Catalogs

Catalog 提供了元数据信息,例如数据库、表、分区、视图以及数据库或其他外部系统中存储的函数和信息。

数据处理最关键的方面之一是管理元数据。 元数据可以是临时的,例如临时表、或者通过 TableEnvironment 注册的 UDF。 元数据也可以是持久化的,例如 Hive Metastore 中的元数据。Catalog 提供了一个统一的API,用于管理元数据,并使其可以从 Table API 和 SQL 查询语句中来访问。

Catalog 类型

GenericInMemoryCatalog

GenericInMemoryCatalog 是基于内存实现的 Catalog,所有元数据只在 session 的生命周期内可用。

JdbcCatalog

JdbcCatalog 使得用户可以将 Flink 通过 JDBC 协议连接到关系数据库。PostgresCatalog 是当前实现的唯一一种 JDBC Catalog。 参考 JdbcCatalog 文档 获取关于配置 JDBC catalog 的详细信息。

HiveCatalog

HiveCatalog 有两个用途:作为原生 Flink 元数据的持久化存储,以及作为读写现有 Hive 元数据的接口。 Flink 的 Hive 文档 提供了有关设置 HiveCatalog 以及访问现有 Hive 元数据的详细信息。

警告 Hive Metastore 以小写形式存储所有元数据对象名称。而 GenericInMemoryCatalog 区分大小写。

用户自定义 Catalog

Catalog 是可扩展的,用户可以通过实现 Catalog 接口来开发自定义 Catalog。 想要在 SQL CLI 中使用自定义 Catalog,用户除了需要实现自定义的 Catalog 之外,还需要为这个 Catalog 实现对应的 CatalogFactory 接口。

CatalogFactory 定义了一组属性,用于 SQL CLI 启动时配置 Catalog。 这组属性集将传递给发现服务,在该服务中,服务会尝试将属性关联到 CatalogFactory 并初始化相应的 Catalog 实例。

使用 SQL DDL

用户可以使用 DDL 通过 Table API 或者 SQL Client 在 Catalog 中创建表。

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

更多详细信息,请参考Flink SQL CREATE DDL

使用 Java/Scala

用户可以用编程的方式使用Java 或者 Scala 来创建 Catalog 表。

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

注意:这里只列出了编程方式的 Catalog API,用户可以使用 SQL DDL 实现许多相同的功能。 关于 DDL 的详细信息请参考 SQL CREATE DDL

数据库操作

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

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

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

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

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

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

表操作

// 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")

视图操作

// 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")

分区操作

// 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.listPartitions(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)

函数操作

// 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")

通过 Table API 和 SQL Client 操作 Catalog

注册 Catalog

用户可以访问默认创建的内存 Catalog default_catalog,这个 Catalog 默认拥有一个默认数据库 default_database。 用户也可以注册其他的 Catalog 到现有的 Flink 会话中。

tableEnv.registerCatalog(new CustomCatalog("myCatalog"));
t_env.register_catalog(catalog)

使用 YAML 定义的 Catalog 必须提供 type 属性,以表示指定的 Catalog 类型。 以下几种类型可以直接使用。

Catalog Type Value
GenericInMemory generic_in_memory
Hive hive
catalogs:
   - name: myCatalog
     type: custom_catalog
     hive-conf-dir: ...

修改当前的 Catalog 和数据库

Flink 始终在当前的 Catalog 和数据库中寻找表、视图和 UDF。

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;

通过提供全限定名 catalog.database.object 来访问不在当前 Catalog 中的元数据信息。

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;

列出可用的 Catalog

tableEnv.listCatalogs();
t_env.list_catalogs()
Flink SQL> show catalogs;

列出可用的数据库

tableEnv.listDatabases();
t_env.list_databases()
Flink SQL> show databases;

列出可用的表

tableEnv.listTables();
t_env.list_tables()
Flink SQL> show tables;