Article by one of our Subject Matter Expert
Have you encountered a scenario where the data size you need to process exceeds the memory size of your system? This is what happens when you enter arena of big data. Say you have data volume in the range of 20-30 GB, how can you sort it if it can’t even be loaded in memory?
Answer to this is Apache Spark and its distributed computing principles. In layman terms, you can split the data over several different machines over the network and process the same data in-memory which will be the fastest way possible. Let’s dive in it in detail.
What is Apache Spark? :
Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. It provides development APIs in Java, Scala, Python and R, and supports code reuse across multiple workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing.
Spark is a general-purpose distributed processing system used for big data workloads. It has been deployed in every type of big data use case to detect patterns and provide real-time insight.
How does Apache Spark work?
Hadoop MapReduce is a programming model for processing big data sets with a parallel, distributed algorithm. Developers can write massively parallelized operators, without having to worry about work distribution, and fault tolerance. However, a challenge to MapReduce is the sequential multi-step process it takes to run a job. With each step, MapReduce reads data from the cluster, performs operations, and writes the results back to HDFS. Because each step requires a disk read, and write, MapReduce jobs are slower due to the latency of disk I/O.
Spark was created to address the limitations to MapReduce, by doing processing in-memory, reducing the number of steps in a job, and by reusing data across multiple parallel operations. With Spark, only one-step is needed where data is read into memory, operations performed, and the results written back resulting in a much faster execution. Spark also reuses data by using an in-memory cache to greatly speed up machine learning algorithms that repeatedly call a function on the same dataset. Data re-use is accomplished through the creation of DataFrames, an abstraction over Resilient Distributed Dataset (RDD), which is a collection of objects that is cached in memory and reused in multiple Spark operations. This dramatically lowers the latency making Spark multiple times faster than MapReduce, especially when doing machine learning, and interactive analytics.
What are the benefits of Apache Spark?
There are many benefits of Apache Spark to make it one of the most active projects in the Hadoop ecosystem. These include:
- Speed− Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing number of read/write operations to disk. It stores the intermediate processing data in memory.
- Supports multiple languages– It is developer friendly. Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
- Advanced Analytics− Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms.
- Multiple workload -Apache Spark comes with the ability to run multiple workloads, including interactive queries, real-time analytics, machine learning, and graph processing. One application can combine multiple workloads seamlessly.
Components of Spark
The Spark framework includes:
- Spark Core as the foundation for the platform
- Spark SQL for interactive queries
- Spark Streaming for real-time analytics
- Spark MLlib for machine learning
- Spark GraphX for graph processing
Spark Core is the foundation of the platform. It is responsible for memory management, fault recovery, scheduling, distributing & monitoring jobs, and interacting with storage systems. Spark Core is exposed through an application programming interface (APIs) built for Java, Scala, Python and R. These APIs hide the complexity of distributed processing behind simple, high-level operators.
MLlib – Machine Learning
Spark includes MLlib, a library of algorithms to do machine learning on data at scale. Machine Learning models can be trained by data scientists with R or Python on any Hadoop data source, saved using MLlib, and imported into a Java or Scala-based pipeline. Spark was designed for fast, interactive computation that runs in memory, enabling machine learning to run quickly. The algorithms include the ability to do classification, regression, clustering, collaborative filtering, and pattern mining.
Spark Streaming Real-time
Spark Streaming is a real-time solution that leverages Spark Core’s fast scheduling capability to do streaming analytics. It ingests data in mini-batches and enables analytics on that data with the same application code written for batch analytics. This improves developer productivity, because they can use the same code for batch processing, and for real-time streaming applications. Spark Streaming supports data from Twitter, Kafka, Flume, HDFS, and ZeroMQ, and many others found from the Spark Packages ecosystem.
Spark SQL – Interactive Queries
Spark SQL is a distributed query engine that provides low-latency, interactive queries up to 100x faster than MapReduce. It includes a cost-based optimizer, columnar storage, and code generation for fast queries, while scaling to thousands of nodes. Business analysts can use standard SQL or the Hive Query Language for querying data. Developers can use APIs, available in Scala, Java, Python, and R. It supports various data sources out-of-the-box including JDBC, ODBC, JSON, HDFS, Hive, ORC, and Parquet.
GraphX – Graph Processing
Spark GraphX is a distributed graph processing framework built on top of Spark. GraphX provides ETL, exploratory analysis, and iterative graph computation to enable users to interactively build and transform a graph data structure at scale. It comes with a highly flexible API, and a selection of distributed Graph algorithms.
One can start programming with sample code from its git hub repository with below link.
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