palanteer-scripting 0.8.0

Look into Palanteer and get an omniscient view of your program

Palanteer is a set of lean and efficient tools to improve the quality of software, for C++ and Python programs.

Simple code instrumentation, mostly automatic in Python, delivers powerful features:

• Collection of meaningful atomic events on timings, memory, locks wait and usage, context switches, data values..
• Visual and interactive observation of record: timeline, plot, histograms, flame graph...
• Remote command call and events observation can be scripted in Python: deep testing has never been simpler
• C++:
  • ultra-light single-header cross-platform instrumentation library
  • compile-time selection of groups of instrumentation
  • compile-time hashing of static string to minimize their cost
  • compile-time striping of all instrumentation static strings
  • enhanced assertions, stack trace dump...
• Python:
  • Automatic instrumentation of functions enter/leave, memory allocations, raised exceptions, garbage collection runs
  • Support of multithread, coroutines, asyncio/gevent

Palanteer is an efficient, lean and comprehensive solution for better and enjoyable software development!

Usage

This scripting module processes the events sent by instrumented programs, independently of their language (Python or C++).

Typical usages are:

• Tests based on stimulations/configuration with CLI and events observation, as data can also be traced
• Evaluation of the program performance
• Monitoring
• ...

Below is a simple example of a Python program instrumented with Palanteer and generating 100 000 random integers.
The range can be remotely configured with a user-defined Palanteer CLI.

#! /usr/bin/env python3
import sys
import random
from palanteer import *

globalMinValue, globalMaxValue =  0, 10

# Handler (=implementation) of the example CLI, which sets the range
def setBoundsCliHandler(minValue, maxValue):              # 2 parameters (both integer) as declared
    global globalMinValue, globalMaxValue
    if minValue>maxValue:                                 # Case where the CLI execution fails (non null status). The text answer contains some information about it
        return 1, "Minimum value (%d) shall be lower than the maximum value (%d)" % (minValue, maxValue)

    # Modify the state of the program
    globalMinValue, globalMaxValue = minValue, maxValue
    # CLI execution was successful (null status)
    return 0, ""


def main(argv):
    global globalMinValue, globalMaxValue

    plInitAndStart("example")                             # Start the instrumentation
    plDeclareThread("Main")                               # Declare the current thread as "Main", so that it can be identified more easily in the script
    plRegisterCli(setBoundsCliHandler, "config:setRange", "min=int max=int", "Sets the value bounds of the random generator")  # Declare the CLI
    plFreezePoint()                                       # Add a freeze point here to be able to configure the program at a controlled moment

    plBegin("Generate some random values")
    for i in range(100000):
        value = int(globalMinValue + random.random()*(globalMaxValue+1-globalMinValue))
        plData("random data", value)                      # Here are the "useful" values
    plEnd("")                                             # Shortcut for plEnd("Generate some random values")

    plStopAndUninit()                                     # Stop and uninitialize the instrumentation

# Bootstrap
if __name__ == "__main__":
    main(sys.argv)

The Python scripting module can remotely control this program, in particular:

• call the setBoundsCliHandler to change the configuration
• temporarily halt the program at the freeze point
• see all "random data" values and the timing of the scope event "Generate some random values"

#! /usr/bin/env python3
import sys
import palanteer_scripting as ps

def main(argv):
    if len(sys.argv)<2:
        print("Error: missing parameters (the program to launch)")
        sys.exit(1)

    # Initialize the scripting module
    ps.initialize_scripting()

    # Enable the freeze mode so that we can safely configure the program once stopped on its freeze point
    ps.program_set_freeze_mode(True)

    # Launch the program under test
    ps.process_launch(sys.argv[1], args=sys.argv[2:])
    # From here, we are connected to the remote program

    # Configure the selection of events to receive
    my_selection = ps.EvtSpec(thread="Main", events=["random data"]) # Thread "Main", only the event "random data"
    ps.data_configure_events(my_selection)

    # Configure the program
    status, response = ps.program_cli("config:setRange min=300 max=500")
    if status!=0:
        print("Error when configuring: %s\nKeeping original settings." % response)

    # Disable the freeze mode so that the program resumes its execution
    ps.program_set_freeze_mode(False)

    # Collect the events as long as the program is alive or we got some events in the last round
    qty, sum_values, min_value, max_value, has_worked = 0, 0, 1e9, 0, True
    while ps.process_is_running() or has_worked:
        has_worked = False
        for e in ps.data_collect_events(timeout_sec=1.):  # Loop on received events, per batch
            has_worked, qty, sum_values, min_value, max_value = True, qty+1, sum_values+e.value, min(min_value, e.value), max(max_value, e.value)

    # Display the result of the processed collection of data
    print("Quantity: %d\nMinimum : %d\nAverage : %d\nMaximum : %d" % (qty, min_value, sum_values/max(qty,1), max_value))

    # Cleaning
    ps.process_stop()            # Kills the launched process, if still running
    ps.uninitialize_scripting()  # Uninitialize the scripting module


# Bootstrap
if __name__ == "__main__":
    main(sys.argv)

The execution of this last script, with the first one as parameter, gives the following output:

> ./remoteScript.py example.py
Quantity: 100000
Minimum : 300
Average : 400
Maximum : 500

Details can be found here.

Installation of the scripting module

Directly from the PyPI storage (from sources on Linux, binary on Windows)

pip install palanteer_scripting

Directly from GitHub sources

pip install "git+https://github.com/dfeneyrou/palanteer#egg=palanteer_scripting&subdirectory=server/scripting"

From locally retrieved sources

Get the sources:

git clone https://github.com/dfeneyrou/palanteer
cd palanteer
mkdir build
cd build

Build on Linux:

cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc) install

Build on Windows:
(vcvarsall.bat or equivalent shall be called beforehand, so that the MSVC compiler is accessible)

cmake .. -DCMAKE_BUILD_TYPE=Release -G "NMake Makefiles"
nmake install

Important!

To be useful, this module requires an "instrumentation side" in the program under analysis (C++ or Python):

• For Python language, the instrumentation module is available on PyPI or from the github sources
• For C++ language, the instrumentation library is a single header file available in the github sources

Also, a graphical viewer is available for non-scripted analysis of the program behaviors.

NOTE: It is strongly recommended to have a matching version between the scripting module and the instrumentation sides