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(), but doing so usually results in O(N^2)
cost. If at all possible, avoid any flattening of depsets except for debugging
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 inside a loop is often a mistake. It can lead to depsets with
very deep nesting, which perform poorly. For example:
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:
transitive =  for i in inputs: transitive.append(i.deps) x = depset(transitive = transitive)
This can sometimes be reduced using a list comprehension:
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(). 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.
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 explicitly 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 # 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, 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 ... )
If Bazel appears to be hung, you can hit Ctrl-\ or send
SIGQUIT signal (
kill -3 $(bazel info server_pid)) to get a thread
dump in the file
$(bazel info output_base)/server/jvm.out.
Since you may not be able to run
bazel info if bazel is hung, the
output_base directory is usually the parent of the
symlink in your workspace directory.
The JSON trace profile can be very useful to quickly understand what Bazel spent time on during the invocation.
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:<path to java-allocation-instrumenter-3.3.0.jar> \ --host_jvm_args=-DRULE_MEMORY_TRACKER=1
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
As an example, look at the target
foo and see what it does. To only
run the analysis and not run the build execution phase, add the
$ bazel $(STARTUP_FLAGS) build --nobuild //foo:foo
Next, see how much memory the whole Bazel instance consumes:
$ bazel $(STARTUP_FLAGS) info used-heap-size-after-gc > 2594MB
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)
Look at where the memory is going by producing a
bazel dump --skylark_memory:
$ bazel $(STARTUP_FLAGS) dump --skylark_memory=$HOME/prof.gz > Dumping Starlark heap to: /usr/local/google/home/$USER/prof.gz
pprof tool to investigate the heap. A good starting point is
getting a flame graph by using
pprof -flame $HOME/prof.gz.
pprof from https://github.com/google/pprof.
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)