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Basically, to run queries on petabytes of data we all know that hive is a query language which is similar to SQL built on Hadoop ecosystem. So, there are several Hive optimization techniques to improve its performance which we can implement when we run our hive queries.
Following are the Hive optimization techniques for Hive Performance Tuning:
a. Tez-Execution Engine in Hive
b. Usage of Suitable File Format in Hive
c. Hive Partitioning
d. Bucketing in Hive
e. Vectorization In Hive
f. Cost-Based Optimization in Hive (CBO)
g. Hive IndexingHowever, to learn all in detail, follow the link: 7 Best Hive Optimization Techniques – Hive Performance
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As we know, users can use HiveQL or traditional MapReduce systems, along with Hive, on the basis of individual needs as well as preferences. Especially, for analyzing large datasets (petabytes), Hive is an ideal choice. Also, it includes a variety of storage options for the users.
Although, there are multiple unique tools present in Apache Hive which permits users to efficiently and quickly perform data queries as well as analysis. But, it’s important for users to use some best practices for Hive implementation to make full use of all these tools. However, then question occurs that how to scale Apache Hive? So, here are here is 7 best Hive optimization techniques to make the most of Hive performance, such as:
a. Tez-Execution Engine in Hive
b. Usage of Suitable File Format in Hive
c. Hive Partitioning
d. Bucketing in Hive
e. Vectorization In Hive
f. Cost-Based Optimization in Hive (CBO)
g. Hive IndexingWell, to learn all these Hive optimization techniques in detail, follow the link: 7 Best Hive Optimization Techniques – Hive Performance
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