You can implement many Hive optimization techniques to improve its performance while running the queries. Hive is a powerful query language like SQL that is a part of the Hadoop framework. This enables it to process several petabytes of data with relative ease. Some of the popular queries in Hive include – execution engine, vectorization, bucketing, partitioning, indexing, etc.
Techniques for Hive Optimization
The variants of query simplification techniques regarding performance tuning for Hive are as follows:
- Execution engine (Tez) – It is a new app framework with its origin in Hadoop Yarn. It can run complicated graphs and basic data analysis tasks. It offers more flexibility and processing power than the map-reduce framework.
- File formatting – Using the correct file format can improve query performance in Hive by a huge margin. To get the most performance, using the Optimized Row Columnar file format is the way to go. This is because it can lower the size of data by up to 75% – greatly increasing the data processing speed.
- Partitioning – The process of reading data can be sped up significantly by dividing the directory into multiple partitions. This also shortens the time it takes to fetch data after processing a user query.
- Bucketing – This concept is all about separating the table data into multiple less complex chunks, without degrading the overall data integrity. The user can also define the size of a ‘bucket’ by applying this technique.
- Vectorization – By using this query protocol we can greatly enhance the operation’s performance in Hive. An operation can mean filters, scans, summations, etc. It makes use of batch processing instead of processing a single row separately.
- Cost Optimization – This refers to optimizing each logical component of a query that forms the Hive execution plan. Cost-based Optimization (CBO) makes unique optimizations depending on the cost of a query.
- Indexing – This technique can vastly improve query performance by generating an index in the data table, which functions as a point of reference. This prevents the waste of energy and time by not making the query scan every row in a given table. Furthermore, it initially validates the index and only then goes to the specific column to operate.
Conclusion
We hope this article will clear your doubts regarding the different optimization techniques in Hive and help you execute your queries faster. The analytical possibilities of this open-source framework are vast and allow users to read, write, and manage a huge pool of data using Structured Query Language.
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