A long-running job may be the last thing you want to run into on a Friday afternoon before heading out of the office for the weekend, but what are long running jobs? A long-running job in the world of data processing can mean many things, but for most programmers, the term is relative. What are long running jobs going to do to your schedule? Well, they could hold you up for seconds, hours or even days.

Once again, it all depends on what type of data processing you're dealing with when it comes to defining what is or is not a long-running job. For some programmers and data scientists, a long-running job is anything that takes longer than it takes to time out a system. In some cases, this may be 15 minutes.

What Causes Long-Running Jobs?

Long-running jobs can be caused by a variety of factors, including available processing power, any background processes running concurrently and the availability of cloud resources when running data tasks through the web. User expectations can also cause jobs to run long if too much is requested of a system at one time.

What’s important to remember is that, even in the age of immediate gratification and high-power computing, technology still has limits. In some cases, these are hard limits, such as when a machine has maxed out its RAM. When a machine is pushed too hard, it literally can’t go any faster, potentially causing jobs to run longer than a user would like.

How to Speed Up Long-Running Jobs

For the most part, you can speed up jobs that are running long by planning ahead. When you take into account the limitations of your hardware resources, you can plan out processing operations to avoid bottlenecks.

You can also try to avoid batch processing when possible. Batch processing means everything within a batch of data undergoes processing, whether it needs it or not. This can waste valuable resources and increase the time it takes to complete a data operation.

Author Resource:-

Emily Clarke writes about the best data observability tools and data analysis softwares. You can find her thoughts at data usage blog.