Deep Dive: The RustyBase Query Engine

Executing a query like { "age": { "$gt": 25 } } might seem simple, but doing it across millions of documents in microseconds requires a sophisticated execution pipeline.

Query Parsing and AST

Every query sent to RustyBase's API is first parsed into an Abstract Syntax Tree (AST). We use the Serde framework in Rust to handle JSON deserialization with extreme efficiency.

The Optimizer

Before execution, the AST is passed through our optimizer. The optimizer checks:

Execution Pipeline

RustyBase uses a pull-based execution model (similar to the Volcano model used in traditional SQL engines but optimized for JSON). Each operator in the pipeline pulls data from its child, applies a filter or transformation, and passes it up.

// Abstract representation of an execution node
trait Operator {
    fn next(&mut self) -> Option<Document>;
}

Vectorized Execution

In our latest version, we are implementing Vectorized Execution, which allows us to process batches of documents at once using SIMD instructions on modern CPUs. This leads to a 5x-10x speedup for aggregation queries.

To learn more about modern database architecture, we highly recommend reading Architecture of a Database System.