Book Image

Learning Apache Apex

By : Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles
Book Image

Learning Apache Apex

By: Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles

Overview of this book

Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Simulation of a real-time feed using historical data


Before you run this example, download some Yellow Cab trip data CSV files from the aforementioned website at nyc.gov. At the time of writing, this example is compatible with the data format used in the CSV files between 2015-01 and 2016-06. Let's say you have chosen2016-01 and saved the data as yellow_tripdata_2016-01.csv.

We want to simulate a real-time feed. However, because the trip data source is wildly unordered, we want to sort the data with some random deviation. A real-time feed usually contains some out-of-order data, but not to the extent of the original trip data files.

So, let's sort the data by timestamp:

bash> sort -t, -k2 yellow_tripdata_2016-01.csv > yellow_tripdata_sorted_2016-01.csv

Next, add some random deviation to the sorted data:

bash> cat yellow_tripdata_sorted_2016-01.csv | perl -e '@lines = (); while (<>) { if (@lines && rand(10) < 1) { print shift @lines;  } if (rand(20) < 1) { push @lines...