pyccolo 0.0.62

Declarative instrumentation for Python

Pyccolo

Pyccolo (pronounced like the instrument "piccolo") is a library for declarative instrumentation in Python; i.e., it lets you specify the what of the instrumentation you wish to perform, and takes care of the how for you. It aims to be ergonomic, composable, and portable, by providing an intuitive interface, making it easy to layer multiple levels of instrumentation, and allowing the same code to work across multiple versions of Python (3.6 to 3.12), with few exceptions. Portability across versions is accomplished by embedding instrumentation at the level of source code (as opposed to bytecode-level instrumentation).

Pyccolo can be used (and has been used) to implement various kinds of dynamic analysis tools and other instrumentation:

• Code coverage (see pyccolo/examples/coverage.py)
• Syntactic macros such as quasiquotes (like MacroPy's) or quick lambdas; see pyccolo/examples/quasiquote.py and pyccolo/examples/quick_lambda.py
• Syntax-augmented Python (3.8 and up, see pyccolo/examples/optional_chaining.py)
• Dynamic dataflow analysis performed by ipyflow
• Tools to perform (most) imports lazily (see pyccolo/examples/lazy_imports.py)
• Tools to uncover semantic memory leaks
• <Your tool here!>

Install

pip install pyccolo

Hello World

Below is a simple script that uses Pyccolo to print "Hello, world!" before every statement that executes:

import pyccolo as pyc


class HelloTracer(pyc.BaseTracer):
    @pyc.before_stmt
    def handle_stmt(self, *_, **__):
        print("Hello, world!")


if __name__ == "__main__":
    with HelloTracer:
        # prints "Hello, world!" 11 times
        pyc.exec("for _ in range(10): pass")

Instrumentation is provided by a tracer class that inherit from pyccolo.BaseTracer. This class rewrites Python source code with instrumentation that triggers whenever events of interest occur, such as when a statement is about to execute. By registering a handler with the associated event (with the @pyc.before_stmt decorator, in this case), we can enrich our programs with additional observability, or even alter their behavior altogether.

What is up with pyc.exec(...)?

A program's abstract syntax tree is fixed at import / compile time, and when our script initially started running, the tracer was not active, so unquoted Python in the same file will lack instrumentation. It is possible to instrument modules at import time, but only when the imports are performed inside a tracing context. Thus, we must quote any code appearing in the same module where the tracer class was defined in order to instrument it.

Composing tracers

A core feature of Pyccolo is that its instrumentation is composable. It's usually tricky to use two or more ast.NodeTransformer classes simultaneously --- sometimes you can just have one inherit from the other, but if they both define visit methods for the same AST node type, then typically you would need to define a bespoke node transformer that uses logic from each base transformer, handling corner cases to resolve incompatibilities. With Pyccolo, you simply compose the context managers of each tracer class whose instrumentation you wish to use, and everything usually Just Works^TM:

with tracer1:
    with tracer2:
        pyc.exec(...)

Compatibility with sys.settrace(...)

Pyccolo is designed to support not only AST-level instrumentation, but also instrumentation involving Python's built in tracing utilities. To use it, you simply register handlers for one of the corresponding Pyccolo events (call, line, return_, exception, or opcode). Here's a minimal example:

import pyccolo as pyc


class SysTracer(pyc.BaseTracer):
    @pyc.call
    def handle_call(self, *_, **__):
        print("Pushing a stack frame!")

    @pyc.return_
    def handle_return(self, *_, **__):
        print("Popping a stack frame!")


if __name__ == "__main__":
    with SysTracer:
        def f():
            def g():
                return 42
            return g()
        # push, push, pop, pop
        answer_to_life_universe_everything = f()

Note that we didn't need to use pyc.exec(...) in the above example, because Python's built-in tracing does not involve any AST-level transformations. If, however, we had registered handlers for other events, such as pyc.before_stmt, we would need to use pyc.exec(...) to ensure those handlers get called, when running code in the same file where our tracer class is defined.

What if I'm already using sys.settrace(...) with my own tracing function?

Pyccolo is designed to be composable, and should execute both your tracing function as well as any handlers defined in any active Pyccolo tracers. For example Pyccolo's unit tests for call and return events work even when coverage.py is active (and without breaking it), which also uses Python's built-in tracing utilities.

Instrumenting Imported Modules

Instrumentation is opt-in for modules imported within tracing contexts. To determine whether a module gets instrumented, the method should_instrument_file(...) is called with the module's corresponding filename as input. For example:

class MyTracer(pyc.BaseTracer):
    def should_instrument_file(self, filename: str) -> bool:
        return filename.endswith("foo.py")
    
    # handlers, etc. defined below
    ...

with MyTracer:
    import foo  # contents of `foo` module get instrumented
    import bar  # contents of `bar` module do not get instrumented

Imports are instrumented by registering a custom finder / loader with sys.meta_path. This loader ignores cached bytecode (which may possibly be uninstrumented), and avoids generating new cached bytecode (which would be instrumented, possibly causing confusion later when instrumentation is not desired).

Command Line Interface

You can execute arbitrary scripts with instrumentation enabled with the pyc command line tool. For example, to use the OptionalChainer tracer defined in pyccolo/examples/optional_chaining.py, you can call pyc as follows, given some example script bar.py:

# bar.py
bar = None
# prints `None` since bar?.foo coalesces to `None`
print(bar?.foo)

> pyc bar.py -t pyccolo.examples.OptionalChainer

You can also run bar as a module (indeed, pyc performs this internally when provided a file):

> pyc -m bar -t pyccolo.examples.OptionalChainer

Note that you can specify multiple tracer classes after the -t argument; in case you were not already aware, Pyccolo is composable! :)

The above example demonstrates a tracer class that performs syntax augmentation on its instrumented Python source to modify the default Python syntax. This feature is available only on Python >= 3.8 for now and is lacking documentation for the moment, but you can see some examples in the test_syntax_augmentation.py unit tests.

More Events

Pyccolo handlers can be registered for many kinds of events. Some of the more common ones are:

• pyc.before_stmt, emitted before a statement executes;
• pyc.after_stmt, emitted after a statement executes;
• pyc.before_attribute_load, emitted in load contexts before an attribute is accessed;
• pyc.after_attribute_load, emitted in load contexts after an attribute is accessed;
• pyc.load_name, emitted when a variable is used in a load context (e.g. foo in bar = foo.baz);
• pyc.call and pyc.return_, two non-AST trace events built-in to Python.

There are many different Pyccolo events, and more are always being added. See pyccolo/trace_events.py for a full list.

Note that, for AST events, Python source is only transformed to emit some event when there is at least one tracer active that has at least one handler registered for that event. This prevents the transformed source from becoming extremely bloated when only a few events are needed.

Handler Interface

Every Pyccolo handler is passed four positional arguments:

1. The return value, for instrumented expressions;
2. The AST node (or node id, if using register_raw_handler(...), or None, for sys events);
3. The stack frame, at the point where instrumentation kicks in;
4. The event (useful when the same handler is registered for multiple events).

Some events pass additional keyword arguments, which I'm still in the process of documenting, but the above four tend to suffice for most use cases.

Not every handler receives a return value; for example, this argument is always None for pyc.after_stmt handlers. For certain handlers, the return value can be overridden. For example, by returning a value in a pyc.before_attribute_load, we override the object whose attribute is accessed. If we return nothing or None, then we do not override this object. (If we actually want to override it as None for some reason, then we can return pyc.Null.) For a particular event, handler return values compose with other handlers defined on the same tracer class as well as with handlers defined on other tracer classes.

Performance

Pyccolo instrumentation adds significant overhead to Python. In some cases, this overhead can be partially mitigated if, for example, you only need instrumentation the first time a statement runs. In such cases, you can deactivate instrumentation after, e.g., the first time a function executes, or after the first iteration in a loop for that respective function or loop, so that further calls (iterations, respectively) use uninstrumented code with all the mighty performance of native Python. This is implemented by activating "guards" associated with the function or loop, as in the below example:

class TracesOnce(pyc.BaseTracer):
    @pyc.register_raw_handler((pyc.after_for_loop_iter, pyc.after_while_loop_iter))
    def after_loop_iter(self, *_, guard, **__):
        self.activate_guard(guard)

    @pyc.register_raw_handler(pyc.after_function_execution)
    def after_function_exec(self, *_, guard, **__):
        self.activate_guard(guard)

Subsequent calls / iterations will be instrumented only after calling self.deactivate_guard(...) on the associated function / loop guard.

License

Code in this project licensed under the BSD-3-Clause License.