Gravitino connector - Hive catalog

The Hive catalog allows Trino querying data stored in an Apache Hive data warehouse.

Requirements

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

Apache Hadoop HDFS 2.x supported.

Many distributed storage systems including HDFS, Amazon S3 or S3-compatible systems, Google Cloud Storage, Azure Storage, and IBM Cloud Object 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.

Data files must be in a supported file format. Some file formats can be configured using file format configuration properties per catalog:

• 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

Schema operations

Create a schema

Users can create a schema with properties through Gravitino Trino connector as follows:

CREATE SCHEMA catalog.schema_name 

Table operations

Create table

The Gravitino connector currently supports basic Hive table creation statements, such as defining fields, allowing null values, and adding comments. The Gravitino connector does not support CREATE TABLE AS SELECT.

The following example shows how to create a table in the Hive catalog:

CREATE TABLE catalog.schema_name.table_name
(
  name varchar,
  salary int
)

Alter table

Support for the following alter table operations:

• Rename table
• Add a column
• Drop a column
• Rename a column
• Change a column type
• Set a table property

Select

The Gravitino connector supports most SELECT statements, allowing the execution of queries successfully. Currently, it doesn't support certain query optimizations, such as pushdown and pruning functionalities.

Schema and table properties

You can set additional properties for tables and schemas in the Hive catalog using "WITH" keyword in the "CREATE" statement.

Create a schema with properties

Users can use the following example to create a schema with properties:

CREATE SCHEMA "metalake.catalog".dbname
WITH (
  location = 'hdfs://hdfs-host:9000/user/hive/warehouse/dbname'
);

The following tables are the properties supported by the Hive schema:

Property	Description	Default Value	Required	Reserved	Since Version
location	HDFS location for table storage	(none)	No	No	0.2.0

Reserved properties: A reserved property is one can't be set by users but can be read by users.

Create a table with properties

Users can use the following example to create a table with properties:

CREATE TABLE catalog.dbname.tabname
(
  name varchar,
  salary int
) WITH (
  format = 'TEXTFILE',
  KEY = 'VALUE',
  ...      
);

The following tables are the properties supported by the Hive table:

Property	Description	Default Value	Required	Reserved	Since Version
format	Hive storage format for the table	TEXTFILE	No	No	0.2.0
total_size	Total size of the table	(none)	No	Yes	0.2.0
num_files	Number of files	0	No	Yes	0.2.0
external	Indicate whether it's an external table	(none)	No	No	0.2.0
location	HDFS location for table storage	(none)	No	No	0.2.0
table_type	The type of Hive table	(none)	No	No	0.2.0
input_format	The input format class for the table	org.apache.hadoop.mapred.TextInputFormat	No	No	0.2.0
output_format	The output format class for the table	org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat	No	No	0.2.0
serde_lib	The serde library class for the table	org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe	No	No	0.2.0
serde_name	Name of the serde	table name by default	No	No	0.2.0
partitioned_by	Partition columns for the table	(none)	No	No	0.4.0
bucketed_by	Bucket columns for the table	(none)	No	No	0.4.0
bucket_count	Number of buckets for the table	(none)	No	No	0.4.0
sorted_by	Sorted columns for the table	(none)	No	No	0.4.0

Basic usage examples

You need to do the following steps before you can use the Hive catalog in Trino through Gravitino.

• Create a metalake and catalog in Gravitino. Assuming that the metalake name is test and the catalog name is hive_test, then you can use the following code to create them in Gravitino:

curl -X POST -H "Content-Type: application/json" \
-d '{
  "name": "test",
  "comment": "comment",
  "properties": {}
}' http://gravitino-host:8090/api/metalakes

curl -X POST \
-H "Content-Type: application/json" \
-d '{
  "name": "hive_test",
  "type": "RELATIONAL",
  "comment": "comment",
  "provider": "hive",
  "properties": {
    "metastore.uris": "thrift://hive-host:9083"
  }
}' http://gravitino-host:8090/api/metalakes/test/catalogs

For More information about the Hive catalog, please refer to Hive catalog.

• Set the value of configuration gravitino.metalake to the metalake you have created, named 'test', and start the Trino container.

Use the Trino CLI to connect to the Trino container and run a query.

Listing all Gravitino managed catalogs:

SHOW CATALOGS;

The results are similar to:

    Catalog
----------------
 gravitino
 jmx
 system
 hive_test
(4 rows)

Query 20231017_082503_00018_6nt3n, FINISHED, 1 node

The gravitino catalog is a catalog defined By Trino catalog configuration. The test.hive_test catalog is the catalog created by you in Gravitino. Other catalogs are regular user-configured Trino catalogs.

Creating tables and schemas

Create a new schema named database_01 in hive_test catalog.

CREATE SCHEMA hive_test.database_01;

Create a new schema using HDFS location:

CREATE SCHEMA hive_test.database_01 WITH (
  location = 'hdfs://hdfs-host:9000/user/hive/warehouse/database_01'
);

Create a new table named table_01 in schema hive_test.database_01 and stored in a TEXTFILE format, partitioning by salary, bucket by name and sorted by salary.

CREATE TABLE  hive_test.database_01.table_01
(
name varchar,
salary int
)
WITH (
  format = 'TEXTFILE',
  partitioned_by = ARRAY['salary'],
  bucketed_by = ARRAY['name'],
  bucket_count = 2,
  sorted_by = ARRAY['salary']  
);

Writing data

Insert data into the table table_01:

INSERT INTO hive_test.database_01.table_01 (name, salary) VALUES ('ice', 12);

Insert data into the table table_01 from select:

INSERT INTO hive_test.database_01.table_01 (name, salary) SELECT * FROM hive_test.database_01.table_01;

Querying data

Query the table_01 table:

SELECT * FROM hive_test.database_01.table_01;

Modify a table

Add a new column age to the table_01 table:

ALTER TABLE hive_test.database_01.table_01 ADD COLUMN age int;

Drop a column age from the table_01 table:

ALTER TABLE hive_test.database_01.table_01 DROP COLUMN age;

Rename the table_01 table to table_02:

ALTER TABLE hive_test.database_01.table_01 RENAME TO hive_test.database_01.table_02;

DROP

Drop a schema:

DROP SCHEMA hive_test.database_01;

Drop a table:

DROP TABLE hive_test.database_01.table_01;

HDFS config and permissions

For basic setups, Gravitino connector configures the HDFS client automatically and does not require any configuration files. Gravitino connector is not support user to config the hdfs-site.xml and core-site.xml files to the HDFS client.

Before running any Insert statements for Hive tables in Trino, you must check that the user Trino is using to access HDFS has access to the Hive warehouse directory. 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