Spark Streaming

Go through the Spark Streaming Programming Guide
before beginning this section. In particular, it covers the concept of DStreams.

This section requires another dependency on the Spark Streaming library:

<dependency> <!-- Spark Streaming -->
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming_2.11</artifactId>
    <version>2.0.1</version>
</dependency>

The earlier examples demonstrates how to compute statistics on an existing log file - but not how to do realtime monitoring of logs. Spark Streaming enables that functionality.

To run the streaming examples, you will tail a log file into netcat to send to Spark.
This is not the ideal way to get data into Spark in a production system,
but is an easy workaround for a first Spark Streaming example. We will cover best practices for how to import data for Spark Streaming in Chapter 2.

In a terminal window, just run this command on a logfile which you will append to:

% tail -f [[YOUR_LOG_FILE]] | nc -lk 9999

If you don’t have a live log file that is being updated on the fly, you
can add lines manually with the included data file or another your own log file:

% cat ../../data/apache.access.log >> [[YOUR_LOG_FILE]]

You can use a convenience script that emulates a network stream. The script stream.sh
is located in the same directory as the sample log file apache.access.log. This script
periodically sends portions of the sample file to a network socket.
Run the script on another console:

% cd ../../data
% ./stream.sh apache.access.log

When data is streamed into Spark, there are two common use cases covered:

1. Windowed Calculations means that you only care about data
   received in the last N amount of time. When monitoring your web servers,
   perhaps you only care about what has happened in the last hour.
   • Spark Streaming conveniently
     splits the input data into the desired time windows for easy processing, using the window function of the streaming library.
   • The forEachRDD function allows you to access the RDD’s created each time interval.

• Cumulative Calculations means that you want to keep cumulative
  statistics, while streaming in new data to refresh those statistics. In that
  case, you need to maintain the state for those statistics.
  • The Spark Streaming library has some convenient functions for maintaining state
    to support this use case, updateStateByKey.
• Reusing code from Batching covers how to organize business logic code from the batch examples so that code can be reused in Spark Streaming.
  • The Spark Streaming library has transform functions which allow you to apply arbitrary RDD-to-RDD functions, and thus to reuse code from the batch mode of Spark.