Updates to use update_graph in task journey docs
@@ -41,15 +41,15 @@ Step 2: Arrive in the Scheduler
A few milliseconds later, the scheduler receives this message on an open socket.
The scheduler updates its state with this little graph that shows how to compute
-``z``.::
+``z``::
- scheduler.tasks.update(msg['tasks'])
+ scheduler.update_graph(tasks=msg['tasks'], keys=msg['keys'])
The scheduler also updates *a lot* of other state. Notably, it has to identify
that ``x`` and ``y`` are themselves variables, and connect all of those
dependencies. This is a long and detail oriented process that involves
updating roughly 10 sets and dictionaries. Interested readers should
-investigate ``distributed/scheduler.py::update_state()``. While this is fairly
+investigate ``distributed/scheduler.py::update_graph()``. While this is fairly
complex and tedious to describe rest assured that it all happens in constant
time and in about a millisecond.
