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JA4. Apache Spark vs Hive SQL

Statement

Compare processing data using Apache Spark and Hive SQL. Provide a detailed explanation of how these systems are used with big data processing.

Answer

Introduction

Big data processing frameworks (or engines) are used to plan and execute a set of operations on large datasets in a distributed and efficient manner following the MapReduce paradigm. There were a lot of improvements over the original MapReduce which gave birth to lots of new frameworks like Apache Hadoop, Storm, Spark, and Flink (Gurusamy et al., 2017).

Almost all processing frameworks accept SQL-like syntax for querying data. It is a way of utilizing the famous SQL language for querying big data without forcing analysts to learn a new thing. The data is loaded (on disk or memory) and then into relevant data structures such as DataFrames and Datasets; an engine will convert those queries into a series of operations that the query processor passes over the data to evaluate them (Damji et al., 2020).

Apache Spark and Hive SQL are two famous components in the big data processing ecosystem. They both accept SQL-like syntax queries. The text will discuss each of them separately and then summarize the comparison between them.

Hive SQL

Apache Hive is a data warehousing framework working on top of the Hadoop platform for big data processing. It provides an interface for writing queries in Hive Query Language (HiveQL) which is similar to SQL. HiveQL queries are converted into MapReduce jobs and executed on the Hadoop cluster against data stored in HDFS (Małysiak-Mrozek et al., 2022).

Hive is suitable for batch processing where the use case requires going through the entire finite dataset to extract insights. It is easy to use and scale; it is compatible with numerous storage types such as HBse, ORC, and Parquet; and its architecture is relatively simple (ProjectPro, 2021).

Hive suffers from a few limitations such as its slow performance due to the overhead of converting queries into MapReduce jobs, its inability to handle real-time processing, and its lack of resuming functionality as jobs need to restart from the beginning on failures (Dayananda, 2022).

Apache Spark

Apache Spark is a distributed computing framework that provides components for data processing, machine learning, and streaming. It is designed to be faster and more efficient than Hadoop’s MapReduce. Spark SQL is a component of Spark that provides a programming interface for writing queries in SQL and it works as a distributed SQL query engine. (Dayananda, 2022).

Spark is suitable for stream processing due to its in-memory cashing and mico-batch processing mode, and it was created to overcome the limitations of Hive. Spark SQL integrates well with other Spark components; supports many storage formats such as Hive, Avro, Parquet, ORC, JSON, and JDBC; it is compatible with Hive; and it allows for user-defined functions (UDFs) and user-defined aggregates (UDAs)(Dayananda, 2022).

Spark also suffers from a few limitations such as its complexity, its lack of a file management system as it relies on external user-defined solutions which may not always be fully compatible; its lack of automatic optimizations although they are manually possible; and its high cost due to memory needs (Malhotra, 2018).

Comparison

From the discussion above we notice that Spark is faster, and more flexible in supporting both batch and stream processing (Gurusamy et al., 2017), it was created later, and it is meant to solve the limitations of Hive. However, the final choice should depend on the use case and the resources available for the project and the organization.

Here is a summary of the comparison between Apache Spark and Hive SQL:

Feature Apache Spark Hive SQL
Processing Method In-memory processing Disk-based processing on top of HDFS
Processing Model Batch and stream processing Batch processing only
Supported Languages Scala, Java, Python Java
Query Language Spark SQL + APIs HiveQL
Performance Faster Slower
File System Manager External solutions Built-in (HDFS)
Cost High Low

Conclusion

The main theme between all processing frameworks besides their efficiency and scalability is their interoperability, components-based architecture, and flexibility. Thus, it is possible to combine different framework components to create a custom solution; but, some components are shipped together as one solution.

While Hive is usually shipped with Hadoop, and Spark is usually shipped with its own SQL solution, it is possible to use Hive with Spark, but not the other way around. The idea is that this is not limited to a packaged solution, but it can go as far as the use case requires.

References

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