Comparing Real Time Analytics and Batch Processing Applications with Hadoop MapReduce and Spark

Apache Spark is an engine for fast, large scale data processing. It claims to run the programs up to 100x faster than Hadoop MapReduce in-memory, while 10x faster with the disks. Introduction of Hadoop Mapreduce framework greatly simplified the problem of big data management and analysis in a cost-efficient way. With the help of commodity hardware, we can apply several algorithms on large volumes of data. But MapReduce failed to show its performance while implementing complex and multi-stage algorithms. Through this article, we tried to dig deep to understand why Apache Spark upstages Apache Hadoop MapReduce framework.

Unified Architecture

Introduction of big data mandated the development of sophisticated tools that runs faster and are easy to use. We need such tools for various applications such as interactive query processing, ad-hoc queries on real-time streaming data and sophisticated data processing on historical data for better decision making.

Continue reading

Analyzing Wikipedia Text with pySpark

Spark improves usability by offering a rich set of APIs and making it easy for developers to write code. Programs in spark are 5x smaller than MapReduce. The Spark Python API (PySpark) exposes the Spark programming model to Python. To learn the basics of Spark, read through the Scala programming guide; it should be easy to follow even if you don’t know Scala. pySpark provides an easy-to-use programming abstraction and parallel runtime, we can think of it as – “Here’s an operation, run it on all of the data”.

To use Spark, developers write a driver program that implements the high-level control flow of their application and launches various operations in parallel on the nodes of the cluster.

The typical life cycle of a Spark program is –

  • Create RDDs from some external data source or parallelize a collection in your driver program.
  • Lazily transform the base RDDs into new RDDs using transformations.
  • Cache some of those RDDs for future reuse.
  • Perform actions to execute parallel computation and to produce results.

Continue reading

Introduction to Big Data with Apache Spark (Part-2)

In part-1 of this series we saw a brief overview of Apache Spark, Resilient Distributed Dataset (RDD) and Spark Ecosystem. In this article, we will have a closer look at Spark’s primary and fault-tolerant memory abstraction for in-memory cluster computing called the Resilient Distributed Dataset (i.e RDD).

Motivation

One of the most popular parallel data processing paradigm – MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable to efficiently solve the complex and iterative machine learning and graph processing algorithms, as well as the interactive or ad-hoc queries. All of these complex algorithms need one thing in common that MapReduce lacks : efficient primitives for data sharing. In MapReduce, the data is shared across different jobs (or different stages of a single job) with the help of stable storage. As discussed in the previous article, MapReduce stores results on the disk, and thus, the reads and writes are very slow. Also, the existing storage abstraction interfaces uses the data replication or update log replication for fault-tolerance. This method is considerably costly if we are dealing with data-intensive applications.

Continue reading

Introduction to Big Data with Apache Spark (Part-1)

With the advent of new technologies, there has been an increase in the number of data sources. Web server logs, machine log files, user activity on social media, recording a user’s clicks on the website and many other data sources have caused an exponential growth of data. Individually this content may not be very large, but when taken across billions of users, it produces terabytes or petabytes of data. For example, Facebook is collecting 500 terabytes(TB) of data everyday with more than 950 million users. Such a massive amount of data which is not only structured but also unstructured and semi-structured  is considered under the roof known as Big Data.

Big data is of more importance today, because in past we collected a lot of data and built models to predict the future, called forecasting, but now we collect data and build models to predict what is happening now, called nowcasting. So a phenomenal amount of data is collected, but only a tiny amount is ever analysed. The term Data Science means deriving knowledge from big data, efficiently and intelligently.

The common tasks involved in data science are :

  1. Dig data to find useful data to analyse
  2. Clean and prepare that data
  3. Define a model
  4. Evaluate the model
  5. Repeat until we get statistically good results, and hence a good model
  6. Use this model for large scale data processing

Continue reading