Flink vs Spark comparison
As we learned, we learned that both Spark and Flink support batch and stream processing, so let’s compare the two popular data processing frameworks in all aspects. First, the two data processing frameworks have a lot in common.
• all based on memory calculations;
• There are unified batch and stream processing APl, both supporting SQL-like programming interfaces;
• Both support many of the same conversion operations, programmed using a functional programming pattern similar to scala collection APl;
• all have a well-established error recovery mechanism;
• Both support exactly once for semantic consistency.
of course, their differences are also quite obvious, and we can look at them from 4 different perspectives.
From the perspective of stream processing, Spark is based on micro-batch processing, which treats stream data as a small batch of data blocks processed separately, so the latency can only be achieved in seconds. Flink, on the other hand, is based on each event processing, and is processed immediately whenever there is a new data input, which is a true streaming calculation and supports millisecond computation. For the same reason, Spark only supports time-based window operations (processing time or event time), while Flink supports window operations that are very flexible, not only supporting time windows, but also windows based on the data itself, and developers can freely define the window operations they want.
From a SQL functional perspective, Spark and Flink provide SQL interaction support for SparkSQL and Table APl, respectively.
Compared with the two, Spark has better support for SQL, corresponding optimization, scaling, and performance, while Flink has a lot of room for improvement in SQL support.
From an iterative computing perspective, Spark supports machine learning well because intermediate computation results can be cached in memory to speed up machine learning algorithms. But most machine learning algorithms are actually a looped stream of data, which in Spark is represented by an acyclic graph. Flink, on the other hand, supports looped data streams over runtime, allowing machine learning algorithms to be more efficiently evaluated.
From the perspective of the corresponding ecosystem, Spark’s community is undoubtedly more active. Spark arguably has the largest number of open source contributors to Apache, and there are many different libraries to use in different scenarios. Due to the newer Flink, the current open source community is not as active as Spark, and the various libraries are not as comprehensive as Spark. But Flink is still evolving, and various functions are gradually improving.
How to choose Spark and Flink
For the following scenarios, you can choose Spark.
• batch data processing with very large data volume and complex logic, and high requirements for computing efficiency (such as using big data analysis to build a recommendation system for personalized recommendation, advertising fixed-point delivery, etc.);
• interactive queries based on historical data, requiring faster responses;
• data processing based on real-time data streams, latency requirements are between hundreds of milliseconds and seconds.
Spark is perfect for these scenarios, and it can solve these problems from a single source, without the need for another data processing platform. Because Flink was created as a platform to improve stream processing, it is suitable for a variety of real-time data processing scenarios that require very low latency (microseconds to milliseconds), such as real-time log report analysis.
And Flink’s idea of using stream processing to simulate batch processing is better extensible than Spark’s idea of simulating stream processing with batch processing.