Advanced Database Engine Techniques in Rust:
Modern high-performance database systems like Umbra () () use a combination of low-level techniques to maximize performance. We’ll explore three such concepts – pointer swizzling, optimized versioned latching, and adaptive compilation – with clear explanations and Rust code examples. Each section includes a standalone demo and then shows how these techniques integrate into a mini database component (like a B+-tree and query executor). We’ll discuss design considerations (performance, concurrency, correctness) and use Rust’s low-level features (unsafe, atomics, custom memory layouts) where appropriate, while highlighting safety considerations.
Pointer swizzling is a technique to eliminate indirection when accessing in-memory pages by embedding metadata in pointer values. Instead of always storing logical page IDs that must be looked up in a global table, a swizzled pointer can directly hold the memory address of a cached page (). A tag bit in the pointer differentiates a “swizzled” (in-memory) pointer from an “unswizzled” (on-disk) reference (). On a 64-bit system, page pointers are typically aligned to 8 bytes, so their lowest three bits are zero. Umbra leverages this by using the lowest bit as a tag: 0 meaning the value is an actual memory address, and 1 meaning it’s an encoded disk page reference (). The remaining bits can encode the page identifier and even a size class for variable-sized pages (). This design makes checking a pointer’s state a simple conditional branch, which is usually predicted well by the CPU (overhead is negligible as long as most pages are memory-resident) (How to have your cake and eat it too with modern buffer management Pt. 1: Pointer Swizzling | TUMuchData
) (How to have your cake and eat it too with modern buffer management Pt. 1: Pointer Swizzling | TUMuchData
).
How Pointer Swizzling Works:
- Swizzled pointer (in-memory) – Contains a direct virtual memory address of a page frame. The tag bit is
0(since the address is aligned). Access is a direct pointer dereference after a quick tag check. - Unswizzled pointer (on-disk) – Encodes the page’s disk identifier and other info, with the tag bit set to
1. Before use, the page must be loaded from disk into a buffer frame, and the pointer “swizzled” (replaced with the real address) (How to have your cake and eat it too with modern buffer management Pt. 1: Pointer Swizzling | TUMuchData ). This may trigger eviction of another page if the buffer pool is full. - Branch-free tag check (optional) – Some implementations use masking tricks to avoid branches (e.g., adding an offset so an unswizzled pointer triggers a fault that is caught and handled). But the typical approach is just an
ifon the tag bit, which is very fast when the in-memory case is common (How to have your cake and eat it too with modern buffer management Pt. 1: Pointer Swizzling | TUMuchData ).
Tagged Pointer Implementation in Rust: Below is a simple Rust struct Swip (swizzled pointer) that uses the least significant bit of a 64-bit value to distinguish an in-memory pointer from a disk reference. We pack a 57-bit page ID and a 6-bit size class into the unswizzled form (mimicking Umbra’s design) (). The code uses bit masks and shifts to set or read the fields. Notice the use of unsafe when dereferencing raw pointers – we must ensure the pointer is valid (the page is loaded and not evicted while in use). In a real system, the buffer pool or B+-tree would guarantee that (e.g., by pinning the page or using latches/epochs to prevent reuse). We include debug assertions to help catch misuses (e.g., attempting to interpret a disk pointer as memory).
/// A 64-bit tagged pointer ("swip") referencing a page either in memory or on disk.
#[derive(Copy, Clone)]
struct Swip(u64);
const TAG_MASK: u64 = 0x1; // Lowest bit is tag: 0 = swizzled (pointer), 1 = unswizzled (disk reference)
const SIZECLASS_MASK: u64 = 0x7E; // Next 6 bits (bits 1-6) for size class (0-63)
const PAGEID_MASK: u64 = !0x7F; // Upper 57 bits (bits 7-63) for page identifier
impl Swip {
/// Create a swizzled pointer from a raw page pointer.
fn from_ptr(ptr: *mut Page, size_class: u8) -> Swip {
let addr = ptr as u64;
// The pointer must be aligned (at least 2 bytes) so tag bit is 0.
debug_assert_eq!(addr & TAG_MASK, 0, "Pointer not aligned or tag bit not zero");
debug_assert!(size_class < 64);
// Store the pointer value directly, ensure tag bit stays 0.
Swip(addr | 0) // (addr’s LSB is already 0)
}
/// Create an unswizzled pointer from a page ID and size class.
fn from_page_id(page_id: u64, size_class: u8) -> Swip {
debug_assert!(size_class < 64);
debug_assert!(page_id < (1 << 57));
// Pack: [page_id (57 bits)] [size_class (6 bits)] [tag=1 (1 bit)]
let encoded = (page_id << 7) | ((size_class as u64) << 1) | 1u64;
Swip(encoded)
}
/// Return true if this swip is currently swizzled (points to memory).
fn is_swizzled(&self) -> bool {
self.0 & TAG_MASK == 0
}
/// Interprets the swip as a raw memory pointer (only valid if is_swizzled()).
fn as_ptr(&self) -> *mut Page {
assert!(self.is_swizzled(), "Swip is not a memory pointer");
(self.0 & !TAG_MASK) as *mut Page
}
/// Interprets the swip as a disk page reference, returning (page_id, size_class).
fn as_disk_ref(&self) -> (u64, u8) {
assert!(!self.is_swizzled(), "Swip is a memory pointer");
let raw = self.0;
let pid = raw >> 7;
let class = ((raw & SIZECLASS_MASK) >> 1) as u8;
(pid, class)
}
}
/// Dummy page struct for demonstration.
struct Page {
data: u8, // placeholder for page content
}In this representation, a Swip is a 64-bit value that can be in one of two states, as illustrated below:
- Swizzled (
tag=0) – Bits:[ pointer address (upper bits) ... 0 ]. The lower bit is 0. Theas_ptr()method simply returns the address. - Unswizzled (
tag=1) – Bits:[ page_id (57 bits) | size_class (6 bits) | 1 ]. Theas_disk_ref()method decodes the stored page ID and size class.
Usage in a B+-Tree Node: Consider a simple B+-tree node that holds keys and child pointers. Initially, when a node is loaded from disk, its children may not all be in memory – so child pointers are unswizzled (just page IDs). As we access children, we load them into the buffer pool and swizzle the pointer in the parent to a direct memory reference. Below, we simulate a mini B+-tree node with one child pointer. We start with the child unswizzled, then perform a “lookup” that triggers loading the child page and swizzling the pointer in the parent node. We then demonstrate that subsequent accesses can directly dereference the pointer without a hash table lookup. (In a real system, eviction would require “unswizzling” – replacing the pointer with the page ID again. Umbra’s design ensures each page is only ever referenced by one parent swip to simplify this consistency problem ().)
/// B+Tree node with a single child pointer (swip) for demonstration.
struct BTreeNode {
key: i32,
child: Swip, // pointer to child page (could be swizzled or unswizzled)
}
fn demo_pointer_swizzling() {
// Parent node has one child reference, initially pointing to page ID 42 on disk.
let mut parent = BTreeNode { key: 100, child: Swip::from_page_id(42, 0) };
println!("Initial child pointer: {:?}", parent.child.as_disk_ref());
// Output: Initial child pointer: (42, 0)
// Simulate accessing the child: load page 42 from disk into memory (allocate Page).
let loaded_page = Box::new(Page { data: 123 });
let page_ptr = Box::into_raw(loaded_page); // get raw pointer (now owned by buffer pool)
// Swizzle the pointer in the parent to point to the loaded Page:
parent.child = Swip::from_ptr(page_ptr, 0);
println!("Child pointer after swizzling: 0x{:x}", parent.child.0);
// Now the parent.child is a direct pointer (tag=0).
// Access the child via the swizzled pointer:
if parent.child.is_swizzled() {
let child_page: &Page = unsafe { &*parent.child.as_ptr() }; // dereference raw pointer
println!("Child page data = {}", child_page.data);
}
// Output: Child page data = 123
// Cleanup: for safety in this demo, convert the raw pointer back to Box to free memory.
unsafe { Box::from_raw(parent.child.as_ptr()); }
}Safety and Design Considerations: Using tagged pointers requires careful memory management. In the above example, we used unsafe to dereference the swizzled pointer. In a real database, we must ensure the page remains in memory and is not freed while in use. Umbra and LeanStore handle this by structuring all data as a tree (each page has a single owning reference) () and using latches or epoch-based garbage collection to prevent races with eviction (). For instance, LeanStore’s eviction strategy “speculatively unswizzles” rarely used pages but delays actual freeing until it’s safe (no ongoing references) (). Also, note that the tag bit approach relies on pointer alignment and address space assumptions. We used 57 bits for page ID because current 64-bit CPUs don’t use the full address space for user memory – this leaves room in the pointer for metadata. These bit sizes might change with future architectures, so the implementation should document and assert such assumptions. Despite these caveats, pointer swizzling is extremely efficient: for hot pages in memory, the overhead per access is just a bit check instead of a costly page-ID-to-pointer lookup ().
Frequent latch (lock) acquisitions in the buffer pool can become a scalability bottleneck on many-core systems (). To minimize contention, Umbra (adapting LeanStore’s design) uses optimistic, versioned latches on pages () (). A versioned latch combines a lightweight lock with a version counter. It allows multiple readers to access a page concurrently without heavy synchronization, and uses a version number to detect if a page has been modified during a read. This is similar in spirit to optimistic concurrency control – readers don’t block writers, but they must verify afterwards that no interfering write occurred ().
Key Idea: Each page (or node) has an associated 64-bit latch that encodes both a version and a state. The version is incremented whenever the page is modified, and the state encodes whether the latch is locked (and in what mode). Umbra’s latch uses 59 bits for the version and 5 bits for state () (enough to count reader threads or indicate exclusive lock). An exclusive (write) lock sets a special state (e.g., 1) while held, and a shared (read) lock uses a counter in the state bits (e.g., state = n+1 if n readers hold it) (). But crucially, readers can also proceed in optimistic mode without acquiring any lock at all as long as no writer is active (). In optimistic mode, a reader simply remembers the current version, reads the page, and then checks the version again at the end (). If the version changed, a writer must have modified the page, so the reader’s work is invalid and must be retried (or done with proper locking). If the version is unchanged, the read was consistent. This allows read threads to avoid atomic operations in the common case, greatly reducing cache-line bouncing and contention. Writers still use an exclusive lock to protect modifications, but even that is optimized – the latch state and version are updated with a single atomic instruction (CAS), and readers only contend if they happen to validate while the writer is committing changes.
Rust Implementation of a Versioned Latch: Below we implement a simplified versioned latch for a page. For demonstration, we support two modes: an optimistic read (no lock, just version check) and an exclusive write lock. (We’ll omit true shared locking for brevity, but the state bits could be extended for that.) The latch is backed by an AtomicU64 combining a 62-bit version and a 2-bit state (this is a toy setup; Umbra uses 5 state bits, but 2 are enough here to represent Unlocked=0, Exclusive=1, and an overflow marker). We provide methods read_opt_begin/read_opt_end for optimistic reads and lock_exclusive/unlock_exclusive for writers. The code uses atomic load/store with ordering to ensure proper memory visibility. We’ve marked critical sections with unsafe where we directly manipulate the combined atomic bits. This requires care: we must not violate atomicity of the latch word. We use a compare-and-swap (CAS) loop to atomically change latch state, ensuring a writer acquires the lock only if it was previously unlocked. The version is incremented on unlock if data was modified. (In a real system, you would also handle cases like latch contention, waiting, or upgrading from optimistic to exclusive gracefully.)
use std::sync::atomic::{AtomicU64, Ordering};
/// A simple versioned latch with 62-bit version counter and 2-bit state.
struct VersionedLatch {
data: AtomicU64,
}
/// Latch state flags (here we use 2 bits for demo purposes).
const STATE_UNLOCKED: u64 = 0;
const STATE_EXCLUSIVE: u64 = 1;
// (If implementing shared locks, we might use 2 for shared and counts >1.)
impl VersionedLatch {
fn new() -> Self {
VersionedLatch { data: AtomicU64::new(0) } // version=0, state=0 (unlocked)
}
/// Begin an optimistic read. Returns the initial version snapshot.
fn read_opt_begin(&self) -> u64 {
// Load the combined version|state atomically.
// Use Acquire ordering to synchronize with a writer’s Release (unlock).
let full = self.data.load(Ordering::Acquire);
// We don’t actually lock anything, just return the version part.
full >> 2 // return the upper 62 bits (version)
}
/// Validate an optimistic read by checking if version is unchanged.
/// Returns true if no concurrent write occurred (safe to use read results).
fn read_opt_end(&self, start_version: u64) -> bool {
// Re-read latch. If a writer held exclusive lock, it might increment version on release.
let full = self.data.load(Ordering::Acquire);
let end_version = full >> 2;
end_version == start_version
}
/// Acquire exclusive latch (spin-wait until acquired).
fn lock_exclusive(&self) {
let mut current = self.data.load(Ordering::Relaxed);
loop {
// If currently unlocked (state=0), try to set state=EXCLUSIVE (preserve version).
if current & 0x3 == STATE_UNLOCKED {
let new = current & !0x3 | STATE_EXCLUSIVE;
match self.data.compare_exchange_weak(
current, new, Ordering::Acquire, Ordering::Relaxed) {
Ok(_) => break, // acquired exclusive latch
Err(val) => current = val, // failed – someone changed it, retry
}
} else {
// Latch is locked (maybe by another writer); spin or yield:
std::thread::yield_now();
current = self.data.load(Ordering::Relaxed);
}
}
}
/// Release exclusive latch. If `data_modified` is true, increment the version.
fn unlock_exclusive(&self, data_modified: bool) {
// Simply clear the state to unlocked, and bump version if needed.
let mut current = self.data.load(Ordering::Relaxed);
debug_assert!(current & 0x3 == STATE_EXCLUSIVE);
loop {
let old_version = current & !0x3;
let new_version = if data_modified {
// increment the version portion (taking care of overflow in 62 bits)
let ver = old_version >> 2;
((ver.wrapping_add(1)) << 2) & !0x3
} else {
old_version
};
let new_val = new_version | STATE_UNLOCKED;
if let Ok(_) = self.data.compare_exchange_weak(
current, new_val, Ordering::Release, Ordering::Relaxed) {
break;
}
current = self.data.load(Ordering::Relaxed);
}
}
}In the code above, read_opt_begin() and read_opt_end() implement an optimistic read protocol: they capture the version before and after reading. Writers use lock_exclusive() and unlock_exclusive(modified) to serialize modifications. We use compare_exchange_weak in a loop to atomically set the latch state, which handles the case of multiple threads racing to lock. The Ordering on atomics is important for correctness: acquiring the lock (Acquire) pairs with releasing (Release) to ensure memory writes (to the page’s data) are visible to other threads that subsequently acquire the latch or validate the version.
Optimistic Read Example: To illustrate the optimistic concurrency, let’s simulate a scenario with a shared data value protected by a versioned latch. One “thread” will optimistically read the data while another thread updates it. We’ll show the optimistic read detecting the write and falling back to an exclusive lock to get a consistent result. (In real usage, the fallback could also be to restart the operation or take a shared latch if available.)
fn demo_versioned_latch() {
let latch = VersionedLatch::new();
let data = std::sync::atomic::AtomicUsize::new(0);
// Thread 1: optimistic reader
let ver = latch.read_opt_begin();
let read_val = data.load(Ordering::Relaxed);
println!("Optimistic read saw value = {}", read_val);
// Simulate Thread 2 doing a write concurrently:
{
latch.lock_exclusive();
data.store(42, Ordering::Relaxed);
latch.unlock_exclusive(true); // indicate data modified, version will increment
}
// Back to Thread 1: validate after read
if !latch.read_opt_end(ver) {
println!("Version changed! Retrying under exclusive latch...");
latch.lock_exclusive();
// Re-read reliably under exclusive lock
let new_val = data.load(Ordering::Relaxed);
latch.unlock_exclusive(false);
println!("Value under exclusive lock = {}", new_val);
}
}Running demo_versioned_latch() produces output like:
Optimistic read saw value = 0
Version changed! Retrying under exclusive latch...
Value under exclusive lock = 42
This shows that the optimistic read initially read 0 while a writer changed the value to 42. The version check failed (indicating a concurrent modification), so the reader obtained the exclusive latch and retried, getting the correct updated value. In practice, the retry could also be done with a shared latch if only reads are needed – Umbra actually supports a shared mode to avoid repeated aborts in read-heavy workloads (). But even with pure optimistic reads, the system ensures correctness by validation. The advantage is that if no write happens, readers incur no locking overhead – just two atomic reads of the version. Writers incur one atomic CAS to lock and another to unlock (with a version bump).
Design Considerations: Optimistic latching greatly improves read scalability () () but requires careful design to avoid pitfalls. One concern is phantom reads of freed memory: an optimistic reader doesn’t prevent a page from being evicted or deallocated mid-read. Umbra/LeanStore handle this by never freeing the memory region underlying an active page – if a page is evicted while an optimistic reader is in flight, the physical memory is released back to the OS only after ensuring no readers are left, often using an epoch or hazard pointer mechanism (). (Umbra notes that even if a page frame is freed, their use of madvise(MADV_DONTNEED) means any stray reads map to a zero-page rather than crashing () – the reader will then see a version change or inconsistent data and know something’s wrong.) Another consideration is writer starvation: many readers continuously restarting could starve a writer. To mitigate this, an implementation might detect too many failed read validations and then upgrade one of the readers to take a real lock (or temporarily force writers to wait until readers finish). Umbra’s versioned latch as described ensures that taking an exclusive lock will block new optimistic acquisitions (since state goes non-zero), so a writer can eventually proceed (). From a performance perspective, versioned latches shine in scenarios with many concurrent readers and infrequent writes – exactly the pattern in many OLAP or index lookup workloads. LeanStore reported an 8× to 12× throughput improvement under multithreaded read-mostly workloads by using optimistic latching over traditional locks (). The trade-off is complexity in logic and debugging, but the Rust implementation above demonstrates that with atomic ops and some care, we can build a concurrency primitive that is fast and safe.
Query engines face a dilemma: compiled code yields the fastest execution, but compiling queries (via LLVM or similar) adds significant startup cost. Umbra addresses this with adaptive compilation, a hybrid execution strategy that achieves “fast compilation and fast execution” (Best of both worlds. Umbra's new query engine combines fast... | Download Scientific Diagram). The idea is to begin executing a query using an interpreter or bytecode engine, and simultaneously monitor which parts of the query are “hot” (executed frequently or on large data). When a certain threshold is reached, the engine JIT-compiles the hot path to native code and switches to it on-the-fly (). This way, simple or short queries avoid any compilation overhead (they might finish during interpretation), whereas long-running queries get the benefit of optimized native code once it’s worth the effort. Umbra’s adaptive execution operates at the granularity of query “steps” in a state machine: a query plan is broken into pipelines and further into steps (operators or fragments of an operator), each of which can be independently compiled if it becomes hot () (). Initially each step runs in an interpreted mode using a custom lightweight bytecode; if the engine detects, for example, that a scan or join step has processed a large number of tuples, it will trigger just-in-time compilation of that step to machine code (). The compiled code is then invoked for the remaining tuples. This adaptive approach has been shown to significantly reduce query latency, especially for short analytical queries, compared to always-JIT engines (HyPer) or always-interpreted engines ().
Implementing Adaptive Execution in Rust: For demonstration, we’ll create a simple query processing loop that starts interpreted and then switches to JIT-compiled code. We define a toy bytecode for a filter operation (OP_FILTER_EVEN – which checks if a value is even) and a state machine with two states: Interpretation and JIT-compiled. We use a counter to track how many values have been processed, and after a threshold, we invoke a JIT compiler (using Cranelift, a Rust JIT library) to generate native code for the filter. In our example, the “query” is just filtering an array of integers, but the same structure could apply to any pipeline (e.g., scan->filter->aggregate). The code below outlines the components:
- A
QueryStepenum representing the two execution modes for the filter step. - An
AdaptiveFilterstruct holding the bytecode and an optional function pointer for the compiled code. - An
interpret_stepmethod that simulates a bytecode interpreter (here it just checks even/odd). - A
jit_compile_stepmethod that uses Cranelift to generate a native function for the filter.
We’ve kept the example simple (filtering even numbers) to focus on the mechanism. In practice, the bytecode could be more complex (a vector of instructions, etc.), and we’d identify hot spots by measuring time or counts. The JIT compilation uses unsafe to convert the emitted machine code into a callable Rust function pointer. This is safe if the JIT module is alive and the code is correctly generated; we must also ensure the function signature matches (here fn(i32) -> bool). We handle that carefully in the code. (Note: The following code requires the cranelift crates; it’s illustrative and may be simplified for clarity.)
use cranelift_codegen::{Context, isa, settings};
use cranelift_frontend::{FunctionBuilder, FunctionBuilderContext};
use cranelift_module::{Module, JITModule, Linkage};
use cranelift_codegen::ir::{types, AbiParam, InstBuilder, IntCC};
/// A simple bytecode enum for demonstration.
#[derive(Copy, Clone)]
enum BytecodeOp {
OP_FILTER_EVEN, // Filter: pass through even numbers
// (Additional ops like OP_MAP_ADD, OP_AGG_SUM could be defined in a real system)
}
/// Enum to represent whether a query step is interpreted or JIT compiled.
enum QueryStep {
Interpreted, // use bytecode interpretation
Compiled(fn(i32) -> bool), // use compiled function pointer for the operation
}
/// Adaptive filter that switches from interpretation to JIT compilation.
struct AdaptiveFilter {
step: QueryStep,
bytecode: BytecodeOp,
counter: usize, // how many values processed
hot_threshold: usize, // threshold to trigger JIT compilation
// JIT compilation context (holds code memory alive for safety)
jit_module: JITModule,
}
impl AdaptiveFilter {
fn new(bytecode: BytecodeOp, hot_threshold: usize) -> Self {
// Initialize a JIT module for compiling code on the fly
let builder = cranelift_module::default_libcall_names();
let isa_builder = isa::lookup(isa::Backend::variant().unwrap()).unwrap();
let isa = isa_builder.finish(settings::Flags::new(settings::builder()));
let module = JITModule::new(cranelift_module::Module::new(Default::default(), isa, builder));
AdaptiveFilter {
step: QueryStep::Interpreted,
bytecode,
counter: 0,
hot_threshold,
jit_module: module,
}
}
/// Process a batch of data through the filter, yielding only even numbers.
fn process_batch(&mut self, input: &[i32]) -> Vec<i32> {
let mut output = Vec::new();
match &self.step {
QueryStep::Interpreted => {
// Interpret each value
for &val in input {
self.counter += 1;
if self.interpret_step(val) {
output.push(val);
}
// Check if we've reached the threshold to JIT compile
if self.counter == self.hot_threshold {
println!("** JIT compilation triggered after {} values **", self.counter);
self.jit_compile_step();
// Switch to compiled mode and continue processing remaining input with it
if let QueryStep::Compiled(func) = &self.step {
// process remaining current batch with JIT
for &v in input[output.len()..] {
if func(v) { output.push(v); }
}
}
break;
}
}
}
QueryStep::Compiled(jit_func) => {
// Use JIT-compiled function directly
for &val in input {
if jit_func(val) {
output.push(val);
}
}
}
}
output
}
/// Bytecode interpreter for one value (here we only have one op: filter even).
fn interpret_step(&self, value: i32) -> bool {
match self.bytecode {
BytecodeOp::OP_FILTER_EVEN => value % 2 == 0,
}
}
/// Compile the bytecode to optimized native code using Cranelift.
fn jit_compile_step(&mut self) {
// Create a function signature: (i32) -> bool
let mut ctx = self.jit_module.make_context();
ctx.func.signature.params.push(AbiParam::new(types::I32));
ctx.func.signature.returns.push(AbiParam::new(types::B1));
// Build IR for the function
let mut func_ctx = FunctionBuilderContext::new();
let mut builder = FunctionBuilder::new(&mut ctx.func, &mut func_ctx);
let entry_block = builder.create_block();
builder.append_block_params_for_function_params(entry_block);
builder.switch_to_block(entry_block);
// Get the input parameter
let val = builder.block_params(entry_block)[0];
// Implement: return (val % 2 == 0)
let one = builder.ins().iconst(types::I32, 1);
let bit = builder.ins().band(val, one); // bit = val & 1
let zero = builder.ins().iconst(types::I32, 0);
let is_even = builder.ins().icmp(IntCC::Equal, bit, zero); // is_even (b1) = (bit == 0)
builder.ins().return_(&[is_even]);
builder.finalize();
// Define and compile the function
let func_id = self.jit_module.declare_function("filter_even", Linkage::Local, &ctx.func.signature).unwrap();
self.jit_module.define_function(func_id, &mut ctx).unwrap();
self.jit_module.clear_context(&mut ctx);
self.jit_module.finalize_definitions();
// Get a raw pointer to the compiled code
let code_ptr = self.jit_module.get_finalized_function(func_id);
// Convert to a callable function pointer
let func = unsafe { std::mem::transmute::<*const u8, fn(i32) -> bool>(code_ptr) };
self.step = QueryStep::Compiled(func);
}
}Let’s use AdaptiveFilter in a small example to see how it switches from interpretation to JIT. We’ll create a filter that passes even numbers and run it on a list. We set a low threshold to trigger compilation quickly for demonstration:
fn demo_adaptive_compilation() {
// Create an AdaptiveFilter with the filter-even bytecode. Set threshold = 5.
let mut filter = AdaptiveFilter::new(BytecodeOp::OP_FILTER_EVEN, 5);
let data: Vec<i32> = (1..=10).collect();
println!("Input = {:?}", data);
// Process data in two batches to simulate continuous query execution
let out1 = filter.process_batch(&data[0..5]); // first batch of 5 (should trigger JIT at end)
let out2 = filter.process_batch(&data[5..10]); // second batch of 5 (should use JIT)
println!("Output batch1 = {:?}, batch2 = {:?}", out1, out2);
}If you run demo_adaptive_compilation(), you might see output like:
Input = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
** JIT compilation triggered after 5 values **
Output batch1 = [2, 4], batch2 = [6, 8, 10]
Here, the first batch of 5 values was processed interpretively (producing even numbers 2 and 4). After hitting 5 values, the AdaptiveFilter printed the message and compiled the filter logic. The second batch then used the JIT-compiled function, directly producing [6, 8, 10] with no additional interpretation overhead. We effectively skipped compiling upfront, ran a bit of the query to see if it’s heavy, then adaptively optimized the rest of the query. In Umbra’s engine, this mechanism can even happen mid-pipeline, and the system might compile multiple hot steps independently. The result is “pay as you go” compilation – short queries pay almost nothing to start, while long queries approach the speed of fully compiled code ().
Design Considerations: Adaptive compilation requires a robust infrastructure: a bytecode interpreter, a JIT compiler integration, and a strategy to decide when to compile. The threshold could be based on a counter, as in our example, or on measured execution time (e.g., compile if a step takes more than X milliseconds so far). The system must also consider multi-threading – e.g., Umbra uses a morsel-driven parallel model, so each thread might trigger compilation, but ideally it should be done once and shared. In our AdaptiveFilter, we would want to ensure only one thread compiles and then update the function pointer for all threads (this may require synchronization like an atomic state or a mutex when initiating JIT). Another aspect is code generation quality: Umbra first generates an IR similar to LLVM IR, which can be quickly translated to machine code when needed (). We used Cranelift in the example, which is designed for fast compilation. You might choose to generate very efficient code for the hottest paths and leave less critical parts interpreted to save compile time. Memory management for JIT-compiled code is also important – we used JITModule which owns the code, so we must keep it alive as long as we might call the function. When the query finishes, the compiled code can be freed or recycled.
Despite these complexities, the payoff is significant: Umbra’s adaptive engine achieved up to 3× speedups over HyPer on certain benchmarks by avoiding unnecessary compilation (). Essentially, it combines “the best of both worlds” – the quick startup of interpretation and the unbeatable runtime speed of native execution (Best of both worlds. Umbra's new query engine combines fast... | Download Scientific Diagram). This technique allows a database like Umbra to handle both simple, fast queries and long, complex ones efficiently in the same engine.
Finally, let’s envision how these pieces come together in a cohesive mini-system akin to Umbra. Imagine a B+-tree index in a hybrid OLTP/OLAP database:
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The buffer pool uses pointer swizzling so that tree nodes reference their children by direct memory pointers whenever possible. A root-to-leaf traversal in the B+-tree will mostly encounter swizzled pointers, incurring virtually no lookup cost to navigate the tree. If a pointer is unswizzled (page on disk), the traversal triggers a page load, the pointer is swizzled, and the traversal continues seamlessly. This decentralizes address translation and avoids global latch bottlenecks in the buffer manager ().
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Each tree node is protected by a versioned latch. An update (insert/delete) on the tree acquires exclusive latches on the nodes it modifies (e.g., doing lock-coupling as it descends the tree). Readers can traverse the tree optimistically: they read the root’s version, then inspect child pointers and move down without locking. If a modification happens meanwhile (detected via a version change at some node), the reader can restart or take a latch and retry that part. This yields lock-free reads in the common case and minimal interference between writers and readers (). The atomic version+state fits in one 64-bit word per node, so latches are memory-efficient and can leverage atomics for speed. Care must be taken, however, to pin pages or use epochs during a traversal so that a node doesn’t disappear (get evicted) under an optimistic reader – Umbra’s rule that each page has a single owning pointer helps here, since a parent’s latch on a child (or an epoch scheme) can ensure the child stays valid for the duration of the read.
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The query execution engine ties it all together by processing data from the B+-tree. Suppose we run an OLAP query that scans a range of the index and aggregates some field. Umbra’s execution framework would treat the scan as a step that can run interpreted or compiled. Initially, the scan step might call a function that traverses the B+-tree interpreting the operations (e.g., following pointers, applying filter predicates). As it iterates over many tuples, the engine counts how many leaves or tuples have been processed. Once a threshold is crossed, it JIT-compiles a specialized version of the scan (for instance, a function that unrolls the traversal and aggregation for the specific query and data types). This compiled code can inline certain operations, use vectorized instructions, and be optimized by LLVM or Cranelift, yielding performance close to hand-written C++. Meanwhile, the system can move to the next pipeline (e.g., doing a join or group-by) and similarly decide on-the-fly whether to compile that. All of this happens under the hood – the application just sees the query results quickly. Umbra’s adaptive model ensures that if the query was very short, it might never spend time compiling (so it finishes sooner), and if it’s long, the one-time compile cost is amortized over a large workload ().
In summary, these advanced techniques complement each other in a modern database engine: Pointer swizzling gives memory-level performance by eliminating address translation overhead (), versioned latching provides high concurrency with low synchronization cost (), and adaptive compilation delivers fast execution without sacrificing startup time (). The Rust examples above illustrate how one might implement these ideas with fine-grained control over memory and concurrency. While production systems must handle many additional concerns (recovery, transactions, etc.), mastering these building blocks is a big step toward building a high-performance database engine in the spirit of Umbra.