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https://github.com/daskos/mentor
Extensible Python Framework for Apache Mesos
https://github.com/daskos/mentor
docker mentos mesos multiprocessing python scheduler
Last synced: about 1 month ago
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Extensible Python Framework for Apache Mesos
- Host: GitHub
- URL: https://github.com/daskos/mentor
- Owner: daskos
- License: apache-2.0
- Created: 2016-02-14T18:05:16.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-10-19T14:33:09.000Z (about 7 years ago)
- Last Synced: 2024-11-03T03:18:53.153Z (about 2 months ago)
- Topics: docker, mentos, mesos, multiprocessing, python, scheduler
- Language: Python
- Homepage:
- Size: 338 KB
- Stars: 33
- Watchers: 11
- Forks: 6
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
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# An extensible Mesos library for Python
###### aka. the distributed snake-charmer
Mentor's intention is to simplify the process of writing python frameworks
for Mesos. Mentor provides multiple components and interfaces to cover various
levels of complexity needs.
## Notable Features
- Comfortable Pythonic interface instead of the C++ syntax
- Magical Protobuf wrapper to easily extend messages with custom functionality
- Multiple weighted Bin-Packing heuristics for optimized scheduling
- Easily extensibe QueueScheduler implementation
- Python multiprocessing.Pool interface
## Install
`pip install mentor` or use [daskos/mentor](https://hub.docker.com/r/daskos/mentor/) Docker image
Requirements:
- mesos.interface (installable via pip)
- mesos.native (binary .egg downloadable from mesosphere.io)
Configuration:
- `MESOS_MASTER=zk://127.0.0.1:2181/mesos`
## Examples
### Futures Interface
It's almost identical to python's
[futures interface](https://docs.python.org/3/library/concurrent.futures.html)
but runs processes on a Mesos cluster (concurrently).
```python
from mentor.apis.futures import MesosPoolExecutor
from mentor.proxies.messages import Cpus, Mem
with MesosPoolExecutor(name='futures-pool') as executor:
def mul(a, b):
return a * b
future = executor.submit(mul, args=[3, 5])
assert future.result(timeout=5) == 3
it = executor.map(mul, range(10), range(10), timeout=5,
resources=[Cpus(0.1), Mem(128)])
assert list(it) == [i**2 for i in range(10)]
```
### Multiprocessing
It's similar to python's
[multiprocessing interface](https://docs.python.org/2/library/multiprocessing.html)
but runs processes on a Mesos cluster (concurrently).
```python
from __future__ import print_function
from mentor.apis.multiprocessing import Pool
with Pool(name='mentor-pool') as pool:
def mul(a, b):
return a * b
res1 = pool.apply_async(lambda a, b: a + b, [1, 2])
res2 = pool.apply_async(mul, [2, 3])
pool.wait()
print(res1.get())
print(res2.get())
```
### Work Queue Scheduler
Basic scheduler to submit various kind of workloads, eg.:
- bash commands
- docker executable containers
- python callables
- customized tasks (e.g. function executed via pypy)
```python
from __future__ import print_function
from mentor.scheduler import QueueScheduler, Running
from mentor.messages import PythonTask
from mentor.proxies.messages import Disk, Mem, Cpus
scheduler = QueueScheduler()
task = PythonTask(fn=sum, args=[range(10)], name='mentor-task',
resources=[Cpus(0.1), Mem(128), Disk(512)])
with Running(scheduler, name='mentor-scheduler'):
res = scheduler.submit(task) # return AsyncResult
print(res.get(timeout=30))
```
### Custom Scheduler
You can make your own scheduler built on QueueScheduler or for more complex
needs there's a [Scheduler](mentor/interface.py) interface which you can use
to create one from scratch. (However in this case you'll have to implement
some of the functionalities already in [QueueScheduler](mentor/scheduler.py))
```python
from __future__ import print_function
from mentor.scheduler import QueueScheduler, Running
from mentor.messages import PythonTask
from mentor.proxies.messages import Disk, Mem, Cpus
class CustomScheduler(QueueScheduler):
def on_update(self, driver, status):
"""You can hook on the events defined in the Scheduler interface.
They're just more conveniantly named methods for the basic
mesos.interface functions but this is how you can add some
custom logic to your framework in an easy manner.
"""
logging.info(
"Status update received for task {}".format(status.task_id))
super(CustomScheduler, self).on_update(driver, status)
scheduler = CustomScheduler()
task = PythonTask(fn=sum, args=[range(9)], name='mentor-task',
resources=[Cpus(0.1), Mem(128), Disk(512)])
with Running(scheduler, name='mentor-custom-scheduler'):
res = scheduler.submit(task)
print(res.get(timeout=60))
```
Also this way you can easily implement your own resource offer handling logic by
overriding the `on_offers(self, driver, offers)` method in which we give you a
helping hand with comparable Offers and TaskInfos (basic arithmetic operators
are also overloaded).
```python
from mentor.interface import Scheduler
from mentor.proxies.messages import Offer, TaskInfo
class CustomScheduler(Scheduler):
...
def on_offers(self, driver, offers):
...
task = self.get_next_task()
for offer in offers
if task < offer:
task.slave_id = offer.slave_id
driver.launch(offer, [task])
# decline unused offers or launch with empty task list
...
```
## Optimized Task Placement
Mentor implements multiple weighted heuristics to solve the
[Bin-Packing Problem](https://en.wikipedia.org/wiki/Bin_packing_problem):
- First-Fit
- First-Fit-Decreasing
- Max-Rest
- Best-Fit
- Best-Fit-Decreasing
see [binpack.py](mentor/binpack.py).
The benefits of using bin-packing has been proven by
[Netflix/Fenzo](https://github.com/Netflix/Fenzo) in
[Heterogeneous Resource Scheduling Using Apache Mesos](http://events.linuxfoundation.org/sites/events/files/slides/Prezo-at-MesosCon2015-Final.pdf)
## Built in Task Types
### Command
The most basic task executes a simple command, Mesos will run CommandInfo's
value with `/bin/sh -c`. Also, if you want to run your task in a Docker
container you can provide some additional information for the task.
```python
from mentor.proxies.messages import TaskInfo, CommandInfo
task = TaskInfo(name='command-task', command=CommandInfo(value='echo 100'))
task.container.type = 'DOCKER'
task.container.docker.image = 'daskos/mentor:latest'
```
### Python
[PythonTask](/mentor/messages.py) is capable of running arbitrary python code on
your cluster. It sends [cloudpickled](https://github.com/cloudpipe/cloudpickle)
methods and arguments to the matched mesos-slave for execution.
Note that python tasks run in [daskos/mentor](https://hub.docker.com/r/daskos/mentor/)
Docker container by default.
```python
from mentor.messages import PythonTask
# You can pass a function or a lambda in place of sum for fn.
task = PythonTask(name='python-task', fn=sum, args=[range(5)])
```
## Custom Task
Customs tasks can be written by extending [TaskInfo](/mentor/proxies/messages.py)
or any existing descendants.
If you're walking down the former path you'll most likely have to deal with
protobuf in your code; worry not, we have some magic wrappers for you to provide
customizable messages.
```python
from __future__ import print_function
from mentor.proxies.messages import TaskInfo
from mesos.interface import mesos_pb2
class CustomTask(TaskInfo):
# descriptive protobuf template the wrapper matched against
proto = mesos_pb2.TaskInfo(
labels=mesos_pb2.Labels(
labels=[mesos_pb2.Label(key='custom')]))
@property
def uppercase_task_name():
return self.name.upper()
def on_update(self, status):
logging.info('Custom task has received a status update')
def custom_method(self):
print("Arbitrary stuff")
```
## One-Off Executor
This Executor implementation simply runs the received python function with the
provided arguments, then sends back the result in a reliable fashion.
```py
class OneOffExecutor(Executor):
def on_launch(self, driver, task):
def run_task():
driver.update(task.status('TASK_RUNNING'))
logging.info('Sent TASK_RUNNING status update')
try:
logging.info('Executing task...')
result = task()
except Exception as e:
logging.exception('Task errored')
driver.update(task.status('TASK_FAILED', message=e.message))
logging.info('Sent TASK_RUNNING status update')
else:
driver.update(task.status('TASK_FINISHED', data=result))
logging.info('Sent TASK_FINISHED status update')
thread = threading.Thread(target=run_task)
thread.start()
```
## Warning (at the end)
This is a pre-release!
- proper documentation
- python futures api
- more detailed examples
- and CONTRIBUTION guide
- dask.mesos backend
are coming!