pairs.

You can retrieve the properties of the Delta table test_table by using the following query:

SELECT * FROM "test_table$properties"
 key                        | value           |
----------------------------+-----------------+
delta.minReaderVersion      | 1               |
delta.minWriterVersion      | 4               |
delta.columnMapping.mode    | name            |
delta.feature.columnMapping | supported       |

Metadata columns#

In addition to the defined columns, the Delta Lake connector automatically exposes metadata in a number of hidden columns in each table. You can use these columns in your SQL statements like any other column, e.g., they can be selected directly or used in conditional statements.

  • $path

    Full file system path name of the file for this row.

    - $file_modified_time

    Date and time of the last modification of the file for this row.

    - $file_size

    Size of the file for this row.

Fault-tolerant execution support#

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

Table functions#

The connector provides the following table functions:

table_changes#

Allows reading Change Data Feed (CDF) entries to expose row-level changes between two versions of a Delta Lake table. When the change_data_feed_enabled table property is set to true on a specific Delta Lake table, the connector records change events for all data changes on the table. This is how these changes can be read:

SELECT
  *
FROM
  TABLE(
    system.table_changes(
      schema_name => 'test_schema',
      table_name => 'tableName',
      since_version => 0
    )
  );

schema_name - type VARCHAR, required, name of the schema for which the function is called

table_name - type VARCHAR, required, name of the table for which the function is called

since_version - type BIGINT, optional, version from which changes are shown, exclusive

In addition to returning the columns present in the table, the function returns the following values for each change event:

  • _change_type

    Gives the type of change that occurred. Possible values are insert, delete, update_preimage and update_postimage.

    - _commit_version

    Shows the table version for which the change occurred.

    - _commit_timestamp

    Represents the timestamp for the commit in which the specified change happened.

This is how it would be normally used:

Create table:

CREATE TABLE test_schema.pages (page_url VARCHAR, domain VARCHAR, views INTEGER)
    WITH (change_data_feed_enabled = true);

Insert data:

INSERT INTO test_schema.pages
    VALUES
        ('url1', 'domain1', 1),
        ('url2', 'domain2', 2),
        ('url3', 'domain1', 3);
INSERT INTO test_schema.pages
    VALUES
        ('url4', 'domain1', 400),
        ('url5', 'domain2', 500),
        ('url6', 'domain3', 2);

Update data:

UPDATE test_schema.pages
    SET domain = 'domain4'
    WHERE views = 2;

Select changes:

SELECT
  *
FROM
  TABLE(
    system.table_changes(
      schema_name => 'test_schema',
      table_name => 'pages',
      since_version => 1
    )
  )
ORDER BY _commit_version ASC;

The preceding sequence of SQL statements returns the following result:

page_url    |     domain     |    views    |    _change_type     |    _commit_version    |    _commit_timestamp
url4        |     domain1    |    400      |    insert           |     2                 |    2023-03-10T21:22:23.000+0000
url5        |     domain2    |    500      |    insert           |     2                 |    2023-03-10T21:22:23.000+0000
url6        |     domain3    |    2        |    insert           |     2                 |    2023-03-10T21:22:23.000+0000
url2        |     domain2    |    2        |    update_preimage  |     3                 |    2023-03-10T22:23:24.000+0000
url2        |     domain4    |    2        |    update_postimage |     3                 |    2023-03-10T22:23:24.000+0000
url6        |     domain3    |    2        |    update_preimage  |     3                 |    2023-03-10T22:23:24.000+0000
url6        |     domain4    |    2        |    update_postimage |     3                 |    2023-03-10T22:23:24.000+0000

The output shows what changes happen in which version. For example in version 3 two rows were modified, first one changed from ('url2', 'domain2', 2) into ('url2', 'domain4', 2) and the second from ('url6', 'domain2', 2) into ('url6', 'domain4', 2).

If since_version is not provided the function produces change events starting from when the table was created.

SELECT
  *
FROM
  TABLE(
    system.table_changes(
      schema_name => 'test_schema',
      table_name => 'pages'
    )
  )
ORDER BY _commit_version ASC;

The preceding SQL statement returns the following result:

page_url    |     domain     |    views    |    _change_type     |    _commit_version    |    _commit_timestamp
url1        |     domain1    |    1        |    insert           |     1                 |    2023-03-10T20:21:22.000+0000
url2        |     domain2    |    2        |    insert           |     1                 |    2023-03-10T20:21:22.000+0000
url3        |     domain1    |    3        |    insert           |     1                 |    2023-03-10T20:21:22.000+0000
url4        |     domain1    |    400      |    insert           |     2                 |    2023-03-10T21:22:23.000+0000
url5        |     domain2    |    500      |    insert           |     2                 |    2023-03-10T21:22:23.000+0000
url6        |     domain3    |    2        |    insert           |     2                 |    2023-03-10T21:22:23.000+0000
url2        |     domain2    |    2        |    update_preimage  |     3                 |    2023-03-10T22:23:24.000+0000
url2        |     domain4    |    2        |    update_postimage |     3                 |    2023-03-10T22:23:24.000+0000
url6        |     domain3    |    2        |    update_preimage  |     3                 |    2023-03-10T22:23:24.000+0000
url6        |     domain4    |    2        |    update_postimage |     3                 |    2023-03-10T22:23:24.000+0000

You can see changes that occurred at version 1 as three inserts. They are not visible in the previous statement when since_version value was set to 1.

Performance#

The connector includes a number of performance improvements detailed in the following sections:

Table statistics#

Use ANALYZE statements in Trino to populate data size and number of distinct values (NDV) extended table statistics in Delta Lake. The minimum value, maximum value, value count, and null value count statistics are computed on the fly out of the transaction log of the Delta Lake table. The cost-based optimizer then uses these statistics to improve query performance.

Extended statistics enable a broader set of optimizations, including join reordering. The controlling catalog property delta.table-statistics-enabled is enabled by default. The equivalent catalog session property is statistics_enabled.

Each ANALYZE statement updates the table statistics incrementally, so only the data changed since the last ANALYZE is counted. The table statistics are not automatically updated by write operations such as INSERT, UPDATE, and DELETE. You must manually run ANALYZE again to update the table statistics.

To collect statistics for a table, execute the following statement:

ANALYZE table_schema.table_name;

To recalculate from scratch the statistics for the table use additional parameter mode:

ANALYZE table_schema.table_name WITH(mode = „full_refresh“);

There are two modes available full_refresh and incremental. The procedure use incremental by default.

To gain the most benefit from cost-based optimizations, run periodic ANALYZE statements on every large table that is frequently queried.

Fine-tuning#

The files_modified_after property is useful if you want to run the ANALYZE statement on a table that was previously analyzed. You can use it to limit the amount of data used to generate the table statistics:

ANALYZE example_table WITH(files_modified_after = TIMESTAMP '2021-08-23
16:43:01.321 Z')

As a result, only files newer than the specified time stamp are used in the analysis.

You can also specify a set or subset of columns to analyze using the columns property:

ANALYZE example_table WITH(columns = ARRAY['nationkey', 'regionkey'])

To run ANALYZE with columns more than once, the next ANALYZE must run on the same set or a subset of the original columns used.

To broaden the set of columns, drop the statistics and reanalyze the table.

Disable and drop extended statistics#

You can disable extended statistics with the catalog configuration property delta.extended-statistics.enabled set to false. Alternatively, you can disable it for a session, with the catalog session property extended_statistics_enabled set to false.

If a table is changed with many delete and update operation, calling ANALYZE does not result in accurate statistics. To correct the statistics, you have to drop the extended statistics and analyze the table again.

Use the system.drop_extended_stats procedure in the catalog to drop the extended statistics for a specified table in a specified schema:

CALL example.system.drop_extended_stats('example_schema', 'example_table')

Memory usage#

The Delta Lake connector is memory intensive and the amount of required memory grows with the size of Delta Lake transaction logs of any accessed tables. It is important to take that into account when provisioning the coordinator.

You must decrease memory usage by keeping the number of active data files in the table low by regularly running OPTIMIZE and VACUUM in Delta Lake.

Memory monitoring#

When using the Delta Lake connector, you must monitor memory usage on the coordinator. Specifically, monitor JVM heap utilization using standard tools as part of routine operation of the cluster.

A good proxy for memory usage is the cache utilization of Delta Lake caches. It is exposed by the connector with the plugin.deltalake.transactionlog:name=<catalog-name>,type=transactionlogaccess JMX bean.

You can access it with any standard monitoring software with JMX support, or use the JMX коннектор with the following query:

SELECT * FROM jmx.current."*.plugin.deltalake.transactionlog:name=<catalog-name>,type=transactionlogaccess"

Following is an example result:

datafilemetadatacachestats.hitrate      | 0.97
datafilemetadatacachestats.missrate     | 0.03
datafilemetadatacachestats.requestcount | 3232
metadatacachestats.hitrate              | 0.98
metadatacachestats.missrate             | 0.02
metadatacachestats.requestcount         | 6783
node                                    | trino-master
object_name                             | io.trino.plugin.deltalake.transactionlog:type=TransactionLogAccess,name=delta

In a healthy system, both datafilemetadatacachestats.hitrate and metadatacachestats.hitrate are close to 1.0.

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 Delta Lake tables to Hive tables with the delta.hive-catalog-name catalog configuration property.

Performance tuning configuration properties#

The following table describes performance tuning catalog properties specific to the Delta Lake connector.

Предупреждение

Performance tuning configuration properties are considered expert-level features. Altering these properties from their default values is likely to cause instability and performance degradation. It is strongly suggested that you use them only to address non-trivial performance issues, and that you keep a backup of the original values if you change them.

Delta Lake performance tuning configuration properties#

Property name

Description

Default

delta.domain-compaction-threshold

Minimum size of query predicates above which Trino compacts the predicates. Pushing a large list of predicates down to the data source can compromise performance. For optimization in that situation, Trino can compact the large predicates. If necessary, adjust the threshold to ensure a balance between performance and predicate pushdown.

1000

delta.max-outstanding-splits

The target number of buffered splits for each table scan in a query, before the scheduler tries to pause.

1000

delta.max-splits-per-second

Sets the maximum number of splits used per second to access underlying storage. Reduce this number if your limit is routinely exceeded, based on your filesystem limits. This is set to the absolute maximum value, which results in Trino maximizing the parallelization of data access by default. Attempting to set it higher results in Trino not being able to start.

Integer.MAX_VALUE

delta.max-initial-splits

For each query, the coordinator assigns file sections to read first at the initial-split-size until the max-initial-splits is reached. Then it starts issuing reads of the max-split-size size.

200

delta.max-initial-split-size

Sets the initial data size for a single read section assigned to a worker until max-initial-splits have been processed. You can also use the corresponding catalog session property <catalog-name>.max_initial_split_size.

32MB

delta.max-split-size

Sets the largest data size for a single read section assigned to a worker after max-initial-splits have been processed. You can also use the corresponding catalog session property <catalog-name>.max_split_size.

64MB

delta.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 might improve performance on tables with small files. A higher value might improve performance for queries with highly skewed aggregations or joins.

0.05

delta.projection-pushdown-enabled

Read only projected fields from row columns while performing SELECT queries

true

delta.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