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Apache Spark vs Spark Framework: What are the differences?
Introduction
Apache Spark and Spark Framework are both widely used technologies in the field of data processing and web development, respectively. While they share similar names, they serve different purposes and have distinct features. In this article, we will explore the key differences between Apache Spark and Spark Framework.
Data Processing vs. Web Development: The primary difference between Apache Spark and Spark Framework lies in their domain of application. Apache Spark is a powerful data processing engine that enables large-scale data processing tasks, such as data analytics, machine learning, and stream processing. On the other hand, Spark Framework is a lightweight Java web framework that simplifies the development of web applications, RESTful APIs, and microservices.
Big Data Processing vs. HTTP-based Applications: Apache Spark is designed to handle big data processing tasks efficiently by leveraging distributed computing techniques. It provides built-in support for parallel processing, fault tolerance, and data caching. In contrast, Spark Framework focuses on building HTTP-based applications by providing an easy-to-use API for routing HTTP requests, handling request/response objects, and implementing middleware.
Complexity and Scalability: Apache Spark is a highly scalable and complex framework that is optimized for processing large volumes of data across distributed clusters. It leverages in-memory computing and optimization techniques to achieve high performance. On the other hand, Spark Framework is a lightweight framework that focuses on simplicity and ease of use. It can be used for building small to medium-sized web applications that do not require the scale and complexity of Apache Spark.
Support for Data Analytics vs. Web Services: Apache Spark provides a rich set of libraries and APIs for data analytics tasks, including machine learning (MLlib), graph processing (GraphX), and stream processing (Spark Streaming). It supports a wide range of data sources and provides advanced data manipulation capabilities. In contrast, Spark Framework is primarily focused on building web services and does not provide built-in support for data analytics. However, it can be integrated with other data processing tools and frameworks.
Programming Language Support: Apache Spark supports multiple programming languages, including Java, Scala, Python, and R. It allows developers to write data processing jobs in their preferred language and provides language-specific APIs and libraries. Spark Framework, on the other hand, is primarily built using Java and provides a Java-based API for building web applications. However, it also has limited support for other JVM-based languages like Kotlin and Groovy.
Community and Ecosystem: Apache Spark has a large and active community of developers and users. It is widely adopted in industry and academia and has a rich ecosystem of third-party libraries, tools, and integrations. Spark Framework, although less popular than Apache Spark, also has an active community and provides a range of plugins and extensions for common web development tasks. However, its community and ecosystem are relatively smaller compared to Apache Spark.
In summary, Apache Spark is a powerful and scalable data processing engine for big data analytics, while Spark Framework is a lightweight web framework for building HTTP-based applications. These technologies differ in their domain of application, complexity, scalability, language support, and community/ecosystem size.
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"
I developed Hexagon heavily inspired in these great tools because of the following reasons:
- Take full advantage of the Kotlin programming language without any strings attached to Java (as a language).
- I wanted to be able to replace the HTTP server library used with different adapters (Jetty, Netty, etc.) and though right now there is only one, more are coming.
- Have a complete tool to do full applications, though you can use other libraries, Hexagon comes with a dependency injection helper, settings loading from different sources and HTTP Client, so it comes with (batteries included).
Right now I'm using it for my pet projects, and I'm happy with it.
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
Pros of Spark Framework
- Very easy to get up and running. Lovely API2
- Java1
- Native paralelization1
- Ideal for microservices1
- Fast1
- Easy1
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Cons of Apache Spark
- Speed4
Cons of Spark Framework
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