On startup, when we were populating the Settings object, we failed to coerce the dry_run parameter from a string to a boolean, which resulted in is_dry_run always believing that it was a preview. This PR fixes this oversight by explicitly coercing to a boolean prior to sending the value to Settings.
99 lines
4.6 KiB
Python
99 lines
4.6 KiB
Python
#!/usr/bin/env python
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# Copyright 2016-2018, Pulumi Corporation. All rights reserved.
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import argparse
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import asyncio
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import logging
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import os
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import sys
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import traceback
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import runpy
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# The user might not have installed Pulumi yet in their environment - provide a high-quality error message in that case.
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try:
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import pulumi
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import pulumi.runtime
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except ImportError:
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# For whatever reason, sys.stderr.write is not picked up by the engine as a message, but 'print' is. The Python
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# langhost automatically flushes stdout and stderr on shutdown, so we don't need to do it here - just trust that
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# Python does the sane thing when printing to stderr.
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print(traceback.format_exc(), file=sys.stderr)
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print("""
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It looks like the Pulumi SDK has not been installed. Have you run pip install?
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If you are running in a virtualenv, you must run pip install -r requirements.txt from inside the virtualenv.""", file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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# Parse the arguments, program name, and optional arguments.
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ap = argparse.ArgumentParser(description='Execute a Pulumi Python program')
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ap.add_argument('--project', help='Set the project name')
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ap.add_argument('--stack', help='Set the stack name')
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ap.add_argument('--parallel', help='Run P resource operations in parallel (default=none)')
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ap.add_argument('--dry_run', help='Simulate resource changes, but without making them')
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ap.add_argument('--pwd', help='Change the working directory before running the program')
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ap.add_argument('--monitor', help='An RPC address for the resource monitor to connect to')
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ap.add_argument('--engine', help='An RPC address for the engine to connect to')
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ap.add_argument('--tracing', help='A Zipkin-compatible endpoint to send tracing data to')
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ap.add_argument('PROGRAM', help='The Python program to run')
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ap.add_argument('ARGS', help='Arguments to pass to the program', nargs='*')
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args = ap.parse_args()
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# If any config variables are present, parse and set them, so subsequent accesses are fast.
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config_env = pulumi.runtime.get_config_env()
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for k, v in config_env.items():
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pulumi.runtime.set_config(k, v)
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# Configure the runtime so that the user program hooks up to Pulumi as appropriate.
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pulumi.runtime.configure(
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pulumi.runtime.Settings(
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monitor=args.monitor,
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engine=args.engine,
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project=args.project,
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stack=args.stack,
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parallel=int(args.parallel),
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dry_run=args.dry_run == "true"
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)
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)
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# Finally, swap in the args, chdir if needed, and run the program as if it had been executed directly.
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sys.argv = [args.PROGRAM] + args.ARGS
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if not args.pwd is None:
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os.chdir(args.pwd)
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successful = False
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loop = asyncio.get_event_loop()
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# We are (unfortunately) suppressing the log output of asyncio to avoid showing to users some of the bad things we
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# do in our programming model.
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#
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# Fundamentally, Pulumi is a way for users to build asynchronous dataflow graphs that, as their deployments
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# progress, resolve naturally and eventually result in the complete resolution of the graph. If one node in the
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# graph fails (i.e. a resource fails to create, there's an exception in an apply, etc.), part of the graph remains
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# unevaluated at the time that we exit.
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#
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# asyncio abhors this. It gets very upset if the process terminates without having observed every future that we
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# have resolved. If we are terminating abnormally, it is highly likely that we are not going to observe every single
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# future that we have created. Furthermore, it's *harmless* to do this - asyncio logs errors because it thinks it
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# needs to tell users that they're doing bad things (which, to their credit, they are), but we are doing this
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# deliberately.
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#
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# In order to paper over this for our users, we simply turn off the logger for asyncio. Users won't see any asyncio
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# error messages, but if they stick to the Pulumi programming model, they wouldn't be seeing any anyway.
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logging.getLogger("asyncio").setLevel(logging.CRITICAL)
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try:
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coro = pulumi.runtime.run_in_stack(lambda: runpy.run_path(args.PROGRAM, run_name='__main__'))
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loop.run_until_complete(coro)
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successful = True
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except pulumi.RunError as e:
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pulumi.log.error(str(e))
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except Exception as e:
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pulumi.log.error("Program failed with an unhandled exception:")
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pulumi.log.error(traceback.format_exc())
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finally:
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loop.close()
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sys.stdout.flush()
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sys.stderr.flush()
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exit_code = 0 if successful else 1
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sys.exit(exit_code)
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