The surprising truth about Vitess WASM query planning is that it allows you to run completely custom, sandboxed logic for query optimization within Vitess itself, without needing to modify Vitess’s core Go code.
Let’s see it in action. Imagine you have a common query pattern that Vitess’s default planner isn’t optimizing perfectly. Maybe it’s a specific JOIN that always benefits from a particular index hint, or a WHERE clause that needs to be rewritten for performance on your specific schema.
Here’s a simplified Vitess configuration snippet showing how you might enable WASM for query planning:
cell: zone1
tablet:
tablet_uid: 1
grpc_port: 15999
mysql_server:
host: localhost
port: 3306
tablet_uid: 1
vt_tablet:
enable_query_plan_optimization_wasm: true
query_plan_optimization_wasm_module_path: /etc/vitess/plugins/query_optimizer.wasm
query_plan_optimization_wasm_function_name: optimize_query
In this setup, enable_query_plan_optimization_wasm tells Vitess to look for a WASM module. The query_plan_optimization_wasm_module_path points to the compiled WebAssembly binary, and query_plan_optimization_wasm_function_name specifies the entry point within that module.
When a query arrives at a Vitess tablet configured this way, Vitess will first attempt to optimize it using its standard Go-based planner. If the WASM option is enabled, before sending the query to MySQL, Vitess will load the specified WASM module and execute the optimize_query function. This function receives the original SQL query as input and can return a modified SQL query. Vitess then uses this modified query for execution.
The WASM module is written in a language like Rust or AssemblyScript, compiled to WebAssembly, and then deployed to the server. This means you can write complex, custom optimization logic in a familiar language without needing deep knowledge of Vitess’s internal Go structures.
Consider a scenario where you have a table orders and often query it with a WHERE created_at > NOW() - INTERVAL '7' DAY clause. Your analysis might show that on your specific MySQL version and hardware, using an explicit DATE_SUB function with a fixed date calculation is marginally faster due to internal MySQL optimizations.
Your optimize_query WASM function might look something like this (conceptual Rust, compiled to WASM):
#[no_mangle]
pub extern "C" fn optimize_query(query_ptr: *mut u8, query_len: usize) -> *mut u8 {
// Safety: Assume query_ptr is valid and query_len is correct.
let query_slice = unsafe { std::slice::from_raw_parts(query_ptr, query_len) };
let query_str = std::str::from_utf8(query_slice).unwrap_or("");
let optimized_query = if query_str.contains("created_at > NOW() - INTERVAL '7' DAY") {
query_str.replace(
"created_at > NOW() - INTERVAL '7' DAY",
"created_at > DATE_SUB(NOW(), INTERVAL 7 DAY)",
)
} else {
query_str.to_string()
};
// In a real scenario, you'd need to allocate memory for the new string
// and return a pointer and its length. For simplicity, we're just
// showing the transformation logic.
// ... memory allocation and return pointer ...
std::ptr::null_mut() // Placeholder
}
Vitess, when interacting with the WASM runtime, provides a memory buffer where the original query is placed. Your WASM function reads from this buffer, performs its transformations, and writes the new query back into a newly allocated buffer (which Vitess then reads). This sandboxing is crucial: the WASM code runs in an isolated environment, preventing it from accessing arbitrary host system resources or interfering with other Vitess processes.
The ability to inject custom query transformation logic at the query planning stage offers a powerful way to fine-tune performance for specific workloads or databases. You can implement rule-based transformations, apply complex heuristics, or even integrate machine-learned models for query optimization without modifying Vitess itself. This decouples your custom optimizations from Vitess releases, allowing for independent development and deployment.
The most potent aspect of this system is that the WASM module is stateless from Vitess’s perspective on each invocation. It receives a query and returns a transformed query. Any state management or complex logic must be self-contained within the WASM module itself or managed through external means that the WASM can access (e.g., by making network calls if the WASM runtime permits it, though this is less common for pure query planning).
The next immediate challenge you’ll encounter is debugging these WASM modules, as the execution environment is isolated and standard debugging tools might not directly attach.