corda/docs/source/flow-state-machines.rst

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Writing flows
=============

This article explains our approach to modelling business processes and the lower level network protocols that implement
them. It explains how the platform's flow framework is used, and takes you through the code for a simple
2-party asset trading flow which is included in the source.

Introduction
------------

Shared distributed ledgers are interesting because they allow many different, mutually distrusting parties to
share a single source of truth about the ownership of assets. Digitally signed transactions are used to update that
shared ledger, and transactions may alter many states simultaneously and atomically.

Blockchain systems such as Bitcoin support the idea of building up a finished, signed transaction by passing around
partially signed invalid transactions outside of the main network, and by doing this you can implement
*delivery versus payment* such that there is no chance of settlement failure, because the movement of cash and the
traded asset are performed atomically by the same transaction. To perform such a trade involves a multi-step flow
in which messages are passed back and forth privately between parties, checked, signed and so on.

Despite how useful these flows are, platforms such as Bitcoin and Ethereum do not assist the developer with the rather
tricky task of actually building them. That is unfortunate. There are many awkward problems in their implementation
that a good platform would take care of for you, problems like:

* Avoiding "callback hell" in which code that should ideally be sequential is turned into an unreadable mess due to the
  desire to avoid using up a thread for every flow instantiation.
* Surviving node shutdowns/restarts that may occur in the middle of the flow without complicating things. This
  implies that the state of the flow must be persisted to disk.
* Error handling.
* Message routing.
* Serialisation.
* Catching type errors, in which the developer gets temporarily confused and expects to receive/send one type of message
  when actually they need to receive/send another.
* Unit testing of the finished flow.

Actor frameworks can solve some of the above but they are often tightly bound to a particular messaging layer, and
we would like to keep a clean separation. Additionally, they are typically not type safe, and don't make persistence or
writing sequential code much easier.

To put these problems in perspective, the *payment channel protocol* in the bitcoinj library, which allows bitcoins to
be temporarily moved off-chain and traded at high speed between two parties in private, consists of about 7000 lines of
Java and took over a month of full time work to develop. Most of that code is concerned with the details of persistence,
message passing, lifecycle management, error handling and callback management. Because the business logic is quite
spread out the code can be difficult to read and debug.

As small contract-specific trading flows are a common occurrence in finance, we provide a framework for the
construction of them that automatically handles many of the concerns outlined above.

Theory
------

A *continuation* is a suspended stack frame stored in a regular object that can be passed around, serialised,
unserialised and resumed from where it was suspended. This concept is sometimes referred to as "fibers". This may
sound abstract but don't worry, the examples below will make it clearer. The JVM does not natively support
continuations, so we implement them using a library called Quasar which works through behind-the-scenes
bytecode rewriting. You don't have to know how this works to benefit from it, however.

We use continuations for the following reasons:

* It allows us to write code that is free of callbacks, that looks like ordinary sequential code.
* A suspended continuation takes far less memory than a suspended thread. It can be as low as a few hundred bytes.
  In contrast a suspended Java thread stack can easily be 1mb in size.
* It frees the developer from thinking (much) about persistence and serialisation.

A *state machine* is a piece of code that moves through various *states*. These are not the same as states in the data
model (that represent facts about the world on the ledger), but rather indicate different stages in the progression
of a multi-stage flow. Typically writing a state machine would require the use of a big switch statement and some
explicit variables to keep track of where you're up to. The use of continuations avoids this hassle.

A two party trading flow
------------------------

We would like to implement the "hello world" of shared transaction building flows: a seller wishes to sell some
*asset* (e.g. some commercial paper) in return for *cash*. The buyer wishes to purchase the asset using his cash. They
want the trade to be atomic so neither side is exposed to the risk of settlement failure. We assume that the buyer
and seller have found each other and arranged the details on some exchange, or over the counter. The details of how
the trade is arranged isn't covered in this article.

Our flow has two parties (B and S for buyer and seller) and will proceed as follows:

1. S sends a ``StateAndRef`` pointing to the state they want to sell to B, along with info about the price they require
   B to pay.
2. B sends to S a ``SignedTransaction`` that includes two inputs (the state owned by S, and cash owned by B) and three
   outputs (the state now owned by B, the cash now owned by S, and any change cash still owned by B). The
   ``SignedTransaction`` has a single signature from B but isn't valid because it lacks a signature from S authorising
   movement of the asset.
3. S signs the transaction and sends it back to B.
4. B *finalises* the transaction by sending it to the notary who checks the transaction for validity, recording the
   transaction in B's local vault, and then sending it on to S who also checks it and commits the transaction to S's
   local vault.

You can find the implementation of this flow in the file ``finance/src/main/kotlin/net/corda/finance/TwoPartyTradeFlow.kt``.

Assuming no malicious termination, they both end the flow being in possession of a valid, signed transaction that
represents an atomic asset swap.

Note that it's the *seller* who initiates contact with the buyer, not vice-versa as you might imagine.

We start by defining two classes that will contain the flow definition. We also pick what data will be used by
each side.

.. note:: The code samples in this tutorial are only available in Kotlin, but you can use any JVM language to
   write them and the approach is the same.

.. container:: codeset

    .. literalinclude:: ../../docs/source/example-code/src/main/kotlin/net/corda/docs/tutorial/flowstatemachines/TutorialFlowStateMachines.kt
        :language: kotlin
        :start-after: DOCSTART 1
        :end-before: DOCEND 1

This code defines several classes nested inside the main ``TwoPartyTradeFlow`` singleton. Some of the classes are
simply flow messages or exceptions. The other two represent the buyer and seller side of the flow.

Going through the data needed to become a seller, we have:

- ``otherSideSession: FlowSession`` - a flow session for communication with the buyer
- ``assetToSell: StateAndRef<OwnableState>`` - a pointer to the ledger entry that represents the thing being sold
- ``price: Amount<Currency>`` - the agreed on price that the asset is being sold for (without an issuer constraint)
- ``myParty: PartyAndCertificate`` - the certificate representing the party that controls the asset being sold

And for the buyer:

- ``sellerSession: FlowSession`` - a flow session for communication with the seller
- ``notary: Party`` - the entry in the network map for the chosen notary. See “Notaries” for more information on
  notaries
- ``acceptablePrice: Amount<Currency>`` - the price that was agreed upon out of band. If the seller specifies
  a price less than or equal to this, then the trade will go ahead
- ``typeToBuy: Class<out OwnableState>`` - the type of state that is being purchased. This is used to check that the
  sell side of the flow isn't trying to sell us the wrong thing, whether by accident or on purpose
- ``anonymous: Boolean`` - whether to generate a fresh, anonymous public key for the transaction

Alright, so using this flow shouldn't be too hard: in the simplest case we can just create a Buyer or Seller
with the details of the trade, depending on who we are. We then have to start the flow in some way. Just
calling the ``call`` function ourselves won't work: instead we need to ask the framework to start the flow for
us. More on that in a moment.

Suspendable functions
---------------------

The ``call`` function of the buyer/seller classes is marked with the ``@Suspendable`` annotation. What does this mean?

As mentioned above, our flow framework will at points suspend the code and serialise it to disk. For this to work,
any methods on the call stack must have been pre-marked as ``@Suspendable`` so the bytecode rewriter knows to modify
the underlying code to support this new feature. A flow is suspended when calling either ``receive``, ``send`` or
``sendAndReceive`` which we will learn more about below. For now, just be aware that when one of these methods is
invoked, all methods on the stack must have been marked. If you forget, then in the unit test environment you will
get a useful error message telling you which methods you didn't mark. The fix is simple enough: just add the annotation
and try again.

.. note:: Java 9 is likely to remove this pre-marking requirement completely.

Whitelisted classes with the Corda node
---------------------------------------

For security reasons, we do not want Corda nodes to be able to just receive instances of any class on the classpath
via messaging, since this has been exploited in other Java application containers in the past.  Instead, we require
every class contained in messages to be whitelisted. Some classes are whitelisted by default (see ``DefaultWhitelist``),
but others outside of that set need to be whitelisted either by using the annotation ``@CordaSerializable`` or via the
plugin framework.  See :doc:`serialization`.  You can see above that the ``SellerTradeInfo`` has been annotated.

Starting your flow
------------------

The ``StateMachineManager`` is the class responsible for taking care of all running flows in a node. It knows
how to register handlers with the messaging system (see ":doc:`messaging`") and iterate the right state machine
when messages arrive. It provides the send/receive/sendAndReceive calls that let the code request network
interaction and it will save/restore serialised versions of the fiber at the right times.

Flows can be invoked in several ways. For instance, they can be triggered by scheduled events (in which case they need to
be annotated with ``@SchedulableFlow``), see ":doc:`event-scheduling`" to learn more about this. They can also be triggered
directly via the node's RPC API from your app code (in which case they need to be annotated with `StartableByRPC`). It's
possible for a flow to be of both types.

You request a flow to be invoked by using the ``CordaRPCOps.startFlowDynamic`` method. This takes a
Java reflection ``Class`` object that describes the flow class to use (in this case, either ``Buyer`` or ``Seller``).
It also takes a set of arguments to pass to the constructor. Because it's possible for flow invocations to
be requested by untrusted code (e.g. a state that you have been sent), the types that can be passed into the
flow are checked against a whitelist, which can be extended by apps themselves at load time.  There are also a series
of inlined Kotlin extension functions of the form ``CordaRPCOps.startFlow`` which help with invoking flows in a type
safe manner.

The process of starting a flow returns a ``FlowHandle`` that you can use to observe the result, and which also contains
a permanent identifier for the invoked flow in the form of the ``StateMachineRunId``. Should you also wish to track the
progress of your flow (see :ref:`progress-tracking`) then you can invoke your flow instead using
``CordaRPCOps.startTrackedFlowDynamic`` or any of its corresponding ``CordaRPCOps.startTrackedFlow`` extension functions.
These will return a ``FlowProgressHandle``, which is just like a ``FlowHandle`` except that it also contains an observable
``progress`` field.

.. note:: The developer `must` then either subscribe to this ``progress`` observable or invoke the ``notUsed()`` extension
   function for it. Otherwise the unused observable will waste resources back in the node.

Implementing the seller
-----------------------

Let's implement the ``Seller.call`` method that will be run when the flow is invoked.

.. container:: codeset

    .. literalinclude:: ../../finance/src/main/kotlin/net/corda/finance/flows/TwoPartyTradeFlow.kt
        :language: kotlin
        :start-after: DOCSTART 4
        :end-before: DOCEND 4
        :dedent: 8

We start by sending information about the asset we wish to sell to the buyer. We fill out the initial flow message with
the trade info, and then call ``otherSideSession.send``. which takes two arguments:

- The party we wish to send the message to
- The payload being sent

``otherSideSession.send`` will serialise the payload and send it to the other party automatically.

Next, we call a *subflow* called ``IdentitySyncFlow.Receive`` (see :ref:`subflows`). ``IdentitySyncFlow.Receive``
ensures that our node can de-anonymise any confidential identities in the transaction it's about to be asked to sign.

Next, we call another subflow called ``SignTransactionFlow``. ``SignTransactionFlow`` automates the process of:

* Receiving a proposed trade transaction from the buyer, with the buyer's signature attached.
* Checking that the proposed transaction is valid.
* Calculating and attaching our own signature so that the transaction is now signed by both the buyer and the seller.
* Sending the transaction back to the buyer.

The transaction then needs to be finalized. This is the the process of sending the transaction to a notary to assert
(with another signature) that the timestamp in the transaction (if any) is valid and there are no double spends.
In this flow, finalization is handled by the buyer, so we just wait for the signed transaction to appear in our
transaction storage. It will have the same ID as the one we started with but more signatures.

Implementing the buyer
----------------------

OK, let's do the same for the buyer side:

.. container:: codeset

    .. literalinclude:: ../../finance/src/main/kotlin/net/corda/finance/flows/TwoPartyTradeFlow.kt
         :language: kotlin
         :start-after: DOCSTART 1
         :end-before: DOCEND 1
         :dedent: 8

This code is longer but no more complicated. Here are some things to pay attention to:

1. We do some sanity checking on the proposed trade transaction received from the seller to ensure we're being offered
   what we expected to be offered.
2. We create a cash spend using ``Cash.generateSpend``. You can read the vault documentation to learn more about this.
3. We access the *service hub* as needed to access things that are transient and may change or be recreated
   whilst a flow is suspended, such as the wallet or the network map.
4. We call ``CollectSignaturesFlow`` as a subflow to send the unfinished, still-invalid transaction to the seller so
   they can sign it and send it back to us.
5. Last, we call ``FinalityFlow`` as a subflow to finalize the transaction.

As you can see, the flow logic is straightforward and does not contain any callbacks or network glue code, despite
the fact that it takes minimal resources and can survive node restarts.

Flow sessions
-------------

It will be useful to describe how flows communicate with each other. A node may have many flows running at the same
time, and perhaps communicating with the same counterparty node but for different purposes. Therefore flows need a
way to segregate communication channels so that concurrent conversations between flows on the same set of nodes do
not interfere with each other.

To achieve this in order to communicate with a counterparty one needs to first initiate such a session with a ``Party``
using ``initiateFlow``, which returns a ``FlowSession`` object, identifying this communication. Subsequently the first
actual communication will kick off a counter-flow on the other side, receiving a "reply" session object. A session ends
when either flow ends, whether as expected or pre-maturely. If a flow ends pre-maturely then the other side will be
notified of that and they will also end, as the whole point of flows is a known sequence of message transfers. Flows end
pre-maturely due to exceptions, and as described above, if that exception is ``FlowException`` or a sub-type then it
will propagate to the other side. Any other exception will not propagate.

Taking a step back, we mentioned that the other side has to accept the session request for there to be a communication
channel. A node accepts a session request if it has registered the flow type (the fully-qualified class name) that is
making the request - each session initiation includes the initiating flow type. The *initiated* (server) flow must name the
*initiating* (client) flow using the ``@InitiatedBy`` annotation and passing the class name that will be starting the
flow session as the annotation parameter.

.. _subflows:

Sub-flows
---------

Flows can be composed via nesting. Invoking a sub-flow looks similar to an ordinary function call:

.. container:: codeset

   .. sourcecode:: kotlin

      @Suspendable
      fun call() {
          val unnotarisedTransaction = ...
          subFlow(FinalityFlow(unnotarisedTransaction))
      }

   .. sourcecode:: java

      @Suspendable
      public void call() throws FlowException {
          SignedTransaction unnotarisedTransaction = ...
          subFlow(new FinalityFlow(unnotarisedTransaction))
      }

Let's take a look at the three subflows we invoke in this flow.

FinalityFlow
^^^^^^^^^^^^
On the buyer side, we use ``FinalityFlow`` to finalise the transaction. It will:

* Send the transaction to the chosen notary and, if necessary, satisfy the notary that the transaction is valid.
* Record the transaction in the local vault, if it is relevant (i.e. involves the owner of the node).
* Send the fully signed transaction to the other participants for recording as well.

.. warning:: If the seller stops before sending the finalised transaction to the buyer, the seller is left with a
   valid transaction but the buyer isn't, so they can't spend the asset they just purchased! This sort of thing is not
   always a risk (as the seller may not gain anything from that sort of behaviour except a lawsuit), but if it is, a future
   version of the platform will allow you to ask the notary to send you the transaction as well, in case your counterparty
   does not. This is not the default because it reveals more private info to the notary.

We simply create the flow object via its constructor, and then pass it to the ``subFlow`` method which
returns the result of the flow's execution directly. Behind the scenes all this is doing is wiring up progress
tracking (discussed more below) and then running the object's ``call`` method. Because the sub-flow might suspend,
we must mark the method that invokes it as suspendable.

Within FinalityFlow, we use a further sub-flow called ``ReceiveTransactionFlow``. This is responsible for downloading
and checking all the dependencies of a transaction, which in Corda are always retrievable from the party that sent you a
transaction that uses them. This flow returns a list of ``LedgerTransaction`` objects.

.. note:: Transaction dependency resolution assumes that the peer you got the transaction from has all of the
   dependencies itself. It must do, otherwise it could not have convinced itself that the dependencies were themselves
   valid. It's important to realise that requesting only the transactions we require is a privacy leak, because if
   we don't download a transaction from the peer, they know we must have already seen it before. Fixing this privacy
   leak will come later.

CollectSignaturesFlow/SignTransactionFlow
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We also invoke two other subflows:

* ``CollectSignaturesFlow``, on the buyer side
* ``SignTransactionFlow``, on the seller side

These flows communicate to gather all the required signatures for the proposed transaction. ``CollectSignaturesFlow``
will:

* Verify any signatures collected on the transaction so far
* Verify the transaction itself
* Send the transaction to the remaining required signers and receive back their signatures
* Verify the collected signatures

``SignTransactionFlow`` responds by:

* Receiving the partially-signed transaction off the wire
* Verifying the existing signatures
* Resolving the transaction's dependencies
* Verifying the transaction itself
* Running any custom validation logic
* Sending their signature back to the buyer
* Waiting for the transaction to be recorded in their vault

We cannot instantiate ``SignTransactionFlow`` itself, as it's an abstract class. Instead, we need to subclass it and
override ``checkTransaction()`` to add our own custom validation logic:

.. container:: codeset

    .. literalinclude:: ../../finance/src/main/kotlin/net/corda/finance/flows/TwoPartyTradeFlow.kt
        :language: kotlin
        :start-after: DOCSTART 5
        :end-before: DOCEND 5
        :dedent: 12

In this case, our custom validation logic ensures that the amount of cash outputs in the transaction equals the
price of the asset.

Persisting flows
----------------

If you look at the code for ``FinalityFlow``, ``CollectSignaturesFlow`` and ``SignTransactionFlow``, you'll see calls
to both ``receive`` and ``sendAndReceive``. Once either of these methods is called, the ``call`` method will be
suspended into a continuation and saved to persistent storage. If the node crashes or is restarted, the flow will
effectively continue as if nothing had happened. Your code may remain blocked inside such a call for seconds,
minutes, hours or even days in the case of a flow that needs human interaction!

.. note:: There are a couple of rules you need to bear in mind when writing a class that will be used as a continuation.
   The first is that anything on the stack when the function is suspended will be stored into the heap and kept alive by
   the garbage collector. So try to avoid keeping enormous data structures alive unless you really have to.  You can
   always use private methods to keep the stack uncluttered with temporary variables, or to avoid objects that
   Kryo is not able to serialise correctly.

   The second is that as well as being kept on the heap, objects reachable from the stack will be serialised. The state
   of the function call may be resurrected much later! Kryo doesn't require objects be marked as serialisable, but even so,
   doing things like creating threads from inside these calls would be a bad idea. They should only contain business
   logic and only do I/O via the methods exposed by the flow framework.

   It's OK to keep references around to many large internal node services though: these will be serialised using a
   special token that's recognised by the platform, and wired up to the right instance when the continuation is
   loaded off disk again.

``receive`` and ``sendAndReceive`` return a simple wrapper class, ``UntrustworthyData<T>``, which is
just a marker class that reminds us that the data came from a potentially malicious external source and may have been
tampered with or be unexpected in other ways. It doesn't add any functionality, but acts as a reminder to "scrub"
the data before use.

Exception handling
------------------

Flows can throw exceptions to prematurely terminate their execution. The flow framework gives special treatment to
``FlowException`` and its subtypes. These exceptions are treated as error responses of the flow and are propagated
to all counterparties it is communicating with. The receiving flows will throw the same exception the next time they do
a ``receive`` or ``sendAndReceive`` and thus end the flow session. If the receiver was invoked via ``subFlow``
then the exception can  be caught there enabling re-invocation of the sub-flow.

If the exception thrown by the erroring flow is not a ``FlowException`` it will still terminate but will not propagate to
the other counterparties. Instead they will be informed the flow has terminated and will themselves be terminated with a
generic exception.

.. note:: A future version will extend this to give the node administrator more control on what to do with such erroring
flows.

Throwing a ``FlowException`` enables a flow to reject a piece of data it has received back to the sender. This is typically
done in the ``unwrap`` method of the received ``UntrustworthyData``. In the above example the seller checks the price
and throws ``FlowException`` if it's invalid. It's then up to the buyer to either try again with a better price or give up.

.. _progress-tracking:

Progress tracking
-----------------

Not shown in the code snippets above is the usage of the ``ProgressTracker`` API. Progress tracking exports information
from a flow about where it's got up to in such a way that observers can render it in a useful manner to humans who
may need to be informed. It may be rendered via an API, in a GUI, onto a terminal window, etc.

A ``ProgressTracker`` is constructed with a series of ``Step`` objects, where each step is an object representing a
stage in a piece of work. It is therefore typical to use singletons that subclass ``Step``, which may be defined easily
in one line when using Kotlin. Typical steps might be "Waiting for response from peer", "Waiting for signature to be
approved", "Downloading and verifying data" etc.

A flow might declare some steps with code inside the flow class like this:

.. container:: codeset

    .. literalinclude:: ../../finance/src/main/kotlin/net/corda/finance/flows/TwoPartyTradeFlow.kt
        :language: kotlin
        :start-after: DOCSTART 2
        :end-before: DOCEND 2
        :dedent: 8

    .. literalinclude:: ../../docs/source/example-code/src/main/java/net/corda/docs/java/tutorial/flowstatemachines/TutorialFlowStateMachines.java
        :language: java
        :start-after: DOCSTART 1
        :end-before: DOCEND 1
        :dedent: 4

Each step exposes a label. By defining your own step types, you can export progress in a way that's both human readable
and machine readable.

Progress trackers are hierarchical. Each step can be the parent for another tracker. By setting
``Step.childProgressTracker``, a tree of steps can be created. It's allowed to alter the hierarchy at runtime, on the
fly, and the progress renderers will adapt to that properly. This can be helpful when you don't fully know ahead of
time what steps will be required. If you *do* know what is required, configuring as much of the hierarchy ahead of time
is a good idea, as that will help the users see what is coming up. You can pre-configure steps by overriding the
``Step`` class like this:

.. container:: codeset

    .. literalinclude:: ../../finance/src/main/kotlin/net/corda/finance/flows/TwoPartyTradeFlow.kt
        :language: kotlin
        :start-after: DOCSTART 3
        :end-before: DOCEND 3
        :dedent: 12

    .. literalinclude:: ../../docs/source/example-code/src/main/java/net/corda/docs/java/tutorial/flowstatemachines/TutorialFlowStateMachines.java
        :language: java
        :start-after: DOCSTART 2
        :end-before: DOCEND 2
        :dedent: 4

Every tracker has not only the steps given to it at construction time, but also the singleton
``ProgressTracker.UNSTARTED`` step and the ``ProgressTracker.DONE`` step. Once a tracker has become ``DONE`` its
position may not be modified again (because e.g. the UI may have been removed/cleaned up), but until that point, the
position can be set to any arbitrary set both forwards and backwards. Steps may be skipped, repeated, etc. Note that
rolling the current step backwards will delete any progress trackers that are children of the steps being reversed, on
the assumption that those subtasks will have to be repeated.

Trackers provide an `Rx observable <http://reactivex.io/>`_ which streams changes to the hierarchy. The top level
observable exposes all the events generated by its children as well. The changes are represented by objects indicating
whether the change is one of position (i.e. progress), structure (i.e. new subtasks being added/removed) or some other
aspect of rendering (i.e. a step has changed in some way and is requesting a re-render).

The flow framework is somewhat integrated with this API. Each ``FlowLogic`` may optionally provide a tracker by
overriding the ``flowTracker`` property (``getFlowTracker`` method in Java). If the
``FlowLogic.subFlow`` method is used, then the tracker of the sub-flow will be made a child of the current
step in the parent flow automatically, if the parent is using tracking in the first place. The framework will also
automatically set the current step to ``DONE`` for you, when the flow is finished.

Because a flow may sometimes wish to configure the children in its progress hierarchy *before* the sub-flow
is constructed, for sub-flows that always follow the same outline regardless of their parameters it's conventional
to define a companion object/static method (for Kotlin/Java respectively) that constructs a tracker, and then allow
the sub-flow to have the tracker it will use be passed in as a parameter. This allows all trackers to be built
and linked ahead of time.

In future, the progress tracking framework will become a vital part of how exceptions, errors, and other faults are
surfaced to human operators for investigation and resolution.

Future features
---------------

The flow framework is a key part of the platform and will be extended in major ways in future. Here are some of
the features we have planned:

* Exception management, with a "flow hospital" tool to manually provide solutions to unavoidable
  problems (e.g. the other side doesn't know the trade)
* Being able to interact with people, either via some sort of external ticketing system, or email, or a custom UI.
  For example to implement human transaction authorisations
* A standard library of flows that can be easily sub-classed by local developers in order to integrate internal
  reporting logic, or anything else that might be required as part of a communications lifecycle