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  • Writer's pictureKartikay Luthra

Comparing Apache Spark and Akka Streams for Stream Processing


Apache spark v Akka streams
apache spark v akka streams


In the world of data processing and stream handling, Apache Spark and Akka Streams are like two distinct tools in a craftsman's toolbox. Each serves a unique purpose and offers its own set of features and advantages. In this blog, we will delve into the world of stream processing and draw real-world analogies to understand when to use Apache Spark and when to opt for Akka Streams.


Craftsman's Toolbox Analogy


Imagine you're a craftsman with a toolbox, and in this toolbox, you have two specialized tools: Apache Spark and Akka Streams.


1. The Spark Power Drill (Apache Spark)


Just like a powerful electric drill, Apache Spark is a versatile, high-speed tool that can handle a wide range of tasks. It's like the power drill you'd use for various projects in your workshop. Apache Spark can process not only streams but also large datasets and batch data efficiently. It's your go-to tool when you need robust, in-memory data processing for a variety of tasks.


2. The Precision Screwdriver (Akka Streams)

Akka Streams, on the other hand, is like a precision screwdriver—a specialized tool designed for fine, delicate work. It's the tool you reach for when you're dealing specifically with streams of data. Akka Streams excels at managing and processing data as it flows through your system in a reactive, lightweight, and non-blocking manner.


Real-World Scenarios


Now, let's relate these tools to real-world scenarios to better understand when to use each of them.


Scenario 1: Building a Grand Library


Imagine you're constructing a grand library. You have thousands of books arriving daily, and you need to sort and catalog them efficiently.


- Use Apache Spark: When you have a massive collection of books to organize, you'd employ Apache Spark. It's like having a team of workers with power drills to quickly process and categorize the books. Spark is excellent for batch processing of large volumes of data, and in this case, it's your tool of choice for managing the library's vast collection.


Scenario 2: Tracking a Busy Postal Service


Now, suppose you're running a busy postal service, and you need to track the delivery status of parcels in real-time.


- Use Akka Streams: When you're dealing with a constant stream of parcels, much like a flowing river of packages, you will turn to Akka Streams. It's like having an array of precision screwdrivers to carefully track each parcel's progress. Akka Streams are designed for real-time, reactive data processing, making them the perfect fit for monitoring the flow of parcels as they move through the postal network.


In both scenarios, you can see how the choice of tool depends on the specific requirements. Apache Spark shines when you need to process large volumes of data efficiently, while Akka Streams excels at handling continuous, real-time streams.


I hope the real-world analogies and scenarios I mentioned above were enough to explain to you in a general sense are differences between the two, Now let us look at the technical features that constitute both Akka Streams and Apache Spark and how they both differ in implementing these features which are very useful if one is trying to develop a real-time application.




1. Scalability and Parallelism:


In the craftsman's workshop, think of scalability and parallelism like how you handle a big task. You have a choice: you can either use more power drills (like Apache Spark) or more precision screwdrivers (like Akka Streams) to get the job done.


How Apache Spark Handles Scalability and Parallelism:


- Imagine you're in charge of organizing a grand library with thousands of books arriving every day. To keep up, you'd hire more people with power drills (Apache Spark). Each person works on a section of the library independently, and together, they process books in parallel. If the workload gets even bigger, you can simply hire more people with power drills, and your library gets organized faster. This is how Spark scales out—by adding more workers (nodes) to the task.


How Akka Streams Handles Scalability and Parallelism:


- Now, picture running a busy postal service tracking parcel deliveries in real-time. You need to process parcels as they come in. You can't just hire more people; it's not practical. Instead, you'd use more precision screwdrivers (Akka Streams). Each precision screwdriver worker is highly efficient, and able to process parcels as they arrive one by one. If more parcels arrive, each precision screwdriver works faster to keep up. This is how Akka Streams scales up—by making individual workers (actors) more efficient without adding more.


In the Craftsman's Workshop:


- If you had to build both the grand library and run the postal service, you'd use Apache Spark for the library (batch processing) and Akka Streams for the postal service (real-time processing).


In simpler terms, Apache Spark is like having more power drills for big tasks where you can easily add more workers, while Akka Streams is like having more efficient workers (precision screwdrivers) for tasks that need real-time attention without hiring more hands. Both tools are handy, but you choose the one that suits your project's size and nature.


Basically, Apache Spark focuses more comprehensively on Horizontal Scaling by adding more nodes, whereas Akka Streams focuses more comprehensively on Vertical Scaling by making one node efficient enough to its maximum performance.




2. Fault Tolerance:


In our craftsman's workshop, fault tolerance is like making sure nothing goes terribly wrong during a project. Both Apache Spark and Akka Streams have their ways of ensuring that even if something unexpected happens, your work doesn't fall apart.


Exploring Fault Tolerance in Apache Spark:


- Imagine you're organizing the grand library with Apache Spark. The power drill workers are hard at work, but sometimes a worker might suddenly stop working due to a drill malfunction (like a computer crashing). In such cases, Apache Spark keeps track of the work that was completed by that worker. It's like having a log of what books they've already organized. If a worker stops, another worker can pick up where the previous one left off, using the log as a guide. This way, even if there are hiccups, you can be sure that all the books will eventually get organized.


Comparing Fault Tolerance in Akka Streams:


- Now, let's switch to the postal service scenario using Akka Streams. While parcels are being processed, one of the precision screwdrivers may encounter a problem (like an actor failing). But, Akka Streams are designed to react quickly. It's like having a dedicated supervisor watching over each precision screwdriver. If one of them stops, the supervisor immediately replaces it with another worker to ensure that parcel processing continues without delay. In the postal service, even if one worker faces an issue, your parcels keep moving.


Data Integrity in Stream Processing:


In both cases, fault tolerance ensures that even if something breaks down, the work still goes on. This is crucial for data integrity, especially in stream processing, where data is constantly flowing, and you can't afford to lose any of it.


So, in our craftsman's workshop, fault tolerance is like having a backup plan for when a tool or worker unexpectedly stops working. Both Apache Spark and Akka Streams have this backup plan in place, making them reliable options for stream processing tasks, where data integrity is paramount.



3. Integration and Ecosystem:


In the craftsman's workshop, integration and the ecosystem are like the tools and resources you have access to. Let's explore how Apache Spark and Akka Streams fit into the workshop's setup.


Discussing Integration with Other Tools and Libraries in Apache Spark:


- Think of Apache Spark as a versatile workbench in your workshop. It's designed to cooperate with a wide range of tools and resources. Just like a craftsman can use different tools with the workbench, Apache Spark can integrate with various data storage systems, databases, and even other big data tools. It's like your workbench being compatible with different types of saws, drills, and measuring instruments. This versatility allows you to combine Spark with the right tools for your specific project, making it a valuable part of your workshop.


Examining Ecosystem Support and Compatibility in Akka Streams:


- On the other hand, Akka Streams is like having a specialized worktable dedicated to specific tasks in your workshop. While it's not as versatile as the workbench, it's expert at what it does. Similarly, Akka Streams has a well-defined ecosystem that aligns with its specialization in handling data streams. It's like having a set of specialized tools designed to work seamlessly with your dedicated worktable. This ecosystem includes Akka actors, Akka HTTP, and other Akka modules that are specifically tailored to real-time and stream processing. This focused ecosystem ensures that Akka Streams excels in its niche.


In Your Craftsman's Workshop:


- So, just as a craftsman combines the general workbench with specialized worktables, you can use Apache Spark's wide integration capabilities for a variety of data processing projects. When you have specific, real-time stream processing tasks, you'd bring out your dedicated Akka Streams worktable, complete with a set of specialized tools that make stream processing efficient and reliable.


In essence, the choice between Apache Spark and Akka Streams depends on the kind of projects you're working on and the tools you need in your workshop. The integration and ecosystem compatibility of each framework ensure that you have the right tools for the job, whether you're building something versatile or something highly specialized.


Conclusion:


In our craftsman's workshop, we explored the world of stream processing with two versatile tools—Apache Spark and Akka Streams. Each tool has its unique strengths and areas of expertise, just like a power drill and a precision screwdriver have distinct roles in a craftsman's toolbox.


- Apache Spark is the power drill of data processing, adept at handling massive amounts of data efficiently. It's the go-to choice when you need scalability and versatility in your projects. With in-memory processing, fault tolerance, and seamless integration with various tools, it's your trusted companion for organizing grand libraries of data.


- Akka Streams, in contrast, is the precision screwdriver for real-time stream processing. It excels when every detail and every moment counts. With its lightweight, reactive nature and focused ecosystem, Akka Streams is like having a team of efficient workers in your postal service, ensuring that parcels keep moving even in the face of unexpected challenges.


Just as a craftsman chooses the right tool for the task at hand, you can select either Apache Spark or Akka Streams based on the nature of your data processing project. These tools are designed to make your work more efficient and reliable, ensuring that your data remains organized, tracked, and delivered, just as you'd expect in a well-managed workshop. So, remember, whether you're handling vast libraries of information or real-time streams of data, you have the right tool in your workshop to get the job done.


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