Bahaa Al Zubaidi shares his knowledge on Hive Optimization and defines it as a process to optimize the structure of a table in Hive. The tables are optimized based on their usage patterns, which means that they are reorganized to enhance performance.

1. What is Hive Optimization?
Bahaa Al Zubaidi shares his knowledge on Hive Optimization and defines it as a
process to optimize the structure of a table in Hive. The tables are optimized based on
their usage patterns, which means that they are reorganized to enhance performance.
The two main types of Hive optimizations are:
Partitioned Tables: These tables have been partitioned into multiple sub-tables to
improve query speed and reduce memory usage. This is done by splitting the large data
set into smaller partitions and storing each partition separately. When querying for data,
Hive uses only the required partitions instead of scanning all the partitions. This
improves performance significantly because only one partition needs to be scanned for
each query instead of scanning all partitions at once.
Compressed Tables: These tables have been compressed using Snappy compression
or LZO compression. For example, If you have a 100 million-row table with 1 million
unique values (index keys), Hive can compress it using Snappy or LZO. As a result, it
takes less space and makes your queries faster.
Best Hive Optimization Techniques
Bahaa Al Zubaidi takes a closer look at each of the Hive optimization strategies we can
use for fine-tuning our server’s performance in Hive.
Tez-Execution Engine in Hive
The goal of using the Tez Execution Engine – Hive Optimization Techniques is to
improve the speed with which our hive queries run. Tez is a new application framework
based on Hadoop Yarn, to put it simply.
Which processes general-purpose data processing jobs represented as
complex-directed acyclic graphs. However, it can be seen as the successor to the
map-reduce framework that is both more versatile and powerful.
Usage of Suitable File
If we use a suitable file format based on the data, we can optimize our use of the hive
using ORCFILE. Our ability to conduct queries will improve dramatically as a result.
The ORC file format is ideal for maximizing query performance. ORC stands for
“Optimized Row Columnar” in this context. As a result, we have a more efficient means
of storing data than is possible with conventional file formats.

2. Hive Partitioning
Partitioning in Hive – Hive Optimization Methods, Hive reads all files in a directory
without splitting them up. It then processes the information using the query filters. This
is a time-consuming and costly process because all data must be read.
The necessity to filter the information based on values in particular columns is another
common scenario. However, in order to implement the partitioning in the Hive, users
need to understand the domain of the data on which they are performing analysis.
Bucketing in Hive
Consider the case of bucketing in Hive – Hive Optimization Methods. Occasionally, a
massive data set is accessible. Partitioning on a certain field or fields does reduce the
overall file size, but it still ends up being much larger than necessary.
Vectorization In Hive
Vectorization We employ Vectorized query execution to boost the speed of operations in
Hive-Hive Optimization Techniques. Scans, aggregates, filters, and joins are all
examples of operations in this context. As a result, operations are carried out in batches
of 1024 rows at once rather than on a single row at a time.
Cost-Based Optimization
Before submitting a Query for final execution, Hive improves its logical and physical
execution plan based on its cost, as described in Cost-Based Optimization in Hive –
Hive Optimization Techniques. Although up to now, the cost of the query has not been
used to guide these optimizations.
A new feature of Hive, CBO, however, executes further optimizations based on query
cost. Decisions like join order, join type, parallelism level, and others may arise as a
result.
Hive Indexing
Hive Index is one of the most effective Hive Optimization Methods. Indexing will boost
your query performance immensely. Indexing, in essence, involves the creation of a
second, reference table that is distinct from the original table itself.
We all know that a Hive table will have many rows and columns. To put it simply, without
indexing, running queries on certain columns will take an extremely long time. Thank
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3. Bahaa Al Zubaidi