Catalogs

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 = TableEnvironment.create(settings)

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

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

# Create a catalog database
database = CatalogDatabase.create_instance({"k1": "v1"}, None)
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()

catalog_table = CatalogBaseTable.create_table(
    schema=table_schema, properties=table_properties, comment="my comment")

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

# create database
catalog_database = CatalogDatabase.create_instance({"k1": "v1"}, None)
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.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()

catalog_table = CatalogBaseTable.create_table(schema=table_schema, properties=table_properties, comment="my comment")

# 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

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

catalog_table = CatalogBaseTable.create_view(
    original_query="select * from t1",
    expanded_query="select * from test-catalog.db1.t1",
    schema=table_schema,
    properties={},
    comment="This is a 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

catalog_partition = CatalogPartition.create_instance({}, "my 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

catalog_function = CatalogFunction.create_instance(class_name="my.python.udf")

# 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;