---
layout: documentation
title: Optimizing Performance
---

# Optimizing Performance

When writing rules, the most common performance pitfall is to traverse or copy
data that is accumulated from dependencies. When aggregated over the whole
build, these operations can easily take O(N^2) time or space. To avoid this, it
is crucial to understand how to use depsets effectively.

This can be hard to get right, so Bazel also provides a memory profiler that
assists you in finding spots where you might have made a mistake. Be warned:
The cost of writing an inefficient rule may not be evident until it is in
widespread use.

## Contents
{:.no_toc}

* ToC
{:toc}

## Use depsets

Whenever you are rolling up information from rule dependencies you should use
[depsets](lib/depset.html). Only use plain lists or dicts to publish information
local to the current rule.

A depset represents information as a nested graph which enables sharing.

Consider the following graph:

```
C -> B -> A
D ---^
```

Each node publishes a single string. With depsets the data looks like this:

```
a = depset(direct=['a'])
b = depset(direct=['b'], transitive=[a])
c = depset(direct=['c'], transitive=[b])
d = depset(direct=['d'], transitive=[b])
```

Note that each item is only mentioned once. With lists you would get this:

```
a = ['a']
b = ['b', 'a']
c = ['c', 'b', 'a']
d = ['d', 'b', 'a']
```

Note that in this case `'a'` is mentioned four times! With larger graphs this
problem will only get worse.

Here is an example of a rule implementation that uses depsets correctly to
publish transitive information. Note that it is OK to publish rule-local
information using lists if you want since this is not O(N^2).

```
MyProvider = provider()

def _impl(ctx):
  my_things = ctx.attr.things
  all_things = depset(
      direct=my_things,
      transitive=[dep[MyProvider].all_things for dep in ctx.attr.deps]
  )
  ...
  return [MyProvider(
    my_things=my_things,  # OK, a flat list of rule-local things only
    all_things=all_things,  # OK, a depset containing dependencies
  )]
```

See the [depset overview](depsets.md) page for more information.

### Avoid calling `depset.to_list()`

You can coerce a depset to a flat list using
[`to_list()`](lib/depset.html#to_list), but doing so usually results in O(N^2)
cost. If at all possible, avoid any flattening of depsets except for debugging
purposes.

A common misconception is that you can freely flatten depsets if you only do it
at top-level targets, such as an `<xx>_binary` rule, since then the cost is not
accumulated over each level of the build graph. But this is *still* O(N^2) when
you build a set of targets with overlapping dependencies. This happens when
building your tests `//foo/tests/...`, or when importing an IDE project.

### Reduce the number of calls to `depset`

Calling `depset` inside a loop is often a mistake. It can lead to depsets with
very deep nesting, which perform poorly. For example:

```python
x = depset()
for i in inputs:
    # Do not do that.
    x = depset(transitive = [x, i.deps])
```

This code can be replaced easily. First, collect the transitive depsets and
merge them all at once:

```python
transitive = []

for i in inputs:
    transitive.append(i.deps)

x = depset(transitive = transitive])
```

This can sometimes be reduced using a list comprehension:

```python
x = depset(transitive = [i.deps for i in inputs])
```

## Use `ctx.actions.args()` for command lines

When building command lines you should use [ctx.actions.args()](lib/Args.html).
This defers expansion of any depsets to the execution phase.

Apart from being strictly faster, this will reduce the memory consumption of
your rules -- sometimes by 90% or more.

Here are some tricks:

* Pass depsets and lists directly as arguments, instead of flattening them
yourself. They will get expanded by `ctx.actions.args()` for you.
If you need any transformations on the depset contents, look at
[ctx.actions.args#add](lib/Args.html#add) to see if anything fits the bill.

* Are you passing `File#path` as arguments? No need. Any
[File](lib/File.html) is automatically turned into its
[path](lib/File.html#path), deferred to expansion time.

* Avoid constructing strings by concatenating them together.
The best string argument is a constant as its memory will be shared between
all instances of your rule.

* If the args are too long for the command line an `ctx.actions.args()` object
can be conditionally or unconditionally written to a param file using
[`ctx.actions.args#use_param_file`](lib/Args.html#use_param_file). This is
done behind the scenes when the action is executed. If you need to explicitly
control the params file you can write it manually using
[`ctx.actions.write`](lib/actions.html#write).

Example:

```
def _impl(ctx):
  ...
  args = ctx.actions.args()
  file = ctx.declare_file(...)
  files = depset(...)

  # Bad, constructs a full string "--foo=<file path>" for each rule instance
  args.add("--foo=" + file.path)

  # Good, shares "--foo" among all rule instances, and defers file.path to later
  # It will however pass ["--foo", <file path>] to the action command line,
  # instead of ["--foo=<file_path>"]
  args.add("--foo", file)

  # Use format if you prefer ["--foo=<file path>"] to ["--foo", <file path>]
  args.add(format="--foo=%s", value=file)

  # Bad, makes a giant string of a whole depset
  args.add(" ".join(["-I%s" % file.short_path for file in files])

  # Good, only stores a reference to the depset
  args.add_all(files, format_each="-I%s", map_each=_to_short_path)

# Function passed to map_each above
def _to_short_path(f):
  return f.short_path
```

## Transitive action inputs should be depsets

When building an action using [ctx.actions.run](lib/actions.html?#run), do not
forget that the `inputs` field accepts a depset. Use this whenever inputs are
collected from dependencies transitively.

```
inputs = depset(...)
ctx.actions.run(
  inputs = inputs,  # Do *not* turn inputs into a list
  ...
)
```

## Performance profiling

To profile your code and analyze the performance you have two options:

- use the `--profile` flag and the `analyze-profile` command, or
- use the new `--experimental_generate_json_trace_profile` flag and load the
  resulting JSON profile in `chrome://tracing` (recommended).

### --profile and analyze-profile

This profiling method consists of two steps, first you have to execute your
build/test with the `--profile` flag, for example

```
$ bazel build --nobuild --profile=/tmp/prof //path/to:target
```

The file generated (in this case `/tmp/prof`) is a binary file, which can be
postprocessed and analyzed by the `analyze-profile` command:

```
$ bazel analyze-profile /tmp/prof
```

By default, it prints summary analysis information for the specified profile
datafile. This includes cummaltive statistics for different task types for each
build phase and an analysis of the critical path.

The first section of the default output is an overview of the time spent
on the different build phases:

```
=== PHASE SUMMARY INFORMATION ===

Total launch phase time          978 ms    2.67%
Total init phase time            178 ms    0.49%
Total loading phase time         381 ms    1.04%
Total analysis phase time       2.618 s    7.15%
Total preparation phase time    46.0 ms    0.13%
Total execution phase time     32.366 s   88.46%
Total finish phase time         19.3 ms    0.05%
Total run time                 36.586 s  100.00%
```

The following sections show the execution time of different tasks happening
during that particular phase, for example:

```
=== EXECUTION PHASE INFORMATION ===

Total preparation time                   46.0 ms
Total execution phase time              32.366 s
Total time finalizing build              19.3 ms

Actual execution time                   32.366 s

Total time (across all threads) spent on:
              Type    Total    Count     Average
            ACTION    0.01%      131     0.44 ms
    ACTION_EXECUTE   31.72%      131     2.635 s
              INFO    0.00%       10     1.32 ms
          VFS_STAT    0.02%    32187     0.01 ms
           VFS_DIR    0.01%     4401     0.02 ms
      VFS_READLINK    0.00%        6     0.01 ms
           VFS_MD5    0.50%     4652     1.16 ms
        VFS_DELETE    0.02%     9336     0.03 ms
          VFS_OPEN    0.01%     4706     0.01 ms
          VFS_READ    0.06%   182413     0.00 ms
         VFS_WRITE    0.01%    17691     0.00 ms
              WAIT   16.12%      544      322 ms
     SKYFRAME_EVAL    2.97%        1    32.343 s
       SKYFUNCTION   48.59%    57301     9.23 ms
```

The last section shows the critical path:

```
Critical path (31.410 s):
    Id        Time Percentage   Description
132283     1.219 s    3.88%   action 'Executing genrule //src bazel-bin'
132282    13.591 s   43.27%   action 'Executing genrule //src package-zip'
132281     1.263 s    4.02%   action 'Executing genrule //src install_base_key-file'
132280      381 ms    1.21%   action 'Building deploy jar src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar'
132279     1.10 ms    0.00%   runfiles for //src/main/java/com/google/devtools/build/lib bazel/BazelServer
132278      398 ms    1.27%   action 'Building Java resource jar'
132277     8.292 s   26.40%   action 'Building src/main/java/com/google/devtools/build/lib/libbazel-rules-class.jar (133 source files) and running annotation processors (AutoCodecProcessor, OptionProcessor, AutoAnnotationProcessor, AutoValueProcessor)'
...
```

You can use the following options to display more detailed information:

- `--dump=text`: Print all recorded tasks in the order they occurred.
- `--dump=raw`: Use this for automated analysis with scripts.
- `--html`: Writes a file called `<profile-file>.html` in the directory of the
  profile file. Open it in you browser to see a Gantt type chart that displays
  time on the horizontal axis and threads of execution along the vertical axis.
- `--html_details`: Renders a more detailed execution chart. Beware that this
  increases file size and browser load considerably.

### JSON profile

When you add the flag `--experimental_generate_json_trace_profile` to your Bazel
invocations, Bazel will write a JSON profile in [Chrome Trace
Format](https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview).

We suggest to use the flag together with the following flags:
- `--experimental_json_trace_compression`: The profile will be compressed with
  gzip.
- `--experimental_profile_cpu_usage`: Bazel will measure and include local CPU
  usage in the JSON profile.

Example usage:
```
$ bazel build --experimental_generate_json_trace_profile --experimental_profile_cpu_usage //third_party/zlib/...
INFO: Writing tracer profile to '/home/johndoe/.cache/bazel/_bazel_twerth/f01bc937da326f5bb0feb15c854c110c/command.profile'
INFO: Invocation ID: 34995931-ef6f-4838-9ab1-9a0bc39a8712
INFO: Analysed 3 targets (7 packages loaded, 316 targets configured).
INFO: Found 3 targets...
INFO: Elapsed time: 1.223s, Critical Path: 0.27s
INFO: 17 processes: 17 linux-sandbox.
INFO: Build completed successfully, 20 total actions
```

The resulting profile file
(`/home/johndoe/.cache/bazel/_bazel_twerth/f01bc937da326f5bb0feb15c854c110c/command.profile`
in this case; can be configured by the `--profile=<path>` flag) can then be
loaded and viewed in Chrome. For this, open `chrome://tracing` in a new tab,
click `Load` and pick the profile file.

Example profile:
<img src="profile.png" alt="Example Profile" />

The top row ('cpu counters') shows the local CPU usage, which is high in this
build during the analysis phase and then gets lower during execution.
The second row ('Critical Path') refers to the critical path of the build, that
is even with infinite parallelism the build would not be faster than the actions
in this path.
The third row ('grpc-command-1') displays everything that's happening on
Bazel's main thread, giving a high level overview of what Bazel is doing.
The remaining rows show what the worker threads are doing.

You can interact with the profile, for example zoom in, inspect particular tasks,
filter for task descriptions and select multiple tasks to get an overview. Press
`?` to get an overview of what you can do.

When analyzing these kind of profiles look for the following:
- Slow analysis phase (RunAnalysisPhase), especially on incremental builds. This
  can be a sign of a poor rule implementation, for example one that flattens
  nested sets.
- Individual slow actions, especially those on the critical path. It might be
  possible to split large actions into multiple smaller actions or reduce the
  dependencies to speed them up.
- Bottlenecks, that is a small number of threads is busy while all others are
  idleing waiting for the result. Optimizing this will most likely require
  touching the rule implementations to introduce more parallelism.

Note that we filter out fast tasks and certain task types completely to keep the
profile files small enough to render fast in the Chrome Trace Viewer.


## Memory Profiling

Bazel comes with a built-in memory profiler that can help you check your rule's
memory use. If there is a problem you can dump the heap to find the
exact line of code that is causing the problem.

### Enabling Memory Tracking

You must pass these two startup flags to *every* Bazel invocation:

  ```
  STARTUP_FLAGS=\
  --host_jvm_args=-javaagent:$(BAZEL)/third_party/allocation_instrumenter/java-allocation-instrumenter-3.0.1.jar \
  --host_jvm_args=-DRULE_MEMORY_TRACKER=1
  ```
  **NOTE**: The bazel repository comes with an allocation instrumenter.
  Make sure to adjust '$(BAZEL)' for your repository location.

These start the server in memory tracking mode. If you forget these for even
one Bazel invocation the server will restart and you will have to start over.

### Using the Memory Tracker

Let's have a look at the target `foo` and see what it's up to. We add
`--nobuild` since it doesn't matter to memory consumption if we actually build
or not, we just have to run the analysis phase.

```
$ bazel $(STARTUP_FLAGS) build --nobuild //foo:foo
```

Let's see how much memory the whole Bazel instance consumes:

```
$ bazel $(STARTUP_FLAGS) info used-heap-size-after-gc
> 2594MB
```

Let's break it down by rule class by using `bazel dump --rules`:

```
$ bazel $(STARTUP_FLAGS) dump --rules
>

RULE                                 COUNT     ACTIONS          BYTES         EACH
genrule                             33,762      33,801    291,538,824        8,635
config_setting                      25,374           0     24,897,336          981
filegroup                           25,369      25,369     97,496,272        3,843
cc_library                           5,372      73,235    182,214,456       33,919
proto_library                        4,140     110,409    186,776,864       45,115
android_library                      2,621      36,921    218,504,848       83,366
java_library                         2,371      12,459     38,841,000       16,381
_gen_source                            719       2,157      9,195,312       12,789
_check_proto_library_deps              719         668      1,835,288        2,552
... (more output)
```

And finally let's have a look at where the memory is going by producing a
`pprof` file using `bazel dump --skylark_memory`:

```
$ bazel $(STARTUP_FLAGS) dump --skylark_memory=$HOME/prof.gz
> Dumping skylark heap to: /usr/local/google/home/$USER/prof.gz
```

Next, we use the `pprof` tool to investigate the heap. A good starting point is
getting a flame graph by using `pprof -flame $HOME/prof.gz`.

  You can get `pprof` from [https://github.com/google/pprof](https://github.com/google/pprof).

In this case we get a text dump of the hottest call sites annotated with lines:

```
$ pprof -text -lines $HOME/prof.gz
>
      flat  flat%   sum%        cum   cum%
  146.11MB 19.64% 19.64%   146.11MB 19.64%  android_library <native>:-1
  113.02MB 15.19% 34.83%   113.02MB 15.19%  genrule <native>:-1
   74.11MB  9.96% 44.80%    74.11MB  9.96%  glob <native>:-1
   55.98MB  7.53% 52.32%    55.98MB  7.53%  filegroup <native>:-1
   53.44MB  7.18% 59.51%    53.44MB  7.18%  sh_test <native>:-1
   26.55MB  3.57% 63.07%    26.55MB  3.57%  _generate_foo_files /foo/tc/tc.bzl:491
   26.01MB  3.50% 66.57%    26.01MB  3.50%  _build_foo_impl /foo/build_test.bzl:78
   22.01MB  2.96% 69.53%    22.01MB  2.96%  _build_foo_impl /foo/build_test.bzl:73
   ... (more output)
```
