go-perfbook/performance.md

# 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. Optimizing distributed systems 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
1. Tips for writing fast software
    * Go-specific sections on how to get the best from Go
1. 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"
1. "Be deliberate"
1. "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%."
>
> -- <cite>Knuth</cite>

Add: https://www.youtube.com/watch?time_continue=429&v=RT46MpK39rQ
   * 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."
>
> -- <cite>The Practice of Programming, Kernighan and Pike</cite>

Premature optimization can also hurt you by tying you into certain decisions.
The optimized code can be harder to modify if requirements change and harder to
throw away (sunk-cost falacy) if needed.

[BitFunnel performance estimation](http://bitfunnel.org/strangeloop) 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.

<cite>[What is the best comment in source code you have ever encountered? - Stack Overflow](https://stackoverflow.com/questions/184618/what-is-the-best-comment-in-source-code-you-have-ever-encountered)</cite>:
<pre>
//
// 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
//
</pre>

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](https://golang.org/x/perf/benchstat) or equivalent statistical tests and won't be able to 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](https://youtu.be/lJ8ydIuPFeU)

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](http://bitfunnel.org/strangeloop) shows an example of using rough
calculations to determine if your target performance figures are reasonable.

Simon Eskildsen has a talk from SRECon covering this topic in more depth:
[Advanced Napkin Math: Estimating System Performance from First Principles](https://www.youtube.com/watch?v=IxkSlnrRFqc)

Finally, Jon Bentley's "Programming Pearls" has a chapter titled "The Back of
the Envelope" covering Fermi problems.  Sadly, these kind of estimation skills
got a bad wrap thanks to their use in Microsoft style "puzzle interview
questions" in the 1990s and early 2000s.

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.
Solution: "Premature pessimization is the root of all evil".  This matches with
my Rule 1: Be deliberate.  You don't need to write every line of code
to be fast, but neither should by default do wasteful things.

> "Premature pessimization is when you write code that is slower than it needs to
be, usually by asking for unnecessary extra work, when equivalently complex code
would be faster and should just naturally flow out of your fingers."
>
> -- <cite>Herb Sutter</cite>

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, possibly redesigning
large portions of the system with these performance goals in mind.

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](https://en.wikipedia.org/wiki/Amdahl%27s_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](http://proebsting.cs.arizona.edu/law.html) 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](https://agtb.wordpress.com/2010/12/23/progress-in-algorithms-beats-moore%E2%80%99s-law/).
For a more concrete example, consider [this breakdown](https://medium.com/@buckhx/unwinding-uber-s-most-efficient-service-406413c5871d)
of replacing a brute force geo-spatial 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:

* <http://www.crowl.org/lawrence/programming/Bentley82.html>
* <http://www.geoffprewett.com/BookReviews/WritingEfficientPrograms.html>

and the program tuning rules:

* <https://web.archive.org/web/20080513070949/http://www.cs.bell-labs.com/cm/cs/pearls/apprules.html>

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. This may even include preprocessing the input to be in a
different, more useful format.

Ideas for augmenting your data structure:

* Extra fields

  The classic example of this is storing the length of a linked list in a field in
  the root node. It takes a bit more work to keep it updated, but then querying
  the length becomes a simple field lookup instead of an O(n) traversal. Your data
  structure might present a similar win: a bit of bookkeeping during some
  operations in exchange for some faster performance on a common use case.

  Similarly, storing pointers to frequently needed nodes instead of performing
  additional searches.  This covers things like the "backwards" links in a
  doubly-linked list to make node removal O(1). Some skip lists keep a "search
  finger", where you store a pointer to where you just were in your data
  structure on the assumption it's a good starting point for your next
  operation.

* 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, a set of structs might have a primary ID (integer)
  that you use to look up in a slice, but sometimes need to look up with a
  secondary ID (string). Instead of iterating over the slice, you can augment
  your data structure with a map either from string to ID or directly to the
  struct itself.

* Extra information about elements

  For example, keeping a bloom filter of all the elements you've inserted can let
  you quickly return "no match" for lookups. These need to be small and fast to
  not overwhelm the rest of the data structure.  (If a lookup in your main data
  structure is cheap, the cost of the bloom filter will outweigh any savings.)

* If queries are expensive, add a cache.

  At a larger level, an in-process or external cache (like memcache) can help.
  It might be excessive for a single data structure.  We'll cover more about
  caches below.

These sorts of changes are useful when the data you need is cheap to store and
easy to keep up-to-date.

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](https://en.wikipedia.org/wiki/Space%E2%80%93time_tradeoff).

It's important to examine how this tradeoff can affect your solutions -- it's
not always straight-forward. Sometimes a small amount of memory can give a
significant speed, sometimes the tradeoff is linear (2x memory usage == 2x
performance speedup), sometimes it's significantly worse: a huge amount of
memory gives only a small speedup. Where you need to be on this
memory/performance curve can affect what algorithm choices are reasonable.
It's not always possible to just tune an algorithm parameter. Different
memory usages might be completely different algorithmic approaches.

Lookup tables also fall into this space-time trade-off. A simple lookup table
might just be a cache of previously requested computations.

If the domain is small enough, the *entire* set of results could be
precomputed and stored in the table. As an example, this could be the
approach taken for a fast popcount implementation, where by the number of set
bits in byte is stored in a 256-entry table. A larger table could store the
bits required for all 16-bit words. In this case, they're storing exact
results.

A number of algorithms for trigonometric functions use lookup tables as a
starting point for a calculation.

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](http://smallmemory.com/book.html) 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

  Compression algorithms depend very heavily on what is being compressed.  It's
  best to choose one that suites your data.  If you have []byte, the something
  like snappy, gzip, lz4, behaves well. For floating point data there is go-tsz
  for time series and fpc for scientific data. Lots of research has been done
  around compressing integers, generally for information retrieval in search
  engines.  Examples include delta encoding and varints to more complex schemes
  involving Huffman encoded xor-differences.  You can also come up with custom
  compression formats optimized for exactly your data.

  Do you need to inspect the data or can it stay compressed? Do you need random
  access or only streaming?  If you need access to individual entries but don't
  want to decompress the entire thing, you can compress the data in smaller
  blocks and keep an index indicating what range of entries are in each block.
  Access to a single entry just needs to check the index and unpack the smaller
  data block.

  If your data is not just in-process but will be written to disk, what about
  data migration or adding/removing fields. You'll now be dealing with raw
  []byte instead of nice structured Go types, so you'll need unsafe and to
  consider serialization options.

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](https://arxiv.org/pdf/1406.2294.pdf) 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 change. 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)`) with a binary search (`O(log n)`)
or 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 (but keep in mind the constant factor.)

* 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

Link: <http://bigocheatsheet.com>

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.

Being able to analyze your problem in terms of big-O notation also means you can
figure out if you're already at the limit for what is possible for your problem,
and if you need to change approaches in order to speed things up.  For example,
finding the minimum of an unsorted list is `O(n)`, because you have to look at
every single item.  There's no way to make that faster.

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.

<http://daslab.seas.harvard.edu/rum-conjecture/>

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.
> -- <cite>Rudyard Kipling</cite>

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 details are more likely to be more studied and well understood
than more esoteric or exotic and complex ones.

Simpler algorithms can also be faster.  These two examples are not isolated cases
  https://go-review.googlesource.com/c/crypto/+/169037
  https://go-review.googlesource.com/c/go/+/170322/

TODO: notes on algorithm selection

TODO:
  improve worst-case behaviour at slight cost to average runtime
  linear-time regexp matching
  randomized algorithms: MC vs. LV: MC gamble with correctness, LV with time/termination
    improve worse-case running time by avoiding bad choices with randomness
    treap (tree space: "mostly" balanced with high probability), skip-list similarly O(log n) w/h/p, other caching algorithms
    primality testing: (Miller-Rabin: just run 200 times), randomized pivot for quicksort (easy, simpler than median-of-medians, low probability of failure)
    power of two random choices: move from O(log n) to O(log log n) with high probability, with an entire field of applications
    statistical approximations (frequently depend on sample size and not population size)

 TODO: batching to reduce overhead: https://lemire.me/blog/2018/04/17/iterating-in-batches-over-data-structures-can-be-much-faster/

### Benchmark Inputs

Real-world inputs rarely match the theoretical "worst case".  Benchmarking is
vital to understanding how your system behaves in production.

You need to know what class of inputs your system will be seeing once deployed,
and your benchmarks must use instances pulled from that same distribution. 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. Using purely randomized inputs may skew the behaviour of your algorithm.
Caching and compression algorithms both exploit skewed distributions not present
in random data and so will perform worse, while a binary tree will perform
better with random values as they will tend to keep the tree balanced. (This is
the idea behind a treap, by the way.)

On the other hand, consider the case of testing a system with a cache. If your
benchmark input consists only a single query, then every request will hit the
cache giving potentially a very unrealistic view of how the system will behave
in the real world with a more varied request pattern.

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. Similarly, the behaviour of the garbage collector during benchmarking
can misrepresent real-world impact.  There are (rare) cases where a
microbenchmark will show a slow-down, but real-world performance improves.
Microbenchmarks can help nudge you in the right direction, but being able to
fully test the impact of a change across the entire system is best.

Writing good benchmarks can be difficult.

* <https://timharris.uk/misc/five-ways.pdf>

Use geometric mean to compare groups of benchmarks.

* <https://www.cse.unsw.edu.au/~cs9242/current/papers/Fleming_Wallace_86.pdf>

Evaluating Benchmark Accuracy:

* <http://www.brendangregg.com/blog/2018-06-30/benchmarking-checklist.html>

### 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.

My RC6 cipher implementation had a 10% speed up for the inner loop just by
switching to `encoding/binary` and `math/bits` instead of my hand-rolled
versions.

Similarly, the `compress/bzip2` package was sped by switching to [simpler
code the compiler was better able to
optimize](https://github.com/golang/go/commit/9eb219480e8de08d380ee052b7bff293856955f8)

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 too 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:

We're all familiar with memcache, but there are also in-process caches.  Using
an in-process cache saves the cost of both the network call and the cost of
serialization. On the other hand, this increases GC pressure as there is more
memory to keep track of.  You also need to consider eviction strategies, cache
invalidation, and thread-safety.  An external cache will generally handle
eviction for you, but cache invalidation remains a problem.  Thread-safety can
also be an issue with external caches as it becomes effectively shared mutable
state either between different goroutines in the same service or even different
service instances if the external cache is shared.

A cache saves information you've just spent time computing in the hopes that
you'll be able to reuse it again soon and save the computation time. A cache
doesn't need to be complex.  Even storing a single item -- the most recently
seen query/response -- can be a big win, as seen in the `time.Parse()` example
below.

With caches it's important to compare the cost (in terms of actual wall-clock
and code complexity) of your caching logic to simply refetching or recomputing
the data.  The more complex algorithms that give higher hit rates are generally
not cheap themselves.  Randomized cache eviction is simple and fast and can be
effective in many cases.  Similarly, randomized cache *insertion* can limit your
cache to only popular items with minimal logic.  While these may not be as effective
as the more complex algorithms, the big improvement will be adding a cache in the first
place: choosing exactly which caching algorithm gives only minor improvements.

It's important to benchmark your choice of cache eviction algorithm with
real-world traces. If in the real world repeated requests are sufficiently rare,
it can be more expensive to keep cached responses around than to simply
recompute them when needed. I've had services where testing with production data
showed even an optimal cache wasn't worth it. we simply did't have sufficient
repeated requests to make the added complexity of a cache make sense.

Your expected cache 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.

A large cache can increase GC pressure. At the extreme (little or no eviction,
caching all requests to an expensive function) this can turn into
[memoization](https://en.wikipedia.org/wiki/Memoization)

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:
  * Note that implementing and optimizing numerical calculations is almost its own field
    * <https://github.com/golang/go/commit/ed6c6c9c11496ed8e458f6e0731103126ce60223>
    * <https://gist.github.com/dgryski/67e6a7ff94c3a1add30eb26ec0ad8b0f>
    * multiplication with addition
    * use WolframAlpha, Maxima, sympy and similar to specialize, optimize or create lookup-tables
    * (Also, https://users.ece.cmu.edu/~franzf/papers/gttse07.pdf)
    * moving from floating point math to integer math
    * or mandelbrot removing sqrt, or lttb removing abs,  `a < b/c` => `a * c < b`
    * 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
  * "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
  * 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: https://play.golang.org/p/6tnySwNxG6O
    * 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).
  * remove bounds checks and nil checks from loops: https://go-review.googlesource.com/c/go/+/151158
  * other tricks for the prove pass
  * this is where pieces of Hacker's Delight fall

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
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
    (+ other fancy ones: k6, fortio, fbender)
    - if possible, test ramp-up/ramp-down in addition to steady-state load
1. 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.


## 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.
> -- <cite>@bboreham</cite>

* 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
  * passing in a struct could allow caller to stack allocate it
* reducing pointers to reduce gc scan times
  * pointer-free slices
  * maps with both pointer-free keys and values
* GOGC
* buffer reuse (sync.Pool vs or custom via go-slab, etc)
* slicing vs. offset: pointer writes while GC is running need writebarrier: https://github.com/golang/go/commit/b85433975aedc2be2971093b6bbb0a7dc264c8fd
  * no writebarrier if writing to stack https://github.com/golang/go/commit/2140975ebde164ea1eaa70fc72775c03567f2bc9
* 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
* beware pinning larger allocation with smaller substrings or slices

## 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
  * map for byte/uint16 not optimized; use a slice instead.
  * You can fake a float64-optimized with math.Float{32,64}{from,}bits, but beware float equality issues
  * https://github.com/dgryski/go-gk/blob/master/exact.go says 100x faster; need benchmarks
* bounds check elimination
* []byte <-> string copies, map optimizations
* two-value range will copy an array, use the slice instead:
  * <https://play.golang.org/p/4b181zkB1O>
  * <https://github.com/mdempsky/rangerdanger>
* use string concatenation instead of fmt.Sprintf where possible; runtime has optimized routines for it

## Unsafe

* And all the dangers that go with it
* Common uses for unsafe
* mmap'ing data files
  * struct padding
  * but not always sufficiently faster to justify complexity/safety cost
  * but "off-heap", so ignored by gc (but so would a pointerless slice)
* need to think about serialization format: how to deal with pointers, indexing (mph, index header)
* speedy de-serialization
* binary wire protocol to struct when you already have the buffer
* string <-> slice conversion, []byte <-> []uint32, ...
* int to bool unsafe hack (but cmov) (but != 0 is also branch-free)
* padding:
  - https://dave.cheney.net/2015/10/09/padding-is-hard
  - http://www.catb.org/esr/structure-packing/#_go_and_rust
  - https://golang.org/ref/spec#Size_and_alignment_guarantees
  - https://github.com/dominikh/go-tools structlayout, structlayout-optimize
  - write tests for struct layout with unsafe.Offsetof to notice breakage from unsafe or asm

## 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. (https://github.com/conformal/yubikey/commit/613e3b04ae2eeb78e6a19636b8ff8e9106d2e7bc)
* use strconv instead of fmt if possible
* Use `strings.EqualFold(str1, str2)` instead of `strings.ToLower(str1) == strings.ToLower(str2)` or `strings.ToUpper(str1) == strings.ToUpper(str2)` to efficiently compare strings if possible.
* ...

## Alternate implementations

Popular replacements for standard library packages:

* encoding/json -> [ffjson](https://github.com/pquerna/ffjson), [easyjson](https://github.com/mailru/easyjson), [jingo](https://github.com/bet365/jingo) (only encoder), etc
* net/http
  * [fasthttp](https://github.com/valyala/fasthttp/) (but incompatible API, not RFC compliant in subtle ways)
  * [httprouter](https://github.com/julienschmidt/httprouter) (has other features besides speed; I've never actually seen routing in my profiles)
* regexp -> [ragel](https://www.colm.net/open-source/ragel/) (or other regular expression package)
* serialization
  * encoding/gob -> <https://github.com/alecthomas/go_serialization_benchmarks>
  * protobuf -> <https://github.com/gogo/protobuf>
  * all serialization formats have trade-offs: choose one that matches what you need
    - Write heavy workload -> fast encoding speed
    - Read-heavy workload -> fast decoding speed
    - Other considerations: encoded size, language/tooling compatibility
    - tradeoffs of packed binary formats vs. self-describing text formats
* 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)

## cgo

> cgo is not go
> -- <cite>Rob Pike</cite>

* 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 (perf)
  * 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: inliner is getting better
* reduce data copies (including for repeated large lists of function params)

* Comment about Jeff Dean's 2002 numbers (plus updates)
  * cpus have gotten faster, but memory hasn't kept up

## Concurrency

* 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, ... )

## Assembly

* 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/rc6/farm32), 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 (purego build tag): testing, arm, gccgo
* brief intro to syntax
* how to type the middle dot
* calling convention: everything is on the stack, followed by the return values.
  - everything is on the stack, followed by the return values
  - this might change https://github.com/golang/go/issues/18597
  - https://science.raphael.poss.name/go-calling-convention-x86-64.html
* using opcodes unsupported by the asm (asm2plan9, but this is getting rarer)
* notes about why inline assembly is hard: https://github.com/golang/go/issues/26891
* all the tooling to make this easier:
  - asmfmt: gofmt for assembly https://github.com/klauspost/asmfmt
  - c2goasm: convert assembly from gcc/clang to goasm https://github.com/minio/c2goasm
   - go2asm: convert go to assembly you can link https://rsc.io/tmp/go2asm
   - peachpy/avo: higher-level assembler in python (peachpy) or Go (avo)
   - differences of above
* https://github.com/golang/go/wiki/AssemblyPolicy
* Design of the Go Assembler: https://talks.golang.org/2016/asm.slide

## 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.

tip.golang.org/doc/diagnostics.html

* 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
* https://docs.microsoft.com/en-us/azure/architecture/antipatterns/

## Tooling

### Introductory Profiling

This is a quick cheat-sheet for using the pprof tooling.  There are plenty of other guides available on this.
Check out https://github.com/davecheney/high-performance-go-workshop.

TODO(dgryski): videos?

1. Introduction to pprof
   * go tool pprof (and <https://github.com/google/pprof>)
1. Writing and running (micro)benchmarks
   * small, like unit tests
   * 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)
1. How to read it pprof output
1. What are the different pieces of the runtime that show up
  * malloc, gc workers
  * runtime.\_ExternalCode
1. Macro-benchmarks (Profiling in production)
   * larger, like end-to-end tests
   * net/http/pprof, debug muxer
   * because it's sampling, hitting 10 servers at 100hz is the same as hitting 1 server at 1000hz
1. Using -base to look at differences
1. Memory options: -inuse_space, -inuse_objects, -alloc_space, -alloc_objects
1. Profiling in production; localhost+ssh tunnels, auth headers, using curl.
1. How to read flame graphs

### Tracer

### Look at some more interesting/advanced tooling

* other tooling in /x/perf
* perf (perf2pprof)
* intel vtune / amd codexl / apple instruments
* https://godoc.org/github.com/aclements/go-perf

## 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.
>
> -- <cite>Douglas W. Jones, University of Iowa</cite>

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.
Similarly, 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.

  <https://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/paper-reading.pdf>

  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:
* <https://www.youtube.com/watch?v=8eRx5Wo3xYA>
* <http://codecapsule.com/2012/01/18/how-to-implement-a-paper/>