Spark Configuration
Spark Configuration: High-Density Compute Node for Distributed Data Processing
This document details the technical specifications, performance metrics, optimal use cases, comparative analysis, and maintenance requirements for the purpose-built server configuration designated as the "Spark Configuration." This configuration is engineered specifically to maximize throughput and minimize latency for Apache Spark workloads, leveraging high core counts, fast interconnects, and NVMe-based storage optimized for shuffle operations.
1. Hardware Specifications
The Spark Configuration is designed around a dual-socket motherboard architecture, prioritizing balanced memory bandwidth and high thread density suitable for iterative in-memory processing and large-scale data shuffling inherent to Spark jobs.
1.1 System Foundation
The chassis utilized is a 2U rackmount form factor, optimized for airflow density required by the high-TDP components.
| Component | Specification | Rationale |
|---|---|---|
| Form Factor | 2U Rackmount (Hot-Swap Capable) | High density; superior front-to-back airflow management compared to 4U. |
| Motherboard | Dual-Socket Intel C741 Platform (e.g., ASUS Z13PE-D16 or Supermicro X13DDW-NT) | Supports dual Intel Xeon Scalable Processors (Sapphire Rapids) with PCIe Gen 5.0 lanes. |
| BIOS/Firmware | Latest stable firmware with BMC support (e.g., IPMI 2.0 or Redfish) | Essential for remote management and PCIe lane bifurcation control. |
| Power Supply Units (PSUs) | 2x 2000W Platinum-rated (1+1 Redundant) | Ensures sufficient headroom for peak CPU/GPU/NVMe power draw under sustained load; Platinum efficiency minimizes operational loss. |
1.2 Central Processing Units (CPUs)
The selection criteria for the CPU focus on high core count per socket, large L3 cache, and robust memory channel support to feed the data-intensive Spark tasks.
| Parameter | Specification (Per Socket) | Total System Specification |
|---|---|---|
| Model Family | Intel Xeon Gold 6448Y (or equivalent AMD EPYC Genoa SKU) | Dual Socket |
| Core Count | 24 Cores / 48 Threads | 48 Cores / 96 Threads Total |
| Base Clock Frequency | 2.5 GHz | N/A |
| Max Turbo Frequency (All-Core) | ~3.8 GHz (Estimated sustained) | N/A |
| L3 Cache (Smart Cache) | 60 MB | 120 MB Total |
| TDP (Thermal Design Power) | 205W | 410W Total Base TDP |
| Instruction Sets | AVX-512, AMX (Advanced Matrix Extensions) | Critical for accelerating certain data shuffling and serialization primitives in Spark. |
The choice of the 'Y' suffix (high-frequency optimized) in the Intel lineup ensures better responsiveness for latency-sensitive shuffle operations compared to 'M' (medium frequency/core count balanced) SKUs. CPU Core Optimization is a key tuning point for this configuration.
1.3 Memory Subsystem
Apache Spark relies heavily on fast, ample RAM for caching DataFrames, persisting intermediate results, and executing in-memory joins. This configuration maximizes memory density while strictly adhering to the memory channel requirements of the chosen CPU architecture (8 channels per socket).
| Parameter | Specification | Details |
|---|---|---|
| Technology | DDR5 ECC RDIMM | Superior bandwidth and lower latency compared to DDR4. |
| Total Capacity | 1.5 TB (1536 GB) | Sufficient capacity for very large in-memory datasets. |
| DIMM Configuration | 12 x 128 GB DIMMs (Total 24 DIMMs across 2 Sockets) | Populates 6 channels per socket at full speed (8 channels available). |
| Speed Rating | DDR5-4800 MT/s (PC5-38400) | Optimal balance between speed and stability at high density. |
| Memory Topology | NUMA Optimized (12 DIMMs per Node) | Ensures that each CPU socket primarily accesses its local memory bank, minimizing NUMA Latency. |
The total memory bandwidth achieved is theoretically substantial, exceeding 768 GB/s per socket when operating at full channel utilization.
1.4 Storage Architecture
Storage in a Spark node serves two primary roles: the operating system/application binaries, and the critical temporary storage for shuffle files, spill-to-disk operations, and intermediate results. High-speed, low-latency NVMe is mandatory.
| Purpose | Type/Interface | Capacity & Quantity | Performance Target (Sequential R/W) |
|---|---|---|---|
| Boot/OS Drive | M.2 NVMe PCIe Gen 4.0 | 2 x 960 GB (RAID 1) | ~7 GB/s |
| Temporary/Spark Scratch Space (Primary) | U.2 or M.2 NVMe PCIe Gen 5.0 (Direct Attached) | 8 x 3.84 TB (Configured as a single large volume) | > 40 GB/s Aggregate |
| Data Ingest Buffer (Secondary) | U.2 NVMe PCIe Gen 4.0 | 4 x 7.68 TB (RAID 10) | ~20 GB/s Aggregate |
| Total Usable Storage | N/A | Approximately 30 TB NVMe | N/A |
The use of PCIe Gen 5.0 drives is critical for the primary scratch space, as shuffle operations often involve massive, rapid read/write bursts that can saturate even high-end Gen 4 arrays. NVMe Storage Optimization is crucial for maximizing shuffle throughput.
1.5 Networking Interconnect
Distributed processing demands high-speed, low-latency network connectivity for data exchange between nodes (e.g., MapReduce shuffle traffic, cluster management).
| Port Usage | Interface Type | Quantity | Rationale |
|---|---|---|---|
| Cluster Data Fabric (Primary) | 200 GbE InfiniBand (HDR/NDR) or 400 GbE RoCEv2 | 2 x Dual-Port Cards (4 logical ports) | Lowest latency path for inter-node communication, essential for minimizing shuffle wait times. |
| Management/Storage Access (Secondary) | 25 GbE BASE-T (RJ45) | 2 x Single-Port Cards | Standardized connectivity for management access and non-critical storage access (e.g., HDFS NameNode metadata). |
The primary network interface must support Remote Direct Memory Access (RDMA) capabilities (InfiniBand or RoCE) to bypass the host CPU stack during data transfers, significantly reducing CPU overhead during shuffling. RDMA in Data Centers is a prerequisite for optimal performance.
1.6 Expansion and Acceleration
While primarily CPU-bound, this configuration includes headroom for specialized acceleration, although these slots are often left empty or populated with high-speed networking cards in pure Spark deployments.
- **PCIe Slots:** 8 x PCIe 5.0 x16 slots available.
- **GPU Support:** Capable of hosting 2x double-width, passively cooled GPUs (e.g., NVIDIA H100 SXM/PCIe), though typically reserved for ML workloads running on Spark/Ray frameworks.
2. Performance Characteristics
The Spark Configuration is benchmarked against standard Big Data processing tasks, focusing on metrics relevant to execution time, data spill rates, and I/O saturation.
2.1 Benchmarking Methodology
Performance validation utilizes industry-standard benchmarks adapted for Spark:
1. **TPC-DS (Scale Factor 1000):** Measures complex analytical query performance, heavily utilizing caching and complex joins. 2. **WordCount Shuffle Test:** Measures raw I/O and network throughput during a massive shuffle phase. 3. **Large-Scale ETL Simulation:** Measures end-to-end execution time for transforming 10TB of semi-structured data.
2.2 Key Performance Indicators (KPIs)
| Benchmark Test | Metric | Result (Spark Config) | Baseline (Older Gen Config) | Improvement Factor |
|---|---|---|---|---|
| TPC-DS (Scale Factor 1000) | Query Throughput (QPH) | 4,200 QPH | 2,850 QPH | 1.47x |
| WordCount Shuffle Test (10TB Data) | Total Shuffle Time (End-to-End) | 11.5 minutes | 18.2 minutes | 1.58x |
| ETL Simulation (10TB Data) | Total Execution Time | 45 minutes | 71 minutes | 1.58x |
| Memory-Bound Join Latency | Average Join Latency (ms) | 1.2 ms | 1.9 ms | 1.58x |
2.3 Analysis of Performance Gains
The performance gains observed (approximately 1.5x to 1.6x over previous generation Ivy Bridge/Cascade Lake based Spark nodes) are directly attributable to three primary hardware improvements:
1. **CPU Memory Bandwidth:** The move to DDR5 and 8-channel architecture drastically reduces the time required to load data into registers, mitigating memory stalls during iterative processing stages. Memory Bandwidth Impact on Spark. 2. **NVMe Gen 5.0 I/O:** The increased IOPS and sustained write throughput of the scratch drives virtually eliminate disk I/O as the bottleneck during data spilling, which is common in highly skewed shuffle operations. 3. **High-Speed Interconnect:** RDMA capabilities ensure that the network latency for data movement between the 48 cores in this node and other nodes in the cluster remains sub-microsecond across the fabric, preventing idle CPU cycles waiting for remote data.
The high core count (96 threads) allows for massive parallelism in map tasks, but the memory subsystem dictates the performance ceiling for intermediate aggregation stages.
3. Recommended Use Cases
This configuration is specifically tuned for distributed data processing environments where the workload exhibits high degrees of in-memory computation, significant intermediate data exchange, and low tolerance for execution latency.
3.1 Primary Workloads
- **Interactive SQL Analytics (Spark SQL/Trino on Spark):** The low latency of the NVMe scratch space and the high core count make this excellent for serving complex, multi-stage queries where results must be returned quickly to BI tools or end-users.
- **Machine Learning Feature Engineering:** Pipelines that involve heavy data cleaning, transformation, and feature vectorization (often implemented via Pandas UDFs or Spark MLlib) benefit immensely from the high memory capacity and fast shuffle performance.
- **Streaming Aggregation (Structured Streaming):** Deploying executors on this hardware allows for maintaining very large state stores in memory, minimizing disk checkpointing during continuous aggregation tasks. Structured Streaming Performance Tuning.
3.2 Workloads Where This Configuration Excels
1. **Data Skew Mitigation:** When data skew leads to frequent spilling, the Gen 5.0 NVMe drives handle the I/O bursts far more effectively than SATA SSDs or older PCIe generations. 2. **In-Memory Caching:** With 1.5TB RAM, large datasets can be persisted across multiple executors on the same node, drastically reducing repeated I/O reads from slower storage layers (e.g., HDFS or S3). 3. **High Concurrency Jobs:** The 96 threads allow the scheduler to rapidly dispatch many small tasks simultaneously without blocking the entire executor set.
3.3 Suboptimal Use Cases
While powerful, this configuration may be over-provisioned or misaligned for:
- **Purely I/O Bound Read Workloads (e.g., Simple HDFS Reads):** If the workload involves minimal transformation and only sequential reads from a remote object store, a configuration prioritizing raw disk/network bandwidth over CPU core density might be more cost-effective.
- **GPU-Intensive Deep Learning Training:** While it supports GPUs, a dedicated GPU server configuration (e.g., 8x H100) would vastly outperform this CPU-centric node for matrix multiplication heavy tasks. GPU vs. CPU for Data Processing.
4. Comparison with Similar Configurations
To justify the investment in high-speed interconnects and Gen 5.0 storage, a comparative analysis against two common alternatives is necessary: the "Storage-Optimized Configuration" and the "General Purpose Configuration."
4.1 Configuration Matrix Comparison
| Feature | Spark Configuration (This Document) | Storage-Optimized Config (HDFS/S3 Worker) | General Purpose Config (Mid-Range) |
|---|---|---|---|
| CPU Cores (Total) | 48 Cores / 96 Threads | 32 Cores / 64 Threads | 40 Cores / 80 Threads |
| System RAM | 1.5 TB DDR5 | 512 GB DDR4 | 768 GB DDR4 |
| Primary Scratch Storage | 8 x 3.84TB NVMe Gen 5.0 | 24 x 24TB SAS HDD (RAID 6) | 4 x 3.84TB NVMe Gen 4.0 |
| Network Fabric | 200/400 GbE RDMA | 100 GbE TCP/IP | 100 GbE TCP/IP |
| Cost Index (Relative) | 1.7x | 1.0x | 1.2x |
- 4.2 Performance Trade-Off Analysis
- Storage-Optimized Configuration:** This configuration excels at workloads where data is read once and written once, such as serving as a dedicated DataNode for a large HDFS cluster or processing data directly from cold storage. Its weakness is the high latency associated with HDD seeks during random I/O patterns common in Spark shuffles, leading to significantly longer execution times (often 2.5x slower in TPC-DS).
- General Purpose Configuration:** This configuration represents a common default choice. It offers good memory capacity and decent NVMe speed. However, the Spark Configuration pulls ahead significantly because:
1. The General Purpose Config runs DDR4, limiting memory bandwidth by approximately 30-40%. 2. Its Gen 4.0 NVMe storage caps out aggregate throughput around 25-30 GB/s, whereas the Spark Config pushes past 40 GB/s, which is crucial when the entire dataset being shuffled exceeds the available RAM. Storage Bottleneck Identification.
The Spark Configuration justifies its higher cost index (1.7x) by providing the lowest time-to-completion for compute-intensive, intermediate-data heavy analytical workloads, resulting in a superior *cost per query executed*.
5. Maintenance Considerations
The high-density, high-power nature of the Spark Configuration necessitates stringent maintenance protocols focusing on thermal management, power delivery stability, and firmware integrity.
5.1 Thermal Management and Cooling
The combined TDP of dual 205W CPUs, high-speed memory modules, and multiple high-power NVMe drives generates significant heat flux within the 2U chassis.
- **Airflow Requirements:** Rack density must be managed. Minimum required static pressure airflow across the server face: 150 CFM (Cubic Feet per Minute) at 1.8 inches of H2O static pressure. Cooling infrastructure must be capable of maintaining ambient rack inlet temperatures below 22°C (71.6°F). Data Center Cooling Standards.
- **Fan Speed Control:** The Baseboard Management Controller (BMC) must be configured to use aggressive fan curves based on CPU package temperatures (Tdie) rather than just ambient inlet temperature. Sudden spikes in Spark shuffle activity can cause rapid, localized heating that requires immediate fan response.
- **Thermal Throttling Thresholds:** Monitoring tools must alert if package temperatures exceed 90°C, as sustained operation above 95°C will trigger downclocking, severely impacting Spark job completion times.
5.2 Power Delivery and Redundancy
With 2000W Platinum PSUs, the system can draw significant power, especially during peak utilization when all cores are boosted and NVMe drives are fully active.
- **PDU Capacity:** Each rack unit hosting these servers must be provisioned with adequate Power Distribution Unit (PDU) capacity, ensuring that the combined peak draw does not exceed 80% of the PDU's rated capacity to allow for overhead.
- **Voltage Stability:** Due to the sensitivity of high-frequency DDR5 memory and Gen 5.0 PCIe signaling, input voltage stability is paramount. Uninterruptible Power Supply (UPS) systems must provide clean, conditioned power. Power Quality in HPC.
- **Power Consumption Baseline:** Idle power draw is estimated at ~450W. Peak power draw under full load (CPU + NVMe saturation) can reach 1600W.
5.3 Software and Firmware Lifecycle Management
Maintaining the performance of this specialized configuration requires meticulous management of drivers and firmware, particularly for the high-speed fabric.
- **NIC Driver Verification:** InfiniBand/RoCE drivers must be kept synchronized with the operating system kernel version to ensure RDMA functionality remains stable and latency is minimized. Outdated drivers frequently introduce unexpected TCP fallbacks. RDMA Driver Best Practices.
- **Storage Controller Firmware:** NVMe firmware updates are crucial, often containing performance enhancements or fixes related to power management states (e.g., preventing drives from entering deep sleep during short lulls in shuffle activity).
- **OS Tuning:** The Linux kernel must be tuned for high-throughput I/O. Key settings include:
* Increasing the maximum number of open file descriptors. * Setting appropriate I/O scheduler (e.g., `mq-deadline` or `none` for NVMe). * Tuning kernel parameters related to network buffer sizes for the RDMA fabric. Linux Kernel Tuning for Spark.
5.4 Hardware Diagnostics and Monitoring
Proactive monitoring is essential to prevent performance degradation that is hard to trace back to the hardware layer.
- **Memory Scrubbing:** Regular, scheduled ECC memory scrubbing should be implemented via BIOS/BMC settings to detect and correct soft errors before they cause application instability or data corruption, especially given the high memory density. ECC Memory Reliability.
- **NVMe Health Monitoring:** SMART data and vendor-specific health logs for the NVMe drives must be polled frequently (e.g., every 15 minutes) to monitor Write Amplification Factor (WAF) and remaining endurance (TBW). Excessive WAF on the scratch volume indicates immediate resource contention or a potential configuration flaw (e.g., incorrect block size mapping). NVMe Endurance Monitoring.
- **CPU Telemetry:** Monitoring Package Power Limits (PL1/PL2) and Core Voltage Frequency (VF curve) telemetry helps diagnose if the system is hitting thermal or power limits prematurely, suggesting cooling inadequacy or substandard silicon binning.
This rigorous maintenance regimen ensures that the Spark Configuration maintains its peak performance profile over its service life, maximizing ROI for intensive data processing tasks. Server Lifecycle Management.
--- End of Technical Documentation.
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️