Lock-Free Skip Lists

Understanding concurrent programming through FerrisDB’s MemTable implementation.

Difficulty: 📕 Advanced • Reading time: 35 minutes

The Problem & Why It Matters

Imagine your web application suddenly goes viral. Thousands of users are simultaneously creating accounts, updating profiles, and posting content. Your database needs to handle all these writes at the same time without making users wait in line.

This is the concurrency challenge every modern database faces. In traditional systems, when multiple users try to write data:

Real-world problems CRUD developers face

• Lock contention: Users wait in a virtual queue, like a single bathroom at a party - Deadlocks: Two users waiting for each other, like two cars at a narrow bridge - Slow response times: Your API endpoints timeout during traffic spikes - Wasted server resources: CPUs sit idle while threads wait for locks

The traditional solution uses locks (mutexes) to ensure only one thread modifies data at a time:

// Naive approach - everyone waits in line
struct SlowDatabase {
    data: Mutex<HashMap<String, String>>,
}

// Every operation locks EVERYTHING
db.data.lock().unwrap().insert(key, value); // All other threads wait!

This is like having a single cashier at a busy store - no matter how many customers arrive, they all wait in one line. FerrisDB’s lock-free skip list solves this by allowing multiple “cashiers” to work simultaneously.

Conceptual Overview

The Core Idea

Skip lists are like a subway system for your data:

Regular linked list (local train):

Station1 → Station2 → Station3 → Station4 → Station5

Skip list (express system):

Express:    Station1 -----------→ Station3 -----------→ Station5
Local:      Station1 → Station2 → Station3 → Station4 → Station5

To find Station4:

1. Take express to Station3 (skip Station2)
2. Switch to local for one stop

This makes finding data much faster - O(log n) instead of O(n).

Visual Architecture

Level 3: HEAD ------------------> 30 -------------------------> NULL
Level 2: HEAD ------> 10 -------> 30 -------> 50 -------------> NULL
Level 1: HEAD -> 5 -> 10 -> 20 -> 30 -> 40 -> 50 -> 60 -------> NULL
Level 0: HEAD -> 5 -> 10 -> 20 -> 30 -> 40 -> 50 -> 60 -> 70 -> NULL
           ↑                          ↑                      ↑
        Start here              Found quickly!          Without skipping

Key principles:

1. Multiple levels: Express lanes for faster traversal
2. Probabilistic structure: Randomly decide how many levels each node gets
3. Lock-free reads: Multiple threads can search simultaneously

FerrisDB Implementation Deep Dive

Core Data Structures

Let’s examine FerrisDB’s concurrent skip list implementation:

ferrisdb-storage/src/memtable/skip_list.rs

pub struct SkipList {
    /// The head node of the skip list (sentinel)
    head: Arc<Node>,
    /// Maximum height of any node in the list
    max_height: AtomicUsize,
    /// Thread-safe random number generator
    rng: Mutex<StdRng>,
}

struct Node {
    /// The actual key-value data
    key: Option<Vec<u8>>,
    value: Option<Vec<u8>>,
    /// Forward pointers for each level
    next: Vec<Atomic<Node>>,
}

Note

The Atomic<Node> type provides atomic pointer operations, enabling lock-free updates. This is the secret sauce for concurrent performance!

Thread-Safe Operations

Lock-Free Search

The beauty of skip lists is that searches never need locks:

pub fn get(&self, key: &[u8]) -> Option<Vec<u8>> {
    let mut current = self.head.clone();
    let mut level = self.max_height.load(Ordering::Relaxed);

    // Start from highest level and work down
    while level > 0 {
        level -= 1;

        // Move forward on current level
        loop {
            let next = current.next[level].load(Ordering::Acquire);

            match next {
                Some(next_node) if next_node.key < key => {
                    current = next_node;
                }
                _ => break, // Go down a level
            }
        }
    }

    // Check if we found the key
    let next = current.next[0].load(Ordering::Acquire);
    match next {
        Some(node) if node.key == key => Some(node.value.clone()),
        _ => None,
    }
}

Why This Works

• No locks needed - just atomic reads - Multiple threads can search simultaneously - Progress guaranteed - no thread blocks another

Performance Impact

• Read throughput scales with CPU cores - No cache line bouncing between cores - Predictable latency (no lock waiting)

Concurrent Insertion

Insertion is more complex but still achieves excellent concurrency:

pub fn insert(&self, key: Vec<u8>, value: Vec<u8>) {
    let height = self.random_height();
    let mut update: Vec<Arc<Node>> = vec![self.head.clone(); height];

    // Phase 1: Find insertion position at each level
    let mut current = self.head.clone();
    for level in (0..height).rev() {
        loop {
            let next = current.next[level].load(Ordering::Acquire);

            match next {
                Some(next_node) if next_node.key < key => {
                    current = next_node;
                }
                _ => {
                    update[level] = current.clone();
                    break;
                }
            }
        }
    }

    // Phase 2: Create new node
    let new_node = Arc::new(Node {
        key: Some(key),
        value: Some(value),
        next: vec![Atomic::null(); height],
    });

    // Phase 3: Link in from bottom to top
    for level in 0..height {
        loop {
            let next = update[level].next[level].load(Ordering::Acquire);
            new_node.next[level].store(next.clone(), Ordering::Release);

            // Compare-and-swap to atomically insert
            match update[level].next[level].compare_exchange(
                next,
                Some(new_node.clone()),
                Ordering::Release,
                Ordering::Acquire,
            ) {
                Ok(_) => break,  // Success!
                Err(_) => {
                    // Another thread modified - retry
                    continue;
                }
            }
        }
    }
}

Memory Ordering Explained

Understanding memory ordering is crucial for lock-free programming:

1. Relaxed: No synchronization, just atomicity

   height.load(Ordering::Relaxed)  // Just need the value

2. Acquire: All subsequent reads see writes before the Release

   next.load(Ordering::Acquire)  // Need to see node data

3. Release: All previous writes visible to Acquire loads

   next.store(new_node, Ordering::Release)  // Publish our writes

4. AcqRel: Both Acquire and Release semantics

   compare_exchange(..., Ordering::AcqRel, ...)  // Full synchronization

Performance Analysis

Theoretical Complexity

Operation	Average Case	Worst Case	Space
Search	O(log n)	O(n)	-
Insert	O(log n)	O(n)	O(log n)
Delete	O(log n)	O(n)	-
Range Query	O(log n + k)	O(n)	-

Real-World Performance

Single-threaded

• 1M ops/sec for searches - 500K ops/sec for insertions - Comparable to RB-tree

Multi-threaded (8 cores)

• 7M ops/sec for searches (7x scaling!) - 2M ops/sec for insertions - Far exceeds locked structures

Benchmark Results

// From FerrisDB benchmarks
test bench_skiplist_insert_single    ... bench:       180 ns/iter
test bench_skiplist_insert_parallel  ... bench:        45 ns/iter (4 threads)
test bench_btreemap_insert_locked    ... bench:       220 ns/iter
test bench_btreemap_insert_parallel  ... bench:       850 ns/iter (4 threads)

Advanced Topics

ABA Problem and Solutions

The ABA problem occurs in lock-free programming when:

1. Thread 1 reads value A
2. Thread 2 changes A → B → A
3. Thread 1’s CAS succeeds but misses the intermediate change

Solution: Hazard pointers or epoch-based reclamation

// FerrisDB uses epoch-based memory reclamation
struct EpochGuard {
    epoch: Arc<AtomicU64>,
}

impl Drop for EpochGuard {
    fn drop(&mut self) {
        // Signal we're done with this epoch
        self.epoch.fetch_add(1, Ordering::Release);
    }
}

Memory Reclamation

Lock-free structures can’t immediately free memory:

// Problem: When can we safely delete this node?
let old = node.next.swap(new_next, Ordering::AcqRel);
// drop(old); // UNSAFE! Another thread might be reading it

// Solution: Defer deletion
garbage_collector.defer_drop(old);

Common Pitfalls & Solutions

Pitfall 1: Incorrect Memory Ordering

Problem: Using Relaxed ordering everywhere Result: Data races and corrupted state Solution: Use Acquire for loads, Release for stores

Pitfall 2: Live-lock

Problem: Threads keep retrying forever Result: High CPU usage, no progress Solution: Add exponential backoff

Pitfall 3: Memory Leaks

Problem: Not handling memory reclamation Result: Unbounded memory growth Solution: Use epoch-based reclamation or hazard pointers

Testing Concurrent Code

Deterministic Testing

#[test]
fn test_concurrent_insert() {
    let list = Arc::new(SkipList::new());
    let mut handles = vec![];

    // Spawn multiple threads
    for i in 0..10 {
        let list_clone = list.clone();
        handles.push(thread::spawn(move || {
            for j in 0..1000 {
                list_clone.insert(
                    format!("key_{}_{}", i, j).into_bytes(),
                    format!("value_{}_{}", i, j).into_bytes(),
                );
            }
        }));
    }

    // Wait for all threads
    for handle in handles {
        handle.join().unwrap();
    }

    // Verify all insertions succeeded
    assert_eq!(list.len(), 10_000);
}

Stress Testing with Loom

#[cfg(loom)]
#[test]
fn loom_concurrent_test() {
    loom::model(|| {
        let list = Arc::new(SkipList::new());

        // Loom will explore all possible interleavings
        let t1 = loom::thread::spawn({
            let list = list.clone();
            move || list.insert(b"key1", b"value1")
        });

        let t2 = loom::thread::spawn({
            let list = list.clone();
            move || list.insert(b"key2", b"value2")
        });

        t1.join().unwrap();
        t2.join().unwrap();

        assert_eq!(list.len(), 2);
    });
}

Real-World Applications

When to Use Skip Lists

Perfect For

• High-concurrency workloads - Read-heavy with some writes - In-memory databases - Real-time systems

Avoid When

• Need deterministic structure - Cache efficiency critical - Simple single-threaded use - Persistent storage needed

Industry Usage

• Redis: Uses skip lists for sorted sets
• LevelDB/RocksDB: MemTable implementation
• Apache Cassandra: Secondary indexes
• MemSQL: In-memory tables

Implementation Exercise

Try implementing a simple concurrent counter:

struct ConcurrentCounter {
    count: AtomicU64,
}

impl ConcurrentCounter {
    fn increment(&self) -> u64 {
        // Your code here:
        // 1. Load current value
        // 2. Add 1
        // 3. Compare-and-swap
        // 4. Retry if failed
    }
}

Key Takeaways

1. Skip lists enable lock-free concurrency: Multiple threads can work simultaneously
2. Probabilistic structure: Simple to implement, good average performance
3. Memory ordering matters: Wrong ordering = data races
4. Testing is crucial: Use tools like Loom for verification

Next Steps

• Memory Ordering in Rust
• Implementing MemTables
• Database Concurrency Patterns

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