HiveCatalog

Hive Metastore has evolved into the de facto metadata hub over the years in Hadoop ecosystem. Many companies have a single Hive Metastore service instance in their production to manage all of their metadata, either Hive metadata or non-Hive metadata, as the source of truth.

For users who have both Hive and Flink deployments, HiveCatalog enables them to use Hive Metastore to manage Flink’s metadata.

For users who have just Flink deployment, HiveCatalog is the only persistent catalog provided out-of-box by Flink. Without a persistent catalog, users using Flink SQL CREATE DDL have to repeatedly create meta-objects like a Kafka table in each session, which wastes a lot of time. HiveCatalog fills this gap by empowering users to create tables and other meta-objects only once, and reference and manage them with convenience later on across sessions.

Set up HiveCatalog

Dependencies

Setting up a HiveCatalog in Flink requires the same dependencies as those of an overall Flink-Hive integration.

Configuration

Setting up a HiveCatalog in Flink requires the same configuration as those of an overall Flink-Hive integration.

How to use HiveCatalog

Once configured properly, HiveCatalog should just work out of box. Users can create Flink meta-objects with DDL, and should see them immediately afterwards.

HiveCatalog can be used to handle two kinds of tables: Hive-compatible tables and generic tables. Hive-compatible tables are those stored in a Hive-compatible way, in terms of both metadata and data in the storage layer. Therefore, Hive-compatible tables created via Flink can be queried from Hive side.

Generic tables, on the other hand, are specific to Flink. When creating generic tables with HiveCatalog, we’re just using HMS to persist the metadata. While these tables are visible to Hive, it’s unlikely Hive is able to understand the metadata. And therefore using such tables in Hive leads to undefined behavior.

Flink uses the property ‘is_generic’ to tell whether a table is Hive-compatible or generic. When creating a table with HiveCatalog, it’s by default considered generic. If you’d like to create a Hive-compatible table, make sure to set is_generic to false in your table properties.

As stated above, generic tables shouldn’t be used from Hive. In Hive CLI, you can call DESCRIBE FORMATTED for a table and decide whether it’s generic or not by checking the is_generic property. Generic tables will have is_generic=true.

Example

We will walk through a simple example here.

step 1: set up a Hive Metastore

Have a Hive Metastore running.

Here, we set up a local Hive Metastore and our hive-site.xml file in local path /opt/hive-conf/hive-site.xml. We have some configs like the following:

<configuration>
   <property>
      <name>javax.jdo.option.ConnectionURL</name>
      <value>jdbc:mysql://localhost/metastore?createDatabaseIfNotExist=true</value>
      <description>metadata is stored in a MySQL server</description>
   </property>

   <property>
      <name>javax.jdo.option.ConnectionDriverName</name>
      <value>com.mysql.jdbc.Driver</value>
      <description>MySQL JDBC driver class</description>
   </property>

   <property>
      <name>javax.jdo.option.ConnectionUserName</name>
      <value>...</value>
      <description>user name for connecting to mysql server</description>
   </property>

   <property>
      <name>javax.jdo.option.ConnectionPassword</name>
      <value>...</value>
      <description>password for connecting to mysql server</description>
   </property>

   <property>
       <name>hive.metastore.uris</name>
       <value>thrift://localhost:9083</value>
       <description>IP address (or fully-qualified domain name) and port of the metastore host</description>
   </property>

   <property>
       <name>hive.metastore.schema.verification</name>
       <value>true</value>
   </property>

</configuration>

Test connection to the HMS with Hive Cli. Running some commands, we can see we have a database named default and there’s no table in it.

hive> show databases;
OK
default
Time taken: 0.032 seconds, Fetched: 1 row(s)

hive> show tables;
OK
Time taken: 0.028 seconds, Fetched: 0 row(s)

Add all Hive dependencies to /lib dir in Flink distribution, and modify SQL CLI’s yaml config file sql-cli-defaults.yaml as following:

execution:
    planner: blink
    type: streaming
    ...
    current-catalog: myhive  # set the HiveCatalog as the current catalog of the session
    current-database: mydatabase
    
catalogs:
   - name: myhive
     type: hive
     hive-conf-dir: /opt/hive-conf  # contains hive-site.xml

step 3: set up a Kafka cluster

Bootstrap a local Kafka 2.3.0 cluster with a topic named “test”, and produce some simple data to the topic as tuple of name and age.

localhost$ bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test
>tom,15
>john,21

These message can be seen by starting a Kafka console consumer.

localhost$ bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning

tom,15
john,21

Start Flink SQL Client, create a simple Kafka 2.3.0 table via DDL, and verify its schema.

Flink SQL> CREATE TABLE mykafka (name String, age Int) WITH (
   'connector.type' = 'kafka',
   'connector.version' = 'universal',
   'connector.topic' = 'test',
   'connector.properties.bootstrap.servers' = 'localhost:9092',
   'format.type' = 'csv',
   'update-mode' = 'append'
);
[INFO] Table has been created.

Flink SQL> DESCRIBE mykafka;
root
 |-- name: STRING
 |-- age: INT

Verify the table is also visible to Hive via Hive Cli, and note that the table has property is_generic=true:

hive> show tables;
OK
mykafka
Time taken: 0.038 seconds, Fetched: 1 row(s)

hive> describe formatted mykafka;
OK
# col_name            	data_type           	comment


# Detailed Table Information
Database:           	default
Owner:              	null
CreateTime:         	......
LastAccessTime:     	UNKNOWN
Retention:          	0
Location:           	......
Table Type:         	MANAGED_TABLE
Table Parameters:
	flink.connector.properties.bootstrap.servers	localhost:9092
	flink.connector.topic	test
	flink.connector.type	kafka
	flink.connector.version	universal
	flink.format.type   	csv
	flink.generic.table.schema.0.data-type	VARCHAR(2147483647)
	flink.generic.table.schema.0.name	name
	flink.generic.table.schema.1.data-type	INT
	flink.generic.table.schema.1.name	age
	flink.update-mode   	append
	is_generic          	true
	transient_lastDdlTime	......

# Storage Information
SerDe Library:      	org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat:        	org.apache.hadoop.mapred.TextInputFormat
OutputFormat:       	org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat
Compressed:         	No
Num Buckets:        	-1
Bucket Columns:     	[]
Sort Columns:       	[]
Storage Desc Params:
	serialization.format	1
Time taken: 0.158 seconds, Fetched: 36 row(s)

Run a simple select query from Flink SQL Client in a Flink cluster, either standalone or yarn-session.

Flink SQL> select * from mykafka;

Produce some more messages in the Kafka topic

localhost$ bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning

tom,15
john,21
kitty,30
amy,24
kaiky,18

You should see results produced by Flink in SQL Client now, as:

             SQL Query Result (Table)
 Refresh: 1 s    Page: Last of 1     

        name                       age
         tom                        15
        john                        21
       kitty                        30
         amy                        24
       kaiky                        18

Supported Types

HiveCatalog supports all Flink types for generic tables.

For Hive-compatible tables, HiveCatalog needs to map Flink data types to corresponding Hive types as described in the following table:

Flink Data Type Hive Data Type
CHAR(p) CHAR(p)
VARCHAR(p) VARCHAR(p)
STRING STRING
BOOLEAN BOOLEAN
TINYINT TINYINT
SMALLINT SMALLINT
INT INT
BIGINT LONG
FLOAT FLOAT
DOUBLE DOUBLE
DECIMAL(p, s) DECIMAL(p, s)
DATE DATE
TIMESTAMP(9) TIMESTAMP
BYTES BINARY
ARRAY<T> LIST<T>
MAP<K, V> MAP<K, V>
ROW STRUCT

Something to note about the type mapping:

  • Hive’s CHAR(p) has a maximum length of 255
  • Hive’s VARCHAR(p) has a maximum length of 65535
  • Hive’s MAP only supports primitive key types while Flink’s MAP can be any data type
  • Hive’s UNION type is not supported
  • Hive’s TIMESTAMP always has precision 9 and doesn’t support other precisions. Hive UDFs, on the other hand, can process TIMESTAMP values with a precision <= 9.
  • Hive doesn’t support Flink’s TIMESTAMP_WITH_TIME_ZONE, TIMESTAMP_WITH_LOCAL_TIME_ZONE, and MULTISET
  • Flink’s INTERVAL type cannot be mapped to Hive INTERVAL type yet

Scala Shell

注意:目前 blink planner 还不能很好的支持 Scala Shell,因此 建议在 Scala Shell 中使用 Hive 连接器。