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Apache Spark vs Apache Storm: What are the differences?
Introduction: Apache Spark and Apache Storm are two prominent big data processing frameworks that differ in their architecture, use cases, and capabilities. Understanding the key differences between them is crucial for choosing the right tool for specific data processing needs.
Processing Model: Apache Spark is a batch processing framework that primarily focuses on processing large datasets efficiently using in-memory computing. In contrast, Apache Storm is a real-time processing framework designed for handling continuous streams of data with low latency requirements, making it suitable for real-time analytics and event processing.
Fault Tolerance: Apache Spark achieves fault tolerance through its resilient distributed dataset (RDD) abstraction, which allows data to be automatically recovered in case of node failures. On the other hand, Apache Storm provides fault tolerance through tuple tracking and acknowledgments, ensuring data processing continuity even in the event of failures.
Data Processing Approach: Apache Spark utilizes a DAG (Directed Acyclic Graph) execution engine that optimizes task execution for batch processing. In contrast, Apache Storm follows a stream processing model where data is processed as it arrives, enabling real-time analytics and data transformations.
Programming Languages: Apache Spark supports multiple programming languages such as Scala, Java, Python, and R, providing flexibility for developers to choose the language of their preference. Apache Storm primarily uses Java as its programming language, limiting the options for developers who prefer other languages for big data processing.
Ecosystem Integration: Apache Spark has a rich ecosystem with libraries such as Apache Spark SQL, MLlib, GraphX, and Spark Streaming, providing comprehensive support for various data processing and analytics tasks. While Apache Storm is more focused on real-time data processing, it can be integrated with external systems such as databases, message queues, and monitoring tools to enhance its capabilities.
Scalability: Apache Spark provides scalability through its distributed computing model and the ability to leverage cluster resources efficiently for processing large datasets. Apache Storm is designed for horizontal scalability, allowing users to scale out by adding more worker nodes to handle increasing data processing requirements effectively.
In Summary, Apache Spark and Apache Storm differ in their processing models, fault tolerance mechanisms, data processing approaches, programming language support, ecosystem integration, and scalability options, making them suitable for distinct use cases in big data processing.
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 Storm
- Flexible10
- Easy setup6
- Event Processing4
- Clojure3
- Real Time2
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 Storm
Cons of Apache Spark
- Speed4
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What is Apache Storm?
What is Apache Spark?
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