Catalogs

This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.

Catalogs provide metadata, such as databases, tables, partitions, views, and functions and information needed to access data stored in a database or other external systems.

One of the most crucial aspects of data processing is managing metadata. It may be transient metadata like temporary tables, or UDFs registered against the table environment. Or permanent metadata, like that in a Hive Metastore. Catalogs provide a unified API for managing metadata and making it accessible from the Table API and SQL Queries.

Catalog enables users to reference existing metadata in their data systems, and automatically maps them to Flink’s corresponding metadata. For example, Flink can map JDBC tables to Flink table automatically, and users don’t have to manually re-writing DDLs in Flink. Catalog greatly simplifies steps required to get started with Flink with users’ existing system, and greatly enhanced user experiences.

Catalog Types
How to Create and Register Flink Tables to Catalog
- Using SQL DDL
- Using Java, Scala or Python
Catalog API
Table API and SQL for Catalog

Catalog Types

GenericInMemoryCatalog

The GenericInMemoryCatalog is an in-memory implementation of a catalog. All objects will be available only for the lifetime of the session.

JdbcCatalog

The JdbcCatalog enables users to connect Flink to relational databases over JDBC protocol. PostgresCatalog is the only implementation of JDBC Catalog at the moment. See JdbcCatalog documentation for more details on setting up the catalog.

HiveCatalog

The HiveCatalog serves two purposes; as persistent storage for pure Flink metadata, and as an interface for reading and writing existing Hive metadata. Flink’s Hive documentation provides full details on setting up the catalog and interfacing with an existing Hive installation.

Warning The Hive Metastore stores all meta-object names in lower case. This is unlike GenericInMemoryCatalog which is case-sensitive

User-Defined Catalog

Catalogs are pluggable and users can develop custom catalogs by implementing the Catalog interface. To use custom catalogs in SQL CLI, users should develop both a catalog and its corresponding catalog factory by implementing the CatalogFactory interface.

The catalog factory defines a set of properties for configuring the catalog when the SQL CLI bootstraps. The set of properties will be passed to a discovery service where the service tries to match the properties to a CatalogFactory and initiate a corresponding catalog instance.

How to Create and Register Flink Tables to Catalog

Using SQL DDL

Users can use SQL DDL to create tables in catalogs in both Table API and SQL.

TableEnvironment tableEnv = ...

// Create a HiveCatalog 
Catalog catalog = new HiveCatalog("myhive", null, "<path_of_hive_conf>");

// Register the catalog
tableEnv.registerCatalog("myhive", catalog);

// Create a catalog database
tableEnv.executeSql("CREATE DATABASE mydb WITH (...)");

// Create a catalog table
tableEnv.executeSql("CREATE TABLE mytable (name STRING, age INT) WITH (...)");

tableEnv.listTables(); // should return the tables in current catalog and database.

val tableEnv = ...

// Create a HiveCatalog 
val catalog = new HiveCatalog("myhive", null, "<path_of_hive_conf>")

// Register the catalog
tableEnv.registerCatalog("myhive", catalog)

// Create a catalog database
tableEnv.executeSql("CREATE DATABASE mydb WITH (...)")

// Create a catalog table
tableEnv.executeSql("CREATE TABLE mytable (name STRING, age INT) WITH (...)")

tableEnv.listTables() // should return the tables in current catalog and database.

from pyflink.table.catalog import HiveCatalog

# Create a HiveCatalog
catalog = HiveCatalog("myhive", None, "<path_of_hive_conf>")

# Register the catalog
t_env.register_catalog("myhive", catalog)

# Create a catalog database
t_env.execute_sql("CREATE DATABASE mydb WITH (...)")

# Create a catalog table
t_env.execute_sql("CREATE TABLE mytable (name STRING, age INT) WITH (...)")

# should return the tables in current catalog and database.
t_env.list_tables()

// the catalog should have been registered via yaml file
Flink SQL> CREATE DATABASE mydb WITH (...);

Flink SQL> CREATE TABLE mytable (name STRING, age INT) WITH (...);

Flink SQL> SHOW TABLES;
mytable

For detailed information, please check out Flink SQL CREATE DDL.

Using Java, Scala or Python

Users can use Java, Scala or Python to create catalog tables programmatically.

import org.apache.flink.table.api.*;
import org.apache.flink.table.catalog.*;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.descriptors.Kafka;

TableEnvironment tableEnv = TableEnvironment.create(EnvironmentSettings.newInstance().build());

// Create a HiveCatalog 
Catalog catalog = new HiveCatalog("myhive", null, "<path_of_hive_conf>");

// Register the catalog
tableEnv.registerCatalog("myhive", catalog);

// Create a catalog database 
catalog.createDatabase("mydb", new CatalogDatabaseImpl(...));

// Create a catalog table
TableSchema schema = TableSchema.builder()
    .field("name", DataTypes.STRING())
    .field("age", DataTypes.INT())
    .build();

catalog.createTable(
        new ObjectPath("mydb", "mytable"), 
        new CatalogTableImpl(
            schema,
            new Kafka()
                .version("0.11")
                ....
                .startFromEarlist()
                .toProperties(),
            "my comment"
        ),
        false
    );
    
List<String> tables = catalog.listTables("mydb"); // tables should contain "mytable"

import org.apache.flink.table.api._
import org.apache.flink.table.catalog._
import org.apache.flink.table.catalog.hive.HiveCatalog
import org.apache.flink.table.descriptors.Kafka

val tableEnv = TableEnvironment.create(EnvironmentSettings.newInstance.build)

// Create a HiveCatalog 
val catalog = new HiveCatalog("myhive", null, "<path_of_hive_conf>")

// Register the catalog
tableEnv.registerCatalog("myhive", catalog)

// Create a catalog database 
catalog.createDatabase("mydb", new CatalogDatabaseImpl(...))

// Create a catalog table
val schema = TableSchema.builder()
    .field("name", DataTypes.STRING())
    .field("age", DataTypes.INT())
    .build()

catalog.createTable(
        new ObjectPath("mydb", "mytable"), 
        new CatalogTableImpl(
            schema,
            new Kafka()
                .version("0.11")
                ....
                .startFromEarlist()
                .toProperties(),
            "my comment"
        ),
        false
    )
    
val tables = catalog.listTables("mydb") // tables should contain "mytable"

from pyflink.table import *
from pyflink.table.catalog import HiveCatalog, CatalogDatabase, ObjectPath, CatalogBaseTable
from pyflink.table.descriptors import Kafka

settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
t_env = BatchTableEnvironment.create(environment_settings=settings)

# Create a HiveCatalog
catalog = HiveCatalog("myhive", None, "<path_of_hive_conf>")

# Register the catalog
t_env.register_catalog("myhive", catalog)

from pyflink.java_gateway import get_gateway
gateway = get_gateway()
database_properties = {"k1": "v1"}
database_comment = None
j_database = gateway.jvm.org.apache.flink.table.catalog.CatalogDatabaseImpl(
    database_properties, database_comment)
database = CatalogDatabase(j_database)

# Create a catalog database
catalog.create_database("mydb", database)

# Create a catalog table
table_schema = TableSchema.builder() \
    .field("name", DataTypes.STRING()) \
    .field("age", DataTypes.INT()) \
    .build()

table_properties = Kafka() \
    .version("0.11") \
    .start_from_earlist() \
    .to_properties()

j_catalog_table = gateway.jvm.org.apache.flink.table.catalog.CatalogTableImpl(
    table_schema._j_table_schema,
    table_properties,
    "my comment")
catalog_table = CatalogBaseTable(j_catalog_table)

catalog.create_table(
    ObjectPath("mydb", "mytable"),
    catalog_table,
    False)

# tables should contain "mytable"
tables = catalog.list_tables("mydb")

Catalog API

Note: only catalog program APIs are listed here. Users can achieve many of the same funtionalities with SQL DDL. For detailed DDL information, please refer to SQL CREATE DDL.

Database operations

// create database
catalog.createDatabase("mydb", new CatalogDatabaseImpl(...), false);

// drop database
catalog.dropDatabase("mydb", false);

// alter database
catalog.alterDatabase("mydb", new CatalogDatabaseImpl(...), false);

// get database
catalog.getDatabase("mydb");

// check if a database exist
catalog.databaseExists("mydb");

// list databases in a catalog
catalog.listDatabases();

from pyflink.table.catalog import CatalogDatabase
from pyflink.java_gateway import get_gateway
gateway = get_gateway()

# create database
database_properties = {"k1": "v1"}
database_comment = None
j_catalog_database = gateway.jvm.org.apache.flink.table.catalog.CatalogDatabaseImpl(
    database_properties, database_comment)
catalog_database = CatalogDatabase(j_catalog_database)
catalog.create_database("mydb", catalog_database, False)

# drop database
catalog.drop_database("mydb", False)

# alter database
catalog.alter_database("mydb", catalog_database, False)

# get database
catalog.get_database("mydb")

# check if a database exist
catalog.database_exists("mydb")

# list databases in a catalog
catalog.list_databases()

Table operations

// create table
catalog.createTable(new ObjectPath("mydb", "mytable"), new CatalogTableImpl(...), false);

// drop table
catalog.dropTable(new ObjectPath("mydb", "mytable"), false);

// alter table
catalog.alterTable(new ObjectPath("mydb", "mytable"), new CatalogTableImpl(...), false);

// rename table
catalog.renameTable(new ObjectPath("mydb", "mytable"), "my_new_table");

// get table
catalog.getTable("mytable");

// check if a table exist or not
catalog.tableExists("mytable");

// list tables in a database
catalog.listTables("mydb");

from pyflink.table import *
from pyflink.java_gateway import get_gateway
from pyflink.table.catalog import CatalogBaseTable, ObjectPath
from pyflink.table.descriptors import Kafka

table_schema = TableSchema.builder() \
    .field("name", DataTypes.STRING()) \
    .field("age", DataTypes.INT()) \
    .build()

table_properties = Kafka() \
    .version("0.11") \
    .start_from_earlist() \
    .to_properties()

gateway = get_gateway()
j_catalog_table = gateway.jvm.org.apache.flink.table.catalog.CatalogTableImpl(
    table_schema._j_table_schema,
    table_properties,
    "my comment")
catalog_table = CatalogBaseTable(j_catalog_table)

# create table
catalog.create_table(ObjectPath("mydb", "mytable"), catalog_table, False)

# drop table
catalog.drop_table(ObjectPath("mydb", "mytable"), False)

# alter table
catalog.alter_table(ObjectPath("mydb", "mytable"), catalog_table, False)

# rename table
catalog.rename_table(ObjectPath("mydb", "mytable"), "my_new_table")

# get table
catalog.get_table("mytable")

# check if a table exist or not
catalog.table_exists("mytable")

# list tables in a database
catalog.list_tables("mydb")

View operations

// create view
catalog.createTable(new ObjectPath("mydb", "myview"), new CatalogViewImpl(...), false);

// drop view
catalog.dropTable(new ObjectPath("mydb", "myview"), false);

// alter view
catalog.alterTable(new ObjectPath("mydb", "mytable"), new CatalogViewImpl(...), false);

// rename view
catalog.renameTable(new ObjectPath("mydb", "myview"), "my_new_view", false);

// get view
catalog.getTable("myview");

// check if a view exist or not
catalog.tableExists("mytable");

// list views in a database
catalog.listViews("mydb");

from pyflink.table import *
from pyflink.table.catalog import CatalogBaseTable, ObjectPath
from pyflink.java_gateway import get_gateway

table_schema = TableSchema.builder() \
    .field("name", DataTypes.STRING()) \
    .field("age", DataTypes.INT()) \
    .build()

gateway = get_gateway()
j_view = gateway.jvm.org.apache.flink.table.catalog.CatalogViewImpl(
            "select * from t1",
            "select * from test-catalog.db1.t1",
            table_schema._j_table_schema,
            {},
            "This is a view")
catalog_table = CatalogBaseTable(j_view)

catalog.create_table(ObjectPath("mydb", "myview"), catalog_table, False)

# drop view
catalog.drop_table(ObjectPath("mydb", "myview"), False)

# alter view
catalog.alter_table(ObjectPath("mydb", "mytable"), catalog_table, False)

# rename view
catalog.rename_table(ObjectPath("mydb", "myview"), "my_new_view", False)

# get view
catalog.get_table("myview")

# check if a view exist or not
catalog.table_exists("mytable")

# list views in a database
catalog.list_views("mydb")

Partition operations

// create view
catalog.createPartition(
    new ObjectPath("mydb", "mytable"),
    new CatalogPartitionSpec(...),
    new CatalogPartitionImpl(...),
    false);

// drop partition
catalog.dropPartition(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...), false);

// alter partition
catalog.alterPartition(
    new ObjectPath("mydb", "mytable"),
    new CatalogPartitionSpec(...),
    new CatalogPartitionImpl(...),
    false);

// get partition
catalog.getPartition(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...));

// check if a partition exist or not
catalog.partitionExists(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...));

// list partitions of a table
catalog.listPartitions(new ObjectPath("mydb", "mytable"));

// list partitions of a table under a give partition spec
catalog.listPartitions(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...));

// list partitions of a table by expression filter
catalog.listPartitionsByFilter(new ObjectPath("mydb", "mytable"), Arrays.asList(epr1, ...));

from pyflink.table.catalog import ObjectPath, CatalogPartitionSpec, CatalogPartition
from pyflink.java_gateway import get_gateway

gateway = get_gateway()
partition_properties = {}
partition_comments = "my partition"
j_partition = gateway.jvm.org.apache.flink.table.catalog.CatalogPartitionImpl(
    partition_properties, partition_comments)
catalog_partition = CatalogPartition(j_partition)

catalog_partition_spec = CatalogPartitionSpec({"third": "2010", "second": "bob"})
catalog.create_partition(
    ObjectPath("mydb", "mytable"),
    catalog_partition_spec,
    catalog_partition,
    False)

# drop partition
catalog.drop_partition(ObjectPath("mydb", "mytable"), catalog_partition_spec, False)

# alter partition
catalog.alter_partition(
    ObjectPath("mydb", "mytable"),
    CatalogPartitionSpec(...),
    catalog_partition,
    False)

# get partition
catalog.get_partition(ObjectPath("mydb", "mytable"), catalog_partition_spec)

# check if a partition exist or not
catalog.partition_exists(ObjectPath("mydb", "mytable"), catalog_partition_spec)

# list partitions of a table
catalog.list_partitions(ObjectPath("mydb", "mytable"))

# list partitions of a table under a give partition spec
catalog.list_partitions(ObjectPath("mydb", "mytable"), catalog_partition_spec)

Function operations

// create function
catalog.createFunction(new ObjectPath("mydb", "myfunc"), new CatalogFunctionImpl(...), false);

// drop function
catalog.dropFunction(new ObjectPath("mydb", "myfunc"), false);

// alter function
catalog.alterFunction(new ObjectPath("mydb", "myfunc"), new CatalogFunctionImpl(...), false);

// get function
catalog.getFunction("myfunc");

// check if a function exist or not
catalog.functionExists("myfunc");

// list functions in a database
catalog.listFunctions("mydb");

from pyflink.table.catalog import ObjectPath, CatalogFunction
from pyflink.java_gateway import get_gateway

gateway = get_gateway()
j_function = gateway.jvm.org.apache.flink.table.catalog.CatalogFunctionImpl(
    "my.python.udf",
    gateway.jvm.org.apache.flink.table.catalog.FunctionLanguage.PYTHON)
catalog_function = CatalogFunction(j_function)

# create function
catalog.create_function(ObjectPath("mydb", "myfunc"), catalog_function, False)

# drop function
catalog.drop_function(ObjectPath("mydb", "myfunc"), False)

# alter function
catalog.alter_function(ObjectPath("mydb", "myfunc"), catalog_function, False)

# get function
catalog.get_function("myfunc")

# check if a function exist or not
catalog.function_exists("myfunc")

# list functions in a database
catalog.list_functions("mydb")

Table API and SQL for Catalog

Registering a Catalog

Users have access to a default in-memory catalog named default_catalog, that is always created by default. This catalog by default has a single database called default_database. Users can also register additional catalogs into an existing Flink session.

tableEnv.registerCatalog(new CustomCatalog("myCatalog"));

t_env.register_catalog(catalog)

All catalogs defined using YAML must provide a type property that specifies the type of catalog. The following types are supported out of the box.

Catalog	Type Value
GenericInMemory	generic_in_memory
Hive	hive

catalogs:
   - name: myCatalog
     type: custom_catalog
     hive-conf-dir: ...

Changing the Current Catalog And Database

Flink will always search for tables, views, and UDF’s in the current catalog and database.

tableEnv.useCatalog("myCatalog");
tableEnv.useDatabase("myDb");

t_env.use_catalog("myCatalog")
t_env.use_database("myDb")

Flink SQL> USE CATALOG myCatalog;
Flink SQL> USE myDB;

Metadata from catalogs that are not the current catalog are accessible by providing fully qualified names in the form catalog.database.object.

tableEnv.from("not_the_current_catalog.not_the_current_db.my_table");

t_env.from_path("not_the_current_catalog.not_the_current_db.my_table")

Flink SQL> SELECT * FROM not_the_current_catalog.not_the_current_db.my_table;

List Available Catalogs

tableEnv.listCatalogs();

t_env.list_catalogs()

Flink SQL> show catalogs;

List Available Databases

tableEnv.listDatabases();

t_env.list_databases()

Flink SQL> show databases;

List Available Tables

tableEnv.listTables();

t_env.list_tables()

Flink SQL> show tables;