go-perfbook/performance.md

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

At the moment, it's a collection of links to videos, slides, and blog posts ("awesome-golang-performance"), but I would like this to evolve into a longer book format where the content is here instead of external. The links should be sorted into categories.

While some discussions will be made for indivudial services faster (caching, etc), designing performant distributed systems is beyond the scope of this work.

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

Optimization Workflow

• 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 (http://golang.org/pkg/testing/) 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 https://godoc.org/golang.org/x/perf/benchstat or https://github.com/codahale/tinystat to verify that a set of timings are 'sufficiently' different for an optimization to be worth the added code complexity.
  6. use https://github.com/tsenart/vegeta for load testing http services
  7. make sure your latency numbers make sense: https://youtu.be/lJ8ydIuPFeU

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.

The basic rules of the game are:

1. minimize CPU usage

• do less work
• this generally means "a faster algorithm"
• but CPU caches and the hidden constants in O() can play tricks on you

1. minimize allocations (which leads to less CPU stolen by the GC)
2. make your data quick to access

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

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.

Choosing the most important thing to work on. Sometimes this isn't an optimization at all. Sometimes it's not an actual CPU optimization, but a user-experience one. Making something start up faster by doing computation in the background after drawing the main window, for example.

Some times this will be obvious: an hourly report that completes in three hours is probably less useful that 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.

Choosing what to optimize. Choosing when to optimize.

Clarify "Premature optimization" quote.

TPOP: Should you optimize? "Yes, but only if the problem is important, the program is guinely too slow, and there is some expectation that it can be made faster while maintaining correctness, robustness, and clarity."

Fast software or fast deployment.

http://bitfunnel.org/strangeloop . has numbers. Hypothetical search engine needing 30k machines @ $1k USD / year. Doubling the speed of your software can save $15M/year. Even a developer spending an entire year to shave off 1% will pay for itself

Once you've decided you're going to do this, keep reading.

How to Optimize

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 means that in addition to a comprehensive set of unit tests (to ensuring 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.

That the benchmarks you're using must be correct and provide reproducible numbers on representative workloads. If individual runs have too high a variance, it will make improvements more difficult to spot. You will need to use benchstat or equivalent statistical tests and won't be able just eye-ball it. The steps to run the benchmarks should be documented, any custom scripts and tooling should be commited to the repository with instructions for how to run it. Be mindful of large benchmark suites that take a long time to get the results you need: it will make the development interations slower.

Entire books have been written on how to performance test web servers. Performance is a distribution. Latency distributions at different request/second levels.

Next, decide what it is you're optimizing for. 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?

Anything that can be measured can be optimized. Make sure you're measuring the right thing. Beware bad metrics. There are generally competing factors.

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.

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 10% will improve runtime by almost 8%. Profiles will help identify where time is actually spent.

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.

Do we have to do this at all? The fastest code is the code that's not there. If yes, is this the best algorithm. If yes, is this the best implementation of this algorithm.

Given a profile that says a particular routine is expensive, before optimizing that routine, see if you can eliminate calls to it all together.

Basic techniques:

http://www.crowl.org/lawrence/programming/Bentley82.html

Approached program optimization as an engineering problem. Many of the
tips from Bentley are now done automatically by compilers (for example,
all the "loop" and "expression" ones). It's the programmers job to use
transformations that compilers can't do.

But the engineering approach is correct:
 Benchmark. Analyze. Improve. Verify. Iterate.

Augment your data structure with more information: - precomputed fields (size instead of interating linked list, etc) - extra indexes for searching, "search fingers", doubly-linked list for O(1) removal - limitations of when this is applicable: must be cheap to keep updated - all these fall under "do less work" (at the data structure level) - bloom filter (for example): these need to be small and fast to not overwhelm the rest of the data structure: (e.g, matcher bench)

Trade space for time:

• smaller data structures: pack things, compress data structures in memory
• precompute things you need (size of a linked list) http://www.smallmemory.com/

Most of the time if you're optimizing for CPU, your program will use more memory. This is the classic space-time trade-off: https://en.wikipedia.org/wiki/Space%E2%80%93time_tradeoff

Note that modern computers and the memory hierarchy make this 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 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. See the graphs 4 and 5 in the Jump Hash paper: https://arxiv.org/pdf/1406.2294.pdf )

Further, while data compression increases CPU time, if there are data transfers involved (disk or network), the CPU time spent decompressing will be trivial compared to the saved transfer time which will be orders of magnitude slower.

algorithmic tuning: keep the old implementation around for testing

program tuning: 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 cheap checks before more expensive checks: e.g., strcmp before regexp, (q.v., bloom filter before query)

some tunings are working around runtime or compiler code generation issue: always flag these with the appropriate issue so you can revisit assembly math.Abs() vs code generation vs function call overhead exploit a mathematical identity: https://go-review.googlesource.com/c/go/+/85477 just clearing the parts you used, rather than an entire array

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 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.

Iterative program improvements:

• ensure progress at each step
• but frequently one improvement will enable others
• which means you need to keep looking at the entire picture

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 program gives the wrong answer in no time at all. It's easy to be fast if you don't have to be correct. But it means you can use an optimization some of the time if you're sure it's in range.

Have an intuitive grasp of the different O() levels:

• simple loop, O(n)
• nested loop, O(n*m)
• binary-search O(log n)
• divide-and-conquer O(n log n)
• combinatoric - look out!!

Know how big each of these input sizes is likely to be when coding. You don't always have to shave cycles, but also don't be dumb.

Beware high constants Look for simpler algorithms with small constants. Debugging an optimized algorithm is harder than debugging a simple one. Look for algorithm the paper you're implementing claims to best and do that one instead.

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.

There are examples of this are in the standard library sorting and string packages.

Choose algorithms based on problem size: (stdlib quicksort) Detect and specialize for common or easy cases: stdlib string

Beware algorithms with high startup costs. For example, search is O(log n), but you have to sort first. If you just have a single search to do, a linear scan will be faster. But if you're doing many sorts, the O(n log n) sort overhead will not matter as much

Your benchmarks must use appropriately-sized inputs. As we've seen, different algorithms make sense at different input sizes. If your expected input range in <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.

Cache common cases: Your cache doesn't even need to be huge. Optimized a log processing script to cache the previous time passed to time.parse() for significant speedup But beware cache invalidation, thread issues, etc Random cache eviction is fast and sufficiently effective. - only put "some" items in cache (probabilistically) to limit cache size to popular items with minimal logic Compare cost of cache logic to cost of refetching the data.

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

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.

Profile regularly to ensure the 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.

Basics

1. choose the best algorithm

• traditional computer science analysis
• O(n^2) vs O(n log n) vs O(log n) vs O(1)
• this should handle the majority of your optimization cases
• be aware of http://accidentallyquadratic.tumblr.com/
• https://agtb.wordpress.com/2010/12/23/progress-in-algorithms-beats-moore%E2%80%99s-law/

1. pre-compute things you need
2. add a cache -> reduces work

Introductory Profiling

Techniques applicable to source code in general

1. introduction to pprof

• go tool pprof (and https://github.com/google/pprof)

1. Writing and running (micro)benchmarks

• -cpuprofile / -memprofile / -benchmem

1. How to read it pprof output
2. What are the different pieces of the runtime that show up
3. Macro-benchmarks (Profiling in production)

• net/http/pprof

Tracer

Advanced Techniques

• Techniques specific to the architecture running the code

• introduction to CPU caches

  • performance cliffs
  • building intuition around cache-lines: sizes, padding, alignment
  • false-sharing
  • OS tools to view cache-misses
  • maps vs. slices
  • SOA vs AOS layouts
  • reducing pointer chasing

• branch prediction

• function call overhead

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

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

Garbage Collection

• Stack vs. heap allocations
• What causes heap allocations?
• Understanding escape analysis (and the current limitation)
• 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
• GOGC

Runtime

• 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

Common gotchas with the standard library

• time.After() leaks until it fires
• Reusing HTTP connections...
• ....
• rand.Int() and friends are 1) mutex protected and 2) expensive to create
  • consider alternate random number generation

Unsafe

• And all the dangers that go with it
• Common uses for unsafe
• mmap'ing data files
  • struct padding
• speedy de-serialization
• string <-> slice conversion, []byte <-> []uint32, ...

cgo

• Performance characteristics of cgo calls
• Tricks to reduce the costs: batching
• Rules on passing pointers between Go and C
• syso files

Assembly

• Stuff about writing assembly code for Go
• always have pure-Go version (noasm build tag): testing,
• brief intro to syntax
• calling convention
• using opcodes unsupported by the asm
• notes about why intrinsics are hard
• all the tooling to make this easier: asmfmt, peachpy, c2goasm, ...

Alternate implementations

• Popular replacements for standard library packages:
  • encoding/json -> ffjson
  • net/http -> fasthttp (but incompatible API)
  • regexp -> ragel (or other regular expression package)
  • serialization
    • encoding/gob -> https://github.com/alecthomas/go_serialization_benchmarks
    • protobuf -> https://github.com/gogo/protobuf
    • all formats have trade-offs: choose one that matches what you need encoded space, decoding speed, language/tooling compatibility, ...

Tooling

Look at some more interesting/advanced tooling

• perf (perf2pprof)