Apache Flink vs Apache Spark vs Presto: What are the differences?

Introduction

Apache Flink, Apache Spark, and Presto are all popular distributed computing frameworks used for processing large-scale data. Each framework has its own unique features and characteristics that differentiate it from the others. In this article, we will explore the key differences between Apache Flink, Apache Spark, and Presto.

1. Data Processing Model:

   • Apache Flink offers both batch and stream processing capabilities in a single unified model. It supports event time processing and provides support for event-driven applications.
   • Apache Spark also supports both batch and stream processing, but it follows a micro-batch model for stream processing, which can introduce a small amount of latency. It focuses more on in-memory processing and provides libraries for machine learning and graph processing.
   • Presto, on the other hand, is primarily designed for interactive query processing on large datasets. It doesn't have built-in support for stream processing or machine learning, but it excels at ad-hoc queries and fast data retrieval.

2. Fault Tolerance and Recovery:

   • Apache Flink provides strong fault tolerance with exactly-once semantics. It uses a distributed snapshotting mechanism to ensure consistent state recovery in case of failures.
   • Apache Spark offers fault tolerance as well but with at-least-once semantics. It uses RDD (Resilient Distributed Datasets) to recover lost data partitions in case of failures.
   • Presto doesn't provide built-in fault tolerance. It assumes data source-level fault tolerance and relies on the underlying storage system for data recovery.

3. Execution Engine:

   • Apache Flink has its own execution engine that works on data streams directly, providing low-latency processing and dynamic resource allocation.
   • Apache Spark uses a general-purpose execution engine that works on RDDs, which introduces a slight overhead and latency compared to Flink's native stream processing engine.
   • Presto also uses a general-purpose execution engine, but it focuses more on query optimization and parallel processing to achieve high query performance.

4. Connectivity and Integration:

   • Apache Flink has built-in connectors to various data sources, including file systems, message queues, and databases. It also supports integration with Apache Kafka for event streaming.
   • Apache Spark has a large ecosystem of connectors and libraries that enable seamless integration with various data sources, machine learning algorithms, and visualization tools.
   • Presto provides connectors for querying data stored in various databases, data lakes, and file systems. It can integrate with external systems like Hive, Hadoop, and Kafka.

5. Real-time Processing:

   • Apache Flink is designed for real-time stream processing and provides low-latency, event-driven processing capabilities. It supports event time processing, windowing, and stateful computations.
   • Apache Spark's micro-batch model for stream processing introduces some latency, making it less suitable for low-latency, event-driven applications compared to Flink.
   • Presto is not designed for real-time processing and is more suitable for interactive, ad-hoc queries on large datasets.

6. Ease of Use and Development:

   • Apache Flink provides a rich and easy-to-use API for both batch and stream processing. It has good documentation and a supportive community. However, it may have a steeper learning curve compared to Spark or Presto.
   • Apache Spark has a user-friendly API and provides high-level abstractions that simplify development. It has a large community and extensive documentation, making it easier for developers to get started.
   • Presto also has a user-friendly SQL-like interface for querying data, which makes it accessible to users familiar with SQL. It has a smaller community compared to Flink or Spark but is gaining popularity.

In summary, Apache Flink excels in real-time stream processing, supports event-driven applications, and offers strong fault tolerance with exactly-once semantics. Apache Spark focuses on in-memory processing, provides libraries for machine learning and graph processing, and has a larger ecosystem. Presto specializes in interactive query processing, fast data retrieval, and supports ad-hoc queries on large datasets.