Salt Security Case study

Salt Security Cuts Spark Workload Costs by 40% with Intel Granulate

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Salt provides a groundbreaking security solution that prevents API attacks, and uses Machine Learning and AI to automatically and continuously identify and protect its customers’ APIs. Found at the core of countless SaaS, web, mobile, microservice, and IoT applications, APIs are a new and critical avenue for breaches, and Salt rallies an impressive cloud infrastructure and vast volumes of data to ensure its customers’ APIs are airtight.

Salt’s API Protection Platform is a patented solution that is especially effective for behavioral protection, which hinges on advanced proprietary Machine Learning algorithms running on enormous datasets. Spark worker clusters hosted on AWS elastic Kubernetes infrastructure handle the task of continuously analyzing copies of all API traffic from customer environments, staying ahead of the next generation of attacks before they occur.

We were pleasantly surprised when Granulate exceeded its own estimate for performance improvement. With better latency conditions and more efficient CPU utilization, we were able to get more out of our machines and cut Spark job completion time drastically.
Gal Porat, Director of DevOps, IT & Security

The Challenge

Salt’s primary Spark workload – responsible for driving the Machine Learning behind its API Context Engine (ACE) – was in need of additional performance and stability, as the company won new customers and existing customers expanded their involvement with Salt by connecting more APIs.

Run on many c5.xlarge EKS instances totaling hundreds of cores, Salt’s Spark jobs were taking more hours and more compute resources each day to process jobs, leading the company to seek out optimization initiatives so it could reduce its AWS costs. But with an engineering team whose time was best spent on product-led growth, and a vital business workload that could not suffer extra downtime, Salt had little incentive to pursue in-house performance optimization.

The Results

Salt selected Intel Granulate because of its non-intrusive, hands-free and high performance optimization. It deployed Intel’s Continuous Profiler and, after receiving an estimate for a 25-30% job completion time improvement, began the Intel Granulate solution’s learning phase. Within a week the optimization solution had identified the resource utilization and management patterns of Salt’s Spark workload and was ready for activation.

After activating Intel Granulate on its EKS environment, the technology began automatically and continuously optimizing resource management at the runtime level. Salt immediately saw compute time drop 15% on the Spark worker jobs, and an average core count reduction from 2.76 to 1.63. The company translated this 40% performance gain into a significant advantage; as it didn’t need to increase instance count as it scaled processing larger and larger volumes of traffic.

Reduced Costs
Reduced Spark Time
Reduced CPU Utilization

With a groundbreaking security solution that prevents and protects customers' API attacks, Salt Security turned to Intel Granulate to optimize its Spark workload – responsible for driving the machine learning behind its API Context Engine (ACE) and reduce its AWS costs with its non-intrusive, hands-free continuous optimization solution.