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.
Before loading data into its destination datastore, you need to clean, prepare, and convert that data using ETL (Extract, Transform, and Load) functionalities. A Hive query can do this. Data is typically extracted from the source, then stored in a versatile or compliant storage like Azure Data Lake Storage or Azure Storage blobs. A series of queries then transforms the data. After this, the data is organized inside Apache Hive before it is loaded in bulk into its destination data warehouse.
Creation of Joins for Merging Different Data Tables
Hive queries can include a join, a feature used to combine specific fields or records from two or more tables by using values shared by each one. Joins exponentially increase the efficiency of a Hive query in terms of speed, depending on how the query is written. For instance, queries with join clauses execute much faster when they stream the smallest table first and the largest table last, as opposed to doing it the other way around.
There are four types of joins, and a deeper understanding of each one will help users pick the right join to use—, and write the right queries. These four types of joins are:
- Inner join in Hive
- Left Outer Join in Hive
- Right Outer Join in Hive
- Full Outer Join in Hive
Examples of Hive Queries
Order By Query
The ORDER BY syntax in HiveQL uses the “SELECT” statement to help sort data. This syntax goes through the columns on Hive tables to find and sift specific column values as instructed in the “Order by” clause. The query will only pick the column name mentioned in the Order by clause, and display the matching column values in ascending or descending order.
Group By Query
When a Hive query comes with a “GROUP BY”, it explores the columns on Hive tables and gathers all column values mentioned with the group by clause. The query will only look at the columns under the name defined as “group by” clause, and it will show the results by grouping the specific and matching column values.
Sort By Query
When a Hive query comes with a “Sort by” clause, it goes through the columns under the name defined by the query. Once executed, the query explores columns of Hive tables to sort the output. If you sort by queries with a “DESC” instruction, you sort and display the results in descending order. Queries with an “ASC” will perform an ascending order of the sort and show the results in a similar manner.
Cluster By Query
Hive queries with a CLUSTER BY clause or command are typically deployed in queries to perform the functions of both DISTRIBUTE BY and SORT BY together. This particular query ensures absolute sorting or ordering across all output data files.
The DISTRIBUTE BY instruction determines how the output is divided among reducers in a MapReduce job. DISTRIBUTE BY functions similarly to a GROUP BY clause as it manages how rows of data will be loaded into the reducer for processing.
Common Commands in Hive
Here is the quick lowdown on the common commands you will come across when managing Hive queries.
Hive Data Definition Language (DDL) commands
- CREATE – The CREATE DATABASE command is used for creating databases in Hive. DATABASE and SCHEMA are used interchangeably.
- SHOW – The SHOW DATABASES clause displays all the databases currently present in the Hive.
- DESCRIBE – The DESCRIBE DATABASE command displays the name of the database in Hive, as well as the comment (if set), and its location on the file system.
- USE – The USE command in Hive picks a particular database for a session on which all following HiveQL statements would be performed.
- DROP – The DROP DATABASE statement drops or deletes the database. RESTRICT is the default action, in which the database is dropped or deleted only when it is empty. The CASCADE command is more suited when dropping the database with tables.
- ALTER – The ALTER DATABASE statement in Hive proceeds to modify the metadata associated with the database in Hive.
- TRUNCATE – The TRUNCATE TABLE command removes all the rows from the table or partition.
Hive Data Manipulation Language (DML) Commands
- LOAD – The LOAD statement transfers data files into the locations that correspond to Hive tables.
- SELECT – The SELECT statement in Hive functions similarly to the SELECT statement in SQL. It is primarily for retrieving data from the database.
- INSERT – The INSERT clause loads the data into a Hive table. Users can also perform an insert to both the Hive table and/or partition.
- DELETE – The DELETE clause deletes all the data in the table. Specific data can be targeted and deleted if the WHERE clause is specified.
- UPDATE – The UPDATE command in Hive updates the data in the table. If the query includes the WHERE clause, then it updates the column of the rows that meet the condition in the WHERE clause.
- EXPORT – The Hive EXPORT command moves the table or partition data together with the metadata to a designated output location in the HDFS.
- IMPORT – The Hive IMPORT statement imports the data from a particularized location to a new or currently existing table.
What is The Hive Cheat Sheet?
The Apache Hive Cheat Sheet is a five-part summary that contains all Apache Hive syntax and functions. It functions as a reference or guide for developers and big data engineers.
The Apache Hive Cheat Sheet is broken down into the following parts:
- Apache Hive Cheat Sheet – Query Syntax
- Apache Hive Cheat Sheet – Metadata
- Apache Hive Cheat Sheet – Query Compatibility
- Apache Hive Cheat Sheet – Command Line
- Apache Hive Cheat Sheet – Shell & CLI
Optimizing Hive Queries
Hive queries consume resources. In the world of big data, resources mean money, and an ineffective use of resources can set back any organization. It is important that all your queries are optimized to keep resource consumption at a minimum, operational costs manageable, and overall performance at optimum levels.
Here are some effective ways to optimize Hive queries:
- Partition keys present an opportunity to target a subset of the table data, rather than scan data you don’t need for your operations.
- Bucketing allows you to target a subset of data. It improves join performance specifically by scanning fewer data.
- Compression minimizes the amount of data that traverses each of those steps and decreases the time spent moving through the query states.
- Eliminating small file operations from your query promotes a healthy Hive ecosystem. Each file is tracked by the Hive metastore and stored in HDFS, which are each performance-optimized to handle larger files over many smaller files.
Optimizing your approach for all our queries is crucial. Pepperdata offers enterprises big data optimization solutions designed to give comprehensive visibility and observability into their queries, Query Spotlight, as well as all of their stacks, applications, processes, and more. Users enjoy real-time performance reports and data-fueled recommendations to optimize the performance of their big data infrastructure and all its components while significantly cutting down operational costs.