System Descriptions

In order to put the rearchitected spark Turbo engine at a disadvantage, and push it to itsextremes, we used some of the most efficient data platforms that uses native Spark engines.

Yeedu Turbo Engine

Yeedu's Turbo Engine is a rearchitected Spark executionengine engineered to improve Spark performance, maximizespeed, and cost efficiency. It is designed to be used withoutchanging your code or migrating data and to eliminatecommon Spark performance issues.

Key Capabilities

Zero Code Refactoring: No Spark pipeline changesrequired
Query Plan Rewriting: Reorders execution DAGs forminimal latency
SIMD-Based Vector Execution: Parallel data path processing for higher CPU throughput
Columnar Data Access: Bypasses row-level overhead byoperating on typed column vectors
Cache-Aware Execution: Optimized use of CPU L1/L2/L3 caches to reduce I/O wait cycles
Smart Scheduling: Dynamic allocation of CPU threadsacross concurrent workloads
Multitenant Execution Engine: Runs high-volume paralleljobs with stable latency under load

Traditional Spark Engines

Most data platforms still rely on traditional general-purpose Spark engines the default open-source Sparkruntime which is powerful but not designed tooptimize for modern cloud hardware or extremeconcurrency.

Typical Characteristics

Row-Oriented Processing: Operates record-by-record, increasing memory pressure and I/O
Minimal SIMD Utilization: Misses modern CPU-levelparallelism
Garbage Collection Overhead: JVM-managedmemory creates unpredictable latency under load
Manual Performance Tuning: Requires developerintervention to optimize stage performance
Poor Idle-Time Recovery: CPUs remainunderutilized during I/O-bound stages
Retry Amplification at Scale: Higher failure ratesunder high concurrency and data skew
Cost Drift: Prolonged job execution translates tounpredictable billing across compute layers

Key Observations Under Load

The stress test revealed clear, repeatable differences between Yeedu’s Turbo Engine and traditional Spark runtimes across the benchmarks.

Execution Time

Configure connection Turbo Engine delivered 4x to 10x faster runtimes depending on query type.details for your chosen metastore type

Concurrency Stability

Maintained low latency under 50+ concurrent jobs traditional engines experienced retries and degraded throughput.

Cost Efficiency

Yeedu’s resource utilization translated to 60–80% lower compute costs, even without tuning.

Operational Simplicity

With Yeedu, setup was frictionless. No configuration branching, no dependency management, no data movement.

Rearchitected Vs Traditional Spark Stress Test

The commonly used dataset, the New York City Yellow Cabs, was used for the stress test to standardize the dataset and results.

Query Type	Execution Time		⏱ Time Saved (%)	Cost		$ Cost Saved (%)
	Yeedu	Databricks		Yeedu	Databricks
Simple	30s ✔	10m 32s ✖	95.25%	$0.0024 ✔	$0.0506 ✖	95.25%
Medium	5m 51s ✔	14m 32s ✖	59.75%	$0.0281 ✔	$0.0698 ✖	59.75%
Selective	1m 58s ✔	5m 54s ✖	66.67%	$0.0094 ✔	$0.0283 ✖	66.67%
Complex	32m 18s ✔	46m 22s ✖	30.33%	$0.1550 ✔	$0.2226 ✖	30.33%
🔥 Total Savings	Up to 9 mins faster		Average 63%	Up to $0.068 cheaper		Average 63%

Traditional Spark Engine vs
‍Turbo Rearchitected Spark Engine

System Descriptions

Key Observations Under Load

Execution Time

Concurrency Stability

Cost Efficiency

Operational Simplicity

Rearchitected Vs Traditional Spark Stress Test

Ready to benchmark Turbo Engine on your real workloads?

Company

Traditional Spark Engine vs ‍Turbo Rearchitected Spark Engine

System Descriptions​

Key Observations Under Load​

Execution Time

Concurrency Stability

Cost Efficiency

Operational Simplicity

Rearchitected Vs Traditional Spark Stress Test

Ready to benchmark Turbo Engine on your real workloads?

Company

Traditional Spark Engine vs
‍Turbo Rearchitected Spark Engine

System Descriptions

Key Observations Under Load