Building a Sub-100 µs Matching Engine with Kotlin Coroutines

In the world of high-frequency trading (HFT), every microsecond counts. Small inefficiencies can quickly snowball into significant costs or missed opportunities. With Kotlin’s robust coroutine model, we can harness lightweight concurrency to power a matching engine that can respond within sub-100 microseconds. In this post, I’ll walk through the design considerations, coroutine architecture, and performance tuning tips that enable Kotlin-based low-latency matching engines suitable for HFT environments.

Why Sub-100 µs Matters in High-Frequency Trading

High-frequency trading (HFT) is defined by the relentless race for speed. In markets where thousands of orders may be placed and canceled in a fraction of a second, the ability to process orders within microseconds often determines whether a strategy wins or loses. Below are a few key reasons why these extremely tight latency requirements matter so much:

Competition and Market Share
HFT participants compete on speed. The difference of a few microseconds can mean the difference between capturing a profitable trade and being beaten by a faster competitor. If your system is consistently behind by even 50 µs, it’s likely that other traders – who have optimized their networks, hardware, and software – will fill the best orders first.
Market Microstructure
Modern markets are fragmented, with multiple exchanges and alternative trading systems (ATSs) all offering slightly different prices and liquidity. HFT firms thrive by detecting short-lived price discrepancies across venues and reacting faster than other participants. A 100 µs advantage (or disadvantage) in routing and matching can make all the difference when simultaneously listening to multiple order books.
Order Queue Priority
Exchanges generally match orders on a price-time priority basis. Even if you post the same price as another participant, the earlier order in the queue receives priority. Sub-100 µs latency ensures your orders are placed (or canceled) ahead of the crowd, boosting the probability of execution at the desired price.
Slippage Reduction
In periods of high volatility, prices can shift dramatically in milliseconds. An HFT system with sub-100 µs latency has the best chance of updating or canceling orders before adverse price movements occur, thereby reducing slippage (the difference between the expected price and the actual execution price).
Cumulative Delays
Modern trading systems consist of multiple stages – market data ingestion, order creation, risk checks, matching, and trade confirmation. Each stage adds latency. A system that consistently keeps each stage under sub-100 µs helps ensure the overall round-trip latency remains competitive. Without strict microsecond-level control, small inefficiencies at each stage can accumulate into a sizable delay.
Opportunity Cost
There are countless fleeting trading opportunities. If your system lags by a few microseconds at key decision points, you miss out on profitable trades altogether. The lost opportunities can quickly snowball, leading to a sizable performance gap over weeks or months.
Latency Arbitrage
Some HFT strategies specialize in “latency arbitrage,” exploiting slow market participants or stale quotes in the system. If your trading engine is slower than theirs, you risk providing those opportunities to arbitrageurs rather than capturing them. By maintaining ultra-low latency, you protect yourself from being arbitraged while also enabling you to capitalize on such inefficiencies.
Strategic Flexibility
The faster your core matching and execution infrastructure, the more flexibility you have to implement complex order types or risk checks without blowing your latency budget. Sub-100 µs performance leaves headroom for additional logic, real-time analytics, or machine learning models without jeopardizing overall speed.

In short, sub-100 µs latency is more than a technical milestone – it’s a strategic necessity for anyone serious about playing in the high-frequency space. Achieving it requires holistic optimization, from hardware and networking to data structures, JVM tuning, and concurrency models. Kotlin coroutines, when used effectively, fit neatly into this domain by providing the lightweight concurrency and minimal overhead needed to squeeze out every last microsecond of performance.

Overview of a Matching Engine

A matching engine is the core of any exchange-like system – part traffic cop, part referee, and part indispensable scapegoat when trades don’t go through. In high-frequency trading, it’s the place where thousands of incoming buy/sell orders converge, only to be sorted, matched, and executed in the blink of an eye. Or at least, that’s the dream. Let’s break down its major responsibilities before your matching engine goes from “works on my machine” to “production meltdown at 2 a.m.” territory.

Order Ingestion
The process begins with a constant stream of new orders flying in from traders, algorithms, or that one legacy system that someone swears was mission-critical in 1997. Your matching engine needs to parse, validate, and funnel these orders quickly – preferably without succumbing to existential dread when the order rate spikes.
Order Book Management
This is where a naive approach quickly falls flat. You’re not just storing data in a “list” or a “map”; you’re managing an in-memory structure that must handle hundreds of thousands of active orders – each competing for time priority. Implementation details matter. Stick to specialized data structures (like custom skip lists, balanced trees, or ring buffers) that let you find best bids and asks in microseconds. Anything slower, and you’ll be that one system everyone else is arbitraging against.
Trade Matching
Ah, the big show. Once an order arrives, the matching engine attempts to pair it with resting orders on the opposite side of the book – “attempts” being the operative word here. Getting it wrong means trades are missed or, worse, incorrectly executed. This function is the biggest consumer of your performance budget. If your matching logic can’t deliver sub-100 µs under pressure, you’ve just gifted your competition a reason to celebrate (and by celebrate, I mean profit off your lag).
Trade Execution & Notification
When a match is found, the trade is executed and the system updates both parties’ order statuses. Notifications then fan out to risk management, clearing, and if they’ve done anything right – the clients who asked for that trade in the first place. Keep in mind that, in HFT, even sending a well-structured update can chew into precious microseconds. Handle it asynchronously with coroutines or get comfortable seeing your system relegated to “that slow one nobody uses.”

A Note on Concurrency

Many developers treat concurrency like a puzzle they’ll “figure out later.” In a matching engine, “later” means never. Threads and locks can quickly become your worst enemy, hogging cycles and turning your microsecond dreams into millisecond nightmares. Kotlin coroutines offer a sweet alternative with lightweight concurrency, if you architect it well. Pass data through channels, isolate state where possible, and keep the message pipelines short. Resist the urge to tie everything to a single global lock; that’s a surefire way to create a concurrency bottleneck large enough to be seen from space.

In short, a matching engine is the ultimate stress test for your system. It demands near-real-time updates, massive throughput, and microsecond-level latency. If you find the prospect daunting, don’t worry – that’s normal. If you don’t find it daunting at all, you probably haven’t deployed to production yet. Once you master the matching engine’s flow, though, you’ll have a fast, reliable core that can form the basis of a truly modern, sub-100 µs trading platform. Or, at the very least, one that doesn’t spontaneously combust during the morning open.

Let’s face it – writing high-concurrency code can be a thankless endeavor. Threads, locks, and shared mutable state often combine into a swirling storm of deadlocks, context switches, and race conditions. That’s where Kotlin coroutines step in, ideally before you end up with a debugging headache that no amount of coffee can fix.

Here’s why coroutines deserve a spot in your sub-100 µs matching engine.

Lightweight Concurrency
Traditional threads are expensive. Spawn a thousand of them and watch your CPU usage spike as your OS thrashes around trying to schedule each one. Coroutines, on the other hand, are basically concurrency ninjas – they suspend rather than block, letting you handle massive volumes of concurrent tasks without summoning the Grim Reaper of performance.

// Launching multiple coroutines
repeat(1_000_000) {
    launch {
        // Each coroutine runs independently and suspends when it needs I/O
        // or some other blocking action
    }
}

Structured Concurrency
Some might say structured concurrency is just “fancy speak” for managing the lifecycle of concurrent tasks in a more civilized manner. But once you’ve tried it, you won’t want to go back. Instead of scattering thread handling logic all over your codebase, you define clear coroutine scopes and hierarchies. When a scope finishes, all child coroutines are automatically cleaned up – no more rogue threads lurking about, waiting to crash your system at 2 a.m.

coroutineScope {
    val job1 = launch { /* do something */ }
    val job2 = launch { /* do something else */ }
    // All launched coroutines complete before the scope ends
}

Channels for Communication
Sharing data across threads is about as enjoyable as explaining to finance why you’re over budget on CPU cycles. Kotlin channels offer a much simpler, safer pipeline for data exchange. You send messages through a channel, and on the other end, a coroutine receives them – no explicit locks, no arcane concurrency constructs.

val channel = Channel<Int>()

// Producer coroutine
launch {
    repeat(10) {
        channel.send(it)
    }
    channel.close()
}

// Consumer coroutine
launch {
    for (value in channel) {
        println("Received $value")
    }
}

Performance Under Pressure
Don’t let the “lightweight” label fool you – coroutines can still move serious throughput. By suspending instead of blocking, they let you do more with fewer threads, reducing context-switch overhead. This approach is critical when you’re dealing with microsecond-level matching. It means that the CPU can focus on actual work rather than juggling threads like a caffeinated circus clown.

Easier Testing and Debugging
Coroutines make testing concurrent logic…well, not exactly fun, but certainly more manageable. You can run tests in a controlled scope, simulate suspensions, and ensure all child tasks have completed. It’s not the magical silver bullet that solves all concurrency woes, but it’s closer to a solution than reading stack traces from 15 threads competing for a single lock.

Integration with Existing JVM Ecosystem
Kotlin coroutines play nice with libraries you already know. Netty, Ktor, database drivers – there’s usually a coroutine-ready integration. This compatibility means you don’t have to shoehorn some exotic concurrency library into your codebase or rewrite your entire stack just to save a few microseconds.

Quick Example: Parallel Data Processing with Channels

Imagine you have multiple order streams that need to be merged and then processed by a matching engine. Here’s a trivialized snippet (don’t judge – your production code is probably scarier):

val feed1 = Channel<Order>()
val feed2 = Channel<Order>()
val mergedFeed = Channel<Order>()

// Coroutine to merge two feeds
launch {
    while (!feed1.isClosedForReceive || !feed2.isClosedForReceive) {
        select<Unit> {
            feed1.onReceive { order -> mergedFeed.send(order) }
            feed2.onReceive { order -> mergedFeed.send(order) }
        }
    }
    mergedFeed.close()
}

// Matching engine coroutine
launch {
    for (order in mergedFeed) {
        // match order logic
    }
}

The select expression helps you handle whichever channel has data first – effectively merging them without explicit locking. This might look too straightforward for real-world HFT, but the principle holds: coroutines keep concurrency logic safer and simpler, letting you focus on the real task – building a matching engine that’s as close to meltdown-proof as possible.

Architecture Overview

In a perfect world, a matching engine would be just a single function that takes orders in one end and spits out trades on the other. But here on planet Earth – with real latencies, real concurrency challenges, and that one corner case we always forget – we need a bit more nuance. Below is a closer look at each component of a sub-100 µs matching engine architecture, plus some thoughts on relevant concurrency patterns.

Inbound Gateway

This is your front door, where all external orders come flooding in. It could be a simple TCP socket, a ZeroMQ pipeline, or a Ktor-based REST endpoint if you enjoy living dangerously. The key focus here is low-overhead message parsing and handoff. You want to keep any sort of blocking or large-scale deserialization out of the hot path. If you must parse giant JSON blobs, at least do so in a separate coroutine or a lightweight pool.

Pattern Thoughts: Some teams use the Reactor or Pipeline patterns to keep input ingestion separate from processing. Kotlin channels essentially let you wire a pipeline from the Inbound Gateway to the rest of the system without writing the concurrency logic yourself.

Order Router

Once an order has been pulled in, it needs to find its rightful home. Enter the Order Router – a small piece of logic that decides which instance of the matching engine (or which partition/shard) will handle this order. For instance, if you’re dealing with multiple symbols or product groups, you might allocate each symbol to a dedicated coroutine-based matching engine.

Potential Pitfalls: A naive approach might do a synchronous call like matchingEngineChannels[symbol].send(order). That works, but if your system has 10,000 symbols, you’ll need to think carefully about how to handle concurrency. A single router coroutine might get overwhelmed if the traffic is intense. Alternatively, you can shard the router itself – distributing incoming orders across a small set of router instances.
Pattern Thoughts: This part leans on the Splitter or Distributor pattern – conceptually splitting or routing messages based on a key (symbol, product ID, etc.).

Matching Engine

This is the main event, where buy and sell orders clash in a heroic (or at least highly profitable) battle. The matching engine’s job is to maintain an in-memory order book, figure out if the incoming order can match with an existing one, and then produce trade results.

Under the hood, you’re likely using:

Efficient Data Structures: Balanced trees, skip lists, or arrays that let you update best bid/ask in microseconds.
Coroutines: Each matching engine instance can run on a dedicated coroutine or set of coroutines. Keep in mind that once you venture into multi-coroutine matching, you can accidentally introduce state-sharing issues. Resist the temptation to share an order book across threads without carefully planning concurrency control.

Here’s a simplified snippet – heavily sanitized for demonstration:

val matchingEngineChannel = Channel<Order>()

// Dedicated coroutine for matching engine
launch {
    for (order in matchingEngineChannel) {
        val matchedTrades = matchOrder(order) // your matching logic
        matchedTrades.forEach { tradeEventsChannel.send(it) }
    }
}

Advanced Pattern: Some folks adopt a SEDA (Staged Event-Driven Architecture) to break matching into distinct stages – receiving orders, updating the book, generating trades, etc. Each stage is handled by its own coroutine or pool. Great for scaling, but adds complexity. Use only if you have the stomach for more concurrency layers.

Outbound Gateway

After matching, trade events need to fan out to various destinations – confirmations for clients, risk management modules, clearing houses, your overworked data scientists, you name it. The Outbound Gateway orchestrates this distribution with as little latency overhead as possible.

Key Concerns:
- You do not want to hold up your matching engine while you wait for an external system to say, “Thanks, got it!”
- Coroutines can elegantly handle asynchronous outbound calls without blocking your main thread.
Pattern Thoughts: This is reminiscent of the Publish-Subscribe pattern. You can keep it simple with one or more dedicated channels that feed the various consumers. Just don’t forget to measure the time it takes to broadcast messages in high-load scenarios.

Putting It All Together

Inbound Gateway ingests orders and hands them off to the Order Router via a channel.
Order Router decides which Matching Engine coroutine should receive the order.
Matching Engine processes the order, updates its order book, and sends resulting trades to a channel.
Outbound Gateway reads from the trades channel and delivers events to downstream subscribers.

At each step, you’re trying to minimize contention and keep data flowing quickly. The beauty of coroutines is that they handle a lot of the “who’s blocking who” fiasco by suspending tasks that can’t proceed yet, thus freeing up the CPU for work that is ready.

Still, let’s be realistic – there’s no concurrency model that can save you from poor data structures or a single giant lock that’s inadvertently guarding your entire pipeline. But if you implement each stage with coroutines and channels, profile often, and store data efficiently, you’ll be on track for that coveted sub-100 µs latency.

By borrowing patterns like Pipeline, Splitter, and Publish-Subscribe – and sprinkling in some cynicism for good measure – you can build a resilient architecture that doesn’t burst into flames as soon as market volumes spike. Good luck, and may your GC pauses be ever short.

Channel Fan-Out Pattern

When you’re building a matching engine that needs to handle a torrential downpour of orders, you’ll likely want to fan them out across multiple coroutines or processing pipelines. That’s where Kotlin channels swoop in, letting you distribute data without inflicting manual thread juggling on your soul.

The idea is simple – you have a single inbound channel that receives orders, and you want multiple matching engine coroutines to consume them, potentially in parallel. Each consumer handles a subset of orders, helping you achieve higher throughput. Below is a deeper look, complete with code examples that (hopefully) won’t melt your eyes 😉

Single Producer, Multiple Consumers

val inboundOrders = Channel<Order>(capacity = Channel.UNLIMITED)

// Single producer (e.g., inbound gateway)
launch {
    val allOrders = fetchIncomingOrders() // Some method that yields orders in real-time
    for (order in allOrders) {
        inboundOrders.send(order)
    }
    inboundOrders.close() // Signal no more orders
}

// Multiple consumers
repeat(4) { index ->
    launch {
        // Each consumer processes some of the orders
        for (order in inboundOrders) {
            println("[Consumer-$index] Processing order: $order")
            // Perform matching logic or forward to a dedicated matching engine
        }
    }
}

What’s Happening:
- One coroutine (the producer) slams orders into inboundOrders.
- Four consumer coroutines pick them up. Each time a consumer pulls an order from the channel, no other consumer can take that exact order.
Why Use It:
- You can scale your matching load across multiple consumers (like multiple partitions for different symbols).
- The channel automatically ensures safe handoff without locks or race conditions – though you’re not off the hook for logic errors that might lurk elsewhere.

Dedicated Channels for Each Partition

Sometimes, you might not want consumers to share the same queue. For example, maybe each symbol belongs in its own partition (read: dedicated matching engine). Here’s how you can route to separate channels:

// Assume we have 3 partitions for matching different symbols
val partitionChannels = mapOf(
    "AAPL" to Channel<Order>(capacity = Channel.UNLIMITED),
    "TSLA" to Channel<Order>(capacity = Channel.UNLIMITED),
    "AMZN" to Channel<Order>(capacity = Channel.UNLIMITED)
)

val inboundOrders = Channel<Order>(capacity = 1000)

launch {
    for (order in inboundOrders) {
        val channel = partitionChannels[order.symbol]
        // If the symbol isn't recognized, handle gracefully or toss it out
        if (channel != null) {
            channel.send(order)
        } else {
            println("Unknown symbol: ${order.symbol}")
        }
    }
}

// Each partition has its own matching coroutine
partitionChannels.forEach { (symbol, channel) ->
    launch {
        println("Starting matching engine for $symbol")
        for (order in channel) {
            matchOrder(order) // your matching logic
        }
    }
}

Key Points:
- The inboundOrders channel feeds into multiple partition channels, each dedicated to a symbol.
- Keep an eye on memory usage – if you have thousands of symbols, you’ll have thousands of channels. A carefully designed dynamic approach might be necessary for scaling.
- This architecture isolates processing per symbol, which helps avoid concurrency conflicts in the order book data structure.

Fan-Out After Matching

You might also want to fan out after the matching process is done. For instance, you have trades that need to go to risk management, real-time analytics, and a clearing system simultaneously. In that case, you can fan out using multiple subscriptions to the same event channel:

val tradeEvents = BroadcastChannel<TradeEvent>(capacity = 100) 
// or a regular Channel, but BroadcastChannel is sometimes used for multiple subscribers
// though it's being replaced by SharedFlow in Kotlin’s newer APIs

// Matching engine produces trades
launch {
    for (order in inboundOrders) {
        val trades = matchOrder(order)
        trades.forEach { tradeEvents.send(it) }
    }
}

// Multiple downstream consumers
launch {
    val subscription = tradeEvents.openSubscription()
    for (trade in subscription) {
        println("Risk Management received trade: $trade")
        // Evaluate risk, update positions
    }
}

launch {
    val subscription = tradeEvents.openSubscription()
    for (trade in subscription) {
        println("Analytics received trade: $trade")
        // Possibly feed a real-time analytics pipeline
    }
}

launch {
    val subscription = tradeEvents.openSubscription()
    for (trade in subscription) {
        println("Clearing received trade: $trade")
        // Send to clearing house
    }
}

Notes on BroadcastChannel / SharedFlow:
- BroadcastChannel is a bit old-school now; if you’re on newer Kotlin versions, consider using SharedFlow for multiple subscribers.
- Each subscriber gets the full stream of events, so you’re not splitting the trades among them. That’s typically what you want if each downstream consumer needs all trades.

Select for Dynamic Fan-Out

If you want to get fancy, you can use Kotlin’s select expression to handle multiple channels in a single coroutine – or to push messages to whichever consumer is ready first:

val consumerA = Channel<String>()
val consumerB = Channel<String>()

launch {
    while (true) {
        select<Unit> {
            consumerA.onSend("From Producer: Hello A") {
                // Sent a message to A
            }
            consumerB.onSend("From Producer: Hello B") {
                // Sent a message to B
            }
        }
        delay(50) // Just to avoid blasting messages too quickly in this example
    }
}

The snippet above is contrived, but it illustrates how you can dynamically choose which channel to send to based on readiness. In a matching engine, you might use this pattern if you’re distributing orders among multiple partitions but want to account for each partition’s backlog or load.

Performance Tuning Tips

So you’ve decided that anything above 100 µs of latency is a personal affront. Fair enough – here are some practical tips to wrest every drop of performance out of your matching engine, sprinkled with enough dry humor to ease the pain of endless benchmarking.

VM Settings & GC Tuning

The default HotSpot JVM might be fine for a blog’s “hello world,” but if your orders-per-second rate looks more like a phone number, you’ll need to fine-tune. Garbage collection (GC) can introduce latency spikes, so aim for collectors designed with low pauses in mind, such as ZGC or Shenandoah.

Example JVM Flags:

-XX:+UnlockExperimentalVMOptions \
-XX:+UseShenandoahGC \
-XX:ShenandoahGCHeuristics=aggressive \
-XX:MaxGCPauseMillis=5 \
-Xms4g -Xmx4g

The above flags unlock Shenandoah, set it to an “aggressive” heuristic, and attempt to cap GC pauses at 5 ms (still high for HFT, but it’s a starting point). Adjust memory to something sensible for your environment – just don’t let it balloon to the point where the GC has a panic attack.

ZGC Alternative:

-XX:+UnlockExperimentalVMOptions \
-XX:+UseZGC \
-Xms4g -Xmx4g

ZGC also targets low latency. Whichever you choose, benchmark meticulously, because real-world workloads have an uncanny ability to mock your best-laid plans.

Data Structures That Don’t Fight You

You can’t just store orders in a random list and pray for microseconds. You need specialized structures:

Order Books: Implement a custom skip list or a carefully tuned tree that gives you O(log n) (or better) for insert, remove, and lookup.
Arrays for Bids/Asks: For certain markets (like crypto or a specific product where the price range is known), a simple array-based approach might be faster than a tree.
Lock-Free Queues: Whether it’s Disruptor or a ring buffer, watch out for memory alignment and false sharing. But they can yield big gains if used correctly.

A naive example (non-production, but you get the idea) for a lock-free queue approach:

// Just a conceptual snippet, not a fully fledged lock-free structure
class LockFreeQueue<T> {
    private val buffer = atomicArrayOfNulls<T>(size = 1024)
    private val head = atomic(0)
    private val tail = atomic(0)
    
    fun offer(item: T) {
        val t = tail.getAndIncrement()
        buffer[t % buffer.size]?.value = item
    }
    
    fun poll(): T? {
        val h = head.getAndIncrement()
        val elem = buffer[h % buffer.size]?.getAndSet(null)
        return elem
    }
}

This is a bare-bones (and incomplete) demonstration – you’ll need more atomic wizardry to handle wrapping and concurrency safely. But it illustrates how specialized data structures can help you avoid heavy locks.

Pinning Threads / CPU Affinity

In some HFT setups, you can improve determinism by pinning your JVM threads to specific CPU cores, reducing context switches and cache misses. Yes, it’s that level of detail you didn’t want to consider, but here you are:

Use taskset on Linux or OS-specific approaches. If you’re using Docker, you can specify --cpuset-cpus. For example:

taskset -c 2,3 java -jar matching-engine.jar

This locks the JVM process to cores 2 and 3. Then, from code, you can try to bind specific coroutines or threads to those cores. This is a black art, but in hardcore HFT, black arts are basically job requirements.

Minimize Object Allocation

Allocating objects in the hot path is the GC’s best friend and your worst enemy. Every short-lived object is a ticking time bomb for the next garbage collection cycle. Kotlin coroutines are quite efficient, but you still have to watch out.

Inline Functions
Use inline to avoid generating function objects for lambdas when possible:

inline fun <T> measureMicroseconds(action: () -> T): Pair<T, Long> { val start = System.nanoTime() val result = action() val end = System.nanoTime() return Pair(result, (end - start) / 1000) }

Object Pools
If you’re creating lots of small, temporary objects (e.g., Order instances that get reused), consider an object pool or a mutable structure that you reset. Just be sure you don’t accidentally reuse objects in a way that leads to concurrency nightmares.

Asynchronous I/O

Blocking I/O is how you turn your microsecond dream into a millisecond fiasco. Asynchronous frameworks let coroutines suspend rather than block, freeing up the CPU for other tasks.

Ktor + Coroutines for inbound HTTP or WebSocket calls:

routing {
    post("/submitOrder") {
        val order = call.receive<Order>()
        inboundChannel.send(order) // do not block
        call.respond(HttpStatusCode.OK)
    }
}

Netty + Coroutines if you prefer the Netty ecosystem or Aeron or ZeroMQ for ultra-low-latency UDP/messaging. Though you may need custom coroutine adapters to handle them non-blocking.

Instrumentation & Benchmarking

You need metrics for throughput, average latency, and – most critically – tail latency (99th or 99.9th percentile). JMH (Java Microbenchmark Harness) is a good start for microbenchmarks, but for real-world throughput, integrate your own performance counters.

Using JMH
A minimal example:

@State(Scope.Benchmark) class MatchingEngineBenchmark { private lateinit var engine: MatchingEngine @Setup fun setup() { engine = MatchingEngine() } @Benchmark fun measureMatch() { // Send an order and measure how fast it's matched engine.matchOrder(Order("AAPL", 100.0, 10)) } }

Tail Latency
Beyond average times, gather histograms to see the worst-case scenario. Tools like HDR Histogram or built-in stats libraries can help. Because if 1% of your trades take 500 µs, guess which 1% everyone complains about?

Other Snags to Avoid

False Sharing: Align frequently updated variables on cache line boundaries to avoid false sharing. Libraries like @Contended (Java annotation) can help.
Lock Contention: If you see heavy CPU usage without corresponding throughput gains, you might be contending on a single lock. Profilers or concurrency-friendly data structures can save you from this invisible bottleneck.
Silly Mistakes: Printing logs on every order, forgetting to disable debug settings, or ignoring ephemeral network overhead. Yes, it happens more often than you’d like.

Challenges & Pitfalls

Building a sub-100 µs matching engine might sound heroic – but it’s also laden with hidden traps. Here are some of the more delightful challenges that will keep you up at night:

Tail Latency: The Elephant in the Room
Sure, your average latency might clock in at 50 µs. Try explaining to your boss (or risk manager) why one in every thousand requests randomly spikes to 500 µs. Tail latency is the silent killer of HFT systems – even a brief pause can get you leapfrogged in the order queue. Mitigating it involves relentless GC tuning, specialized data structures, and enough patience to watch paint dry while you comb through logs.
Concurrency Bugs
Coroutines make concurrency easier, not foolproof. If you share data across multiple coroutines without careful synchronization, race conditions come knocking. And they rarely do you the courtesy of showing up in dev or staging – they’ll appear in production the moment volume spikes and the CFO is on a plane. Debugging concurrency bugs is like whack-a-mole at 3 a.m., except the moles cost you real money.
GC Hiccups
Even fancy garbage collectors (ZGC, Shenandoah) need time to do their housekeeping. And in HFT, any housekeeping is too long. Be ready to do unnatural acts to reduce object creation, manage memory more manually, and possibly adopt pools. If your system is suddenly freezing at the worst possible times (like market open), GC might be your main suspect.
Hardware Nuances
Not all CPU cores are created equal. NUMA architectures, memory bandwidth limitations, and CPU caches can wreck otherwise brilliant code. Affinity pinning might help, but it’s a dark art. Use it sparingly, or you’ll spend your days explaining to people why your code runs fine on Core 3 but crawls on Core 7.
Network Ingress/Egress
Your matching engine can be lightning-fast, but if your inbound and outbound pipelines aren’t similarly tuned, it’s all for naught. TCP buffering, Nagle’s algorithm, kernel bypass techniques – these are the joys of ensuring your data doesn’t get stuck in transit. Bonus points if you have to integrate with a system that insists on blocking I/O.
Operational Complexity
Deploying a sub-100 µs system across multiple regions, with failover, monitoring, and logging, can make your head spin. High-frequency systems don’t tolerate “maintenance windows” at 9 p.m. on a Friday. If you do a zero-downtime upgrade, every microsecond still counts. Prepare for robust orchestration, possibly custom deployment pipelines, and the relentless question: “Why is the system 10 µs slower after that last release?”
Scalability vs. Determinism
Parallelizing the matching engine can improve throughput but might degrade latency determinism. The more concurrency you throw at the problem, the more you risk lock contention, scheduling overhead, and data consistency headaches. Striking the perfect balance between concurrency and speed is as easy as nailing jelly to a wall.
Human Factors
Let’s not forget the chain of events that often do more damage than any technical flaw – the human element. A single config tweak, a misapplied JVM flag, or a well-intentioned “quick fix” can sabotage months of careful tuning. Document everything, or face a future where your newly hired teammate breaks the entire pipeline by setting -Xmx64g on a 16 GB box.

Conclusion

Building a sub-100 µs matching engine is like staring into the abyss of low-level optimizations, then deciding you’d rather enjoy that abyss. Kotlin coroutines smooth out some of the concurrency wrinkles, but the rest is up to you: tune your JVM, choose fast data structures, manage memory meticulously, and measure every microsecond like it’s the only thing that matters. (Because let’s face it – in high-frequency trading, it often is.)

Though the challenges are non-trivial, the payoff for nailing sub-100 µs is huge. You get an edge in the cutthroat world of HFT, and more importantly, you earn the right to poke fun at all the “slow” systems still dealing with millisecond delays. Just be prepared for that moment when the CFO asks why an occasional 300 µs latency spike destroyed everyone’s quarterly bonuses. That’s when you smile knowingly, roll up your sleeves, and remind yourself that low-latency systems are a journey – one sprint of GC tuning at a time.