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Apache Beam vs Apache Spark: What are the differences?
Apache Beam and Apache Spark are both popular big data processing frameworks used for distributed data processing. Let's discuss the key differences between them.
Data Model: Apache Beam provides a unified programming model that is independent of any specific data processing engine. It allows developers to write data processing logic once and run it on various execution engines such as Apache Spark, Apache Flink, and Google Cloud Dataflow. On the other hand, Apache Spark has its own data model called Resilient Distributed Dataset (RDD), which is a fault-tolerant collection of elements that can be processed in parallel.
Ease of Use: Apache Beam provides a higher-level API and abstracts away the complexities of distributed processing. It offers a simple and consistent programming model which makes it easier for developers to write and maintain code. In contrast, Apache Spark has a steeper learning curve due to its more low-level API and complex execution model.
Flexibility: Apache Beam offers a wider range of options for data sources and sinks compared to Apache Spark. It provides connectors for various data storage systems and streaming platforms, allowing developers to process data from different sources and write the results to different destinations. Apache Spark, on the other hand, has a more limited set of built-in connectors.
Streaming and Batch Processing: Apache Beam is primarily designed with a focus on streaming data processing, although it also supports batch processing. It provides built-in windowing and triggering capabilities for handling event time-based computations. Apache Spark, on the other hand, was originally designed for batch processing but has added streaming capabilities. However, its streaming capabilities are not as advanced as those provided by Apache Beam.
Execution Engine Compatibility: Apache Beam is designed to be portable and run on different execution engines, making it more flexible in terms of deployment options. It can run on Apache Spark, Apache Flink, and Google Cloud Dataflow, among others. Apache Spark, on the other hand, is a standalone big data processing engine and does not have the same level of compatibility with other execution engines.
Ecosystem and Community: Apache Spark has a larger and more mature ecosystem compared to Apache Beam. It has a wide range of libraries, connectors, and tools built around it, making it easier to integrate with other big data technologies. Apache Beam, while growing in popularity, has a smaller ecosystem and community.
In summary, Apache Beam and Apache Spark both provide powerful distributed data processing capabilities, but Apache Beam offers a more flexible and portable programming model, while Apache Spark has a larger ecosystem and more mature streaming capabilities.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Beam
- Open-source5
- Cross-platform5
- Portable2
- Unified batch and stream processing2
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Apache Beam
Cons of Apache Spark
- Speed4
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What is Apache Beam?
What is Apache Spark?
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