How to Install and Configure Hive with High Availability?

Hive is an open-source data warehousing framework built on top of Apache Hadoop. It allows users to query large datasets stored in Hadoop using a SQL-like language called HiveQL. Hive provides an interface for data analysts and developers to work with Hadoop without having to write complex MapReduce jobs. In this article, we will discuss how to install and configure Hive with high availability.

High availability (HA) is a critical requirement for any production system. HA ensures that system is always available, even in event of hardware or software failures. In context of Hive, HA means that Hive server is always available to process queries, even if one of nodes in cluster fails. To achieve HA, we need to set up multiple instances of Hive server and configure them to work together in a fault-tolerant manner.

Here are steps to install and configure Hive with high availability −

Step 1: Install Hadoop

Before installing Hive, we need to install Hadoop. Hadoop is a distributed file system and foundation for many big data processing frameworks, including Hive. Follow steps outlined in Hadoop installation guide to set up Hadoop on your cluster.

Step 2: Install Hive

Once Hadoop is installed, we can install Hive. Download latest stable release of Hive from Apache Hive website. Extract downloaded package to a directory of your choice. For example, if you extracted package to /usr/local/, Hive installation directory would be /usr/local/apache-hive-x.x.x-bin/.

Step 3: Configure Hive

After Hive is installed, we need to configure it. Hive configuration is stored in hive-site.xml file, located in conf/ directory of Hive installation directory. We need to configure following properties in hive-site.xml file −

<property>
   <name>javax.jdo.option.ConnectionURL</name>
   <value>jdbc:mysql://<mysql-hostname>:<mysql-port>/<hive-db>?createDatabaseIfNotExist=true</value>
   <description>JDBC connect string for a JDBC metastore</description>
</property>

<property>
   <name>javax.jdo.option.ConnectionDriverName</name>
   <value>com.mysql.jdbc.Driver</value>
   <description>Driver class name for a JDBC metastore</description>
</property>

<property>
   <name>javax.jdo.option.ConnectionUserName</name>
   <value><hive-mysql-user></value>
   <description>Username to use against metastore database</description>
</property>

<property>
   <name>javax.jdo.option.ConnectionPassword</name>
   <value><hive-mysql-password></value>
   <description>Password to use against metastore database</description>
</property>

<property>
   <name>hive.server2.support.dynamic.service.discovery</name>
   <value>true</value>
   <description>Enable dynamic service discovery for HiveServer2</description>
</property>

<property>
   <name>hive.server2.zookeeper.namespace</name>
   <value>hiveserver2</value>
   <description>ZooKeeper namespace for HiveServer2 dynamic service discovery</description>
</property>

<property>
   <name>hive.server2.zookeeper.quorum</name>
   <value><zookeeper-hostname>:<zookeeper-port></value>
   <description>ZooKeeper quorum for HiveServer2 dynamic service discovery</description>
</property>

In above configuration, replace following placeholders −

• <mysql-hostname> − hostname of MySQL database server where Hive metadata will be stored.

• <mysql-port> − port number of MySQL database server.

• <hive-db> − name of MySQL database where Hive metadata will be stored.

• <hive-mysql-user> − MySQL username that Hive will use to connect to database.

• <hive-mysql-password> − password for MySQL user.

The above configuration sets up a MySQL database as Hive metastore, which stores Hive metadata such as table definitions, column names, and partitions. hive.server2.support.dynamic.service.discovery property enables dynamic service discovery for HiveServer2, which allows clients to discover active Hive server in cluster. hive.server2.zookeeper.namespace and hive.server2.zookeeper.quorum properties configure ZooKeeper, which is used for dynamic service discovery.

Step 4: Set Up High Availability

To set up high availability, we need to run multiple instances of Hive server and configure them to work together. Here are steps to set up high availability −

Copy Hive installation directory to each node in cluster that will host a Hive server instance.

Modify hive-env.sh file in conf/ directory of Hive installation directory on each node to set HIVE_CONF_DIR environment variable to path of conf/ directory.

Start Hive servers on each node using following command −

$HIVE_HOME/bin/hiveserver2 &

This starts HiveServer2 process, which listens for client connections and processes queries.

Verify that Hive servers are running by checking logs in logs/ directory of Hive installation directory.

Load balance client connections across Hive servers using a load balancer such as HAProxy or a DNS round-robin setup.

By running multiple instances of Hive server and load balancing client connections across them, we achieve high availability for Hive. If one of Hive servers fails, clients can still connect to other active servers and process queries.

While high availability provides fault-tolerance and ensures that Hive is always available, it is important to note that it comes with some trade-offs. Running multiple instances of Hive server requires additional resources, including CPU, memory, and storage. Additionally, setting up high availability adds complexity to system, making it more difficult to manage and troubleshoot.

To minimize impact of these trade-offs, it is important to carefully plan and design Hive cluster architecture. Some best practices to follow include −

Start with a small number of Hive servers and scale out as needed. Adding more servers than necessary can increase resource utilization and decrease performance.

Use hardware load balancers or DNS round-robin setups for load balancing client connections. Software load balancers such as HAProxy can introduce additional overhead and reduce performance.

Monitor Hive cluster performance and resource utilization to identify bottlenecks and optimize system. Tools such as Ganglia or Ambari can provide real-time metrics and alerts for Hive cluster.

Follow backup and disaster recovery best practices to ensure that Hive metadata is protected and can be recovered in event of a failure. This includes regularly backing up Hive metastore and storing backups in a separate location from cluster.

In addition to best practices mentioned above, it is also important to consider security when setting up a highly available Hive cluster. Hive may contain sensitive data, so it is important to ensure that data is protected from unauthorized access.

Some security measures to consider include −

Enable authentication and authorization for Hive. Hive supports various authentication and authorization mechanisms, including Kerberos, LDAP, and Apache Ranger. Enabling authentication and authorization ensures that only authorized users can access and manipulate data in Hive

Use encryption to protect data in transit and at rest. Hive supports encryption for data in transit using SSL/TLS and for data at rest using HDFS encryption. Enabling encryption ensures that data is protected from interception or theft.

Use firewalls to restrict access to Hive cluster. Configure firewalls to allow only authorized IP addresses or subnets to access Hive cluster, and block all other traffic.

Regularly update and patch Hive cluster and its dependencies to address security vulnerabilities. Set up a regular maintenance schedule to ensure that Hive cluster is up-to-date with latest security patches and updates.

By following these security measures, you can ensure that highly available Hive cluster is secure and protected from unauthorized access or data breaches.

Here are some factors to consider when choosing a storage backend for Hive −

• Performance − storage backend should provide fast and efficient access to data for Hive queries. This includes factors such as read and write performance, data compression, and caching.

• Scalability − storage backend should be able to handle volume and growth of data in Hive cluster. This includes factors such as data partitioning, sharding, and replication.

• Cost − storage backend should be cost-effective and fit within budget of Hive cluster. This includes factors such as storage pricing, network bandwidth costs, and data transfer fees.

• Availability − storage backend should be highly available and provide fault-tolerance for data in Hive cluster. This includes factors such as backup and disaster recovery, data replication, and data consistency.

Based on these factors, HDFS is a popular choice for storage backend for Hive. HDFS provides high performance, scalability, and fault-tolerance, and is integrated with Hadoop, making it a natural fit for Hive. However, HDFS requires additional resources and maintenance, and may not be cost-effective for small or medium-sized Hive clusters.

Alternatively, cloud-based storage services such as Amazon S3 or Azure Blob Storage provide scalable and cost-effective storage options for Hive. These services are highly available and provide data replication and backup features, but may have higher network bandwidth costs and data transfer fees.

Conclusion

In this article, we discussed how to install and configure Hive with high availability. High availability is critical for any production system, and Hive is no exception. By following steps outlined in this article, you can set up multiple instances of Hive server and configure them to work together in a fault-tolerant manner, ensuring that Hive service is always available to process queries.