Hive Compatibility Beta

With its wide adoption in streaming processing, Flink has also shown its potentials in batch processing. Improving Flink’s batch processing, especially in terms of SQL, would offer user a complete set of solutions for both their streaming and batch processing needs.

On the other hand, Hive has established its focal point in big data technology and its complete ecosystem. For most of big data users, Hive is not only a SQL engine for big data analytics and ETL, but also a data management platform, on which data are discovered, defined, and evolved. In another words, Hive is a de facto standard for big data on Hadoop.

Therefore, it’s imperative for Flink to integrate with Hive ecosystem to further its reach to batch and SQL users. In doing that, integration with Hive metadata and data is necessary.

The goal here is neither to replace nor to replicate Hive. Rather, we leverage Hive as much as we can. Flink is an alternative batch engine to Hive’s batch engine, and Flink SQL Cli offers a Hive-syntax-compatible SQL client. With Flink and Flink SQL, both Hive and Flink users can enjoy Hive’s rich SQL functionality and ecosystem as well as Flink’s outstanding batch processing performance.

Supported Hive version

The target version is Hive 2.3.4, which is the latest stable version.

Other versioned Hive may also be used with Flink, but there’s no guarantee on compatibility.

Hive Metastore Integration

There are two aspects of Hive metastore integration:

  1. Make Hive’s meta-objects such as tables and views available to Flink and Flink is also able to create such meta-objects for and in Hive. This is achieved through HiveCatalog.

  2. Persist Flink’s meta-objects (tables, views, and UDFs) using Hive metastore as an persistent storage. This is achieved through GenericHiveMetastoreCatalog, which is under active development.

For how to use HiveCatalog and GenericHiveMetastoreCatalog in Flink, see Catalogs

Hive Data Integration

Please refer to Connecting to other systems for how to connect an existing Hive service with Flink using Flink’s Hive data connector.


Here we present a quick example of querying Hive metadata and data in Flink.

Environment :

Assume all physical machines can be accessed in the working environment, and the following components have been successfully setup:

  • Hadoop Cluster (HDFS + YARN)
  • Hive 2.3.4
  • Flink cluster

Start a yarn session

$ ./bin/ -n 4 -qu root.default -s 4 -tm 2048 -nm test_session_001

Simply run

$ ./bin/

Let’s set the SQL Cli yaml config file. For more detailed instructions on Flink SQL Cli, see Flink SQL CLI.

    # Use batch mode
    type: batch
    time-characteristic: event-time
    periodic-watermarks-interval: 200
    # Use table result mode
    result-mode: table
    parallelism: 1
    max-parallelism: 12
    min-idle-state-retention: 0
    max-idle-state-retention: 0

  response-timeout: 5000
  gateway-address: ""
  gateway-port: 0
  # (Optional) For users who use yarn-session mode
  yid: application_1543205128210_0045

   - name: myHive
      type: hive
        # Hive metastore thrift uri
        Hive.metastore.uris: thrift://<ip1>:<port1>,thrift://<ip2>:<port2>

Note that, if users are using Flink yarn-session mode, you’ll get the sessionId as \${appId}. Set it in yid: ${appId} of deployment section in the conf/sql-client-defaults.yaml file

If users are using Flink local mode, no other config is required.

Make sure all the required jars are in the /lib dir, including jars of flink-connector-hive and flink-hadoop-compatibility.

Get Flink SQL Cli running by execute command

$ ./bin/ embedded


Prepare Hive

Assuming that Hive has been successfully set up and running, let’s prepare some data in Hive.

First, we locate the current database in Hive, which is default in this case, and make sure no table exists in the database at this time.

hive> show databases;
Time taken: 0.841 seconds, Fetched: 1 row(s)

hive> show tables;
Time taken: 0.087 seconds

Second, we create a simple table with two columns - name as string and value as double - in a textfile format and each row is delimited by ‘,’.

hive> CREATE TABLE mytable(name string, value double) row format delimited fields terminated by ',' stored as textfile;
Time taken: 0.127 seconds

This way, we created a table named mytable under the default database in Hive.

Then let’s load the data into table mytable and make sure it’s successful. Here’s some data we prepared to load into the table, and assume the file path is ‘/tmp/data.txt’.


Load and check data by running:

hive> load data local inpath '/tmp/data.txt' into table mytable;
Loading data to table default.mytable
Time taken: 0.324 seconds

hive> select * from mytable;
Tom	4.72
John	8.0
Tom	24.2
Bob	3.14
Bob	4.72
Tom	34.9
Mary	4.79
Tiff	2.72
Bill	4.33
Mary	77.7
Time taken: 0.097 seconds, Fetched: 10 row(s)

hive data prepare

In Flink SQL Cli, we can start using Hive.

# ------ See the catalog 'myhive' in the yaml config file is registered successfully and showing up here ------

Flink SQL> show catalogs;

# ------ Set the default catalog and database to be 'myhive' catalog and the 'default' database ------

Flink SQL> use myhive.default;

# ------ See the previously registered table 'mytable' ------

Flink SQL> show tables;

# ------ The table schema that Flink sees is the same that we created in Hive, two columns - name as string and value as double ------ 
Flink SQL> describe mytable;
 |-- name: name
 |-- type: StringType
 |-- isNullable: true
 |-- name: value
 |-- type: DoubleType
 |-- isNullable: true

Flink SQL> select * from mytable;

      name      value
__________ __________

      Tom        4.72
      John	     8.0
      Tom	     24.2
      Bob	     3.14
      Bob	     4.72
      Tom	     34.9
      Mary	     4.79
      Tiff	     2.72
      Bill	     4.33
      Mary	     77.7

query hive data

Another Example

We have prepared two tables in Hive, order_details and products, which can be described in Hive SQL Cli:

Hive> describe order_details;
orderid               bigint
productid             bigint
unitprice             double
quantity              int
discount              double

Hive> describe products;
productid             bigint
productname           string
supplierid            bigint
categoryid            bigint
quantityperunit       string
unitprice             double
unitsinstock          bigint
unitsonorder          bigint
reorderlevel          int
discontinued          int

We can run a few SQL query to get Hive data

Flink SQL> select * from products;

Flink SQL> select count(*) from order_details;

Flink SQL> select
   sum(t.price) as sale
      A.productname as productname,
        B.unitprice * discount as price
      products as A, order_details as B
     where A.productid = B.productid) as t
  group by t.productid, t.productname;

query hive data

Limitations & Future

Integrations of both Hive metadata and data are still in progress. Flink currently only supports reading metadata and data from Hive.