title: “Overview” weight: 1 type: docs aliases:
Apache Hive has established itself as a focal point of the data warehousing ecosystem. It serves as not only a SQL engine for big data analytics and ETL, but also a data management platform, where data is discovered, defined, and evolved.
Flink offers a two-fold integration with Hive.
The first is to leverage Hive‘s Metastore as a persistent catalog with Flink’s HiveCatalog for storing Flink specific metadata across sessions. For example, users can store their Kafka or Elasticsearch tables in Hive Metastore by using HiveCatalog, and reuse them later on in SQL queries.
The second is to offer Flink as an alternative engine for reading and writing Hive tables.
The HiveCatalog is designed to be “out of the box” compatible with existing Hive installations. You do not need to modify your existing Hive Metastore or change the data placement or partitioning of your tables.
Flink supports the following Hive versions.
Please note Hive itself have different features available for different versions, and these issues are not caused by Flink:
DATE column statistics are supported in 1.2.0 and later.To integrate with Hive, you need to add some extra dependencies to the /lib/ directory in Flink distribution to make the integration work in Table API program or SQL in SQL Client. Alternatively, you can put these dependencies in a dedicated folder, and add them to classpath with the -C or -l option for Table API program or SQL Client respectively.
Apache Hive is built on Hadoop, so you need to provide Hadoop dependencies, by setting the HADOOP_CLASSPATH environment variable:
export HADOOP_CLASSPATH=`hadoop classpath`
There are two ways to add Hive dependencies. First is to use Flink‘s bundled Hive jars. You can choose a bundled Hive jar according to the version of the metastore you use. Second is to add each of the required jars separately. The second way can be useful if the Hive version you’re using is not listed here.
NOTE: the recommended way to add dependency is to use a bundled jar. Separate jars should be used only if bundled jars don't meet your needs.
The following tables list all available bundled hive jars. You can pick one to the /lib/ directory in Flink distribution.
| Metastore version | Maven dependency | SQL Client JAR |
|---|---|---|
| 2.3.0 - 2.3.9 | flink-sql-connector-hive-2.3.9 | {{< stable >}}[Download](https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-hive-2.3.9{{< scala_version >}}/{{< version >}}/flink-sql-connector-hive-2.3.9{{< scala_version >}}-{{< version >}}.jar) {{< /stable >}}{{< unstable >}} Only available for stable releases {{< /unstable >}} |
| 3.0.0 - 3.1.3 | flink-sql-connector-hive-3.1.3 | {{< stable >}}[Download](https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-hive-3.1.3{{< scala_version >}}/{{< version >}}/flink-sql-connector-hive-3.1.3{{< scala_version >}}-{{< version >}}.jar) {{< /stable >}}{{< unstable >}} Only available for stable releases {{< /unstable >}} |
Please find the required dependencies for different Hive major versions below.
{{< tabs “8623cd64-8623-4922-92d2-ee82ec410d96” >}} {{< tab “Hive 2.3.4” >}}
/flink-{{< version >}} /lib // Flink's Hive connector.Contains flink-hadoop-compatibility and flink-orc jars flink-connector-hive{{< scala_version >}}-{{< version >}}.jar // Hive dependencies hive-exec-2.3.4.jar // add antlr-runtime if you need to use hive dialect antlr-runtime-3.5.2.jar
{{< /tab >}} {{< tab “Hive 3.1.0” >}}
/flink-{{< version >}}
/lib
// Flink's Hive connector
flink-connector-hive{{< scala_version >}}-{{< version >}}.jar
// Hive dependencies
hive-exec-3.1.0.jar
libfb303-0.9.3.jar // libfb303 is not packed into hive-exec in some versions, need to add it separately
// add antlr-runtime if you need to use hive dialect
antlr-runtime-3.5.2.jar
{{< /tab >}} {{< /tabs >}}
If you are building your own program, you need the following dependencies in your mvn file. It‘s recommended not to include these dependencies in the resulting jar file. You’re supposed to add dependencies as stated above at runtime.
<!-- Flink Dependency --> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-hive{{< scala_version >}}</artifactId> <version>{{< version >}}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table-api-java-bridge{{< scala_version >}}</artifactId> <version>{{< version >}}</version> <scope>provided</scope> </dependency> <!-- Hive Dependency --> <dependency> <groupId>org.apache.hive</groupId> <artifactId>hive-exec</artifactId> <version>${hive.version}</version> <scope>provided</scope> </dependency>
Connect to an existing Hive installation using the [catalog interface]({{< ref “docs/dev/table/catalogs” >}}) and [HiveCatalog]({{< ref “docs/connectors/table/hive/hive_catalog” >}}) through the table environment or YAML configuration.
Following is an example of how to connect to Hive:
{{< tabs “5d3cc7e1-a304-4f9e-b36e-ff1f32394ec7” >}} {{< tab “Java” >}}
EnvironmentSettings settings = EnvironmentSettings.inStreamingMode(); TableEnvironment tableEnv = TableEnvironment.create(settings); String name = "myhive"; String defaultDatabase = "mydatabase"; String hiveConfDir = "/opt/hive-conf"; HiveCatalog hive = new HiveCatalog(name, defaultDatabase, hiveConfDir); tableEnv.registerCatalog("myhive", hive); // set the HiveCatalog as the current catalog of the session tableEnv.useCatalog("myhive");
{{< /tab >}} {{< tab “Scala” >}}
val settings = EnvironmentSettings.inStreamingMode() val tableEnv = TableEnvironment.create(settings) val name = "myhive" val defaultDatabase = "mydatabase" val hiveConfDir = "/opt/hive-conf" val hive = new HiveCatalog(name, defaultDatabase, hiveConfDir) tableEnv.registerCatalog("myhive", hive) // set the HiveCatalog as the current catalog of the session tableEnv.useCatalog("myhive")
{{< /tab >}} {{< tab “Python” >}}
from pyflink.table import * from pyflink.table.catalog import HiveCatalog settings = EnvironmentSettings.in_batch_mode() t_env = TableEnvironment.create(settings) catalog_name = "myhive" default_database = "mydatabase" hive_conf_dir = "/opt/hive-conf" hive_catalog = HiveCatalog(catalog_name, default_database, hive_conf_dir) t_env.register_catalog("myhive", hive_catalog) # set the HiveCatalog as the current catalog of the session tableEnv.use_catalog("myhive")
{{< /tab >}} {{< tab “YAML” >}}
execution: ... 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
{{< /tab >}} {{< tab “SQL” >}}
CREATE CATALOG myhive WITH ( 'type' = 'hive', 'default-database' = 'mydatabase', 'hive-conf-dir' = '/opt/hive-conf' ); -- set the HiveCatalog as the current catalog of the session USE CATALOG myhive;
{{< /tab >}} {{< /tabs >}}
Below are the options supported when creating a HiveCatalog instance with YAML file or DDL.
It's recommended to use [Hive dialect]({{< ref “docs/dev/table/hive-compatibility/hive-dialect/overview” >}}) to execute DDLs to create Hive tables, views, partitions, functions within Flink.
Flink supports DML writing to Hive tables. Please refer to details in [Reading & Writing Hive Tables]({{< ref “docs/connectors/table/hive/hive_read_write” >}})