Things to consider submitting Spark Jobs on Kubernetes in cluster mode

Big Data


February 8, 2024


With the evolution of big data, Sparks and Kubernetes continue to be powerful duos for scalable and efficient data processing.

The big data market is expected to touch $106.3 billion with Kubernetes being pivotal in the role of orchestration by the end of 2027. People are expecting a big picture of growth in the adaptation and potential of Spark on Kubernetes in 2024.

But there are certain things that you need to keep in mind when submitting a Spark job on Kubernetes in Cluster mode. If you can leverage the strengths of these technologies, organizations can achieve efficient, scalable, and cost-effective big data processing, unlocking value insights and escalating business growth.

But the actual struggle arises when you have to do the job flawlessly. There are common mistakes that people commit while submitting their Spark job on Kubernetes. A few things if kept in mind can help in delivering the job efficiently and successfully.

In this write-up, you will get to know everything that will assist you in delivering the Spark job on Kubernetes.

What is Spark and Spark Job?

In the realm of big data processing Spark is known as Apache Spark. It is an open-source, distributed computing system renowned for its fast and unified analytics engine.

It has been observed that using tools like Spark Dynamics Allocation can lead to 20-30% resource savings compared to other traditional cluster setups.

A Spark job, within this framework, signifies a specific task or computation conducted for processing large-scale data. It enables complex data analytics and computation tasks across distributed clusters. This facilitates efficient data processing and analysis in diverse environments.

What is Kuberbernetes?

Kubernetes is an open-source container orchestration platform that is designed to automate the deployment, scaling and management of containerized applications. It simplifies the containerized application lifecycle by providing tools for seamless scaling, load balancing, and resource allocation.

Read More: – Key Benefits of Running Apache Spark on Kubernetes 

What does Apache Spark Jobs on Kubernetes mean?

Apache Spark Jobs on Kubernetes refers to the execution of data processing tasks using Spark Application on Kubernetes in Cluster Mode. This integration leverages the strength of Spark’s analytics engine for distributed computing and Kubernetes’s orchestration capabilities. Also, this spark job running on Kubernetes streamlines resource management and optimizes cluster utilization.

Challenges in Spark Job Execution on Kubernetes

  • One of the most common challenges is managing resources efficiently within a Kubernetes environment.
  • Network overhead in the orchestration of Spark jobs on Kubernetes impacts data transfer and communication between the Spark components.
  • While integrating Spark with Kubernetes both technologies continue to evolve independently and involve overcoming compatibility challenges.
  • Ensuring reliable and efficient persistent storage for Spark jobs on Kubernetes poses a challenge.
  • Achieving dynamic scaling for Spark clusters in response to varying workloads is another challenge faced during the job execution.

Things to Keep in Mind While Kubernetes Spark Job Submission

Sparks and Kubernetes are the best combination of the big data world. They both offer unparalleled power and flexibility for data processing. Navigating the intricacies of Spark job submission on Kubernetes in Cluster mode can feel like traversing a complex maze.

Here are a few things that you need to keep in mind while executing the job:

Packing Efficiently

Choosing the right format is the very first thing that you need to adapt. You can opt for efficient packaging like JARs for your Spark jobs in Kubernetes cluster mode. You can also choose tools like the Maven shade plugin to optimize size and avoid resource hogs.

Resource Roulette

You have to be specific about your Spark jobs cluster requirements. It is better to specify the resource requests and limit for your Spark job so that resource-hungry jobs monopolizing the cluster can be avoided.

Dependency Dilemma

So, the external dependencies can be a problem. You can securely manage them using Spark docker image for Kubernetes, share volumes, or a tool like Maven.


It is essential to keep an eye on Spark job execution and cluster health. Tools like Prometheus and Grafana act as your oven timer, monitoring your Spark Jobs cluster’s health and preventing disasters.

Advanced Adventures

Security comes first:

When it comes to Kubernetes Spark Job submission, security is a priority. Hence, to secure it with authentication, authorization, and network isolation is a must. Privacy is a big concern and also matters a lot in the data world.

Fault Tolerance:

The next thing that you need to check is to equip your Spark jobs with restart mechanisms and error handling to make your Kubernetes cluster Spark job journey resilient.

Perform Perfection:

Tune configurations, optimize data locality, and use tools like Spark Dynamic Allocation to extract peak performance from your Spark jobs cluster on Kubernetes.

So, these are Challenges and solutions for Spark Job execution that can help you improve your efficiency.


Submitting Spark Jobs on Kubernetes in cluster mode isn’t about overcoming challenges, it is about unlocking immense potential. Simply by embracing this powerful duo and mastering their intricacies, you can transform your big data processing into a symphony of efficiency, scalability, and success.

Sometimes this whole task can be tedious. You can hire Apache Spark Development Company to assist you with the perfect execution of the job. Ksolves is one of the best companies that has more than a decade of experience and has helped businesses accelerate their growth.

We are a call away if you are in a dilemma. Hope this was helpful!

authore image
ksolves Team

Leave a Comment

Your email address will not be published. Required fields are marked *

(Text Character Limit 350)