Tag Archives: MapReduce

If you use MapReduce for any real-world application, chances are your workflow consists of more than one MapReduce job. Rapleaf has workflows consisting of over one hundred jobs. A lot of times, you need to make configurations to the workflow that should apply to every job. For example, you may want each job to run [...]

Hadoop is designed to be a fault tolerant system. Jobs should be resilient to nodes going down and other random failures. Hadoop isn’t perfect however, as I still see jobs failing due to random causes every now and again. I decided to investigate the significance of the different factors that play into a job failing. [...]