If you use Hive on Hadoop, learning about Hive queries is key to achieving effective cluster management. Hive queries consume time and resources, so achieving efficiency through Hive query tuning is a must.
In this article, you’ll learn what Hive queries are, how they can affect your clusters (both positively and negatively), useful Hive query approaches, and what good Hive query examples look like. Let’s get started.
1. What Are Hive Queries?
Hive queries are specific information requests from your Hadoop database. These information requests are performed by Apache Hive, an open-source data warehousing platform developed on top of Hadoop. Facebook created Hive to perform data analysis, distributed processing, and reduce work in terms of writing the Java MapReduce platform.
A Hive query utilizes a set of predefined codes native to your database’s language. The database then receives the instruction, and once it understands that instruction, gathers and releases the information requested.
Hive was made for efficiency, and that is why its queries need to be perfectly tuned and well-written. You can also set dependencies to enable the auto-scheduling of queries. This will guarantee that as soon as one action is finished, the next action starts immediately.
Improving the RAM capacity and CPU power of your system results in faster Hive response times, compared to increasing your network bandwidth.
2. The Effects of Poorly-Tuned Hive Queries
Poorly-tuned queries can cause major setbacks for an organization. The biggest of these would be missed SLAs (service level agreements).
These agreements signify the level of service that enterprises and their clients agree upon, and they include performance guarantees, data security, uptimes, and customer service standards. So if inefficient queries lead to missed SLAs, the result can be penalties, refunds, or, in some cases, termination of the contract.
Poorly tuned Hive queries also consume resources. These can affect your Hadoop clusters on two fronts. One: Poorly-tuned queries can use up resources intended for other users or functionalities in your cluster. This results in reduced performance and slower response times. Two: The resources used incur costs. Wasted resources due to poorly-tuned Hive queries can add up on your AWS bill and give you a major headache.
Some other effects of inefficient queries can be disrupted cluster performance, slowing down the database, and downtimes.
Because of the numerous negative effects inefficient queries can create, it’s crucial to optimize your queries. And while you can use manual approaches like partitioning, bucketing, and compression, leveraging analytics stack performance tools like Pepperdata Query Spotlight will make the job a lot easier.
3. Hive Query Tuning Approaches
There are some extremely handy Hive query tuning approaches, depending on whether you’re optimizing for time or resource usage:
Proper Hive tuning allows you to manipulate as little data as possible. One way to do this is through partitioning, where you assign “keys” to subdirectories where your data is segregated. When your query asks for information, you can target the specific subset where your data is, which saves you time from scanning data you don’t need.
Bucketing is similar to partitioning, albeit it helps to improve join performance by scanning less data.
This helps minimize the amount of the data that traverses the steps in the query process, as well as the time it takes to move through query states.
In terms of execution, utilizing execution engines (like Tez, Hive on Spark, and LLAP) can help improve query performance without low-level tuning approaches. Leveraging parallel execution whenever sequential operations are not needed is also wise. The amount of parallelism that your system can perform depends on the resources available and the overall data structure.
Furthermore, it helps to maintain an even distribution of work across all tasks to avoid skew. Remember: Your overall query can only be as fast as your slowest task.
Finally, when you look at testing, sampling (also called unit testing) is one of the best Hive query tuning techniques. By taking a subset of your data and running queries a thousand rows at a time, you can find the failures, odd results, and errors a lot quicker. This helps you fine-tune your queries for more accuracy and efficiency.
Each query should also undergo scrutiny and performance measurement automatically. This ensures they meet the minimum requirements before they get promoted to higher-level environments. Keep the bad queries out of production.
4. Hive Query Examples
There are various types of query operations that you can perform in Hive. Here are some examples:
- ORDER BY: This syntax in HiveQL uses the SELECT statement to sort data. As instructed by the ORDER BY clause, it goes through the Hive tables’ columns to find and filter specific column values.
- GROUP BY: This clause explores all the columns on Hive tables to gather all values mentioned. Results are shown by grouping the specific and matching column values.
- SORT BY: The Hive query that utilizes this clause sorts through the columns under the name defined, then sorts the output. Affixing a DESC instruction will sort the values in descending order, while an ASC instruction will yield results in ascending order.
- DISTRIBUTE BY: This determines how output is divided among the reducers in a MapReduce task. It works a little like GROUP BY since it manages how the rows of data will be loaded for processing into the reducer.
- CLUSTER BY: Among Hive query examples, this command basically performs the functions SORT BY and DISTRIBUTE BY together, ensuring absolute sorting and ordering on all output data.
There’s still a lot more to learn about Hive queries, and there are more techniques you can leverage to optimize them. Watch the latest webinar on Hive from Pepperdata, Proven Approaches to Hive Query Tuning, to learn more about:
- Pinpointing problem queries
- Improving query utilization and performance
- Securing your infrastructure from being impacted by query performance