We have the answers to your questions! - Don't miss our next open house about the data universe!

Apache Presto: everything you need to know about this distributed SQL query engine

- Reading Time: 3 minutes
Apache Presto: everything you need to know about this distributed SQL query engine

The ability to efficiently manage large datasets has become an unavoidable necessity. Apache Presto, a distributed SQL query engine designed for high-speed performance on huge data volumes, is the answer to this challenge.

Initially developed by Facebook to meet their own massive data processing needs, Presto quickly evolved into the industry’s solution of choice, offering remarkable flexibility and efficiency.

Key features of Apache Presto

Apache Presto offers a range of features that set it apart from other data processing technologies, making it particularly well-suited for fast, efficient analysis of large volumes of data.

Support for multiple data sources

Extensive connectivity

Presto can connect to a variety of data sources, such as distributed file systems (like HDFS), relational databases, and even cloud storage services. This capability enables users to perform queries on data from heterogeneous sources, without the need to first move or transform the data.

Data federation

With this feature, users can run queries involving multiple data sources in a single SQL query, greatly simplifying the analysis of disparate data.

Query performance and optimization

Fast execution

Designed for high query performance, even on very large datasets, it uses an in-memory processing model and parallelizes queries across the cluster to accelerate response times.

Advanced optimizations

The engine incorporates sophisticated optimizations such as distributed query planning, predicate pushdown and other optimization techniques to maximize query efficiency.

Flexibility and scalability

Horizontal scalability

Presto can be easily scaled to handle load increases simply by adding more nodes to the cluster. This makes it ideal for environments where data volumes and compute requirements can fluctuate.

Ad hoc and analytical query support

Flexible in terms of the types of queries it can execute, from simple ad hoc queries to complex analyses, making it useful for a wide range of analytical applications.

SQL support and extensions

SQL compatibility

It supports much of the SQL standard, including complex functions, joins, aggregations and subqueries, making it easy to learn for those familiar with SQL.

Extensions and customization

It also offers the possibility of extending its capabilities with user-defined functions and plug-ins, enabling advanced customization according to specific needs.

Ease of use and maintenance

Simple configuration

Presto is relatively simple to configure and maintain, with a minimum of external dependencies. This ease of configuration makes it attractive to teams with limited resources.

Active community and support

With an active open-source community and growing support from major technology companies, Presto benefits from constant evolution and strong user support.

Comparison with other tools

Versus Hive

Performance

Presto is generally faster than Hive for most queries. Presto is designed for rapid analysis and ad hoc queries, while Hive is better suited to batch data processing tasks.

Processing model

Hive uses MapReduce for batch processing, which can be slower for some queries. Presto, on the other hand, uses an in-memory processing model, which speeds up query processing.

SQL on Hadoop

While Hive was one of the first tools to enable SQL queries to be written on Hadoop, Presto offers a more modern approach with better performance.

Versus Apache Spark

Data processing

Spark is primarily designed for batch processing and in-memory calculations, while Presto is optimized for ad hoc queries on large datasets.

Ecosystem and integration

Spark is part of a wider ecosystem, including Spark Streaming, MLlib for machine learning, and GraphX for graph processing. Presto is more specialized in SQL query execution.

Programming languages

Spark supports several programming languages (Scala, Java, Python, R), offering greater flexibility for application development. Presto focuses primarily on SQL.

Advantages and disadvantages

Presto Rapidity of queries, support for multiple data sources, and ease of use for those familiar with SQL. Less suitable for batch processing and intensive computations.
Hive Better suited for batch processing and ETL tasks, and widely adopted in the industry. Slower performance for ad hoc queries.
Spark Fast batch processing, support for real-time streaming, and flexibility with multiple programming languages. May be more complex to configure and optimize, especially for simple SQL queries.

Conclusion

Apache Presto stands out as a fast and flexible distributed SQL query engine, ideal for ad hoc analysis of large datasets. Its ability to query a variety of data sources and its efficient architecture make it a valuable choice in the Big Data ecosystem.

You are not available?

Leave us your e-mail, so that we can send you your new articles when they are published!
icon newsletter

DataNews

Get monthly insider insights from experts directly in your mailbox