With the Apache Spark 3.1 release in March 2021, the Spark on Kubernetes project is now officially declared as production-ready and Generally Available. This is the achievement of 3 years of fast-growing community contributions and adoptions of the project — since initial support for Spark-on-Kubernetes was added in Spark 2.3 (February 2018). In this article, we will go over the main features of Spark 3.1, with a special focus on the improvements to Spark-on-Kubernetes.

Related Resources:

• For an introduction about using Kubernetes as a resource manager for Spark (instead of YARN), look at the Pros & Cons of Running Spark…

November 20th, 2020: I just attended the first edition of the Data + AI Summit — the new name of the Spark Summit conference organized twice a year by Databricks. This was the European edition, meaning the talks took place at a European-friendly time zone. In reality it drew participants from everywhere, as the conference was virtual (and free) because of the epidemic.

The conference featured 125 pre-recorded video talks aired at a specific time (as if they were live), with the speakers available to answer questions in real time on a “Live Chat”. …

For “live” Spark applications, accessing the Spark UI — the open-source monitoring interface of Apache Spark — is easy, as it is served directly by the Spark driver on port 4040.

But once an application has completed, accessing the Spark UI requires setting up a Spark History Server, which takes a bit of work. In this article, we’re going to show you how to access the Spark UI for completed Spark applications with very little effort by leveraging a free open-source monitoring project called Data Mechanics Delight (see the github page).

Accessing the Spark UI for live Spark applications is easy, but having access to it for terminated applications requires persisting logs to a cloud storage and running a server called the Spark History Server. While some commercial Spark platform provides this automatically (e.g. Databricks, EMR) ; for many Spark users (e.g. on Dataproc, Spark-on-Kubernetes) getting this requires a bit of work.

Today, we’re releasing a free, hosted, partly open sourced, Spark UI and Spark History Server that work on top of any Spark platform (whether it’s on-premise or in the cloud, over Kubernetes or over YARN, using a commercial platform…

Note: If you’re looking for an introduction to Spark on Kubernetes — what is it, what’s its architecture, why is it beneficial — start with The Pros And Cons of Running Spark on Kubernetes. For a one-liner introduction, let’s just say that Spark native integration with Kubernetes (instead of Hadoop YARN) generates a lot of interest from the community and is about to be declared Generally Available and Production-Ready with Spark 3.1.

I’m an ex-Databricks engineer, now co-founder of Data Mechanics, a managed Spark platform deployed on a Kubernetes cluster inside our customers cloud account (AWS, Azure, or GCP). …

The benefits that come with using Docker containers are well known: they provide consistent and isolated environments so that applications can be deployed anywhere — locally, in dev / testing / prod environments, across all cloud providers, and on-premise — in a repeatable way.

The software engineering world has fully adopted Docker, and a lot of tools around Docker have changed the way we build and deploy software — testing, CI/CD, dependency management, versioning, monitoring, security. The popularity of Kubernetes as the new standard for container orchestration and infrastructure management follows from the popularity of Docker.

In the big data…

At the 2020 Spark + AI Summit, we presented a session on the best practices and pitfalls of running Apache Spark on Kubernetes (K8s).

In this post we’d like to expand on that presentation and talk to you about:

1. What is Kubernetes?
2. Why run Spark on Kubernetes?
3. Getting started with Spark on Kubernetes
4. Optimizing performance and cost
5. Monitoring your Spark applications on Kubernetes
6. Spark on Kubernetes now Generally Available (Update: March 2021)

If you’re already familiar with k8s and why Spark on Kubernetes might be a fit for you, feel free to skip the first couple of sections and get…

Our mission at Data Mechanics is to give data engineers and data scientists the ability to build pipelines and models over large datasets with the simplicity of running a script on their laptop. Let them focus on their data, while we handle the mechanics of infrastructure management.

So, we built a serverless Spark platform, a more easy-to-use and more performant alternative to services like Amazon EMR, Google Dataproc, Azure HDInsight, Databricks, Qubole, Cloudera, and Hortonworks.

In this video, we will give you a product tour of our platform and some of its core features:

1. How to connect a Jupyter notebook…