(Source: Apache Spark for the Impatient on DZone.) Three Issues with Spark Jobs, On-Premises and in the Cloud
#Unravel two challenges driver#
Sparkitecture diagram – the Spark application is the Driver Process, and the job is split up across executors. You may have improved the configuration, but you probably won’t have exhausted the possibilities as to what the best settings are. Repeat this three or four times, and it’s the end of the week.
#Unravel two challenges how to#
With so many configuration options, how to optimize? Well, if a job currently takes six hours, you can change one, or a few, options, and run it again. Getting one or two critical settings right is hard when several related settings have to be correct, guesswork becomes the norm, and over-allocation of resources, especially memory and CPUs (see below) becomes the safe strategy.ĥ.
#Unravel two challenges software#
And Spark interacts with the hardware and software environment it’s running in, each component of which has its own configuration options. Spark has hundreds of configuration options. Each variant offers some of its own challenges, and a somewhat different set of tools for solving them.Ĥ. And Spark works somewhat differently across platforms – on-premises on cloud-specific platforms such as AWS EMR, Azure HDInsight, and Google Dataproc and on Databricks, which is available across the major public clouds. There are major differences among the Spark 1 series, Spark 2.x, and the newer Spark 3. Spark is open source, so it can be tweaked and revised in innumerable ways. (You specify the data partitions, another tough and important decision.) But when a processing workstream runs into trouble, it can be hard to find and understand the problem among the multiple workstreams running at once.ģ. Spark takes your job and applies it, in parallel, to all the data partitions assigned to your job. And it makes problems hard to diagnose – only traces written to disk survive after crashes.Ģ. It can also make it easy for jobs to crash due to lack of sufficient available memory. However, this can cost a lot of resources and money, which is especially visible in the cloud. Spark gets much of its speed and power by using memory, rather than disk, for interim storage of source data and results. Here are some key Spark features, and some of the issues that arise in relation to them:ġ. Some of the things that make Spark great also make it hard to troubleshoot. Five Reasons Why Troubleshooting Spark Applications is Hard There is also a good introductory guide here.
And for more depth about the problems that arise in creating and running Spark jobs, at both the job level and the cluster level, please see the links below. These problems are usually handled by operations people/administrators and data engineers.įor more on Spark and its use, please see this piece in Infoworld. Then, we’ll look at problems that apply across a cluster. We’ll start with issues at the job level, encountered by most people on the data team – operations people/administrators, data engineers, and data scientists, as well as analysts. In this blog post, we’ll describe ten challenges that arise frequently in troubleshooting Spark applications. But troubleshooting Spark applications is hard – and we’re here to help. And Spark serves as a platform for the creation and delivery of analytics, AI, and machine learning applications, among others. Spark has become one of the most important tools for processing data – especially non-relational data – and deriving value from it.
Most of the time, it’s OOM errors…” Jagat Singh, Quora “ The most difficult thing is finding out why your job is failing, which parameters to change.
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