Switch branches/tags
Nothing to show
Find file Copy path
1132 lines (883 sloc) 53 KB

Writing and Optimizing Go code

This document outlines best practices for writing high-performance Go code.

While some discussions will be made for making individual services faster (caching, etc), designing performant distributed systems is beyond the scope of this work. There are already good texts on monitoring and distributed system design. It encompasses an entirely different set of research and design trade-offs.

All the content will be licensed under CC-BY-SA.

This book is split into different sections:

  1. Basic tips for writing not-slow software
    • CS 101-level stuff
  2. Tips for writing fast software
    • Go-specific sections on how to get the best from Go
  3. Advanced tips for writing really fast software
    • For when your optimized code isn't fast enough

We can summarize these three sections as:

  1. "Be reasonable"
  2. "Be deliberate"
  3. "Be dangerous"

When and Where to Optimize

I'm putting this first because it's really the most important step. Should you even be doing this at all?

Every optimization has a cost. Generally this cost is expressed in terms of code complexity or cognitive load -- optimized code is rarely simpler than the unoptimized version.

But there's another side that I'll call the economics of optimization. As a programmer, your time is valuable. There's the opportunity cost of what else you could be working on for your project, which bugs to fix, which features to add. Optimizing things is fun, but it's not always the right task to choose. Performance is a feature, but so is shipping, and so is correctness.

Choose the most important thing to work on. Sometimes it's not an actual CPU optimization, but a user-experience one. Something as simple as adding a progress bar, or making a page more responsive by doing computation in the background after rendering the page.

Sometimes this will be obvious: an hourly report that completes in three hours is probably less useful than one that completes in less than one.

Just because something is easy to optimize doesn't mean it's worth optimizing. Ignoring low-hanging fruit is a valid development strategy.

Think of this as optimizing your time.

You get to choose what to optimize and when to optimize. You can move the slider between "Fast Software" and "Fast Deployment"

People hear and mindlessly repeat "premature optimization is the root of all evil", but they miss the full context of the quote.

"Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%." -- Knuth


  • don't ignore the easy optimizations
  • more knowledge of algorithms and data structures makes more optimizations "easy" or "obvious"

"Should you optimize? "Yes, but only if the problem is important, the program is genuinely too slow, and there is some expectation that it can be made faster while maintaining correctness, robustness, and clarity." -- The Practice of Programming, Kernighan and Pike

BitFunnel performance estimation has some numbers that make this trade-off explicit. Imagine a hypothetical search engine needing 30,000 machines across multiple data centers. These machines have a cost of approximately $1,000 USD per year. If you can double the speed of the software, this can save the company $15M USD per year. Even a single developer spending an entire year to improve performance by only 1% will pay for itself.

In the vast majority of cases, the size and speed of a program is not a concern. Easiest optimization is not having to do it. The second easiest optimization is just buying faster hardware.

Once you've decided you're going to change your program, keep reading.

How to Optimize

Optimization Workflow

Before we get into the specifics, lets talk about the general process of optimization.

Optimization is a form of refactoring. But each step, rather than improving some aspect of the source code (code duplication, clarity, etc), improves some aspect of the performance: lower CPU, memory usage, latency, etc. This improvement generally comes at the cost of readability. This means that in addition to a comprehensive set of unit tests (to ensure your changes haven't broken anything), you also need a good set of benchmarks to ensure your changes are having the desired effect on performance. You must be able to verify that your change really is lowering CPU. Sometimes a change you thought would improve performance will actually turn out to have a zero or negative change. Always make sure you undo your fix in these cases.

What is the best comment in source code you have ever encountered? - Stack Overflow:

// Dear maintainer:
// Once you are done trying to 'optimize' this routine,
// and have realized what a terrible mistake that was,
// please increment the following counter as a warning
// to the next guy:
// total_hours_wasted_here = 42

The benchmarks you are using must be correct and provide reproducible numbers on representative workloads. If individual runs have too high a variance, it will make small improvements more difficult to spot. You will need to use benchstat or equivalent statistical tests and won't be able just eyeball it. (Note that using statistical tests is a good idea anyway.) The steps to run the benchmarks should be documented, and any custom scripts and tooling should be committed to the repository with instructions for how to run them. Be mindful of large benchmark suites that take a long time to run: it will make the development iterations slower.

Note also that anything that can be measured can be optimized. Make sure you're measuring the right thing.

The next step is to decide what you are optimizing for. If the goal is to improve CPU, what is an acceptable speed? Do you want to improve the current performance by 2x? 10x? Can you state it as "a problem of size N in less than time T"? Are you trying to reduce memory usage? By how much? How much slower is acceptable for what change in memory usage? What are you willing to give up in exchange for lower space requirements?

Optimizing for service latency is a trickier proposition. Entire books have been written on how to performance test web servers. The primary issue is that for a single function, performance is fairly consistent for a given problem size. For webservices, you don't have a single number. A proper web-service benchmark suite will provide a latency distribution for a given reqs/second level. This talk gives a good overview of some of the issues: "How NOT to Measure Latency" by Gil Tene

TODO: See the later section on optimizing web services

The performance goals must be specific. You will (almost) always be able to make something faster. Optimizing is frequently a game of diminishing returns. You need to know when to stop. How much effort are you going to put into getting the last little bit of work. How much uglier and harder to maintain are you willing to make the code?

Dan Luu's previously mentioned talk on BitFunnel performance estimation shows an example of using rough calculations to determine if your target performance figures are reasonable.

TODO: Programming Pearls has "Fermi Problems". Knowing Jeff Dean's slide helps.

For greenfield development, you shouldn't leave all benchmarking and performance numbers until the end. It's easy to say "we'll fix it later", but if performance is really important it will be a design consideration from the start. Any significant architectural changes required to fix performance issues will be too risky near the deadline. Note that during development, the focus should be on reasonable program design, algorithms, and data structures. Optimizing at lower-levels of the stack should wait until later in the development cycle when a more complete view of the system performance is available. Any full-system profiles you do while the system is incomplete will give a skewed view of where the bottlenecks will be in the finished system.

TODO: How to avoid/detect "Death by 1000 cuts" from poorly written software.

Benchmarking as part of CI is hard due to noisy neighbours and even different CI boxes if it's just you. Hard to gate on performance metrics. A good middle ground is to have benchmarks run by the developer (on appropriate hardware) and included in the commit message for commits that specifically address performance. For those that are just general patches, try to catch performance degradations "by eye" in code review.

TODO: how to track performance over time?

Write code that you can benchmark. Profiling you can do on larger systems. Benchmarking you want to test isolated pieces. You need to be able to extract and setup sufficient context that benchmarks test enough and are representative.

The difference between what your target is and the current performance will also give you an idea of where to start. If you need only a 10-20% performance improvement, you can probably get that with some implementation tweaks and smaller fixes. If you need a factor of 10x or more, then just replacing a multiplication with a left-shift isn't going to cut it. That's probably going to call for changes up and down your stack.

Good performance work requires knowledge at many different levels, from system design, networking, hardware (CPU, caches, storage), algorithms, tuning, and debugging. With limited time and resources, consider which level will give the most improvement: it won't always be algorithm or program tuning.

In general, optimizations should proceed from top to bottom. Optimizations at the system level will have more impact than expression-level ones. Make sure you're solving the problem at the appropriate level.

This book is mostly going to talk about reducing CPU usage, reducing memory usage, and reducing latency. It's good to point out that you can very rarely do all three. Maybe CPU time is faster, but now your program uses more memory. Maybe you need to reduce memory space, but now the program will take longer.

Amdahl's Law tells us to focus on the bottlenecks. If you double the speed of routine that only takes 5% of the runtime, that's only a 2.5% speedup in total wall-clock. On the other hand, speeding up routine that takes 80% of the time by only 10% will improve runtime by almost 8%. Profiles will help identify where time is actually spent.

When optimizing, you want to reduce the amount of work the CPU has to do. Quicksort is faster than bubble sort because it solves the same problem (sorting) in fewer steps. It's a more efficient algorithm. You've reduced the work the CPU needs to do in order to accomplish the same task.

Program tuning, like compiler optimizations, will generally make only a small dent in the total runtime. Large wins will almost always come from an algorithmic change or data structure change, a fundamental shift in how your program is organized. Compiler technology improves, but slowly. Proebsting's Law says compilers double in performance every 18 years, a stark contrast with the (slightly misunderstood interpretation) of Moore's Law that doubles processor performance every 18 months. Algorithmic improvements work at larger magnitudes. Algorithms for mixed integer programming improved by a factor of 30,000 between 1991 and 2008. For a more concrete example, consider this breakdown of replacing a brute force geo-spacial algorithm described in an Uber blog post with more specialized one more suited to the presented task. There is no compiler switch that will give you an equivalent boost in performance.

TODO: Optimizing floating point FFT and MMM algorithm differences in gttse07.pdf

A profiler might show you that lots of time is spent in a particular routine. It could be this is an expensive routine, or it could be a cheap routine that is just called many many times. Rather than immediately trying to speed up that one routine, see if you can reduce the number of times it's called or eliminate it completely. We'll discuss more concrete optimization strategies in the next section.

The Three Optimization Questions:

  • Do we have to do this at all? The fastest code is the code that's never run.
  • If yes, is this the best algorithm.
  • If yes, is this the best implementation of this algorithm.

Concrete optimization tips

Jon Bentley's 1982 work "Writing Efficient Programs" approached program optimization as an engineering problem: Benchmark. Analyze. Improve. Verify. Iterate. A number of his tips are now done automatically by compilers. A programmers job is to use the transformations compilers can't do.

There are summaries of the book:

and the program tuning rules:

When thinking of changes you can make to your program, there are two basic options: you can either change your data or you can change your code.

Data Changes

Changing your data means either adding to or altering the representation of the data you're processing. From a performance perspective, some of these will end up changing the O() associated with different aspects of the data structure.

Ideas for augmenting your data structure:

  • Extra fields

    For example, store the size of a linked lists rather than iterating when asked for it. Or storing additional pointers to frequently needed other nodes to multiple searches (for example, "backwards" links in a doubly-linked list to make removal O(1) ). These sorts of changes are useful when the data you need is cheap to store and keep up-to-date.

  • Extra search indexes

    Most data structures are designed for a single type of query. If you need two different query types, having an additional "view" onto your data can be large improvement. For example, []struct, referenced by ID but sometimes string -> map[string]id (or *struct).

  • Extra information about elements

    For example, a bloom filter. These need to be small and fast to not overwhelm the rest of the data structure.

  • If queries are expensive, add a cache.

    We're all familiar with memcache, but there are in-process caches.

    • Over the wire, the network + cost of serialization will hurt.
    • In-process caches, but now you need to worry about expiration and added GC pressure
    • Even a single item can help (logfile time parse example).

    TODO: "cache" might not even be key-value, just a pointer to where you were working. This can be as simple as a "search finger"

These are all clear examples of "do less work" at the data structure level. They all cost space. Most of the time if you're optimizing for CPU, your program will use more memory. This is the classic space-time trade-off.

If your program uses too much memory, it's also possible to go the other way. Reduce space usage in exchange for increased computation. Rather than storing things, calculate them every time. You can also compress the data in memory and decompress it on the fly when you need it.

Small Memory Software is a book available online covering techniques for reducing the space used by your programs. While it was originally written targeting embedded developers, the ideas are applicable for programs on modern hardware dealing with huge amounts of data.

  • Rearrange your data

    Eliminate structure padding. Remove extra fields. Use a smaller data type.

  • Change to a slower data structure

    Simpler data structures frequently have lower memory requirements. For example, moving from a pointer-heavy tree structure to use slice and linear search instead.

  • Custom compression format for your data

    []byte (snappy, gzip, lz4), floating point (go-tsz), integers (delta, xor + huffman) Lots of resources on compression. Do you need to inspect the data or can it stay compressed? Do you need random access or only streaming? Compress blocks with extra index. If not just in-process but written to disk, what about migration or adding/removing fields. You'll now be dealing with raw []byte instead of nice structured Go types.

We will talk more about data layouts later.

Modern computers and the memory hierarchy make the space/time trade-off less clear. It's very easy for lookup tables to be "far away" in memory (and therefore expensive to access) making it faster to just recompute a value every time it's needed.

This also means that benchmarking will frequently show improvements that are not realized in the production system due to cache contention (e.g., lookup tables are in the processor cache during benchmarking but always flushed by "real data" when used in a real system. Google's Jump Hash paper in fact addressed this directly, comparing performance on both a contended and uncontended processor cache. (See graphs 4 and 5 in the Jump Hash paper)

TODO: how to simulate a contended cache, show incremental cost

Another aspect to consider is data-transfer time. Generally network and disk access is very slow, and so being able to load a compressed chunk will be much faster than the extra CPU time required to decompress the data once it has been fetched. As always, benchmark. A binary format will generally be smaller and faster to parse than a text one, but at the cost of no longer being as human readable.

For data transfer, move to a less chatty protocol, or augment the API to allow partial queries. For example, an incremental query rather than being forced to fetch the entire dataset each time.

Algorithmic Changes

If you're not changing the data, the other main option is to change the code.

The biggest improvement is likely to come from an algorithmic changes. This is the equivalent of replacing bubble sort (O(n^2)) with quicksort (O(n log n)) or replacing a linear scan through an array (O(n)) that used to be small with a map lookup (O(1)).

This is how software becomes slow. Structures originally designed for one use is repurposed for something it wasn't designed for. This happens gradually.

It's important to have an intuitive grasp of the different big-O levels. Choose the right data structure for your problem. You don't have to always shave cycles, but this just prevents dumb performance issues that might not be noticed until much later.

The basic classes of complexity are:

  • O(1): a field access, array or map lookup

    Advice: don't worry about it

  • O(log n): binary search

    Advice: only a problem if it's in a loop

  • O(n): simple loop

    Advice: you're doing this all the time

  • O(n log n): divide-and-conquer, sorting

    Advice: still fairly fast

  • O(n*m): nested loop / quadratic

    Advice: be careful and constrain your set sizes

  • Anything else between quadratic and subexponential

    Advice: don't run this on a million rows

  • O(b ^ n), O(n!): exponential and up

    Advice: good luck if you have more than a dozen or two data points


Let's say you need to search through of an unsorted set of data. "I should use a binary search" you think, knowing that a binary search is O(log n) which is faster than the O(n) linear scan. However, a binary search requires that the data is sorted, which means you'll need to sort it first, which will take O(n log n) time. If you're doing lots of searches, then the upfront cost of sorting will pay off. On the other hand, if you're mostly doing lookups, maybe having an array was the wrong choice and you'd be better off paying the O(1) lookup cost for a map instead.

If your data structure is static, then you can generally do much better than the dynamic case. It becomes easier to build an optimal data structure customized for exactly your lookup patterns. Solutions like minimal perfect hashing can make sense here, or precomputed bloom filters. This also make sense if your data structure is "static" for long enough and you can amortize the up-front cost of construction across many lookups.

Choose the simplest reasonable data structure and move on. This is CS 101 for writing "not-slow software". This should be your default development mode. If you know you need random access, don't choose a linked-list. If you know you need in-order traversal, don't use a map. Requirements change and you can't always guess the future. Make a reasonable guess at the workload.

Data structures for similar problems will differ in when they do a piece of work. A binary tree sorts a little at a time as inserts happen. A unsorted array is faster to insert but it's unsorted: at the end to "finalize" you need to do the sorting all at once.

When writing a package to be used by others, avoid the temptation to optimize up front for every single use case. This will result in unreadable code. Data structures by design are effectively single-purpose. You can neither read minds nor predict the future. If a user says "Your package is too slow for this use case", a reasonable answer might be "Then use this other package over here". A package should "do one thing well".

Sometimes hybrid data structures will provide the performance improvement you need. For example, by bucketing your data you can limit your search to a single bucket. This still pays the theoretical cost of O(n), but the constant will be smaller. We'll revisit these kinds of tweaks when we get to program tuning.

Two things that people forget when discussion big-O notation:

One, there's a constant factor involved. Two algorithms which have the same algorithmic complexity can have different constant factors. Imagine looping over a list 100 times vs just looping over it once. Even though both are O(n), one has a constant factor that's 100 times higher.

These constant factors are why even though merge sort, quicksort, and heapsort all O(n log n), everybody uses quicksort because it's the fastest. It has the smallest constant factor.

The second thing is that big-O only says "as n grows to infinity". It talks about the growth trend, "As the numbers get big, this is the growth factor that will dominate the run time." It says nothing about the actual performance, or how it behaves with small n.

There's frequently a cut-off point below which a dumber algorithm is faster. A nice example from the Go standard library's sort package. Most of the time it's using quicksort, but it has a shell-sort pass then insertion sort when the partition size drops below 12 elements.

For some algorithms, the constant factor might be so large that this cut-off point may be larger than all reasonable inputs. That is, the O(n^2) algorithm is faster than the O(n) algorithm for all inputs that you're ever likely to deal with.

This also means you need to know representative input sizes, both for choosing the most appropriate algorithm and for writing good benchmarks. 10 items? 1000 items? 1000000 items?

This also goes the other way: For example, choosing to use a more complicated data structure to give you O(n) scaling instead of O(n^2), even though the benchmarks for small inputs got slower. This also applies to most lock-free data structures. They're generally slower in the single-threaded case but more scalable when many threads are using it.

The memory hierarchy in modern computers confuses the issue here a little bit, in that caches prefer the predictable access of scanning a slice to the effectively random access of chasing a pointer. Still, it's best to begin with a good algorithm. We will talk about this in the hardware-specific section.

The fight may not always go to the strongest, nor the race to the fastest, but that's the way to bet. -- Rudyard Kipling

Sometimes the best algorithm for a particular problem is not a single algorithm, but a collection of algorithms specialized for slightly different input classes. This "polyalgorithm" quickly detects what kind of input it needs to deal with and then dispatches to the appropriate code path. This is what the sorting package mentioned above does: determine the problem size and choose a different algorithm. In addition to combining quicksort, shell sort, and insertion sort, it also tracks recursion depth of quicksort and calls heapsort if necessary. The string and bytes packages do something similar, detecting and specializing for different cases. As with data compression, the more you know about what your input looks like, the better your custom solution can be. Even if an optimization is not always applicable, complicating your code by determining that it's safe to use and executing different logic can be worth it.

This also applies to subproblems your algorithm needs to solve. For example, being able to use radix sort can have a significant impact on performance, or using quickselect if you only need a partial sort.

Sometimes rather than specialization for your particular task, the best approach is to abstract it into a more general problem space that has been well-studied by researchers. Then you can apply the more general solution to your specific problem. Mapping your problem into a domain that already has well-researched implementations can be a significant win.

Similarly, using a simpler algorithm means that tradeoffs, analysis, and implementation deals are more likely to be more studied and well understood than more esoteric or exotic and complex ones.

TODO: improve worst-case behaviour at slight cost to average runtime linear-time regexp matching randomized algorithms improve worse-case running time skip-list, treap, randomized marking, primality testing, randomized pivot for quicksort power of two random choices statistical approximations (frequently depend on sample size and not population size)

TODO: batching to reduce overhead:

Benchmark Inputs

Know how big each of your input sizes is likely to be in production.

Your benchmarks must use appropriately-sized inputs. As we've seen, different algorithms make sense at different input sizes. If your expected input range is <100, then your benchmarks should reflect that. Otherwise, choosing an algorithm which is optimal for n=10^6 might not be the fastest.

Be able to generate representative test data. Different distributions of data can provoke different behaviours in your algorithm: think of the classic "quicksort is O(n^2) when the data is sorted" example. Similarly, interpolation search is O(log log n) for uniform random data, but O(n) worst case. Knowing what your inputs look like is the key to both representative benchmarks and for choosing the best algorithm. If the data you're using to test isn't representative of real workloads, you can easily end up optimizing for one particular data set, "overfitting" your code to work best with one specific set of inputs.

This also means your benchmark data needs to be representative of the real world. If repeated requests are sufficiently rare, it's more expensive to keep them around than to recompute them. If your benchmark data consists of only the same repeated request, your cache will give an inaccurate view of the performance.

TODO: randomized inputs may have nice properties but not be representative of real-world inputs (random graphs, random trees, etc)

Also note that some issues that are not apparent on your laptop might be visible once you deploy to production and are hitting 250k reqs/second on a 40 core server.

Writing good benchmarks can be difficult.

TODO: cases where microbenchmarks show a slow down but macro (real-world) performance improves.

Program Tuning

Program tuning used to be an art form, but then compilers got better. So now it turns out that compilers can optimize straight-forward code better than complicated code. The Go compiler still has a long way to go to match gcc and clang, but it does mean that you need to be careful when tuning and especially when upgrading Go versions that your code doesn't become "worse". There are definitely cases where tweaks to work around the lack of a particular compiler optimization became slower once the compiler was improved.


If you are working around a specific runtime or compiler code generation issue, always document your change with a link to the upstream issue. This will allow you to quickly revisit your optimization once the bug is fixed.

Fight the temptation to cargo cult folklore-based "performance tips", or even over-generalize from your own experience. Each performance bug needs to be approached on its own merits. Even if something has worked previously, make sure to profile to ensure the fix is still applicable. Your previous work can guide you, but don't apply previous optimizations blindly.

Program tuning is an iterative process. Keep revisiting your code and seeing what changes can be made. Ensure you're making progress at each step. Frequently one improvement will enable others to be made. (Now that I'm not doing A, I can simplify B by doing C instead.) This means you need to keep looking at the entire picture and not get to obsessed with one small set of lines.

Once you've settled on the right algorithm, program tuning is the process of improving the implementation of that algorithm. In Big-O notation, this is the process of reducing the constants associated with your program.

All program tuning is either making a slow thing fast, or doing a slow thing fewer times. Algorithmic changes also fall into these categories, but we're going to be looking at smaller changes. Exactly how you do this varies as technologies change.

Making a slow thing fast might be replacing SHA1 or hash/fnv1 with a faster hash function. Doing a slow thing fewer times might be saving the result of the hash calculation of a large file so you don't have to do it multiple times.

Keep comments. If something doesn't need to be done, explain why. Frequently when optimizing an algorithm you'll discover steps that don't need to be performed under some circumstances. Document them. Somebody else might think it's a bug and needs to be put back.

Empty programs gives the wrong answer in no time at all.

It's easy to be fast if you don't have to be correct.

"Correctness" can depend on the problem. Heuristic algorithms that are mostly-right most of the time can be fast, as can algorithms which guess and improve allowing you to stop when you hit an acceptable limit.

Cache common cases:

  • Your cache doesn't even need to be huge.
    • see time.Parse() example below; just a single value made an impact
  • But beware cache invalidation, thread issues, etc.
  • Random cache eviction is fast and sufficiently effective.
  • Random cache insertion can limit cache to popular items with minimal logic.
  • Compare cost of cache logic to cost of refetching the data.
  • A large cache can increase GC pressure and keep blowing processor cache.
  • At the extreme (little or no eviction, caching all requests to an expensive function) this can turn into memoization

I've done experiments with a network trace for a service that showed even an optimal cache wasn't worth it. Your expected hit ratio is important. You'll want to export the ratio to your monitoring stack. Changing ratios will show a shift in traffic. Then it's time to revisit the cache size or the expiration policy.

Program tuning:

Program tuning is the art of iteratively improving a program in small steps. Egon Elbre lays out his procedure:

  • Come up with a hypothesis as to why your program is slow.
  • Come up with N solutions to solve it
  • Try them all and keep the fastest.
  • Keep the second fastest just in case.
  • Repeat.

Tunings can take many forms.

  • If possible, keep the old implementation around for testing.
  • If not possible, generate sufficient golden test cases to compare output to.
  • "Sufficient" means including edge cases, as those are the ones likely to get affected by tuning as you aim to improve performance in the general case.
  • Exploit a mathematical identity:
    • multiplication with addition
    • use WolframAlpha, Maxima, sympy and similar to specialize, optimize or create lookup-tables
    • (Also,
    • "pay only for what you use, not what you could have used"
      • zero only part of an array, rather than the whole thing
    • best done in tiny steps, a few statements at a time
    • moving from floating point math to integer math
    • or mandelbrot removing sqrt, or lttb removing abs, a < b/c => a * c < b
    • cheap checks before more expensive checks:
      • e.g., strcmp before regexp, (q.v., bloom filter before query) "do expensive things fewer times"
    • common cases before rare cases i.e., avoid extra tests that always fail
    • unrolling still effective:
      • code size. vs branch test overhead
    • using offsets instead of slice assignment can help with bounds checks, data dependencies, and code gen (less to copy in inner loop).
    • this is where pieces of Hacker's Delight fall
    • consider different number representations: fixed-point, floating-point, (smaller) integers,
      • fancier: integers with error accumulators (e.g. Bresenham's line and circle), multi-base numbers / redundant number systems

Many folklore performance tips for tuning rely on poorly optimizing compilers and encourage the programmer to do these transformations by hand. Compilers have been using shifts instead of multiplying or dividing by a power of two for 15 years now -- nobody should be doing that by hand. Similarly, hoisting invariant calculations out of loops, basic loop unrolling, common sub-expression elimination and many others are all done automatically by gcc and clang and the like. Go's compiler does many of these and continues to improve. As always, benchmark before committing to the new version.

The transformations the compiler can't do rely on you knowing things about the algorithm, about your input data, about invariants in your system, and other assumptions you can make, and factoring that implicit knowledge into removing or altering steps in the data structure.

Every optimization codifies an assumption about your data. These must be documented and, even better, tested for. These assumptions are going to be where your program crashes, slows down, or starts returning incorrect data as the system evolves.

Program tuning improvements are cumulative. 5x 3% improvements is a 15% improvement. When making optimizations, it's worth it to think about the expected performance improvement. Replacing a hash function with a faster one is a constant factor improvement.

Understanding your requirements and where they can be altered can lead to performance improvements. One issue that was presented in the #performance Gophers Slack channel was the amount of time that was spent creating a unique identifier for a map of string key/value pairs. The original solution was to extract the keys, sort them, and pass the resulting string to a hash function. The improved solution we came up was to individually hash the keys/values as they were added to the map, then xor all these hashes together to create the identifier.

Here's an example of specialization.

Let's say we're processing a massive log file for a single day, and each line begins with a time stamp.

Sun  4 Mar 2018 14:35:09 PST <...........................>

For each line, we're going to call time.Parse() to turn it into a epoch. If profiling shows us time.Parse() is the bottleneck, we have a few options to speed things up.

The easiest is to keep a single-item cache of the previously seen time stamp and the associated epoch. As long as our log file has multiple lines for a single second, this will be a win. For the case of a 10 million line log file, this strategy reduces the number of expensive calls to time.Parse() from 10,000,000 to 86400 -- one for each unique second.

TODO: code example for single-item cache

Can we do more? Because we know exactly what format the timestamps are in and that they all fall in a single day, we can write custom time parsing logic that takes this into account. We can calculate the epoch for midnight, then extract hour, minute, and second from the timestamp string -- they'll all be in fixed offsets in the string -- and do some integer math.

TODO: code example for string offset version

In my benchmarks, this reduced the time parsing from 275ns/op to 5ns/op. (Of course, even at 275 ns/op, you're more likely to be blocked on I/O and not CPU for time parsing.)

The general algorithm is slow because it has to handle more cases. Your algorithm can be faster because you know more about your problem. But the code is more closely tied to exactly what you need. It's much more difficult to update if the time format changes.

Optimization is specialization, and specialized code is more fragile to change than general purpose code.

The standard library implementations need to be "fast enough" for most cases. If you have higher performance needs you will probably need specialized implementations.

Profile regularly to ensure to track the performance characteristics of your system and be prepared to re-optimize as your traffic changes. Know the limits of your system and have good metrics that allow you to predict when you will hit those limits.

When the usage of your application changes, different pieces may become hotspots. Revisit previous optimizations and decide if they're still worth it, and revert to more readable code when possible. I had one system that I had optimized process startup time with a complex set of mmap, reflect, and unsafe. Once we changed how the system was deployed, this code was no longer required and I replaced it with much more readable regular file operations.

Optimization workflow summary

All optimizations should follow these steps:

  1. determine your performance goals and confirm you are not meeting them
  2. profile to identify the areas to improve.
    • This can be CPU, heap allocations, or goroutine blocking.
  3. benchmark to determine the speed up your solution will provide using the built-in benchmarking framework (
    • Make sure you're benchmarking the right thing on your target operating system and architecture.
  4. profile again afterwards to verify the issue is gone
  5. use or to verify that a set of timings are 'sufficiently' different for an optimization to be worth the added code complexity.
  6. use for load testing http services (+ other fancy ones: k6, fortio, ...)
  7. make sure your latency numbers make sense

The first step is important. It tells you when and where to start optimizing. More importantly, it also tells you when to stop. Pretty much all optimizations add code complexity in exchange for speed. And you can always make code faster. It's a balancing act.


Introductory Profiling

Techniques applicable to source code in general

  1. Introduction to pprof
  2. Writing and running (micro)benchmarks
    • profile, extract hot code to benchmark, optimize benchmark, profile.
    • -cpuprofile / -memprofile / -benchmem
    • 0.5 ns/op means it was optimized away -> how to avoid
    • tips for writing good microbenchmarks (remove unnecessary work, but add baselines)
  3. How to read it pprof output
  4. What are the different pieces of the runtime that show up
  • malloc, gc workers
  • runtime._ExternalCode
  1. Macro-benchmarks (Profiling in production)
    • net/http/pprof
  2. Using -base to look at differences
  3. Memory options: -inuse_space, -inuse_objects, -alloc_space, -alloc_objects
  4. Profiling in production; localhost+ssh tunnels, auth headers, using curl.


Look at some more interesting/advanced tooling

Garbage Collection

You pay for memory allocation more than once. The first is obviously when you allocate it. But you also pay every time the garbage collection runs.

Reduce/Reuse/Recycle. -- @bboreham

  • Stack vs. heap allocations
  • What causes heap allocations?
  • Understanding escape analysis (and the current limitation)
  • /debug/pprof/heap , and -base
  • API design to limit allocations:
    • allow passing in buffers so caller can reuse rather than forcing an allocation
    • you can even modify a slice in place carefully while you scan over it
  • reducing pointers to reduce gc scan times
    • pointer-free map keys/values
  • GOGC
  • buffer reuse (sync.Pool vs or custom via go-slab, etc)
  • slicing vs. offset: pointer writes while GC is running need writebarrier:
  • use error variables instead of errors.New() / fmt.Errorf() at call site (performance or style? interface requires pointer, so it escapes to heap anyway)
  • use structured errors to reduce allocation (pass struct value), create string at error printing time
  • size classes

Runtime and compiler

  • cost of calls via interfaces (indirect calls on the CPU level)
  • runtime.convT2E / runtime.convT2I
  • type assertions vs. type switches
  • defer
  • special-case map implementations for ints, strings
  • bounds check elimination
  • []byte <-> string copies, map optimizations
  • two-value range will copy an array, use the slice instead:
  • use string concatenation instead of fmt.Sprintf where possible; runtime has optimized routines for it


Common gotchas with the standard library

  • time.After() leaks until it fires; use t := NewTimer(); t.Stop() / t.Reset()
  • Reusing HTTP connections...; ensure the body is drained (issue #?)
  • rand.Int() and friends are 1) mutex protected and 2) expensive to create
    • consider alternate random number generation (go-pcgr, xorshift)
  • binary.Read and binary.Write use reflection and are slow; do it by hand.
  • use strconv instead of fmt if possible
  • ...

Alternate implementations

Popular replacements for standard library packages:

  • encoding/json -> ffjson, easyjson, etc
  • net/http
    • fasthttp (but incompatible API, not RFC compliant in subtle ways)
    • httprouter (has other features besides speed; I've never actually seen routing in my profiles)
  • regexp -> ragel (or other regular expression package)
  • serialization
  • database/sql -> has tradeoffs that affect performance
    • look for drivers that don't use it: jackx/pgx, crawshaw sqlite, ...
  • gccgo (benchmark!), gollvm (WIP)
  • container/list: use a slice instead (almost always)


  • Performance characteristics of cgo calls
  • Tricks to reduce the costs: batching
  • Rules on passing pointers between Go and C
  • syso files (race detector, dev.boringssl)

Advanced Techniques

Techniques specific to the architecture running the code

  • introduction to CPU caches

    • performance cliffs
    • building intuition around cache-lines: sizes, padding, alignment
    • OS tools to view cache-misses
    • maps vs. slices
    • SOA vs AOS layouts: row-major vs. column major; when you have an X, do you need another X or do you need a Y?
    • temporal and spacial locality: use what you have and what's nearby as much as possible
    • reducing pointer chasing
    • explicit memory prefetching; frequently ineffective; lack of intrinsics means function call overhead (removed from runtime)
    • make the first 64-bytes of your struct count
  • branch prediction

    • remove branches from inner loops: if a { for { } } else { for { } } instead of for { if a { } else { } } benchmark due to branch prediction structure to avoid branch

      if i % 2 == 0 { evens++ } else { odds++ }

      counts[i & 1] ++ "branch-free code", benchmark; not always faster, but frequently harder to read TODO: ASCII class counts example, with benchmarks

  • sorting data can help improve performance via both cache locality and branch prediction, even taking into account the time it takes to sort

  • function call overhead

  • reduce data copies

  • Comment about Jeff Dean's 2002 numbers (plus updates)

    • cpus have gotten faster, but memory hasn't kept up


  • Figure out which pieces can be done in parallel and which must be sequential
  • goroutines are cheap, but not free.
  • Optimizing multi-threaded code
    • false-sharing -> pad to cache-line size
    • true sharing -> sharding
  • Overlap with previous section on caches and false/true sharing
  • Lazy synchronization; it's expensive, so duplicating work may be cheaper
  • things you can control: number of workers, batch size

You need a mutex to protect shared mutable state. If you have lots of mutex contention, you need to either reduce the shared, or reduce the mutable. Two ways to reduce the shared are 1) shard the locks or 2) process independently and combine afterwards. To reduce mutable: well, make your data structure read-only. You can also reduce the time the data needs be shared by reducing the critical section -- hold the lock as little as needed. Sometimes a RWMutex will be sufficient, although note that they're slower but they allow multiple readers in.

If you're sharding the locks, be careful of shared cache-lines. You'll need to pad to avoid cache-line bouncing between processors.

var stripe [8]struct{ sync.Mutex; _ [7]uint64 } // mutex is 64-bits; padding fills the rest of the cacheline

Don't do anything expensive in your critical section if you can help it. This includes things like I/O (which are cheap but slow).

TODO: how to decompose problem for concurrency TODO: reasons parallel implementation might be slower (communication overhead, best algorithm is sequential, ... )


  • Stuff about writing assembly code for Go
  • compilers improve; the bar is high
  • replace as little as possible to make an impact; maintenance cost is high
  • good reasons: SIMD instructions or other things outside of what Go and the compiler can provide
  • very important to benchmark: improvements can be huge (10x for go-highway) zero (go-speck), or even slower (no inlining)
  • rebenchmark with new versions to see if you can delete your code yet
    • TODO: link to 1.11 patches removing asm code
  • always have pure-Go version (noasm build tag): testing, arm, gccgo
  • brief intro to syntax
  • how to type the middle dot
  • calling convention
  • using opcodes unsupported by the asm (asm2plan9, but this is getting rarer)
  • notes about why inline assembly is hard
  • all the tooling to make this easier: asmfmt, peachpy, c2goasm, ...

Optimizing an entire service

Most of the time you won't be presented with a single CPU-bound routine. That's the easy case. If you have a service to optimize, you need to look at the entire system. Monitoring. Metrics. Log lots of things over time so you can see them getting worse and so you can see the impact your changes have in production.

  • references for system design: SRE Book, practical distributed system design
  • extra tooling: more logging + analysis
  • The two basic rules: either speed up the slow things or do them less frequently.
  • distributed tracing to track bottlenecks at a higher level
  • query patterns for querying a single server instead of in bulk
  • your performance issues may not be your code, but you'll have to work around them anyway

Appendix: Implementing Research Papers

Tips for implementing papers: (For algorithm read also data structure)

  • Don't. Start with the obvious solution and reasonable data structures.

"Modern" algorithms tend to have lower theoretical complexities but high constant factors and lots of implementation complexity. One of the classic examples of this is Fibonacci heaps. They're notoriously difficult to get right and have a huge constant factor. There has been a number of papers published comparing different heap implementations on different workloads, and in general the 4- or 8-ary implicit heaps consistently come out on top. And even in the cases where Fibonacci heap should be faster (due to O(1) "decrease-key"), experiments with Dijkstra's depth-first search algorithm show it's faster when they use the straight heap removal and addition.

Similarly, treaps or skiplists vs. the more complex red-black or AVL trees. On modern hardware, the "slower" algorithm may be fast enough, or even faster.

The fastest algorithm can frequently be replaced by one that is almost as fast and much easier to understand.

Douglas W. Jones, University of Iowa

The added complexity has to be enough that the payoff is actually worth it. Another example is cache eviction algorithms. Different algorithms can have much higher complexity for only a small improvement in hit ratio. Of course, you may not be able to test this until you have a working implementation and have integrated it into your program.

Sometimes the paper will have graphs, but much like the trend towards publishing only positive results, these will tend to be skewed in favour of showing how good the new algorithm is.

  • Choose the right paper.
  • Look for the paper their algorithm claims to beat and implement that.

Frequently, earlier papers will be easier to understand and necessarily have simpler algorithms.

Not all papers are good.

Look at the context the paper was written in. Determine assumptions about the hardware: disk space, memory usage, etc. Some older papers make different tradeoffs that were reasonable in the 70s or 80s but don't necessarily apply to your use case. For example, what they determine to be "reasonable" memory vs. disk usage tradeoffs. Memory sizes are now orders of magnitude larger, and SSDs have altered the latency penalty for using disk. Simiarly, some streaming algorithms are designed for router hardware, which can make it a pain to translate into software.

Make sure the assumptions the algorithm makes about your data hold.

This will take some digging. You probably don't want to implement the first paper you find.

  • Make sure you understand the algorithm. This sounds obvious, but it will be impossible to debug otherwise.

    A good understanding may allow you to extract the key idea from the paper and possibly apply just that to your problem, which may be simpler than reimplementing the entire thing.

  • The original paper for a data structure or algorithm isn't always the best. Later papers may have better explanations.

  • Some papers release reference source code which you can compare against, but

    1. academic code is almost universally terrible
    2. beware licensing restrictions ("research purposes only")
    3. beware bugs; edge cases, error checking, performance etc.

    Also look out for other implementations on GitHub: they may have the same (or different!) bugs as yours.

Other resources on this topic: