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User guide🔗

This user guide can be used as a starting point for getting a deeper understanding of the inner workings of the bandsaw library. It is meant for users who want to learn about individual details or who plan to extend its features by developing own advices or extensions.


First we start with some high-level concepts that we use throughout the library. The purpose of this sections is to explain the structure of bandsaw and introduce a common set of terms that helps to talk about the underlying ideas


The fundamental basis in bandsaw is the idea of a workflow. We think of a workflow as a single python script, that contains code for all individual steps that are performed in a defined sequence.

Diagram of a workflow

The individual steps can have dependencies on external inputs and on results of other steps, so that they form a directed acyclic graph. Those steps are referred to as Tasks within bandsaw.


Tasks are pieces of code, that are used to process data. Task instances can be created by calling the create_task(cls, obj) class function in the Task class. At the moment, bandsaw supports only free functions defined on module level as tasks.

Usually, tasks are defined by adding the @bandsaw.task decorator to the code that should be executed as a task.

def my_function(x):
    return x

Each task has a unique task_id that is derived from the code that is executed. This can be used to differentiate between different tasks.


Tasks can take arbitrary arguments, which means we can have multiple executions of the same task, that differ in the given arguments. This is captured by a Run object, which encapsulates the arguments for a specific task execution. Similar to the task_id, each run has a separate run_id that differentiates between different runs.


Objects that implement the Advice protocol are the mechanism that allows bandsaw to influence the execution of the task. Each advice class can implement two different methods, before(self, session) and after(self, session).

The before() method is called before the task is actually executed and allows the advice to make changes to the way the task is executed, e.g. running the task on a different system or returning a result early without executing the task at all.

The after() method is called after the task was actually executed and returned a result. This allows an advice to make changes to the result or use it in a different way than just returning it.

Both methods decide on what happens after by calling the appropriate method on the current session, the sole argument both methods are taking.


A Session is the object, that manages the process of executing a task for a specific run. It defines the different actions that advices can take when their before() or after() methods are called.

Diagram of an advised task

When a task is being called within a workflow, a new Session object is instantiated with the task, a run object containing the tasks arguments and a list of advices, called the "advice chain", that should be used for advising the execution.

before() executing a task.🔗

The session calls the before() methods of all advices one after another in the order that the advices are defined in the advice chain. Each Advice has to tell the session to either


  • return early by skipping the following advices AND the actual task execution by calling session.conclude(result) and providing a Result that should be used instead. After the advice has concluded with a result, the only advices whose after() methods will be called are the advices, that come before in the advice chain.

Executing the task🔗

If all advices of the advice chain decided to proceed(), the session will execute that task and keep the value it returns. If an exception or error is raised during the execution it will be stored in the Result instance in the session.

after() executing a task🔗

After the task has been executed, the session begins to call the after() methods of the advices in the REVERSED order from the advice chain. This means the advice that was called last for before() is called first for after(). Now each advice can decide how to continue. Similar to before() there are two possibilities:


  • return a different result and continue with that by calling session.conclude(result) with a different Result instance. All following advices will be called using after() with a session containing the new result.

Once all advices have finished, the session will unpack the result and either return its value to the workflow or re-raise the error.

Serializing the session🔗

In order to move the execution of a task across different python interpreters, an advice can use the capability of a Session to serialize its state to a stream and recreate it at a later point in time or on a different platform.

For this the session contains two methods, save(stream) and restore(stream). Both methods will (de-)serialize the complete session including context, task, run and result. The only thing missing here are the objects from the advice chain, since bandsaw can't enforce them to be serializable. This means, that the same advice chain with the same name must be available from the configuration at the time, the session is restored. For an example, how transfer to a different python interpreter can be implemented, please look at the implementation of the SubprocessAdvice.


Bandsaw needs configuration to know which advices to apply to the individual tasks. This configuration is given in form of an object of the bandsaw.config.Configuration class. Just creating a new Configuration object will create an empty, but working configuration, that actually does nothing and executes tasks without any changes.

import bandsaw

configuration = bandsaw.Configuration()

The class has all the required methods to configure the different aspects of bandsaw.

Advice chains🔗

An advice chain is a sequence of objects implementing the Advice protocol, that should be used for advising task executions. An advice chain is added to the configuration using the add_device_chain(*advices, name='default') method. It takes instances of Advice as positional arguments with an optional name keyword argument.


Each advice chain has a name, that can be used to choose which chain to use per task. If no name is given, the chain with name 'default' is configured. Already existing chains will be overwritten. So if you configure two different advice chains, with no or the same name, the latter will replace the former.


In order to transfer tasks between different python interpreters, bandsaw needs the capability to serialize tasks, their arguments and internal classes. For this bandsaw defines a Serializer base class, that can be implemented to support different types of serialization. Which serializer to use, can be configured as part of the configuration:

from bandsaw.serialization.json import JsonSerializer


Bandsaw comes with two different serialization formats:


The PickleSerializer has the advantage, that it works out of the box with most standard python types. It uses the standard python pickle library to serialize python objects and should work across different python versions. If custom types need to be serialized (e.g. as part of some arguments to a task), that don't work with pickle, support for pickle can easily be added. One disadvantage of pickle is that sometimes the serialized representation of a value is not unique. Since bandsaw uses the serialized form of arguments to derive the run_id of a run, this can lead to inconsistencies, when the same arguments can lead to different run_ids.


The JsonSerializer uses JSON as format for the serialized data. The standard json library supports only primitive types like strings, dict, int etc. so for all complex types one need to explicitly add code to serialize them. Bandsaw implements support for serializing exceptions, tuples and all of its internal classes, so that they can be serialized to json. To serialize a custom type to json, bandsaw offers two options:

Implement SerializableValue🔗

The easiest way to make your custom type json serializable is to inherit from the SerializableValue base class and implement the abstract methods serialized(self) and deserialize(cls, values). serialized(self) must return a value that is json serializable (e.g. a dict containing only primitives). The class method deserialize(cls, values) is given this value and returns a new instance of the class.

Create a ValueSerializer🔗

If the custom type can't be changed, there is the option to create a new ValueSerializer class that can serialize this type. It consists of 3 different methods:

can_serialize_value(self, value) Has to return True if value is of the type that this particular serializer can serialize.

serialize_value(self, value) Has to return the serialized representation of the type, consisting only of primitives that are json serializable.

deserialize_value(self, representation) Has to return a new instance of custom_type from its serialized representation.

Finally, the new ValueSerializer needs to be added to the json serializer as part of the configuration:


How to use bandsaw🔗

Now with some knowledge about the different concepts within bandsaw at our hands, let's dive into the topic of how to put the library to good use.

Install the library🔗

Use stable release from PyPI🔗

All stable versions of bandsaw are available on PyPI and can be downloaded and installed from there. The easiest option to get it installed into your python environment is by using pip:

pip install bandsaw

Use from source🔗

Bandsaw's Git repository is available for everyone and can easily be cloned into a new repository on your local machine:

$ cd /your/local/directory
$ git clone
$ cd bandsaw

If you want to make changes to library, please follow the guidance in the on how to setup the necessary tools for testing your changes.

If you just want to use the library, it is sufficient to add the path to your local bandsaw repository to your $PYTHONPATH variable, e.g.:

$ export PYTHONPATH="$PYTHONPATH:/your/local/directory/bandsaw"

Defining the individual tasks of your workflow🔗

In order to use bandsaw in your workflow, you first have to import its package.

import bandsaw


Splitting up your workflow into individual tasks can be done by annotating the individual functions with the bandsaw.task decorator:

def my_function(x):
    return x

The decorator can be only applied to free functions in the current implementation, so it isn't possible to decorate methods of classes for now:

class MyClass:

    def my_method(self, x):

        return x

Configure bandsaw🔗

Bandsaw loads its configuration automatically at the time it is needed. This happens by dynamically importing a python module that contains an instance of the class Configuration assigned to the variable configuration.

import bandsaw

configuration = bandsaw.Configuration()

Bandsaw expects the configuration to be found in the python module bandsaw_config. The name of the module can be changed in two ways:

  1. Set the BANDSAW_CONFIG environment variable to a different module name. Bandsaw reads the name of the module containing the configuration from this variable if it is set. So by setting it to e.g. my_config it will import my_config and take the configuration from it instead.

  2. Add an additional keyword argument to the @task decorator that contains the name of the configuration to use for this task:

    def my_function(x):
        return x
    By adding the config keyword argument we can choose the configuration we want to use per task.

The existence of the configuration is checked as soon as the decorator is being applied. If no configuration could be loaded, an error is raised, usually at module import time.

Choose the advice chain🔗

Each task can define, which advice chain should be used. This can be done, by adding a keyword argument chain to the task decorator:

def my_function(x):
    return x

If no chain exists with the specified name, a KeyError is raised.

Extending bandsaw🔗

Bandsaw is extensible and allows developers to add new functionality in various ways:

Implementing custom advices🔗

Advices are the main building blocks for adding new functionality. Implementing an advice is easy, the only necessary work is to adhere to a protocol and follow some simple rules.

The protocol is defined in Advice and consists of two methods, before(self, session) and after(self, session).

  1. Advices have to call the session. When called via before() and after(), the advice is responsible for telling the session how to continue. They must call once either session.proceed() or session.conclude(). Failure to do so, will lead to a RuntimeError raised by the session.
  2. Advices shouldn't keep state themselves. Advice instances are used for multiple tasks. Therefore, their implementation shouldn't store any task specific state themselves. If task specific state is necessary, it can be easily added to the context of the session.
  3. Advices may be instantiated multiple times and on different machines. Everytime a task is transferred to a different python interpreter, its configuration and with it the configured advices are instantiated again, even if they aren't actually called. Therefore, advices shouldn't do much work or interact with their environment at instantiation time, but only when they are actually called via after() or before(). Usually they can assume that both methods are called in the same environment for the same task.

Last update: 2021-10-28