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Basic infrastructure for writing scripts

Project description

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Python is an excellent language that makes writing scripts very straightforward. Over the course of writing many scripts, we realized that we were doing some things over and over like creating a logger and accepting command line arguments. Base script is a very simple abstraction that takes care of setting up logging and other basics so you can focus on your application specific logic.

Here are some facilities that Base Script offers: - Logging - Accepting command-line arguments using argparse

Installation

pip install basescript

Usage

Here is a simple example to get started

Hello World

helloworld.py

from basescript import BaseScript

class HelloWorld(BaseScript):
    def run(self):
        print "Hello world"

if __name__ == '__main__':
    HelloWorld().start()

NOTE: all examples showcased here are available under the
``examples`` directory

Run the above by doing:

python helloworld.py run

Run script with log level set to DEBUG

python helloworld.py --log-level DEBUG run

Run script with custom log file

python helloworld.py --log-level DEBUG --log mylog run

Command line args, Using the logger

The following is a more involved example

adder.py

from basescript import BaseScript

class Adder(BaseScript):
    # The following specifies the script description so that it be used
    # as a part of the usage doc when --help option is used during running.
    DESC = 'Adds numbers'

    def __init__(self):
        super(Adder, self).__init__()
        self.a = 10
        self.b = 20

    def define_args(self, parser):
        parser.add_argument('c', type=int, help='Number to add')

    def run(self):
        self.log.info("Starting run of script ...")

        print self.a + self.b + self.args.c

        self.log.info("Script is done")

if __name__ == '__main__':
    Adder().start()

Run the script as follows and observe the usage information shown. Note how the description appears along with the c argument.

python adder.py --help
usage: adder.py [-h] [--name NAME] [--log-level LOG_LEVEL]
                [--log-format {json,pretty}] [--log-file LOG_FILE] [--quiet]
                [--metric-grouping-interval METRIC_GROUPING_INTERVAL]
                [--debug]
                {run} ...

Adds numbers

optional arguments:
  -h, --help            show this help message and exit
  --name NAME           Name to identify this instance
  --log-level LOG_LEVEL
                        Logging level as picked from the logging module
  --log-format {json,pretty}
                        Force the format of the logs. By default, if the
                        command is from a terminal, print colorful logs.
                        Otherwise print json.
  --log-file LOG_FILE   Writes logs to log file if specified, default: None
  --quiet               if true, does not print logs to stderr, default: False
  --metric-grouping-interval METRIC_GROUPING_INTERVAL
                        To group metrics based on time interval ex:10 i.e;(10
                        sec)
  --debug               To run the code in debug mode

commands:
  {run}
python adder.py run --help
usage: adder.py run [-h] c

positional arguments:
  c           Number to add

optional arguments:
  -h, --help  show this help message and exit

Run the script now to see the intended output

python adder.py run 30
60

Run the same with info and higher level logs enabled

python adder.py --log-level INFO 30
2016-04-10 13:48:27,356 INFO Starting run of script ...
60
2016-04-10 13:48:27,356 INFO Script is done

--log-level accepts all the values shown at https://docs.python.org/2/library/logging.html#logging-levels.

log is a log object created using python’s standard logging module. You can read more about it at https://docs.python.org/2/library/logging.html.

Metric-Grouping

When writing a Metric using self.log, you can specify type=metric. If this is done, a background thread will automatically group multiple metrics into one by averaging values (to prevent writing too many log lines). test.py

from basescript import BaseScript
import time
import random

class Stats(BaseScript):
    def __init__(self):
        super(Stats, self).__init__()

    def run(self):
        ts = time.time()
        while True:
            # Metric Format.
            self.log.info("stats", time_duration=(time.time()-ts), type="metric", random_number=random.randint(1, 50))

if __name__ == '__main__':
    Stats().start()

Run the command to see the output.

python test.py --metric-grouping-interval 5 run

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