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hadrosaur — computed resource management

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Hadrosaur makes it easy to track the completion status, errors, and logs of large amounts of resources (files, metadata, analytics, database imports, etc.).

You simply define your resource as a decorated Python function that can create files and save metadata using an identifier in a certain namespace. Later on, you can quickly fetch the status and results of previously computed resources.

This library uses a combination of LevelDB and the file system to track the state of your tasks.

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. This function's job is to generate a single resource for the collection given a unique ID and some arguments.

The collection should have a unique name, and its function 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 during computation:
    • 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) method to compute and cache resources in a collection.

Keyword arguments:

  • args -- an optional dict of extra arguments for the resource compute function
  • recompute -- force the resource to be re-computed, even if it has already been computed

What happens when you fetch a resource:

  • If the resource has not yet been computed, the collection's compute function will be run.
  • If the resource was already computed in the past, then the saved results will get returned instantly (unless recompute=True has been set in the keyword arguments).
  • If an error is thrown in the function, logs will be saved and the status will be updated
>> proj.fetch('collection_name', 'uniq_ident123', optional_args)
<Resource>

The resource object has the following properties:

  • resource.result: any JSON-serializable data returned by the resource's compute function
  • resource.start_time: The unix epoch (in milliseconds) of when the resource started being computed
  • eresource.end_time: the unix epoch (in ms) of when the resource finished computing (or failed)
  • resource.status: whether the resource has been computed already ("completed"), is currently being computed ("pending"), has not yet been fetched at all ("unavailable"), or threw a Python error while running the function ("error")
  • resource.paths: A dictionary of all the filesystem paths associated with your resource, with the following keys:
    • '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
    • 'log': A line-by-line log file produced by the resource's logger (ctx.logger)
    • 'status': Path to the current status ("unavailable", "completed", "pending", "error")
    • 'result': Path to a JSON file of serializable data returned by the resource's function
    • 'storage': Directory path of additional files written by the resource's function (ctx.subdir)

Fetch status and information

Fetch stats for a collection

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

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

Use proj.stats() without an argument to fetch the stats for all collections.

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

> proj.fetch_by_status('collection_name', 'pending')
['1', '2', '3'..]

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')
"complete"

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)
"""

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