Hive connector

The Hive connector allows querying data stored in an Apache Hive data warehouse. Hive is a combination of three components:

• Data files in varying formats, that are typically stored in the Hadoop Distributed File System (HDFS) or in object storage systems such as Amazon S3.
• Metadata about how the data files are mapped to schemas and tables. This metadata is stored in a database, such as MySQL, and is accessed via the Hive metastore service.
• A query language called HiveQL. This query language is executed on a distributed computing framework such as MapReduce or Tez.

Trino only uses the first two components: the data and the metadata. It does not use HiveQL or any part of Hive's execution environment.

Requirements

The Hive connector requires a Hive metastore service (HMS), or a compatible implementation of the Hive metastore, such as AWS Glue Data Catalog.

Apache Hadoop 2.x and 3.x are supported, along with derivative distributions, including Cloudera CDH 5 and Hortonworks Data Platform (HDP).

Many distributed storage systems including HDFS, Amazon S3 or S3-compatible systems, Google Cloud Storage, and Azure Storage can be queried with the Hive connector.

The coordinator and all workers must have network access to the Hive metastore and the storage system. Hive metastore access with the Thrift protocol defaults to using port 9083.

Supported file types

The following file types are supported for the Hive connector:

• ORC
• Parquet
• Avro
• RCText (RCFile using ColumnarSerDe)
• RCBinary (RCFile using LazyBinaryColumnarSerDe)
• SequenceFile
• JSON (using org.apache.hive.hcatalog.data.JsonSerDe)
• CSV (using org.apache.hadoop.hive.serde2.OpenCSVSerde)
• TextFile

Metastore configuration for Avro

In order to enable first-class support for Avro tables when using Hive 3.x, you need to add the following property definition to the Hive metastore configuration file hive-site.xml (and restart the metastore service):

<property>
     <!-- https://community.hortonworks.com/content/supportkb/247055/errorjavalangunsupportedoperationexception-storage.html -->
     <name>metastore.storage.schema.reader.impl</name>
     <value>org.apache.hadoop.hive.metastore.SerDeStorageSchemaReader</value>
 </property>

Materialized views

The Hive connector supports reading from Hive materialized views. In Trino, these views are presented as regular, read-only tables.

Hive views

Hive views are defined in HiveQL and stored in the Hive Metastore Service. They are analyzed to allow read access to the data.

The Hive connector includes support for reading Hive views with three different modes.

• Disabled
• Legacy
• Experimental

You can configure the behavior in your catalog properties file.

By default, Hive views are executed with the RUN AS DEFINER security mode. Set the hive.hive-views.run-as-invoker catalog configuration property to true to use RUN AS INVOKER semantics.

Disabled

The default behavior is to ignore Hive views. This means that your business logic and data encoded in the views is not available in Trino.

Legacy

A very simple implementation to execute Hive views, and therefore allow read access to the data in Trino, can be enabled with hive.hive-views.enabled=true and hive.hive-views.legacy-translation=true.

For temporary usage of the legacy behavior for a specific catalog, you can set the hive_views_legacy_translation catalog session property to true.

This legacy behavior interprets any HiveQL query that defines a view as if it is written in SQL. It does not do any translation, but instead relies on the fact that HiveQL is very similar to SQL.

This works for very simple Hive views, but can lead to problems for more complex queries. For example, if a HiveQL function has an identical signature but different behaviors to the SQL version, the returned results may differ. In more extreme cases the queries might fail, or not even be able to be parsed and executed.

Experimental

The new behavior is better engineered and has the potential to become a lot more powerful than the legacy implementation. It can analyze, process, and rewrite Hive views and contained expressions and statements.

It supports the following Hive view functionality:

• UNION [DISTINCT] and UNION ALL against Hive views
• Nested GROUP BY clauses
• current_user()
• LATERAL VIEW OUTER EXPLODE
• LATERAL VIEW [OUTER] EXPLODE on array of struct
• LATERAL VIEW json_tuple

You can enable the experimental behavior with hive.hive-views.enabled=true. Remove the hive.hive-views.legacy-translation property or set it to false to make sure legacy is not enabled.

Keep in mind that numerous features are not yet implemented when experimenting with this feature. The following is an incomplete list of missing functionality:

• HiveQL current_date, current_timestamp, and others
• Hive function calls including translate(), window functions, and others
• Common table expressions and simple case expressions
• Honor timestamp precision setting
• Support all Hive data types and correct mapping to Trino types
• Ability to process custom UDFs

Configuration

Create etc/catalog/hive.properties with the following contents to mount the hive connector as the hive catalog, replacing example.net:9083 with the correct host and port for your Hive metastore Thrift service:

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

Multiple Hive clusters

You can have as many catalogs as you need, so if you have additional Hive clusters, simply add another properties file to etc/catalog with a different name, making sure it ends in .properties. For example, if you name the property file sales.properties, Trino creates a catalog named sales using the configured connector.

HDFS configuration

For basic setups, Trino configures the HDFS client automatically and does not require any configuration files. In some cases, such as when using federated HDFS or NameNode high availability, it is necessary to specify additional HDFS client options in order to access your HDFS cluster. To do so, add the hive.config.resources property to reference your HDFS config files:

hive.config.resources=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml

Only specify additional configuration files if necessary for your setup. We recommend reducing the configuration files to have the minimum set of required properties, as additional properties may cause problems.

The configuration files must exist on all Trino nodes. If you are referencing existing Hadoop config files, make sure to copy them to any Trino nodes that are not running Hadoop.

HDFS username and permissions

Before running any CREATE TABLE or CREATE TABLE AS statements for Hive tables in Trino, you need to check that the user Trino is using to access HDFS has access to the Hive warehouse directory. The Hive warehouse directory is specified by the configuration variable hive.metastore.warehouse.dir in hive-site.xml, and the default value is /user/hive/warehouse.

When not using Kerberos with HDFS, Trino accesses HDFS using the OS user of the Trino process. For example, if Trino is running as nobody, it accesses HDFS as nobody. You can override this username by setting the HADOOP_USER_NAME system property in the Trino jvm_config, replacing hdfs_user with the appropriate username:

-DHADOOP_USER_NAME=hdfs_user

The hive user generally works, since Hive is often started with the hive user and this user has access to the Hive warehouse.

Whenever you change the user Trino is using to access HDFS, remove /tmp/presto-* on HDFS, as the new user may not have access to the existing temporary directories.

Accessing Hadoop clusters protected with Kerberos authentication

Kerberos authentication is supported for both HDFS and the Hive metastore. However, Kerberos authentication by ticket cache is not yet supported.

The properties that apply to Hive connector security are listed in the Hive Configuration Properties table. Please see the Hive section for a more detailed discussion of the security options in the Hive connector.

Hive configuration properties

Property Name	Description	Default
hive.config.resources	An optional comma-separated list of HDFS configuration files. These files must exist on the machines running Trino. Only specify this if absolutely necessary to access HDFS. Example:/etc/hdfs-site.xml
hive.recursive-directories	Enable reading data from subdirectories of table or partition locations. If disabled, subdirectories are ignored. This is equivalent to the hive.mapred.supports.subdirectories property in Hive.	false
hive.ignore-absent-partitions	Ignore partitions when the file system location does not exist rather than failing the query. This skips data that may be expected to be part of the table.	false
hive.storage-format	The default file format used when creating new tables.	ORC
hive.compression-codec	The compression codec to use when writing files. Possible values are NONE, SNAPPY, LZ4,ZSTD, or GZIP.	GZIP
hive.force-local-scheduling	Force splits to be scheduled on the same node as the Hadoop DataNode process serving the split data. This is useful for installations where Trino is collocated with every DataNode.	false
hive.respect-table-format	Should new partitions be written using the existing table format or the default Trino format?	true
hive.immutable-partitions	Can new data be inserted into existing partitions? Iftrue then settinghive.insert-existing-partitions-behavior toAPPEND is not allowed. This also affects theinsert_existing_partitions_behavior session property in the same way.	false
hive.insert-existing-partitions-behavior	What happens when data is inserted into an existing partition? Possible values are APPEND - appends data to existing partitions OVERWRITE - overwrites existing partitions ERROR - modifying existing partitions is not allowed	APPEND
hive.target-max-file-size	Best effort maximum size of new files.	1GB
hive.create-empty-bucket-files	Should empty files be created for buckets that have no data?	false
hive.partition-statistics-sample-size	Specifies the number of partitions to analyze when computing table statistics.	100
hive.max-partitions-per-writers	Maximum number of partitions per writer.	100
hive.max-partitions-per-scan	Maximum number of partitions for a single table scan.	100,000
hive.hdfs.authentication.type	HDFS authentication type. Possible values are NONEor KERBEROS.	NONE
hive.hdfs.impersonation.enabled hive.hdfs.trino.principal	Enable HDFS end user impersonation. The Kerberos principal that Trino will use when connecting to HDFS.	false
hive.hdfs.trino.keytab	HDFS client keytab location.
hive.dfs.replication	Hadoop file system replication factor.
hive.security security.config-file	See Hive. Path of config file to use when hive.security=file. See catalog-file-based-access-control for details.
hive.non-managed-table-writes-enabled	Enable writes to non-managed (external) Hive tables.	false
hive.non-managed-table-creates-enabled	Enable creating non-managed (external) Hive tables.	true
hive.collect-column-statistics-on-write	Enables automatic column level statistics collection on write. See Table Statistics for details.	true
hive.s3select-pushdown.enabled	Enable query pushdown to AWS S3 Select service.	false
hive.s3select-pushdown.max-connections hive.file-status-cache-tables	Maximum number of simultaneously open connections to S3 for Pushdown. Cache directory listing for specific tables. Examples: fruit.apple,fruit.orange to cache listings only for tables apple and orange in schemafruit fruit.*,vegetable.* to cache listings for all tables in schemas fruit and vegetable * to cache listings for all tables in all schemas	500
hive.file-status-cache-size	Maximum total number of cached file status entries.	1,000,000
hive.file-status-cache-expire-time	How long a cached directory listing should be considered valid.	1m
hive.rcfile.time-zone	Adjusts binary encoded timestamp values to a specific time zone. For Hive 3.1+, this should be set to UTC.	JVM default
hive.timestamp-precision	Specifies the precision to use for Hive columns of typetimestamp. Possible values are MILLISECONDS,MICROSECONDS and NANOSECONDS. Values with higher precision than configured are rounded.	MILLISECONDS
hive.temporary-staging-directory-enabled	Controls whether the temporary staging directory configured athive.temporary-staging-directory-path should be used for write operations. Temporary staging directory is never used for writes to non-sorted tables on S3, encrypted HDFS or external location. Writes to sorted tables will utilize this path for staging temporary files during sorting operation. When disabled, the target storage will be used for staging while writing sorted tables which can be inefficient when writing to object stores like S3.	true
hive.temporary-staging-directory-path	Controls the location of temporary staging directory that is used for write operations. The ${USER} placeholder can be used to use a different location for each user.	/tmp/presto-${USER}
hive.hive-views.enabled	Enable translation for Hive views.	false
hive.hive-views.legacy-translation	Use the legacy algorithm to translate Hive views. You can use thehive_views_legacy_translation catalog session property for temporary, catalog specific use.	false
hive.parallel-partitioned-bucketed-writes	Improve parallelism of partitioned and bucketed table writes. When disabled, the number of writing threads is limited to number of buckets.	true
hive.fs.new-directory-permissions	Controls the permissions set on new directories created for tables. It must be either 'skip' or an octal number, with a leading 0. If set to 'skip', permissions of newly created directories will not be set by Trino.	0777
hive.query-partition-filter-required	Set to true to force a query to use a partition filter. You can use the query_partition_filter_requiredcatalog session property for temporary, catalog specific use.	false
hive.table-statistics-enabled	Enables Table statistics. The equivalent catalog session property isstatistics_enabled for session specific use. Set tofalse to disable statistics. Disabling statistics means that Cost based optimizations can not make smart decisions about the query plan.	true
hive.auto-purge	Set the default value for the auto_purge table property for managed tables. See the Hive for more information on auto_purge.	false

ORC format configuration properties

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

ORC format configuration properties

Property Name	Description	Default
hive.orc.time-zone	Sets the default time zone for legacy ORC files that did not declare a time zone.	JVM default
hive.orc.use-column-names	Access ORC columns by name. By default, columns in ORC files are accessed by their ordinal position in the Hive table definition. The equivalent catalog session property isorc_use_column_names.	false
hive.orc.bloom-filters.enabled	Enable bloom filters for predicate pushdown.	false

Parquet format configuration properties

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

Parquet format configuration properties

Property Name	Description	Default
hive.parquet.time-zone	Adjusts timestamp values to a specific time zone. For Hive 3.1+, set this to UTC.	JVM default
hive.parquet.use-columns-names	Access Parquet columns by name by default. Set this property tofalse to access columns by their ordinal position in the Hive table definition. The equivalent catalog session property isparquet_use_column_names.	true

Metastore configuration properties

The required Hive metastore can be configured with a number of properties. Specific properties can be used to further configure the Thrift or Glue metastore.

Property Name	Description
hive.metastore	The type of Hive metastore to use. Trino currently supports the default Hive Thrift metastore (thrift), and the AWS Glue Catalog (glue) as metadata sources. Default isthrift.
hive.metastore-cache.cache-partitions	Enable caching for partition metadata. You can disable caching to avoid inconsistent behavior that results from it. Default istrue.
hive.metastore-cache-ttl	Duration how long cached metastore data should be considered valid. Default is 0s.
hive.metastore-cache-maximum-size	Maximum number of metastore data objects in the Hive metastore cache. Default is 10000.
hive.metastore-refresh-interval	Asynchronously refresh cached metastore data after access if it is older than this but is not yet expired, allowing subsequent accesses to see fresh data.
hive.metastore-refresh-max-threads	Maximum threads used to refresh cached metastore data. Default is10.
hive.metastore-timeout	Timeout for Hive metastore requests. Default is10s.
hive.hide-delta-lake-tables	Controls whether to hide Delta Lake tables in table listings. Currently applies only when using the AWS Glue metastore. Default isfalse.

Thrift metastore configuration properties

Property Name	Description
hive.metastore.uri	The URIs of the Hive metastore to connect to using the Thrift protocol. If a comma-separated list of URIs is provided, the first URI is used by default, and the rest of the URIs are fallback metastores. This property is required. Example: thrift://192.0.2.3:9083or thrift://192.0.2.3:9083,thrift://192.0.2.4:9083
hive.metastore.username	The username Trino uses to access the Hive metastore.
hive.metastore.authentication.type	Hive metastore authentication type. Possible values areNONE or KERBEROS. Default isNONE.
hive.metastore.thrift.impersonation.enabled	Enable Hive metastore end user impersonation.
hive.metastore.thrift.delegation-token.cache-ttl	Time to live delegation token cache for metastore. Default is1h.
hive.metastore.thrift.delegation-token.cache-maximum-size	Delegation token cache maximum size. Default is1000.
hive.metastore.thrift.client.ssl.enabled	Use SSL when connecting to metastore. Default isfalse.
hive.metastore.thrift.client.ssl.key	Path to private key and client certificate (key store).
hive.metastore.thrift.client.ssl.key-password	Password for the private key.
hive.metastore.thrift.client.ssl.trust-certificate	Path to the server certificate chain (trust store). Required when SSL is enabled.
hive.metastore.thrift.client.ssl.trust-certificate-password	Password for the trust store
hive.metastore.service.principal	The Kerberos principal of the Hive metastore service.
hive.metastore.client.principal	The Kerberos principal that Trino uses when connecting to the Hive metastore service.
hive.metastore.client.keytab	Hive metastore client keytab location.

AWS Glue catalog configuration properties

In order to use a Glue catalog, ensure to configure the metastore with hive.metastore=glue and provide further details with the following properties:

Property Name	Description
hive.metastore.glue.region	AWS region of the Glue Catalog. This is required when not running in EC2, or when the catalog is in a different region. Example:us-east-1
hive.metastore.glue.endpoint-url	Glue API endpoint URL (optional). Example:https://glue.us-east-1.amazonaws.com
hive.metastore.glue.pin-client-to-current-region	Pin Glue requests to the same region as the EC2 instance where Trino is running, defaults to false.
hive.metastore.glue.max-connections	Max number of concurrent connections to Glue, defaults to30.
hive.metastore.glue.max-error-retries	Maximum number of error retries for the Glue client, defaults to10.
hive.metastore.glue.default-warehouse-dir	Default warehouse directory for schemas created without an explicit location property.
hive.metastore.glue.aws-credentials-provider	Fully qualified name of the Java class to use for obtaining AWS credentials. Can be used to supply a custom credentials provider.
hive.metastore.glue.aws-access-key	AWS access key to use to connect to the Glue Catalog. If specified along with hive.metastore.glue.aws-secret-key, this parameter takes precedence overhive.metastore.glue.iam-role.
hive.metastore.glue.aws-secret-key	AWS secret key to use to connect to the Glue Catalog. If specified along with hive.metastore.glue.aws-access-key, this parameter takes precedence overhive.metastore.glue.iam-role.
hive.metastore.glue.catalogid	The ID of the Glue Catalog in which the metadata database resides.
hive.metastore.glue.iam-role	ARN of an IAM role to assume when connecting to the Glue Catalog.
hive.metastore.glue.external-id	External ID for the IAM role trust policy when connecting to the Glue Catalog.
hive.metastore.glue.partitions-segments	Number of segments for partitioned Glue tables, defaults to5.
hive.metastore.glue.get-partition-threads	Number of threads for parallel partition fetches from Glue, defaults to 20.
hive.metastore.glue.read-statistics-threads	Number of threads for parallel statistic fetches from Glue, defaults to 5.
hive.metastore.glue.write-statistics-threads	Number of threads for parallel statistic writes to Glue, defaults to 5.

Google Cloud Storage configuration

The Hive connector can access data stored in GCS, using the gs:// URI prefix. Please refer to the Hive for step-by-step instructions.

GCS configuration properties

Property Name	Description
hive.gcs.json-key-file-path	JSON key file used to authenticate with Google Cloud Storage.
hive.gcs.use-access-token	Use client-provided OAuth token to access Google Cloud Storage. This is mutually exclusive with a global JSON key file.

Performance tuning configuration properties

The following table describes performance tuning properties for the Hive connector.

caution

Performance tuning configuration properties are considered expert-level features. Altering these properties from their default values is likely to cause instability and performance degradation.

Property name	Description	Default value
hive.max-outstanding-splits	The target number of buffered splits for each table scan in a query, before the scheduler tries to pause.	1000
hive.max-splits-per-second	The maximum number of splits generated per second per table scan. This can be used to reduce the load on the storage system. By default, there is no limit, which results in Presto maximizing the parallelization of data access.
hive.max-initial-splits	For each table scan, the coordinator first assigns file sections of up to max-initial-split-size. Aftermax-initial-splits have been assigned,max-split-size is used for the remaining splits.	200
hive.max-initial-split-size	The size of a single file section assigned to a worker untilmax-initial-splits have been assigned. Smaller splits results in more parallelism, which gives a boost to smaller queries.	32 MB
hive.max-split-size	The largest size of a single file section assigned to a worker. Smaller splits result in more parallelism and thus can decrease latency, but also have more overhead and increase load on the system.	64 MB

SQL support

The connector provides read access and write access to data and metadata in the configured object storage system and metadata stores. In addition to the globally available and read operation statements, the connector supports the following features:

• sql-write-operations:
  • sql-data-management, see also Hive
  • sql-schema-table-management
  • sql-view-management
• sql-security-operations, see also Hive
• sql-transactions

Some sql-data-management statements may be affected by the Hive catalog's authorization check policy. In the default legacy policy, some statements are disabled by default. See Hive for more information.

Refer to the migration guide for practical advice on migrating from Hive to Trino.

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 non-transactional table so that it is merged into fewer but larger files. In case that the table is partitioned, the data compaction acts separately on each partition selected for optimization. This operation improves read performance.

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

ALTER TABLE test_table EXECUTE optimize

The following statement merges 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

The optimize command is disabled by default, and can be enabled for a catalog with the <catalog-name>.non_transactional_optimize_enabled session property:

SET SESSION <catalog_name>.non_transactional_optimize_enabled=true

caution

Because Hive tables are non-transactional, take note of the following possible outcomes:

• If queries are run against tables that are currently being optimized, duplicate rows may be read.
• In rare cases where exceptions occur during the optimize operation, a manual cleanup of the table directory is needed. In this situation, refer to the Trino logs and query failure messages to see which files need to be deleted.

Data management

The sql-data-management functionality includes support for INSERT, UPDATE, and DELETE statements, with the exact support depending on the storage system, file format, and metastore:

When connecting to a Hive metastore version 3.x, the Hive connector supports reading from and writing to insert-only and ACID tables, with full support for partitioning and bucketing.

DELETE applied to non-transactional tables is only supported if the table is partitioned and the WHERE clause matches entire partitions. Transactional Hive tables with ORC format support "row-by-row" deletion, in which the WHERE clause may match arbitrary sets of rows.

UPDATE is only supported for transactional Hive tables with format ORC. UPDATE of partition or bucket columns is not supported.

ACID tables created with Hive Streaming Ingest are not supported.

Table statistics

The Hive connector supports collecting and managing table statistics to improve query processing performance.

When writing data, the Hive connector always collects basic statistics (numFiles, numRows, rawDataSize, totalSize) and by default will also collect column level statistics:

Column Type	Collectible Statistics
TINYINT	number of nulls, number of distinct values, min/max values
SMALLINT	number of nulls, number of distinct values, min/max values
INTEGER	number of nulls, number of distinct values, min/max values
BIGINT	number of nulls, number of distinct values, min/max values
DOUBLE	number of nulls, number of distinct values, min/max values
REAL	number of nulls, number of distinct values, min/max values
DECIMAL	number of nulls, number of distinct values, min/max values
DATE	number of nulls, number of distinct values, min/max values
TIMESTAMP	number of nulls, number of distinct values, min/max values
VARCHAR	number of nulls, number of distinct values
CHAR	number of nulls, number of distinct values
VARBINARY	number of nulls
BOOLEAN	number of nulls, number of true/false values

Updating table and partition statistics

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

When analyzing a partitioned table, the partitions to analyze can be specified via the optional partitions property, which is an array containing the values of the partition keys in the order they are declared in the table schema:

ANALYZE table_name WITH (
    partitions = ARRAY[
        ARRAY['p1_value1', 'p1_value2'],
        ARRAY['p2_value1', 'p2_value2']])

This query will collect statistics for two partitions with keys p1_value1, p1_value2 and p2_value1, p2_value2.

On wide tables, collecting statistics for all columns can be expensive and can have a detrimental effect on query planning. It is also typically unnecessary - statistics are only useful on specific columns, like join keys, predicates, grouping keys. One can specify a subset of columns to be analyzed via the optional columns property:

ANALYZE table_name WITH (
    partitions = ARRAY[ARRAY['p2_value1', 'p2_value2']],
    columns = ARRAY['col_1', 'col_2'])

This query collects statistics for columns col_1 and col_2 for the partition with keys p2_value1, p2_value2.

Note that if statistics were previously collected for all columns, they need to be dropped before re-analyzing just a subset:

CALL system.drop_stats('schema_name', 'table_name')

You can also drop statistics for selected partitions only:

CALL system.drop_stats(
    schema_name => 'schema',
    table_name => 'table',
    partition_values => ARRAY[ARRAY['p2_value1', 'p2_value2']])

Dynamic filtering

The Hive connector supports the dynamic filtering optimization. Dynamic partition pruning is supported for partitioned tables stored in any file format for broadcast as well as partitioned joins. Dynamic bucket pruning is supported for bucketed tables stored in any file format for broadcast joins only.

For tables stored in ORC or Parquet file format, dynamic filters are also pushed into local table scan on worker nodes for broadcast joins. Dynamic filter predicates pushed into the ORC and Parquet readers are used to perform stripe or row-group pruning and save on disk I/O. Sorting the data within ORC or Parquet files by the columns used in join criteria significantly improves the effectiveness of stripe or row-group pruning. This is because grouping similar data within the same stripe or row-group greatly improves the selectivity of the min/max indexes maintained at stripe or row-group level.

Delaying execution for dynamic filters

It can often be beneficial to wait for the collection of dynamic filters before starting a table scan. This extra wait time can potentially result in significant overall savings in query and CPU time, if dynamic filtering is able to reduce the amount of scanned data.

For the Hive connector, a table scan can be delayed for a configured amount of time until the collection of dynamic filters by using the configuration property hive.dynamic-filtering.wait-timeout in the catalog file or the catalog session property <hive-catalog>.dynamic_filtering_wait_timeout.

Schema evolution

Hive allows the partitions in a table to have a different schema than the table. This occurs when the column types of a table are changed after partitions already exist (that use the original column types). The Hive connector supports this by allowing the same conversions as Hive:

• varchar to and from tinyint, smallint, integer and bigint
• real to double
• Widening conversions for integers, such as tinyint to smallint

Any conversion failure results in null, which is the same behavior as Hive. For example, converting the string 'foo' to a number, or converting the string '1234' to a tinyint (which has a maximum value of 127).

Avro schema evolution

Trino supports querying and manipulating Hive tables with the Avro storage format, which has the schema set based on an Avro schema file/literal. Trino is also capable of creating the tables in Trino by infering the schema from a valid Avro schema file located locally, or remotely in HDFS/Web server.

To specify that the Avro schema should be used for interpreting table's data one must use avro_schema_url table property. The schema can be placed remotely in HDFS (e.g. avro_schema_url = 'hdfs://user/avro/schema/avro_data.avsc'), S3 (e.g. avro_schema_url = 's3n:///schema_bucket/schema/avro_data.avsc'), a web server (e.g. avro_schema_url = 'http://example.org/schema/avro_data.avsc') as well as local file system. This URL, where the schema is located, must be accessible from the Hive metastore and Trino coordinator/worker nodes.

The table created in Trino using avro_schema_url behaves the same way as a Hive table with avro.schema.url or avro.schema.literal set.

Example:

CREATE TABLE hive.avro.avro_data (
   id bigint
 )
WITH (
   format = 'AVRO',
   avro_schema_url = '/usr/local/avro_data.avsc'
)

The columns listed in the DDL (id in the above example) is ignored if avro_schema_url is specified. The table schema matches the schema in the Avro schema file. Before any read operation, the Avro schema is accessed so the query result reflects any changes in schema. Thus Trino takes advantage of Avro's backward compatibility abilities.

If the schema of the table changes in the Avro schema file, the new schema can still be used to read old data. Newly added/renamed fields must have a default value in the Avro schema file.

The schema evolution behavior is as follows:

• Column added in new schema: Data created with an older schema produces a default value when table is using the new schema.
• Column removed in new schema: Data created with an older schema no longer outputs the data from the column that was removed.
• Column is renamed in the new schema: This is equivalent to removing the column and adding a new one, and data created with an older schema produces a default value when table is using the new schema.
• Changing type of column in the new schema: If the type coercion is supported by Avro or the Hive connector, then the conversion happens. An error is thrown for incompatible types.

Limitations

The following operations are not supported when avro_schema_url is set:

• CREATE TABLE AS is not supported.
• Bucketing(bucketed_by) columns are not supported in CREATE TABLE.
• ALTER TABLE commands modifying columns are not supported.

Procedures

Use the CALL statement to perform data manipulation or administrative tasks. Procedures must include a qualified catalog name, if your Hive catalog is called web:

CALL web.system.example_procedure()

The following procedures are available:

• system.create_empty_partition(schema_name, table_name, partition_columns, partition_values)

  Create an empty partition in the specified table.

• system.sync_partition_metadata(schema_name, table_name, mode, case_sensitive)

  Check and update partitions list in metastore. There are three modes available:

  • ADD : add any partitions that exist on the file system, but not in the metastore.
  • DROP: drop any partitions that exist in the metastore, but not on the file system.
  • FULL: perform both ADD and DROP.

  The case_sensitive argument is optional. The default value is true for compatibility with Hive's MSCK REPAIR TABLE behavior, which expects the partition column names in file system paths to use lowercase (e.g. col_x=SomeValue). Partitions on the file system not conforming to this convention are ignored, unless the argument is set to false.

• system.drop_stats(schema_name, table_name, partition_values)

  Drops statistics for a subset of partitions or the entire table. The partitions are specified as an array whose elements are arrays of partition values (similar to the partition_values argument in create_empty_partition). If partition_values argument is omitted, stats are dropped for the entire table.

• system.register_partition(schema_name, table_name, partition_columns, partition_values, location)

  Registers existing location as a new partition in the metastore for the specified table.

  When the location argument is omitted, the partition location is constructed using partition_columns and partition_values.

  Due to security reasons, the procedure is enabled only when hive.allow-register-partition-procedure is set to true.

• system.unregister_partition(schema_name, table_name, partition_columns, partition_values)

  Unregisters given, existing partition in the metastore for the specified table. The partition data is not deleted.

• system.flush_metadata_cache()

  Flush all Hive metadata caches.

• system.flush_metadata_cache(schema_name => ..., table_name => ..., partition_columns => ARRAY[...], partition_values => ARRAY[...])

  Flush Hive metadata cache entries connected with selected partition. Procedure requires named parameters to be passed

Table properties

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

CREATE TABLE tablename
WITH (format='CSV',
      csv_escape = '"')

See the Hive for more information.

Hive connector table properties

Property name	Description	Default
auto_purge	Indicates to the configured metastore to perform a purge when a table or partition is deleted instead of a soft deletion using the trash.
avro_schema_url	The URI pointing to Hive for the table.
bucket_count	The number of buckets to group data into. Only valid if used withbucketed_by.	0
bucketed_by	The bucketing column for the storage table. Only valid if used withbucket_count.	[]
bucketing_version	Specifies which Hive bucketing version to use. Valid values are1 or 2.
csv_escape	The CSV escape character. Requires CSV format.
csv_quote	The CSV quote character. Requires CSV format.
csv_separator	The CSV separator character. Requires CSV format.
external_location	The URI for an external Hive table on S3, Azure Blob Storage, etc. See the Hive for more information.
format	The table file format. Valid values include ORC,PARQUET, AVRO, RCBINARY,RCTEXT, SEQUENCEFILE, JSON,TEXTFILE, and CSV. The catalog propertyhive.storage-format sets the default value and can change it to a different default.
null_format	The serialization format for NULL value. Requires TextFile, RCText, or SequenceFile format.
orc_bloom_filter_columns	Comma separated list of columns to use for ORC bloom filter. It improves the performance of queries using range predicates when reading ORC files. Requires ORC format.	[]
orc_bloom_filter_fpp	The ORC bloom filters false positive probability. Requires ORC format.	0.05
partitioned_by	The partitioning column for the storage table. The columns listed in the partitioned_by clause must be the last columns as defined in the DDL.	[]
skip_footer_line_count	The number of footer lines to ignore when parsing the file for data. Requires TextFile or CSV format tables.
skip_header_line_count	The number of header lines to ignore when parsing the file for data. Requires TextFile or CSV format tables.
sorted_by	The column to sort by to determine bucketing for row. Only valid ifbucketed_by and bucket_count are specified as well.	[]
textfile_field_separator	Allows the use of custom field separators, such as '|', for TextFile formatted tables.
textfile_field_separator_escape	Allows the use of a custom escape character for TextFile formatted tables.
transactional	Set this property to true to create an ORC ACID transactional table. Requires ORC format. This property may be shown as true for insert-only tables created using older versions of Hive.

Special columns

In addition to the defined columns, the Hive connector automatically exposes metadata in a number of hidden columns in each table:

• $bucket: Bucket number for this row
• $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
• $partition: Partition name for this row

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

SELECT *, "$path", "$file_size", "$partition"
FROM hive.web.page_views;

Retrieve all records that belong to files stored in the partition ds=2016-08-09/country=US:

SELECT *, "$path", "$file_size"
FROM hive.web.page_views
WHERE "$partition" = 'ds=2016-08-09/country=US'

Special tables

The raw Hive table properties are available as a hidden table, containing a separate column per table property, with a single row containing the property values. The properties table name is the same as the table name with $properties appended.

You can inspect the property names and values with a simple query:

SELECT * FROM hive.web."page_views$properties";

Examples

The Hive connector supports querying and manipulating Hive tables and schemas (databases). While some uncommon operations need to be performed using Hive directly, most operations can be performed using Trino.

Create a new Hive schema named web that stores tables in an S3 bucket named my-bucket:

CREATE SCHEMA hive.web
WITH (location = 's3://my-bucket/')

Create a new Hive table named page_views in the web schema that is stored using the ORC file format, partitioned by date and country, and bucketed by user into 50 buckets. Note that Hive requires the partition columns to be the last columns in the table:

CREATE TABLE hive.web.page_views (
  view_time timestamp,
  user_id bigint,
  page_url varchar,
  ds date,
  country varchar
)
WITH (
  format = 'ORC',
  partitioned_by = ARRAY['ds', 'country'],
  bucketed_by = ARRAY['user_id'],
  bucket_count = 50
)

Drop a partition from the page_views table:

DELETE FROM hive.web.page_views
WHERE ds = DATE '2016-08-09'
  AND country = 'US'

Add an empty partition to the page_views table:

CALL system.create_empty_partition(
    schema_name => 'web',
    table_name => 'page_views',
    partition_columns => ARRAY['ds', 'country'],
    partition_values => ARRAY['2016-08-09', 'US']);

Drop stats for a partition of the page_views table:

CALL system.drop_stats(
    schema_name => 'web',
    table_name => 'page_views',
    partition_values => ARRAY['2016-08-09', 'US']);

Query the page_views table:

SELECT * FROM hive.web.page_views

List the partitions of the page_views table:

SELECT * FROM hive.web."page_views$partitions"

Create an external Hive table named request_logs that points at existing data in S3:

CREATE TABLE hive.web.request_logs (
  request_time timestamp,
  url varchar,
  ip varchar,
  user_agent varchar
)
WITH (
  format = 'TEXTFILE',
  external_location = 's3://my-bucket/data/logs/'
)

Collect statistics for the request_logs table:

ANALYZE hive.web.request_logs;

The examples shown here should work on Google Cloud Storage after replacing s3:// with gs://.

Cleaning up

Drop the external table request_logs. This only drops the metadata for the table. The referenced data directory is not deleted:

DROP TABLE hive.web.request_logs

Drop a schema:

DROP SCHEMA hive.web

Hive 3 related limitations

• For security reasons, the sys system catalog is not accessible.
• Hive's timestamp with local zone data type is not supported. It is possible to read from a table with a column of this type, but the column data is not accessible. Writing to such a table is not supported.
• Due to Hive issues HIVE-21002 and HIVE-22167, Trino does not correctly read timestamp values from Parquet, RCBinary, or Avro file formats created by Hive 3.1 or later. When reading from these file formats, Trino returns different results than Hive.
• CREATE TABLE can be used to create transactional tables in ORC format like this:

CREATE TABLE <name>
WITH (
    format='ORC',
    transactional=true
)
AS <query>

Trino does not support gathering table statistics for Hive transactional tables.
You need to use Hive to gather table statistics with ``ANALYZE TABLE COMPUTE STATISTICS`` after table creation.