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
    1. profile to identify the areas to improve.  This can be CPU, heap allocations, or goroutine blocking.
    1. 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.
    1. profile again afterwards to verify the issue is gone
    1. 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.
    1. use <https://github.com/tsenart/vegeta> for load testing http services
    1. 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)
1. 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
1. 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
1. What are the different pieces of the runtime that show up
1. 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
   * true sharing -> sharding
   * 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, ...
   * database/sql -> jackx/pgx, ...

## Tooling

Look at some more interesting/advanced tooling

* perf  (perf2pprof)