Apache Hive is the most prevalent query engine used in many of the largest enterprise environments today. To get the most out of the engine, it’s important to perform Hive performance tuning. But before we dive into that, let’s cover the basics.
What is Hive performance tuning? Hive performance tuning refers to the collective processes and steps designed to improve and accelerate the performance of your Hive environments. When queries are not optimized, simple statements take longer to execute, resulting in performance lags and downtime.
While we now understand its importance, tuning your Hive environments for optimal performance can be tricky. And knowing how to analyze Hive query performance is a must for success. But just how do you optimize a Hive query? What are Hive performance tuning best practices? And what can developers and Ops teams do to ensure optimal Hive query performance?
If you have these questions, this post is for you. Keep reading to learn effective performance tuning best practices across three key categories. Whether you’re tuning for time or efficient use of resources, these tips apply.
Category 1: Data: Manipulate as Little as Possible
How can I improve Hive performance? Most users and developers start with tweaking their data. The use of partitioning, bucketing, compression, avoiding small files, and more are all great Hive query optimization techniques.
Here at Pepperdata, we deal with all sorts of questions about Hive queries, with improving Hive performance chief among them. In this section, we’ll dive into how to manipulate data as little as possible to gain success.
Partitioning is a common Hive query tuning tactic which places table data in separate subdirectories of a table location based on keys. Partition keys present an opportunity to target a subset of the table data rather than scanning data you don’t need for your operations.
No matter how much data exists, when you have partitions, Hive only reads a specific amount of data to generate results. This drastically improves performance, even when you execute complex analytics queries. This is because Hive only has to read data from a few partitions specified in the clause. It already filters out the data needed before query execution is initiated.
Bucketing, similar to partitioning, is a Hive query tuning tactic that allows you to target a subset of data. In this case, to improve join performance specifically by scanning less data. This improves the query across the vectors of time and efficiency as less data has to be input, output, or stored in memory.
Bucketing in Hive entails the decomposition of a table data set into smaller parts. Thus, data is easier to handle. With bucketing, you join similar data types and write them to a single file. This step here greatly enhances performance while joining tables or reading data. This is why bucketing with partitioning is so popular among Hive users.
Compression ranks as one of the best Hive query optimization techniques. Big data compression cuts down the amount of bandwidth and storage required to handle large data sets. In addition, compression eliminates redundant and unimportant pieces from your systems.
Each bit of data that is manipulated by a query has I/O associated with getting the data from disk, into memory, out of memory, and back to disk or another end target. Compression minimizes the amount of data that traverses each of those steps and decreases the time spent moving through the query states.
Avoid Small Files
Eliminating small file operations from your query is an effective Hive performance tuning tactic. Doing so 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. Query performance is limited to the health of