--- layout: documentation title: Optimizing Performance --- # Optimizing Performance Skylark efficiency often involves avoiding O(N^2) in time and/or space. Crucially this involves understanding depsets and avoiding their expansion. 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. ## 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. ### Never call `depset#to_list` You can coerce a depset to a flat list using [to_list](lib/depset.html#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 effectively calling `to_list`. This functionality will soon be removed. ### Never call `len(depset)` 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) ``` ## 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 explictly 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=" 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="] to ["--foo", ] 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, use the `--profile` flag: ``` $ 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). ## 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 Skylark 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 :-1 113.02MB 15.19% 34.83% 113.02MB 15.19% genrule :-1 74.11MB 9.96% 44.80% 74.11MB 9.96% glob :-1 55.98MB 7.53% 52.32% 55.98MB 7.53% filegroup :-1 53.44MB 7.18% 59.51% 53.44MB 7.18% sh_test :-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) ```