Apache Hive vs Apache Spark SQL

Difference Between Apache Hive and Apache Spark SQL

With the massive amount of increase in big data technologies today, it is becoming very important to use the right tool for every process. The process can be anything like Data ingestion, Data processing, Data retrieval, Data Storage, etc. In this post, we are going to read about two such data retrieval tools, Apache Hive and Apache Spark SQL. Hive, on one hand, is known for its efficient query processing by making use of SQL-like HQL(Hive Query Language) and is used for data stored in Hadoop Distributed File System whereas Spark SQL makes use of structured query language and makes sure all the read and write online operations are taken care of. Hive has been known to be the component of Big data ecosystem where legacy mappers and reducers are needed to process data from HDFS whereas Spark SQL is known to be the component of Apache Spark API which has made processing on Big data ecosystem a lot easier and real-time. A major misconception most of the professionals today have is that hive can only be used with legacy big data technology and tools such as PIG, HDFS, Sqoop, Oozie. This statement is not completely true as Hive is compatible not only with the legacy tools but also along with Spark-based other components, like Spark Streaming. The idea behind using them is to reduce the effort and bring better output for the business. Let us study about both Apache Hive and Apache Spark SQL in detail.

Head to Head Comparison Between Apache Hive and Apache Spark SQL (Infographics)

Below is the Top 13 comparison between Apache Hive and Apache Spark SQL:

Key Differences Between Apache Hive and Apache Spark SQL

The differences between Apache Hive and Apache Spark SQL is discussed in the points mentioned below:

1. Hive is known to make use of HQL (Hive Query Language) whereas Spark SQL is known to make use of Structured Query language for processing and querying of data
2. Hive provides schema flexibility, portioning and bucketing the tables whereas Spark SQL performs SQL querying it is only possible to read data from existing Hive installation.
3. Hive provides access rights for users, roles as well as groups whereas no facility to provide access rights to a user is provided by Spark SQL
4. Hive provides the facility of selective replication factor for redundant storage of data whereas spark SQL, on the other hand, does not provide any replication factor for storing data
5. As JDBC, ODBC and thrift drivers are available in Hive, we can use them to generate results whereas in case of Apache Spark SQL we can retrieve results in the form of Datasets and DataFrame APIs if Spark SQL is run with another programming language
6. There are several limitations:

• Row-level updates and real-time OLTP querying is not possible using Apache Hive whereas row-level updates and real-time online transaction processing is possible using Spark SQL.
• Provides acceptable high latency for interactive data browsing whereas in Spark SQL the latency provided is up to the minimum to enhance performance.
• Hive, like SQL statements and queries, supports UNION type whereas Spark SQL is incapable of supporting UNION type.

Apache Hive and Apache Spark SQL Comparision Table

Below are the comparison table between Apache Hive and Apache Spark SQL.

Conclusion

We cannot say that Apache Spark SQL is the replacement for Hive or vice-versa. It’s just that Spark SQL can be seen to be a developer-friendly Spark based API which is aimed to make the programming easier. Hive has its special ability of frequent switching between engines and so is an efficient tool for querying large data sets. The usage and implementation over what to choose is dependent upon your goals and requirements. They both Apache Hive and Apache Spark SQL are players in their own field. I hope after going through the post, you would get a fair enough idea about your organization’s need. Follow our blog for more posts like these and we make sure to provide information that fosters your business.

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