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Rust in Data Engineering
Why Rust when we already have Spark, Hadoop, BigQuery?
Data engineering fits into multiple layers:
Layer 0: Storage/Format
- Parquet/Avro/Delta/Iceberg
- S3/ADLS/GCS/HDFS
Layer 1: Programming Language
- Rust
- Java / Scala
- C++
Used to build engines and systems
Layer 2: Planner Layer
- Spark Catalyst Optimizer
- DataFusion logical/physical planner
Responsible for:
- Query optimization
- Execution planning
Layer 3: Execution Engine
- Spark execution engine (Scala)
- Photon (C++)
- DataFusion (Rust)
- DuckDB (C++)
Responsible for:
- Joins
- Aggregations
- Query execution
- Memory handling
Layer 4: Distributed Platform
- Apache Spark (Planner + Engine + Distributed system)
- Ray (distributed compute framework)
- Databricks / BigQuery / Snowflake (managed platforms)
Responsible for:
- Cluster management
- Scaling
- Fault tolerance
- Distributed execution of workloads
In short
- Language builds the system
- Planner decides how to execute
- Engine executes the plan
- Platform distributes and scales
Modern systems are separating planner and execution.
Problems With Traditional Engines
Spark originally handled both planning and execution, but modern systems are separating these concerns to optimize performance.
Most popular systems (like Spark) are JVM-based.
Limitations
- Garbage collection overhead
- Serialization costs
- Poor CPU cache utilization
- Not SIMD-friendly
What Modern Engines Aim For
- Columnar data processing
- Vectorized execution
- CPU-level optimization
This is where performance gains come from.
Where Rust Fits in?
Rust does NOT replace Spark. Rust is being used to build similar high-performance engines.
Rust is used to:
- Build faster execution engines
- Optimize data processing at CPU level
Examples
- Apache Arrow : columnar memory format
- DataFusion : query engine
- Ballista : distributed query execution
- Polars : fast dataframe library
Rust works better because
- No garbage collector : predictable latency
- Fine-grained memory control : better cache efficiency
- Safe concurrency : fewer runtime failures
Rust gives near C++ performance without unsafe chaos.
| Layer | Role | Examples |
|---|---|---|
| Platform | Distributed system | Apache Spark, Databricks, BigQuery |
| Planner | Query optimization | Catalyst, DataFusion planner |
| Engine | Executes queries | Spark engine, Photon, DataFusion |
| Language | Builds engines | Scala, C++, Rust |
The stack is evolving toward:
- Planner layer (Spark-like)
- Execution layer (C++ / Rust)
- Columnar memory (Arrow)
- Vectorized compute (SIMD)
| Legacy | Nextgen |
|---|---|
| JVM-based | Native (Rust/C++) |
| Row-based | Columnar |
| GC-heavy | Memory controlled |
| Slower CPU usage | SIMD optimized |
Rust Adds Value
- Building high-performance data tools
- Optimizing bottlenecks
- Working on next-gen data platforms
- Understanding how engines actually work