feat: New blog announcement for Kubeflow Spark Operator Benchmarks #164
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: The full list of commands accepted by this bot can be found here.
Needs approval from an approver in each of these files:
Approvers can indicate their approval by writing |
|
@andreyvelich FYI |
vara-bonthu
force-pushed
the
kubeflow-spark-operator-benchmarks
branch
2 times, most recently
from
6cdf9d1 to
865cdcb
Compare
Signed-off-by: Vara Bonthu <[email protected]>
vara-bonthu
force-pushed
the
kubeflow-spark-operator-benchmarks
branch
from
865cdcb to
38785aa
Compare
There was a problem hiding this comment.
Looks great @vara-bonthu, thank you for working on this!
@varodrig @franciscojavierarceo @hbelmiro @akgraner @kubeflow/kubeflow-steering-committee @kubeflow/wg-data-leads @jbottum Appreciate your review on this!
Signed-off-by: Vara Bonthu <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR adds a new blog post titled "🚀 Announcing the Kubeflow Spark Operator Benchmarking Results and Toolkit", focusing on performance benchmarking for the Spark Operator on Kubernetes. The blog highlights key scaling challenges, such as CPU saturation, API server slowdowns, and job scheduling inefficiencies, and provides best practices to optimize large-scale Spark workloads. It also introduces a Benchmarking Toolkit and a Grafana Dashboard to help users monitor and improve performance.
The motivation behind this blog is to share benchmarking insights and practical tuning strategies to help users efficiently run thousands of Spark jobs on Kubernetes. By implementing these optimizations, users can improve job throughput, resource utilization, and system stability. This post serves as a valuable resource for the community to enhance Spark Operator deployments.