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https://github.com/tmm1/stackprof
a sampling call-stack profiler for ruby 2.2+
https://github.com/tmm1/stackprof
Last synced: 5 days ago
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a sampling call-stack profiler for ruby 2.2+
- Host: GitHub
- URL: https://github.com/tmm1/stackprof
- Owner: tmm1
- License: mit
- Created: 2013-10-10T04:25:22.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2024-12-18T17:03:58.000Z (24 days ago)
- Last Synced: 2025-01-02T22:07:41.314Z (9 days ago)
- Language: Ruby
- Homepage:
- Size: 330 KB
- Stars: 2,102
- Watchers: 38
- Forks: 129
- Open Issues: 54
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ruby-cli-apps - stackprof - Profiles code by sampling call-stacks. (Awesome Ruby CLIs / Profiling)
README
# Stackprof
A sampling call-stack profiler for Ruby.
Inspired heavily by [gperftools](https://code.google.com/p/gperftools/), and written as a replacement for [perftools.rb](https://github.com/tmm1/perftools.rb).
## Requirements
* Ruby 2.2+
* Linux-based OS
## Getting Started
### Install
In your Gemfile add:
```ruby
gem 'stackprof'
```
Then run `$ bundle install`. Alternatively you can run `$ gem install stackprof`.
### Run
in ruby:
``` ruby
StackProf.run(mode: :cpu, out: 'tmp/stackprof-cpu-myapp.dump') do
#...
end
```
via rack:
``` ruby
use StackProf::Middleware, enabled: true,
mode: :cpu,
interval: 1000,
save_every: 5
```
reporting:
```
$ stackprof tmp/stackprof-cpu-*.dump --text --limit 1
==================================
Mode: cpu(1000)
Samples: 60395 (1.09% miss rate)
GC: 2851 (4.72%)
==================================
TOTAL (pct) SAMPLES (pct) FRAME
1660 (2.7%) 1595 (2.6%) String#blank?
$ stackprof tmp/stackprof-cpu-*.dump --method 'String#blank?'
String#blank? (gems/activesupport-2.3.14.github30/lib/active_support/core_ext/object/blank.rb:80)
samples: 1595 self (2.6%) / 1660 total (2.7%)
callers:
373 ( 41.0%) ApplicationHelper#current_user
192 ( 21.1%) ApplicationHelper#current_repository
callers:
803 ( 48.4%) Object#present?
code:
| 80 | def blank?
1225 (2.0%) / 1225 (2.0%) | 81 | self !~ /[^[:space:]]/
| 82 | end
$ stackprof tmp/stackprof-cpu-*.dump --method 'Object#present?'
Object#present? (gems/activesupport-2.3.14.github30/lib/active_support/core_ext/object/blank.rb:20)
samples: 59 self (0.1%) / 910 total (1.5%)
callees (851 total):
803 ( 94.4%) String#blank?
32 ( 3.8%) Object#blank?
16 ( 1.9%) NilClass#blank?
code:
| 20 | def present?
910 (1.5%) / 59 (0.1%) | 21 | !blank?
| 22 | end
```
For an experimental version of WebUI reporting of stackprof, see [stackprof-webnav](https://github.com/alisnic/stackprof-webnav)
To generate flamegraphs with Stackprof, additional data must be collected using the `raw: true` flag. Once you've collected results with this flag enabled, generate a flamegraph with:
```
$ stackprof --flamegraph tmp/stackprof-cpu-myapp.dump > tmp/flamegraph
```
After the flamegraph has been generated, you can generate a viewer command with:
```
$ stackprof --flamegraph-viewer=tmp/flamegraph
```
The `--flamegraph-viewer` command will output the exact shell command you need to run in order to open the `tmp/flamegraph` you generated with the built-in stackprof flamegraph viewer:
Alternatively, you can generate a flamegraph that uses [d3-flame-graph](https://github.com/spiermar/d3-flame-graph):
```
$ stackprof --d3-flamegraph tmp/stackprof-cpu-myapp.dump > flamegraph.html
```
And just open the result by your browser.
## Sampling
Four sampling modes are supported:
- `:wall` (using `ITIMER_REAL` and `SIGALRM`) [default mode]
- `:cpu` (using `ITIMER_PROF` and `SIGPROF`)
- `:object` (using `RUBY_INTERNAL_EVENT_NEWOBJ`)
- `:custom` (user-defined via `StackProf.sample`)
Samplers have a tuneable interval which can be used to reduce overhead or increase granularity:
- Wall time: sample every _interval_ microseconds of wallclock time (default: 1000)
```ruby
StackProf.run(mode: :wall, out: 'tmp/stackprof.dump', interval: 1000) do
#...
end
```
- CPU time: sample every _interval_ microseconds of CPU activity (default: 1000 = 1 millisecond)
```ruby
StackProf.run(mode: :cpu, out: 'tmp/stackprof.dump', interval: 1000) do
#...
end
```
- Object allocation: sample every _interval_ allocations (default: 1)
```ruby
StackProf.run(mode: :object, out: 'tmp/stackprof.dump', interval: 1) do
#...
end
```
By default, samples taken during garbage collection will show as garbage collection frames
including both mark and sweep phases. For longer traces, these can leave gaps in a flamegraph
that are hard to follow. They can be disabled by setting the `ignore_gc` option to true.
Garbage collection time will still be present in the profile but not explicitly marked with
its own frame.
Samples are taken using a combination of three new C-APIs in ruby 2.1:
- Signal handlers enqueue a sampling job using `rb_postponed_job_register_one`.
this ensures callstack samples can be taken safely, in case the VM is garbage collecting
or in some other inconsistent state during the interruption.
- Stack frames are collected via `rb_profile_frames`, which provides low-overhead C-API access
to the VM's call stack. No object allocations occur in this path, allowing stackprof to collect
callstacks in allocation mode.
- In allocation mode, samples are taken via `rb_tracepoint_new(RUBY_INTERNAL_EVENT_NEWOBJ)`,
which provides a notification every time the VM allocates a new object.
## Aggregation
Each sample consists of N stack frames, where a frame looks something like `MyClass#method` or `block in MySingleton.method`.
For each of these frames in the sample, the profiler collects a few pieces of metadata:
- `samples`: Number of samples where this was the topmost frame
- `total_samples`: Samples where this frame was in the stack
- `lines`: Samples per line number in this frame
- `edges`: Samples per callee frame (methods invoked by this frame)
The aggregation algorithm is roughly equivalent to the following pseudo code:
``` ruby
trap('PROF') do
top, *rest = caller
top.samples += 1
top.lines[top.lineno] += 1
top.total_samples += 1
prev = top
rest.each do |frame|
frame.edges[prev] += 1
frame.total_samples += 1
prev = frame
end
end
```
This technique builds up an incremental call graph from the samples. On any given frame,
the sum of the outbound edge weights is equal to total samples collected on that frame
(`frame.total_samples == frame.edges.values.sum`).
## Reporting
Multiple reporting modes are supported:
- Text
- Dotgraph
- Source annotation
### `StackProf::Report.new(data).print_text`
```
TOTAL (pct) SAMPLES (pct) FRAME
91 (48.4%) 91 (48.4%) A#pow
58 (30.9%) 58 (30.9%) A.newobj
34 (18.1%) 34 (18.1%) block in A#math
188 (100.0%) 3 (1.6%) block (2 levels) in
185 (98.4%) 1 (0.5%) A#initialize
35 (18.6%) 1 (0.5%) A#math
188 (100.0%) 0 (0.0%)
188 (100.0%) 0 (0.0%) block in
188 (100.0%) 0 (0.0%)
```
### `StackProf::Report.new(data).print_graphviz`
```
digraph profile {
70346498324780 [size=23.5531914893617] [fontsize=23.5531914893617] [shape=box] [label="A#pow\n91 (48.4%)\r"];
70346498324680 [size=18.638297872340424] [fontsize=18.638297872340424] [shape=box] [label="A.newobj\n58 (30.9%)\r"];
70346498324480 [size=15.063829787234042] [fontsize=15.063829787234042] [shape=box] [label="block in A#math\n34 (18.1%)\r"];
70346498324220 [size=10.446808510638299] [fontsize=10.446808510638299] [shape=box] [label="block (2 levels) in \n3 (1.6%)\rof 188 (100.0%)\r"];
70346498324220 -> 70346498324900 [label="185"];
70346498324900 [size=10.148936170212766] [fontsize=10.148936170212766] [shape=box] [label="A#initialize\n1 (0.5%)\rof 185 (98.4%)\r"];
70346498324900 -> 70346498324780 [label="91"];
70346498324900 -> 70346498324680 [label="58"];
70346498324900 -> 70346498324580 [label="35"];
70346498324580 [size=10.148936170212766] [fontsize=10.148936170212766] [shape=box] [label="A#math\n1 (0.5%)\rof 35 (18.6%)\r"];
70346498324580 -> 70346498324480 [label="34"];
70346497983360 [size=10.0] [fontsize=10.0] [shape=box] [label="\n0 (0.0%)\rof 188 (100.0%)\r"];
70346497983360 -> 70346498325080 [label="188"];
70346498324300 [size=10.0] [fontsize=10.0] [shape=box] [label="block in \n0 (0.0%)\rof 188 (100.0%)\r"];
70346498324300 -> 70346498324220 [label="188"];
70346498325080 [size=10.0] [fontsize=10.0] [shape=box] [label="\n0 (0.0%)\rof 188 (100.0%)\r"];
70346498325080 -> 70346498324300 [label="188"];
}
```
### `StackProf::Report.new(data).print_method(/pow|newobj|math/)`
```
A#pow (/Users/tmm1/code/stackprof/sample.rb:11)
| 11 | def pow
91 (48.4% / 100.0%) | 12 | 2 ** 100
| 13 | end
A.newobj (/Users/tmm1/code/stackprof/sample.rb:15)
| 15 | def self.newobj
33 (17.6% / 56.9%) | 16 | Object.new
25 (13.3% / 43.1%) | 17 | Object.new
| 18 | end
A#math (/Users/tmm1/code/stackprof/sample.rb:20)
| 20 | def math
1 (0.5% / 100.0%) | 21 | 2.times do
| 22 | 2 + 3 * 4 ^ 5 / 6
block in A#math (/Users/tmm1/code/stackprof/sample.rb:21)
| 21 | 2.times do
34 (18.1% / 100.0%) | 22 | 2 + 3 * 4 ^ 5 / 6
| 23 | end
```
## Usage
The profiler is compiled as a C-extension and exposes a simple api: `StackProf.run(mode: [:cpu|:wall|:object])`.
The `run` method takes a block of code and returns a profile as a simple hash.
``` ruby
# sample after every 1ms of cpu activity
profile = StackProf.run(mode: :cpu, interval: 1000) do
MyCode.execute
end
```
This profile data structure is part of the public API, and is intended to be saved
(as json/marshal for example) for later processing. The reports above can be generated
by passing this structure into `StackProf::Report.new`.
The format itself is very simple. It contains a header and a list of frames. Each frame has a unique ID and
identifying information such as its name, file, and line. The frame also contains sampling data, including per-line
samples, and a list of relationships to other frames represented as weighted edges.
``` ruby
{:version=>1.0,
:mode=>:cpu,
:inteval=>1000,
:samples=>188,
:missed_samples=>0,
:frames=>
{70346498324780=>
{:name=>"A#pow",
:file=>"/Users/tmm1/code/stackprof/sample.rb",
:line=>11,
:total_samples=>91,
:samples=>91,
:lines=>{12=>91}},
70346498324900=>
{:name=>"A#initialize",
:file=>"/Users/tmm1/code/stackprof/sample.rb",
:line=>5,
:total_samples=>185,
:samples=>1,
:edges=>{70346498324780=>91, 70346498324680=>58, 70346498324580=>35},
:lines=>{8=>1}},
```
Above, `A#pow` was involved in 91 samples, and in all cases it was at the top of the stack on line 12.
`A#initialize` was in 185 samples, but it was at the top of the stack in only 1 sample. The rest of the samples are
divided up between its callee edges. All 91 calls to `A#pow` came from `A#initialize`, as seen by the edge numbered
`70346498324780`.
## Advanced usage
The profiler can be started and stopped manually. Results are accumulated until retrieval, across
multiple `start`/`stop` invocations.
``` ruby
StackProf.running? # => false
StackProf.start(mode: :cpu)
StackProf.running? # => true
StackProf.stop
StackProf.results('/tmp/some.file')
```
## All options
`StackProf.run` accepts an options hash. Currently, the following options are recognized:
Option | Meaning
------- | ---------
`mode` | Mode of sampling: `:cpu`, `:wall`, `:object`, or `:custom` [c.f.](#sampling)
`out` | The target file, which will be overwritten
`interval` | Mode-relative sample rate [c.f.](#sampling)
`ignore_gc` | Ignore garbage collection frames
`aggregate` | Defaults: `true` - if `false` disables [aggregation](#aggregation)
`raw` | Defaults `false` - if `true` collects the extra data required by the `--flamegraph` and `--stackcollapse` report types
`metadata` | Defaults to `{}`. Must be a `Hash`. metadata associated with this profile
`save_every`| (Rack middleware only) write the target file after this many requests
## Todo
* file/iseq blacklist
* restore signal handlers on stop