Saving Apache Spark Big Data Processing Costs on Google Cloud Dataproc

According to IDC, the volume of data generated each year is growing exponentially. IDC’s Global DataSphere projects that the world will generate 221 ZB of data…

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NVIDIA
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Karthikeyan Rajendran
•8 min read•intermediate•
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•View Original
ApacheApache SparkGoogle CloudSQL

Overview

The article discusses how organizations can reduce costs and improve performance in big data processing using Apache Spark on Google Cloud Dataproc with the RAPIDS Accelerator. It highlights the challenges of CPU-based infrastructure and presents solutions for leveraging GPU acceleration to enhance data processing efficiency.

What You'll Learn

1

How to use the RAPIDS Accelerator for Apache Spark to speed up data processing jobs

2

Why migrating Spark jobs to GPU can reduce costs and improve performance

3

When to utilize workload qualification tools for GPU migration

Key Questions Answered

How does the RAPIDS Accelerator for Apache Spark improve data processing on Google Cloud Dataproc?
The RAPIDS Accelerator for Apache Spark allows jobs to be scheduled on NVIDIA GPUs, resulting in processing speeds up to 5x faster and costs reduced by up to 80% compared to CPU-based infrastructure. This integration simplifies the migration process without requiring code changes.
What are the common challenges faced during CPU-to-GPU migration?
Common challenges include unpredictable costs, uncertainty about which jobs benefit from GPU acceleration, and difficulties in computing GPU resource requirements. The RAPIDS Accelerator provides tools to address these issues, offering insights and recommendations for successful migration.
What performance improvements can be expected when using NVIDIA GPUs with Dataproc?
Using NVIDIA GPUs with Dataproc can lead to a near 5x speedup in processing times and a 78% reduction in costs compared to using CPU-only clusters, as demonstrated in benchmark tests.

Key Statistics & Figures

Cost reduction
up to 80%
When running data processing jobs on Google Cloud Dataproc with GPU acceleration compared to CPU-based infrastructure.
Speedup
up to 5x faster
In data processing jobs using the RAPIDS Accelerator for Apache Spark on NVIDIA GPUs.
Cost comparison
$22.51 vs $5.65
Cost for an NDS Power run on CPU nodes compared to using NVIDIA T4 GPUs.
Runtime
184 mins vs 34 mins
Runtime for an NDS Power run on a CPU-only four-node cluster compared to the same cluster with 8xT4 NVIDIA GPUs.

Technologies & Tools

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Backend
Apache Spark
Used for big data processing and analytics.
Cloud Service
Google Cloud Dataproc
Provides a fully managed Apache Spark service in the cloud.
Software
Rapids Accelerator For Apache Spark
Enables GPU acceleration for Apache Spark jobs.
Hardware
Nvidia T4 GPU
Used to accelerate data processing tasks in the cloud.

Key Actionable Insights

1
Leverage the RAPIDS Accelerator for Apache Spark to optimize your data processing workflows without changing your existing codebase.
This approach allows data scientists to enhance performance and reduce costs significantly while maintaining the integrity of their applications.
2
Utilize the workload qualification tool to identify which Spark jobs are best suited for GPU migration.
This tool helps in making informed decisions about resource allocation, ensuring that only jobs that will benefit from GPU acceleration are migrated, thus optimizing costs.

Common Pitfalls

1
Assuming that migrating Spark jobs to GPU will always be more expensive.
This misconception can prevent organizations from exploring GPU acceleration, despite potential cost savings and performance improvements. Using the RAPIDS workload qualification tool can help clarify expected costs before migration.
2
Not knowing which Spark jobs are suitable for GPU migration.
Without proper analysis, resources may be wasted on jobs that do not benefit from GPU acceleration. The workload qualification tool assists in identifying the best candidates for migration.