This chapter will introduce how to install and set up the Hive environment in the cluster and cloud. It also covers the usage of basic Hive commands and the Hive integrated-development environment.
In this chapter, we will cover the following topics:
To introduce the Hive installation, we will use Hive version 2.3.3 as an example. The pre-installation requirements for this installation are as follows:
The following steps describe how to install Apache Hive in the command-line environment:
$cd /opt
$wget https://archive.apache.org/dist/hive/hive-2.3.3/apache-
hive-2.3.3-bin.tar.gz
$tar -zxvf apache-hive-2.3.3-bin.tar.gz
$ln -sfn /opt/apache-hive-2.3.3 /opt/hive
Right now, many companies, such as Cloudera and Hortonworks, have packaged the Hadoop ecosystem and management tools into an easily manageable enterprise distribution. Each company takes a slightly different strategy, but the consensus for all of these packages is to make the Hadoop ecosystem easier and more stable for enterprise usage. For example, we can easily install Hive with the Hadoop management tools, such as Cloudera Manager (https://www.cloudera.com/products/product-components/cloudera-manager.html) or Ambari (https://ambari.apache.org/), which are packed in vendor distributions. Once the management tool is installed and started, we can add the Hive service to the Hadoop cluster with the following steps:
Log in to the Cloudera Manager/Ambari and click the Add a Service option to enter the Add Service Wizard
Choose the service to install...
Right now, all major cloud service providers, such as Amazon, Microsoft, and Google, offer matured Hadoop and Hive as services in the cloud. Using the cloud version of Hive is very convenient. It requires almost no installation and setup. Amazon EMR (http://aws.amazon.com/elasticmapreduce/) is the earliest Hadoop service in the cloud. However, it is not a pure open source version since it is customized to run only on Amazon Web Services (AWS). Hadoop enterprise service and distribution providers, such as Cloudera and Hortonworks, also provide tools to easily deploy their own distributions on different public or private clouds. Cloudera Director (http://www.cloudera.com/content/cloudera/en/products-and-services/director.html) and Cloudbreak (https://hortonworks.com/open-source/cloudbreak/), open up Hadoop deployments in the cloud through a simple, self...
Hive first started with hiveserver1. However, this version of Hive server was not very stable. It sometimes suspended or blocked the client's connection quietly. Since v0.11.0, Hive has included a new thrift server called hivesever2 to replace hiveserver1. hiveserver2 has an enhanced server designed for multiple client concurrency and improved authentication. It also recommends using beeline as the major Hive command-line interface instead of the hive command. The primary difference between the two versions of servers is how the clients connect to them. hive is an Apache-Thrift-based client, and beeline is a JDBC client. The hive command directly connects to the Hive drivers, so we need to install the Hive library on the client. However, beeline connects to hiveserver2 through JDBC connections without installing Hive libraries on the client. That means...
Besides the command-line interface, there are other Integrated Development Environment (IDE) tools available to support Hive. One of the best is Oracle SQL Developer, which leverages the powerful functionalities of the Oracle IDE and is totally free to use. Since Oracle SQL Developer supports general JDBC connections, it is quite convenient to switch between Hive and other JDBC-supported databases in the same IDE. Oracle SQL Developer has supported Hive since v4.0.3. Configuring it to work with Hive is quite straightforward:
In this chapter, we learned how to set up Hive in different environments. We also looked into a few examples of using Hive commands in both the command-line and the interactive mode for beeline and hive. Since it is quite productive to use IDE with Hive, we walked through the setup of Oracle SQL Developer for Hive. Now that you've finished this chapter, you should be able to set up your own Hive environment locally and use Hive.
In the next chapter, we will dive into the details of Hive's data definition languages.
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