Fix occurrences of the lowercase (or mixed case) version of taskflow and rename those occurrences to the cased version to be consistent with usage elsewhere in the docs. Change-Id: I31f552daa015724b443b099c2fcfe38d8e04605a
7.0 KiB
Resumption
Overview
Question: How can we persist the flow so that it can be resumed, restarted or rolled-back on engine failure?
Answer: Since a flow is a set of atoms <atoms>
and
relations between atoms we need to create a model and corresponding
information that allows us to persist the right amount of
information to preserve, resume, and rollback a flow on software or
hardware failure.
To allow for resumption TaskFlow must be able to re-create the flow and re-connect the links between atom (and between atoms->atom details and so on) in order to revert those atoms or resume those atoms in the correct ordering. TaskFlow provides a pattern that can help in automating this process (it does not prohibit the user from creating their own strategies for doing this).
Factories
The default provided way is to provide a factory
function which will create (or recreate your workflow). This function
can be provided when loading a flow and corresponding engine via the
provided :pyload_from_factory()
<taskflow.engines.helpers.load_from_factory>
method. This
factory
function is expected to be a function (or staticmethod
)
which is reimportable (aka has a well defined name that can be located
by the __import__
function in python, this excludes
lambda
style functions and instance
methods).
The factory
function name will be saved into the logbook and it will be imported and
called to create the workflow objects (or recreate it if resumption
happens). This allows for the flow to be recreated if and when that is
needed (even on remote machines, as long as the reimportable name can be
located).
Names
When a flow is created it is expected that each atom has a unique
name, this name serves a special purpose in the resumption process (as
well as serving a useful purpose when running, allowing for atom
identification in the notification <notifications>
process). The
reason for having names is that an atom in a flow needs to be somehow
matched with (a potentially) existing :py~taskflow.persistence.logbook.AtomDetail
during
engine resumption & subsequent running.
The match should be:
- stable if atoms are added or removed
- should not change when service is restarted, upgraded...
- should be the same across all server instances in HA setups
Names provide this although they do have weaknesses:
- the names of atoms must be unique in flow
- it becomes hard to change the name of atom since a name change causes other side-effects
Note
Even though these weaknesses names were selected as a good enough solution for the above matching requirements (until something better is invented/created that can satisfy those same requirements).
Scenarios
When new flow is loaded into engine, there is no persisted data for
it yet, so a corresponding :py~taskflow.persistence.logbook.FlowDetail
object will
be created, as well as a :py~taskflow.persistence.logbook.AtomDetail
object for
each atom that is contained in it. These will be immediately saved into
the persistence backend that is configured. If no persistence backend is
configured, then as expected nothing will be saved and the atoms and
flow will be ran in a non-persistent manner.
Subsequent run: When we resume the flow from a persistent backend (for example, if the flow was interrupted and engine destroyed to save resources or if the service was restarted), we need to re-create the flow. For that, we will call the function that was saved on first-time loading that builds the flow for us (aka; the flow factory function described above) and the engine will run. The following scenarios explain some expected structural changes and how they can be accommodated (and what the effect will be when resuming & running).
Same atoms
When the factory function mentioned above returns the exact same the flow and atoms (no changes are performed).
Runtime change: Nothing should be done -- the engine
will re-associate atoms with :py~taskflow.persistence.logbook.AtomDetail
objects by
name and then the engine resumes.
Atom was added
When the factory function mentioned above alters the flow by adding a new atom in (for example for changing the runtime structure of what was previously ran in the first run).
Runtime change: By default when the engine resumes
it will notice that a corresponding :py~taskflow.persistence.logbook.AtomDetail
does not
exist and one will be created and associated.
Atom was removed
When the factory function mentioned above alters the flow by removing a new atom in (for example for changing the runtime structure of what was previously ran in the first run).
Runtime change: Nothing should be done -- flow structure is reloaded from factory function, and removed atom is not in it -- so, flow will be ran as if it was not there, and any results it returned if it was completed before will be ignored.
Atom code was changed
When the factory function mentioned above alters the flow by deciding that a newer version of a previously existing atom should be ran (possibly to perform some kind of upgrade or to fix a bug in a prior atoms code).
Factory change: The atom name & version will have to be altered. The factory should replace this name where it was being used previously.
Runtime change: This will fall under the same
runtime adjustments that exist when a new atom is added. In the future
TaskFlow could make this easier by providing a upgrade()
function that can be used to give users the ability to upgrade atoms
before running (manual introspection & modification of a :py~taskflow.persistence.logbook.LogBook
can be done
before engine loading and running to accomplish this in the
meantime).
Atom was split in two atoms or merged from two (or more) to one atom
When the factory function mentioned above alters the flow by deciding that a previously existing atom should be split into N atoms or the factory function decides that N atoms should be merged in <N atoms (typically occurring during refactoring).
Runtime change: This will fall under the same
runtime adjustments that exist when a new atom is added or removed. In
the future TaskFlow could make this easier by providing a
migrate()
function that can be used to give users the
ability to migrate atoms previous data before running (manual
introspection & modification of a :py~taskflow.persistence.logbook.LogBook
can be done
before engine loading and running to accomplish this in the
meantime).
Flow structure was changed
If manual links were added or removed from graph, or task requirements were changed, or flow was refactored (atom moved into or out of subflows, linear flow was replaced with graph flow, tasks were reordered in linear flow, etc).
Runtime change: Nothing should be done.