A well organized city requires a robust infrastructure for better administration and management. In a similar way , the Big Data world requires a powerful engine to refine, process and extract value from its vast ocean of information. When it comes to the big data analytics kingdom, Apache Spark is the unprecedented king of the hill. So how does this powerful engine work its magic? To figure this out we need to take a the behind the scenes look at the architecture of Spark. Understanding this engine which powers most of the innovative applications across the global is very critical to enhance your career in data engineering.
When it comes to Big data , Apache Spark is practically unavoidable. You can see spark everywhere be it real time analytics or machine learning pipelines or ETL processes. To truly master Spark, we need to deep dive into its architecture.
Spark’s Core Concepts - The Building Blocks
Spark can be considered as a team of specialized workers ,each with their own role, all orchestrated to achieve a common goal which is processing massive data at lightning speed.
The core team includes.
Spark Driver:
This is the master or the brain behind the operation . The driver is responsible for
Job Coordination: Breaking down your complex data processing tasks (jobs) into smaller, manageable tasks.
Task Scheduling: Deciding which worker (Executor) should handle each task.
Executor Supervision: Keeping tabs on the workers, ensuring they are healthy and doing their jobs.
Executors:
These are the worker nodes that do the heavy lifting . Each executor resides in a JVM on the node. They are responsible for
Task Execution: Running the specific tasks assigned by the Driver.
* Data Storage (in memory and disk): Caching data for faster access.
* Returning task output to the driver: After completing the assigned task they send the data back to the driver.
Cluster Manager:
This acts like a HR department managing the resources or the worker nodes . It allocates resources to the application and manages the lifecycle of the executors . It could be
Standalone: Spark's own simple cluster manager.
YARN: A popular choice, especially in Hadoop environments.
Mesos: Another robust cluster manager.
Kubernetes: One of the new and most used cluster manager in the data engineering field.\
Resilient Distributed Datasets(RDD):
RDD’s are core data structures in spark that allows distributed and parallel processing of the data across various clusters.
Spark Session
This is the entry point of spark. Using Spark session various spark functionalities like dataframe, datasets etc could be accessed.
The Spark Execution Flow
To understand the spark processing better , lets see how the spark team work together when you present a massive data challenge at it.
Code submission - You write your spark application in your preferred language like Python, Java or R and submit it.
Driver take charge - The Spark driver springs into action and it analyzes your code ,creates a logical execution plan and transforms the plan to physical execution units.
Cluster Manager - The driver requests resources from the cluster manager.
Executors - The cluster manager launches the requested executors across the cluster.
Task assignment - The driver distributes the tasks to the available executors.
Executors Get to Work: Each executor works on its assigned task, processing data in parallel.
Results Return: Executors send their results back to the Driver.
Driver Aggregates: The Driver combines the results and returns the final output.
Real World Examples
Spark’s architecture allows it to tackle the real world problems with exceptional performance. Here are some of the examples I have seen and that you may face in the growing tech scene.
E-commerce Recommendation Engines: Spark can process huge amounts of user purchase history and browsing data to provide real-time product recommendations for ecommerce stores like amazon or Flipkart. The parallel nature of Spark is just perfect for analyzing millions of customers' behavior simultaneously.
Financial Fraud Detection: Majority of the Banks and financial companies use Spark to analyze massive transaction volumes in real time. Spark helps to detect and prevent fraudulent activity by spotting the patterns and anomalies quickly.
Social Media Trend Analysis: Social media platforms use Spark to perform sentiment analysis, detect trending topics, and personalize content as per the trend. This mostly involves ingesting and analyzing terabytes of the data.
IoT Data Processing: Spark is used to process the massive streams of data obtained from sensors and devices which enables real-time decision-making. For example, smart cities use Spark to understand traffic flow patterns and optimize public transportation.
Retail Analytics: Retail business chains uses spark to analyze sales patterns , inventory management , customer segmentation etc which helps in increasing their sales.
Healthcare: Spark is used to process patient details , predict major disease outbreaks and streamline its operations.
Benefits of Spark’s Architecture
Speed: When the computation happens in-memory it is very fast compared to where the processing happens in disk.
Scalability: By adding more worker nodes, Spark can seamlessly scale horizontally.
Fault Tolerance: RDDs (Resilient Distributed Datasets) and other mechanisms ensure that Spark can easily recover from node failures and hence they are highly fault tolerant.
Ease of Use: Spark's APIs and its high-level libraries make complex data manipulation very intuitive.
Unified Stack: Spark can be used for various data engineering operations like batch processing or stream processing and hence it is an unified stack.
Importance of Spark for Data Engineers
The IT landscape across the world is changing at a rapid pace. With so many start-ups, and MNC’s coming up with data science teams, there is a huge demand for data engineers who are proficient in tools like spark. Knowing the Spark’s architecture in depth and understanding how things work in the background could help you land the dream data engineering job in no time.
What are your experiences with Spark? What challenges have you faced? Let's discuss in the comments below! Also, if you have any questions, feel free to ask. I am always happy to help the fellow data enthusiasts.