Overview of Spark Components
Spark Components are the features that are provided by spark framework for big data processing with a faster approach. Spark is known for processing large amounts of data for analytics solutions. There are basically 6 components associated with Spark ecosystems such as Spark Core, Spark SQL, Spark Streaming, Spark MLlib, Spark GraphX, and SparkR. Spark is a widely used technology in the big data processing industry. It is a reliable and efficient technology in terms of performance. The Spark components work in-memory computation along with the disk or cluster level storage feature that helps sparks for optimizing the data processing.
Top Components of Spark
Currently, we have 6 components in Spark Ecosystem which are Spark Core, Spark SQL, Spark Streaming, Spark MLlib, Spark GraphX, and SparkR. Let’s see what each of these components do.
1. Spark Core
Spark Core is, as the name suggests, the core unit of a Spark process. It takes care of task scheduling, fault recovery, memory management, and input-output operations, etc. Think of it as something similar to CPU to a computer. It supports programming languages like Java, Scala, Python, and R and provides APIs for respective languages using which you can build your ETL job or do analytics. All the other components of Spark have their own APIs which are built on top of Spark Core. Because of its parallel processing capabilities and in-memory computation, Spark can handle any kind of workload.
Spark Core comes with a special kind of data structure called RDD (Resilient Distributed Dataset) which distributes the data across all the nodes within a cluster. RDDs work on a Lazy evaluation paradigm where the computation is memorized and only executed when it’s necessary. This helps in optimizing the process by only computing the necessary objects.
2. Spark SQL
If you have worked with Databases, you understand the importance of SQL. Wouldn’t it be extremely overwhelming if the same SQL code works N times faster even on a larger dataset? Spark SQL helps you manipulate data on Spark using SQL. It supports JDBC and ODBC connections that establish a relation between Java objects and existing databases, data warehouses and business intelligence tools. Spark incorporates something called Dataframes which are structured collection of data in the form of columns and rows.
Spark allows you to work on this data with SQL. Dataframes are equivalent to relational tables and they can be constructed from any external databases, structured files or already existing RDDs. Dataframes have all the features of RDD such as immutable, resilient, in-memory but with an extra feature of being structured and easy to work with. Dataframe API is also available in Scala, Python, R, and Java.
3. Spark Streaming
Data Streaming is a technique where a continuous stream of real-time data is processed. It requires a framework that offers low latency for analysis. Spark Streaming provides that and also a high throughput, fault-tolerant and scalable API for processing data in real-time. It is abstracted on the Discretized Stream (DStream) which represents a stream of data divided into small batches. DStream is built on RDD hence making Spark Streaming work seamlessly with other spark components. Some of the most notable users of Spark.
Streaming is Netflix, Pinterest, and Uber. Spark Streaming can be integrated with Apache Kafka which is a decoupling and buffering platform for input streams. Kafka acts as the central hub for real-time streams that are processed using algorithms in Spark Streaming.
4. Spark MLLib
Spark’s major attraction is scaling up the computation massively and this feature is the most important requirement for any Machine Learning Project. Spark MLLib is the machine learning component of Spark which contains Machine Learning algorithms such as classification, regression, clustering, and collaborative filtering. It also offers a place for feature extraction, dimensionality reduction, transformation, etc.
You can also save your models and run them on larger datasets without having to worry about sizing issues. It also contains utilities for linear algebra, statistics, and data handling. Because of Spark’s in-memory processing, fault tolerance, scalability and ease of programming, with the help of this library you can run iterative ML algorithms easily.
Graph Analytics is basically determining the relationships between objects in a graph, for example, the shortest distance between two points. This helps is route optimization. Spark GraphX API helps in the graph and graph-parallel computation. It simplifies graph analytics and makes it faster and more reliable. One of the main and well-known applications of graph analytics is Google Maps.
It finds out the distance between two locations and gives an optimal route suggestion. Another example can be Facebook friend’s suggestions. GraphX works with both graphs and computations. Spark offers a range of graph algorithms like page rank, connected components, label propagation, SVD++, strongly connected components, and triangle count.
R is the most widely used statistical language which comprises more than 10,000 packages for different purposes. It used data frames API which makes it convenient to work with and also provides powerful visualizations for the data scientists to analyze their data thoroughly. However, R does not support parallel processing and is limited to the amount of memory available in a single machine. This is where SparkR comes into the picture.
Spark developed a package known as SparkR which solves the scalability issue of R. It is based on distributed data frames and also provides the same syntax as R. Spark’s distributed Processing engine and R’s unparalleled interactivity, packages, visualization combine together to give Data Scientists what they want for their analyses.
Since Spark is a general-purpose framework, it finds itself in a wide range of applications. Spark is being extensively used in most of the big data applications because of its performance and reliability. All these components of Spark are getting updated with new features in its every new release and making our lives easier.
This is a guide to Spark Components. Here we discuss the basic concept and top 6 components of spark with a detailed explanation. You may also look at the following articles to learn more –
- Top 5 Important Hive Alternatives
- Quick Glance of 17 Different Spark Versions
- Complete Guide to Spark Tools
- Apache Spark Architecture
- Spark DataFrame