hadrosaur 0.3.2

[WIP] Manage a large amount of computed resources, such as files, imports, etc.

hadrosaur — computed resource management

Do you want to compute thousands of resources (files, metadata, database imports, etc) in parallel, but have a hard time tracking completion status, errors, logs, and other runtime data? That's what this is for.

Work in progress

Quick usage tutorial

Install

pip install hadrosaur

Define a resource collection

Import the lib and initialize a project using a base directory. Files, metadata, and logs will all get stored under this directory.

from hadrosaur import Project

proj = Project('./base_directory')

Define a collection using a decorator around a function. The collection should have a unique name and must take these params:

• ident — an identifier (unique across the collection) for each computed resource
• args — a dictionary of optional arguments
• ctx — a Context object which holds some extra data you may find useful in the function:
  • ctx.subdir - the path of a directory in which you can store files for this resource
  • ctx.logger - a special Python logging instance that will write to a rotating log file stored in the resource directory, with some nice default formatting

@proj.resource('collection_name')
def compute_resource(ident, args, ctx):
  ctx.logger.info("Starting up")
  # Run some things...
  # Maybe save stuff into ctx.subdir...
  time.sleep(1)
  # Return any JSON-serializable data for the resource, such as metadata, run results, filepaths, etc.
  return {'ts': time.time()}

Fetch a resource

Use the proj.fetch(collection_name, ident, args) method to compute and cache resources in a collection.

• If the resource has not yet been computed, the function will be run
• If the resource was already computed in the past, then the saved results will get returned instantly
• If an error is thrown in the function, logs will be saved and the status will be updated
• If the function is backgrounded, then fetching the resource will show a "pending" status

>> proj.fetch('collection_name', 'uniq_ident123', optional_args)
{
  'result': {'some': 'metadata'},
  'status': {'completed': True, 'pending': False, 'error': False},
  '_paths': {
    'base': 'base_directory/collection_name/uniq_ident123',
    'error': 'base_directory/collection_name/uniq_ident123/error.log',
    'stdout': 'base_directory/collection_name/uniq_ident123/stdout.log',
    'stderr': 'base_directory/collection_name/uniq_ident123/stderr.log',
    'status': 'base_directory/collection_name/uniq_ident123/status.json',
    'result': 'base_directory/collection_name/uniq_ident123/result.json',
    'storage': 'base_directory/collection_name/uniq_ident123/storage/'
  }
}

Descriptions of each of the returned fields:

• 'result': any JSON-serializable data returned by the resource's function
• 'status': whether the resource has been computed already ("completed"), is currently being computed ("pending"), or threw a Python error while running the function ("error")
• 'paths': All the various filesystem paths associated with your resource
  • 'base': The base directory that holds all data for the resource
  • 'error': A Python stacktrace of any error that occured while running the resource's function
  • 'stdout': A line-by-line log file of stdout produced by the resource's function (any print() calls)
  • 'stderr': A line-by-line log of stderr messages printed by the resource's function (any sys.stderr.write calls)
  • 'status': a JSON object of status keys for the resource ("completed", "pending", "error")
  • 'result': Any JSON serializable data returned by the resource's function
  • 'storage': Additional storage for any files written by the resource's function

Fetch status and information

Fetch stats for a collection

To see status counts for a whole collection, use proj.status('collection_name'):

> proj.status('collection_name')
{
  'counts': {
      'total': 100,
      'pending': 75,
      'completed': 20,
      'error': 5,
      'unknown': 0
  }
}

To get a list of resource IDs for a given status, use proj.fetch_by_status:

> proj.fetch_by_status('collection_name', pending=True)
['1', '2', '3'..]
> proj.fetch_by_status('collection_name', completed=True)
['4', '5', '6'..]
> proj.fetch_by_status('collection_name', error=True)
['7', '8', '9'..]

Fetch info about a single resource

Use proj.status('collection_name', 'resource_id') to see the status of a particular resource.

> proj.status('collection_name', 'resource_id')
{
    "completed": true,
    "pending": false,
    "error": false,
    "start_time": 1580948135917,
    "end_time": 1580948135919
}

If an exception was raised during the execution of the function used to compute a resource, then use proj.fetch_error to see the error.

> proj.fetch_error('collection_name', 'resource_id')
"""Traceback (most recent call last):
  File "/home/j/code/hadrosaur/hadrosaur/main.py", line 211, in fetch
    result = func(ident, args, ctx)
  File "/home/j/code/hadrosaur/test/test_general.py", line 26, in throw_something
    raise RuntimeError('This is an error!')
RuntimeError: This is an error!"""

To see the run log (produced by ctx.logger during function execution), then use proj.fetch_log

> proj.fetch_log('collection_name', 'resource_id')
"""
2020-02-05 16:15:35 INFO     output here (test_general.py:25)
2020-02-05 16:15:35 INFO     more output here (test_general.py:25)
"""