Well-written and well-designed Hive queries accelerate data retrieval from datasets. In addition, they help bring down processing costs. This is why writing these queries correctly is essential for big data analytics users and developers.
Fully optimized data queries bring you the data you need at a faster rate compared to other available data processing platforms. Efficient and effective queries can reduce execution time by 50%. When your data processing framework runs faster, the benefits stack up.
But what exactly are Hive Queries?
Answering this question starts with understanding precisely what Hive exactly is. Apache Hive is an open-source data warehousing platform developed on top of Hadoop to perform data analysis and distributed processing. Facebook created Apache Hive to decrease the work required in writing the Java MapReduce platform.
Querying and data analysis using Hive is easier and faster than doing the same using the MapReduce framework, even when dealing with large datasets. For simplicity, we’ll focus on MapReduce as the main execution engine, understanding that Hive can also leverage Tez, Tez LLAP, and Spark. MapReduce is a low-level platform and requires multiple custom programs to run. Developers have to be familiar with Java, which is already a complex platform, to fully leverage MapReduce. In contrast, you don’t need to be a Java expert to work with Hive.
Apache Hive Queries Explained
In common usage, a query is simply a request for information. When used within the context of data science and computer programming, a Hive query is the same thing. The difference is that the information comes straight from a database.
A Hive query isn’t just a random information request. The information you want to retrieve has to be specific. Thus, you write a Hive query using a set of pre-defined code and in a programming language native to the database. Once the database receives and understands that instruction, it gathers all the information specified in the query and releases the data you requested.
To really derive the most value from your queries, they must be written well and expertly tuned. But before that, let’s dive into the rest of what you need to know about them.
What is Hive Query Language?
The standard programming language used to create database management tasks and processes is called Structured Query Language (SQL). However, SQL is not the only programming language used to perform queries and data analysis using Hive. AQL, Datalog, and DMX are also popular choices.
Hive Query Language, or HiveQL, is a declarative language akin to SQL. What HiveQL does is convert these queries into MapReduce programs. It also enables developers to process and analyze structured and semi-structured data by substituting complicated MapReduce programs with Hive queries.
Any developer who is well acquainted with SQL commands will find it easy to create requests using Hive Query Language.
What are Hive Queries for?
Creation of Partitions, Tables, and Buckets
You can create queries in Hive to categorize large datasets stored in Hadoop files into tables, partitions, and buckets. In each model, you group the same kind of data based on partition or column key. There can be one or more partition keys to help pinpoint a specific partition. Partitioning datasets accelerate queries on data slices.