Architecting Apache Spark on the VAST Data Platform

Architecting Apache Spark on the VAST Data Platform#

Audience: Data Architects, DevOps Engineers, and Data Platform Owners

Subject: This document provides our official guidance and best practices for deploying Apache Spark on the VAST Data Platform to achieve maximum performance, scalability, and operational simplicity.

Introduction: A Modern Foundation for Your Analytics Engine#

As you scale your Apache Spark workloads, you will inevitably face the architectural limitations of legacy storage systems. Data silos, complex data movement, and the rigid coupling of compute and storage create performance bottlenecks and operational friction that inhibit the speed of your analytics and AI initiatives.

The VAST Data Platform was engineered to solve these challenges. By providing a revolutionary all-flash, disaggregated architecture, VAST delivers a high-performance, infinitely scalable, and cost-effective foundation for your most demanding Spark jobs. This guide outlines our recommended best practices to ensure your deployment is optimized from day one.

Core Principle: Our Disaggregated, Shared-Everything (DASE) Architecture#

The cornerstone of deploying Spark on VAST is our unique Disaggregated and Shared-Everything (DASE) architecture. We fundamentally separate the compute layer (your Spark cluster) from the storage layer (the VAST platform), but present the storage as a single, unified namespace accessible to all compute nodes.

What This Means for You: You can scale your Spark cluster and your VAST system independently, without downtime. If your jobs require more processing power, add more Spark workers. If your data volume grows, expand the VAST cluster. This eliminates the need for costly overprovisioning and complex capacity planning. Every Spark executor has full, concurrent access to the entire dataset, eliminating data hotspots and the need for data replication.

Networking Guidance: Building a Low-Latency Data Fabric#

Your Spark cluster communicates with the VAST platform over a standard, high-performance Ethernet network. To ensure optimal throughput and low latency, we recommend the following configuration:

  • Network Fabric: A 100GbE (or faster) network is recommended between your Spark worker nodes and the VAST C-Nodes (our front-end protocol servers).

  • Jumbo Frames: Set the Maximum Transmission Unit (MTU) size on your switches, Spark hosts, and VAST C-Node interfaces to 9000. This reduces network overhead and significantly boosts data transfer efficiency.

  • High Availability & Load Balancing: Connect your Spark cluster to a Virtual IP (VIP) pool configured on the VAST cluster. This ensures that client connections are automatically load-balanced across all available C-Nodes and provides seamless failover, delivering a resilient and highly available data path.

Rethinking Data Locality: Logical vs. Physical#

Traditional Hadoop architectures required co-locating compute and storage on the same physical nodes to overcome slow network performance. The VAST platform makes this outdated concept obsolete.

We deliver Logical Data Locality. Our all-NVMe architecture, combined with a high-speed network fabric, delivers data to any Spark executor with such low latency that the entire storage namespace effectively feels like a local resource.

Your Advantage: Every Spark node has high-performance access to the complete dataset. This eliminates the need for complex data placement strategies and the performance penalties associated with moving data before a job can run. It provides ultimate flexibility for your job schedulers and simplifies your entire data pipeline.

Integration Best Practice: The VAST Connector for Spark#

To enable seamless integration and unlock maximum performance, we provide the VAST Connector for Spark. This lightweight connector is essential for any deployment.

Its most critical function is enabling Predicate Pushdown. The connector intelligently offloads query filtering operations (WHERE clauses in SQL) from the Spark cluster directly to the VAST DataBase.

The Result: We filter the data at the source, drastically reducing the volume of data that must be sent over the network to the Spark workers. This results in dramatically faster query times, reduced CPU load on your Spark cluster, and a more efficient network.

Configuration Steps#

  1. Install the Connector: Copy the VAST Connector for Spark JAR file into the $SPARK_HOME/jars directory on all master and worker nodes.

  2. Configure spark-defaults.conf: Add the following properties to the Spark defaults configuration file on all nodes to connect to the VAST cluster:

# VAST Cluster Virtual IP (VIP) for metadata/control
spark.ndb.endpoint		<VAST_CLUSTER_VIP>

# Comma-separated list of all Data VIPs in your VAST VIP Pool
spark.ndb.data_endpoints	<VIP1,VIP2,VIP3,...>

# VAST DataBase S3-style Access Credentials
spark.ndb.access_key_id		<YOUR_ACCESS_KEY>
spark.ndb.secret_access_key	<YOUR_SECRET_KEY>

Summary: The VAST Advantage for Spark#

By architecting your Spark environment on the VAST Data Platform according to these best practices, you will build a system defined by:

  • Performance: Accelerate queries and reduce job run times through predicate pushdown and low-latency, all-flash data access.

  • Scalability: Scale compute and storage tiers independently and non-disruptively.

  • Simplicity: Eliminate data silos and complex data management with a single, unified data platform.

  • Efficiency: Reduce both your data center footprint and your total cost of ownership.

For further detailed planning and environment-specific tuning, please engage with your VAST Data presales team.

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