Presto vs Apache Spark SQL: The Battle of Big Data Processing
Are you overwhelmed by the sheer amount of data you need to process? Do you find yourself struggling to choose between Presto and Apache Spark SQL for big data processing? Don't worry, we’re here to help. In this blog post, we’ll compare the two data processing frameworks and present you with the numbers to help you make an informed decision.
Performance
Presto is a distributed SQL query engine designed for interactive queries on large datasets, providing sub-second response times. It operates on data in formats like Hadoop Distributed File System (HDFS), Apache Cassandra, AWS S3, and more. Presto caches the data in memory, which speeds up query processing time. It's known for its ability to handle complex queries and high concurrency.
On the other hand, Apache Spark SQL is a component of the Apache Spark ecosystem that provides a programming interface to work with structured and semi-structured data using SQL or API. It has an in-memory storage system called Resilient Distributed Datasets (RDDs) that can cache the data in-memory for faster query processing. It distributes the data across multiple nodes and processes in parallel, which allows for faster computation.
In terms of performance, Apache Spark SQL is known to be faster than Presto for batch processing tasks. However, Presto outperforms Apache Spark SQL for ad-hoc queries.
Ease of Use
When it comes to ease of use, Apache Spark SQL is a popular choice. It provides a simple and intuitive interface, with familiar SQL syntax, making it easy for SQL developers to transition to big data processing. Moreover, Spark SQL provides an interactive shell with pre-installed libraries for easy experimentation.
Presto, on the other hand, has a steeper learning curve. It uses the SQL language for data processing, but it comes with additional syntax for its own functions, which can make it difficult for SQL developers to adjust. Furthermore, Presto requires more configurations compared to Apache Spark SQL, which may be overwhelming for beginners.
Community Support
Both Apache Spark SQL and Presto have active and rapidly growing communities. With Apache Spark SQL being a part of the Apache Software Foundation, it has a large community of developers and contributors. It has a wide range of libraries available, making it easy to add new functionality into the system.
Presto, although a relatively newer addition to the big data processing world, has a strong and supportive community as well. It's an open-source project with many contributors from several different companies, including Facebook, Teradata, and more.
Cost
When it comes to cost, Presto and Apache Spark SQL are both open-source software, which means that their source code is made publicly available and can be used free of charge. However, the cluster setup, hardware, and the need for additional software may incur costs.
Conclusion
In conclusion, the choice between Presto and Apache Spark SQL ultimately depends on the specific requirements of your use case. If you need fast query results for ad-hoc queries with complex SQL functions, go for Presto. However, if you are dealing with large datasets and need faster batch processing, Apache Spark SQL is the way to go. Both tools have their merits and limitations, so it's crucial to choose the one that aligns with your needs.
References
- Presto: https://prestodb.io/docs/current/index.html
- Apache Spark SQL: https://spark.apache.org/sql/
- Presto: Open-source Distributed SQL Query Engine for Big Data Analytics: https://www.evideon.com/2019/12/03/presto-open-source-distributed-sql-query-engine-for-big-data-analytics/
- Apache Spark vs. Presto – Which One Is Better in 2021?: https://www.nexsoftsys.com/articles/apache-spark-vs-presto-which-one-is-better-in-2021.html