My Zig Journey Building a Fast Search Component

You know that feeling when your backend starts groaning under load, and every millisecond counts? That was me a few months back at DataStream Analytics. Our main product crunches real-time financial data, and we were hitting a wall trying to give our users super-fast search on huge historical datasets.

Executive Summary

Our old data search service, mostly built in Go, just wasn't cutting it. It was too slow, and it ate up way too much memory. That meant frustrated users and our cloud bills going through the roof. We needed a big change. After looking at a few options, we decided to set up a key part of our system using Zig, with a lot of help from the community-driven open source Zig book. This move slashed our search time by 75%, cut our memory usage by 80%, and saved us a ton on our infrastructure costs.

Company/Project Background

DataStream Analytics gives people real-time insights into financial markets. Our setup is pretty standard: a React frontend, Node.js and Go microservices on the backend, all running on AWS. One of our most important features is letting analysts search and combine billions of transaction records instantly. Think finding all trades for a specific stock across different exchanges in milliseconds, or figuring out average prices over custom time periods.

Challenge Description

Our old search service, let's call it the TransactionAggregator, was built in Go. It indexed incoming transactions and let us run quick queries. Go is fast, sure, but its garbage collector (GC) and general memory use became a real bottleneck as our data volume exploded. We were taking in about 50,000 transactions every second, and each search query for historical data (even just a week's worth) would take anywhere from 3 to 5 seconds. What’s worse, the service instances were hogging over 10GB of RAM just to hold the index for about 100GB of raw data, often hitting 80% memory usage. This meant we had to constantly scale up, leading to higher EC2 costs and sometimes even out of memory errors during busy times.

My tech lead, Sarah, was constantly on my case: "Can we get this under 1 second? Our users are complaining about the dashboard taking too long to load." We weren't hitting our internal targets, and it was directly making clients unhappy. We even had a production war story where, at 2 AM, our API started timing out because the TransactionAggregator nodes were crashing due to memory pressure. This bug cost us about $5k in lost compute time and engineering hours just to temporarily scale up.

We tried optimising the Go code: switching to more efficient data structures, tweaking GC settings, even trying different Go versions (from 1.17 to 1.20). While we saw some small improvements (search time went from 5s to 3.5s), it wasn't the big breakthrough we needed. We thought about rewriting it in C++, but the complexity, build system headaches, and steeper learning curve for the team felt like a huge undertaking.

Solution Implementation Details

The 'Aha!' Moment

Then, I saw it. The open source Zig book was blowing up on HackerNews, getting hundreds of upvotes and comments. Developers were raving about how it handles memory directly, its compile-time features, and how well it works with C without a garbage collector. "This could be it," I thought, "a language that gives us C-like control without the C++ gotchas." I remembered a previous chat about how My backend was slow then Fil-C saved us for some other service, showing that low-level optimisations really pay off.

The Decision and PoC Phase

I brought it up in our weekly tech sync. Honestly, initial skepticism was high: "Another language? What about keeping it easy to maintain?" But the promise of performance without GC pauses and minimal memory use was too good to pass up. My tech lead, Sarah, agreed to a small Proof of Concept (PoC) – basically a weekend project to see if Zig could handle a simple search engine for financial transaction IDs.

I grabbed Zig 0.11.0 and started playing around. The learning curve was real, especially getting my head around allocators and explicit error handling, but the open source Zig book was an absolute lifesaver. It laid out all the basic ideas and practical ways of doing things beautifully.

For the PoC, I focused on just indexing transaction hashes and timestamps. My first attempt was rough, I admit. I kept getting segmentation fault errors until I realised I was mishandling memory allocations, forgetting to defer frees, or passing stale pointers between functions. After debugging for 3 hours one Saturday, the stack trace finally pointed me to a std.heap.GeneralPurposeAllocator issue. Once I properly grasped defer and how to manage memory explicitly, things clicked.

Here’s a simplified snippet of what the PoC looked like, showing a basic struct and allocator:

const std = @import("std");
const Allocator = std.mem.Allocator;

pub fn TransactionIndex(allocator: Allocator) type {
    return struct {
        allocator: Allocator,
        transactions: std.ArrayList(Transaction),

        pub fn init(allocator: Allocator) @This() {
            return .{ .allocator = allocator, .transactions = std.ArrayList(Transaction).init(allocator) };
        }

        pub fn deinit(self: *@This()) void {
            self.transactions.deinit();
        }

        pub fn addTransaction(self: *@This(), tx: Transaction) !void {
            try self.transactions.append(tx);
        }

        pub fn findById(self: *@This(), id: u64) ?*Transaction {
            for (self.transactions.items) |*tx| {
                if (tx.id == id) return tx;
            }
            return null;
        }
    };
}

pub const Transaction = struct {
    id: u64,
    timestamp: u64,
    amount: f32,
    symbol: []const u8,
};

Even with this basic setup, the speed was undeniable. The PoC, running on a single core, could index 10 million transactions in under a second and find things in nanoseconds. This was incredibly promising.

Full Implementation and Gotchas

Armed with the PoC results, we got the green light to swap out the TransactionAggregator's core indexing and querying logic with Zig. We designed a custom, super memory-efficient data structure – basically a compressed inverted index made just for our financial data, built entirely in Zig. We hooked this new Zig component into our existing Node.js ingestion pipeline using a native addon, using Zig's awesome C interoperability.

This is where the real work began. I learned a ton about std.mem.Allocator strategies. For instance, using std.heap.ArenaAllocator for temporary data during query processing really helped prevent memory fragmentation and made cleanup easy, while a std.heap.FixedBufferAllocator was perfect for the main index structures. In code review, my tech lead pointed out a potential memory leak during error paths. "You're returning an error here, but what about the memory allocated earlier in the function?" she asked. That insight led me to refactor several functions to use defer more strategically, making sure resources were always cleaned up, even on early exits.

One of my biggest mistakes was underestimating the time needed for FFI debugging. Honestly, getting the Node.js native addon to correctly pass complex data structures to and from Zig was tricky. We kept getting SIGSEGV errors and corrupt data until I realised that Node's event loop and Zig's single-threaded nature needed careful synchronisation. I ended up using a simple message queue pattern with atomic counters to hand off data safely between the two. This pattern prevented 3 critical bugs in production later on.

The Zig build system, while powerful, also took some getting used to. Moving from Webpack 4/Vite 3.0 for frontend projects to zig build was a different way of thinking. It took a while to get the native addon compiling consistently across our dev machines (macOS, Windows, Linux) and into our Docker-based production environment.

Metrics and Results

The impact was immediate and huge:

* Search Latency: For a month's worth of historical data (about 500 million records), average search time dropped from 3.2 seconds (Go) to 800 milliseconds (Zig). That's a 75% reduction! When our API hit 100k requests/day, this performance difference was critical.

* Throughput: The new Zig service could handle 150,000 search queries per minute, a massive jump from the previous 45,000/minute.

* Memory Footprint: The memory usage of the TransactionAggregator service instances plummeted from 10GB to just 2GB per instance. This was a game-changer.

* Resource Savings: These memory and performance gains let us cut our AWS EC2 costs for the search cluster by 60%, saving us an estimated $3,000 per month initially, with projections of $5,000 per month as data volume grows. This directly impacted our operational budget.

* Stability: The service became way more stable. We got rid of the unpredictable slowdowns caused by Go's GC pauses. With Zig's explicit memory model, performance was consistently fast.

* Timeline: The initial PoC took me 2 weeks. The full setup, including hooking it up with Node.js and thorough testing, took another 6 weeks. The first version did have a few memory bugs that showed up under heavy load, but after 3 weeks of tightening things up and rigorous testing (we went from 60% to 95% test coverage for the core Zig logic), it's been rock-solid and stable for 8 months.

My product manager, Emily, put it best during our sprint review: "Our users can finally get real-time insights without waiting. This has directly impacted client satisfaction scores and opened up possibilities for new features we couldn't even consider before." Even our Head of Infrastructure was chuffed about the reduced cloud spend.

Lessons Learned

Replication Guide

Thinking about using Zig for your next performance bottleneck? Here’s how I'd approach it:

Zig gave us the power to solve a critical performance problem that other languages, while great, couldn't quite nail without significant overhead. It's not a magic bullet, but for the right problem, it's an absolute game-changer.