How Spark Achieves its Speed and Efficiency ?
In the previous story we discussed the challenges and inefficiencies that Spark tries to address.
In this story, we will discuss how Spark achieves its speed and efficiency.
Spark’s speed and efficiency can be attributed to three main factors:
- In-memory computing: Spark keeps data in memory instead of disk, which allows it to access data much faster.
- Directed acyclic graph (DAG) execution engine: Spark’s DAG execution engine breaks down tasks into a series of steps, which can be executed in parallel. This can significantly improve the performance of applications that can be broken down into independent tasks.
- RDDs (Resilient Distributed Datasets): RDDs are a distributed data structure that allows Spark to efficiently manage and distribute data across a cluster of machines.
Let’s take a closer look at each of these factors.
In-memory computing
In-memory computing is a technique that stores data in memory instead of disk. This can significantly improve the performance of applications that access data frequently, because data can be accessed much faster from memory than from disk.
Spark uses in-memory computing to store data that is frequently accessed by applications. This can significantly improve the performance of these applications.
Directed acyclic graph (DAG) execution engine
A DAG is a graph in which there are no cycles. A DAG execution engine breaks down tasks into a series of steps, which can be executed in parallel. This can significantly improve the performance of applications that can be broken down into independent tasks.
Spark uses a DAG execution engine to execute tasks in parallel. This can significantly improve the performance of applications that can be broken down into independent tasks.
RDDs (Resilient Distributed Datasets)
RDDs are a distributed data structure that allows Spark to efficiently manage and distribute data across a cluster of machines. RDDs are immutable, which means that they cannot be changed once they are created. This makes them easy to parallelize and fault-tolerant.
Spark uses RDDs to store and manage data. This allows Spark to efficiently manage and distribute data across a cluster of machines.
I understand that this might seem a bit unclear at first, but rest assured, we will dive into this topic thoroughly.
Conclusion
Spark’s speed and efficiency are due to a combination of in-memory computing, DAG execution engine, and RDDs. These technologies allow Spark to significantly improve the performance of applications that can be broken down into independent tasks.
In the next lesson, we will discuss the RDD in more detail.
