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Executive Summary
As organizations process ever-growing data volumes, the demand for faster insights is relentless. Apache Spark has become the de facto standard for distributed analytics, but its row-based execution model often becomes a performance bottleneck at scale.
Acceldata’s ODP Spark with Gluten + Velox delivers a breakthrough by combining:
Key performance gains on TPC-DS 100 GB benchmarks:
✅ 1–3× faster query execution
✅ 20–30 % fewer CPU cycles per row
✅ 15–20 % lower memory allocation pressure
✅ Reduced shuffle and I/O overhead
Quantified ROI Example
(1000-core Spark cluster)
| Metric | Without Gluten | With Gluten | Annual Savings |
|---|---|---|---|
| Compute hours/day | 24,000 | 12,000–16,000 | $150K–$300K (AWS EC2 rates) |
| Failed jobs due to OOM | 5–10% | 2–3% | $50K–$100K (engineering time + re-runs) |
| Time-to-insight | 4 hours | 1.5–2 hours | Intangible (faster decisions) |
| Infrastructure scaling needs | Year 2 | Year 3–4 | $500K–$1M (deferred CapEx) |
And the best part? These improvements require no changes to your existing Spark applications.
The Problem: Spark’s Row-Based Execution
Traditional Spark processes data one row at a time inside the JVM. While flexible, this model introduces:
- High function call overhead
- JVM GC pressure from frequent object creation
- Poor CPU cache utilization
- Limited opportunities for SIMD/vectorization
This results in suboptimal performance for analytical workloads such as aggregations, joins, and window functions on large datasets.
The Solution: Vectorized Execution with Gluten + Velox
Vectorized execution processes data in columnar batches rather than rows, unlocking:
- SIMD (Single Instruction, Multiple Data) acceleration
- Improved CPU cache locality
- Reduced function call overhead
- Native (C++) execution performance
Gluten Framework
Gluten acts as a bridge between Spark and native engines, replacing parts of Spark’s physical plan while maintaining full API compatibility.
Highlights:
- Catalyst integration for rule-based plan transformation
- Extensible backend support (Velox, ClickHouse, Arrow)
- Apache Arrow-based zero-copy columnar data exchange
Velox Execution Engine
Velox is the native vectorized runtime originally developed by Meta for Presto. It provides:
- Columnar batch processing (1024–4096 rows per batch)
- SIMD-enabled expression evaluation and subexpression elimination
- NUMA-aware memory management
- Vectorized operators for joins, aggregates, and window functions
Benchmark Results: TPC-DS on 100 GB
| Query Type |
Avg Speedup |
Peak Improvement |
Key Benefit |
|---|---|---|---|
| Advanced Analytics | 1.46× | 3.72× (Q93) | Complex window functions |
| Aggregation-Heavy | 1.23× | 1.92× (Q5) | Vectorized aggregation |
| Join-Intensive | 1.21× | 1.87× (Q29) | Columnar hash joins |
| Window Functions | 1.27× | 2.19× (Q51) | SIMD string operations |
Resource Utilization Gains:
- CPU: 20–30 % fewer cycles per row; better instruction cache hit rate
- Memory: 15–20 % lower allocation pressure; reduced JVM GC
- I/O: better predicate pushdown, improved compression, lower shuffle volume
Implementation Guide
Cluster & Storage Requirements
- Native Velox libraries deployed on all executors
- glibc ≥ 2.28, libstdc++ ≥ 11.2
- Parquet/ORC columnar storage formats
- Modern CPUs with SIMD support
Key Spark Configuration
These settings are ideal for TPC-DS style OLAP workloads on multi-socket servers.
Real-World ROI Example
| Metric |
Without Gluten |
With Gluten Velox |
Annual Savings |
|---|---|---|---|
| Compute hours/day | 24 000 | 12 000–16 000 | $150 K–$300 K |
| OOM failure rate | 5–10 % | 2–3 % | $50 K–$100 K |
| Time-to-insight | 4 h | 1.5–2 h | Intangible |
| Infra scaling needs | Year 2 | Year 3–4 | $500 K–$1 M |
Tuning Velox for Peak Performance
- Vector batch size tuned to CPU L1 cache for better locality
- Memory pools adjusted for OLAP vs streaming workloads
- NUMA awareness enabled to reduce cross-socket memory access
- Built-in monitoring scripts for perf counters, memory usage, and Spark executor stats
Deployment & Compatibility
- No application code changes needed — unsupported queries fall back to JVM.
- Best performance on Parquet; ORC partially supported; JSON/CSV may fall back.
- Structured Streaming not yet supported.
Monitoring tips:
- Spark UI → SQL Plan Tab: look for operators like
VeloxHashAggregateExec,VeloxProjectExec - Stage & task metrics show distinctive CPU and memory patterns for native execution
Roadmap
Gluten + Velox is evolving rapidly:
- Broader SQL function coverage
- Streaming support under development
- GPU acceleration integration
- Advanced compression & query compilation optimizations
- Deep Iceberg, Delta Lake, and Hudi support
Conclusion
Acceldata ODP Spark with Gluten Velox marks a new era of vectorized, native execution for Spark. Enterprises can expect:
- 1–3× performance improvements on analytical queries
- Significant infrastructure savings
- Better CPU, memory, and I/O efficiency
- No application code rewrites
Gluten/Velox is production-ready today for OLAP workloads and is rapidly expanding its capabilities — positioning itself as the next-generation execution engine for Spark analytics.
Our whitepaper, Download the full whitepaper here, provides an in-depth explanation of how our Spark engine is powered by Gluten + Velox and the tangible benefits it delivers for our customers.
References
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