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.
Whenever you are rolling up information from rule dependencies you should use depsets. 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 page for more information.
You can coerce a depset to a flat list using to_list. This should be considered debugging functionality. Any flattening of a depset in a rule implementation is almost always O(N^2).
A common misconception is that you can freely flatten at the very top level,
eg. at the
xx_binary level. This is still O(N^2) when you build a set
of overlapping targets. This happens when building your tests
//foo/tests/..., or when importing an IDE project.
Note: Today it is possible to flatten depsets implicitly. Anywhere you
iterate a depset (explicitly or implicitly), or take its size, you are
to_list. This functionality will soon be removed.
It is O(N) to get the number of items in a depset. It is however O(1) to check if a depset is empty. This includes checking the truthiness of a depset:
def _impl(ctx): args = ctx.actions.args() files = depset(...) # Bad, has to iterate over entire depset to get length if len(files) == 0: args.add("--files") args.add_all(files) # Good, O(1) if files: args.add("--files") args.add_all(files)
ctx.actions.args() for command lines
When building command lines you should use ctx.actions.args(). 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 to see if anything fits the bill.
Are you passing
File#pathas arguments? No need. Any File is automatically turned into its 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. This is done behind the scenes when the action is executed. If you need to explictly control the params file you can write it manually using
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 args.add("--foo") args.add(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, 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 ... )
To profile your code and analyze the performance, use the
$ bazel build --nobuild --profile=/tmp/prof //path/to:target $ bazel analyze-profile /tmp/prof --html --html_details
Then, open the generated HTML file (
/tmp/prof.html in the example).
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.
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)