Spark on Google Cloud Brief
Jan 2023, Big Data Spark on GCP VMs
Google Cloud's T2A virtual machines, powered by Ampere® Altra® processors provide outstanding single-threaded performance at an affordable cost. These VMs come in various pre-defined sizes, with a maximum of 48 vCPUs and 48GB of memory per vCPU per VM. T2A VMs are compatible with a wide range of Linux operating systems, including RHEL, Ubuntu, SUSE, and CentOS, etc. Most importantly, they offer several key benefits such as deterministic performance, linear scalability, and the best price-performance in the market. They are engineered to efficiently run scale-out and cloud-native workloads.
Apache Spark is an open source, distributed processing system used for big data workloads. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. It provides APIs in Java, Scala, Python and supports multiple real-time analytic workloads, batch processing, interactive queries, and machine learning. Spark addresses the limitations of Hadoop by performing in-memory processing using RDD (Resilient Distributed Dataset) and reusing data across multiple parallel operations. It relies on other storage systems like HDFS, Couchbase, Cassandra and others.
Spark can run in standalone cluster mode or can run on a Cluster Management system like Yarn, Kubernetes and docker.
Spark’s architecture comprises of a Driver, Cluster Manager, and Executor. The driver is the controller of the Spark execution engine and maintains the state of the cluster. It interfaces with the cluster manager to allocate physical resources like vCPU and memory, and it launches the executors. The executors run the tasks and report back their results and state to the driver. The cluster manager is responsible for maintaining the cluster of nodes that run the Spark application.
Cloud Native: Designed from the ground up for ‘born in the cloud’ workloads, Ampere Altra can deliver much higher price-performance over its x86 peers.
Consistency and Predictability: Ampere Altra processors that are designed for cloud native usage, provide consistent and predictable performance of Hadoop solutions and in particular for bursting workloads.
Scalable: With an innovative scale-out architecture, Ampere Altra processors have a high core count with compelling single-threaded performance combined with consistent frequency for all cores that make Big data workloads scale up and scale out efficiently.
Power Efficient: Industry-leading energy efficiency allows Ampere Altra processors to hit competitive levels of raw performance while consuming much lower power than the competition.
What it Enables
Technology & Functionality
Ampere Altra-powered T2A instances in Google cloud are generally available in several Google Cloud regions: US, Europe and Asia Southeast. T2A virtual machines offer a high level of networking performance with bandwidth speeds up to 32 Gbps. Additionally, storage options such as Zonal, Regional, and SSD disks are available for use with these virtual machines.
Ampere Arm technology uses a high number of cores per socket, maximizing core count per rack. This power-efficient design results in lower power consumption and consistent performance for big data applications. T2A VMs based on Ampere Arm processors provide better value for big data applications when compared to x86 processors. They are ideal for big data applications like Spark due to their predictable and scalable architecture.
In this Solution Brief, we contrast 3 Google VMs, each featuring comparable CPUs from Intel, AMD and Ampere.
We used Intel HiBench benchmarking tool, and ran Spark TeraSort benchmark on the following three Google Cloud VMs:
TeraGen was used to generate a dataset of 250GB, and then the data was sorted using TeraSort capturing throughput in MB/s.
|Kernel||Ubuntu 22.04||Ubuntu 22.04||Ubuntu 22.04|
|Storage||2 x 1024 GB, totaling 1000 MB/s throughput||2 x 1024 GB, totaling 1000 MB/s throughput||2 x 1024 GB, totaling 1000 MB/s throughput|
|JDK||Oracle JDK 8u345||Oracle JDK 8u345||Oracle JDK 8u345|
The relative performance data captured on the Google Cloud with Spark on Yarn is shown below.
(VM pricing calculated with Google Cloud’s public pricing calculator)
Google Cloud virtual machines with Ampere Altra processors excel for big data applications such as Spark. While raw performance is slightly behind Intel, Ampere’s VMs deliver excellent price performance compared to both x86 alternatives on Google cloud. The superior performance of Ampere instances combined with their lower cost makes them a highly valuable option for Spark workloads.
We look forward to helping our customers discuss their unique needs.
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System configurations, components, software versions, and testing environments that differ from those used in Ampere’s tests may result in different measurements than those obtained by Ampere.
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