igalshilman opened a new pull request #7:
URL: https://github.com/apache/flink-statefun-playground/pull/7
copied from README.md
# StateFun Python SDK Showcase
This project is intended for new StateFun users that would like to start
implementing their StateFun application functions using Python.
The tutorial is streamlined and split into a few parts which we recommend to
go through a specific order, as lay out below.
Each part is demonstrated with some code snippets plus comments to guide you
through the SDK fundamentals.
## Prerequisites
- python3
- pip
- docker
- docker-compose
## Building the example
### Using venv
```
python3 -m venv venv
source venv/bin/activate
pip3 install .
```
## Tutorial Sections
The [__main__.py](showcase/__main__.py) file demonstrates SDK concepts at
length, and highly recommend
to read trough it. The sections below are copied from that file with some of
the comments removed.
### Type System
This function demonstrates StateFun's type system using the Python SDK.
```
@functions.bind("showcase/types")
def types(context, message: Message):
# All values, including messages and storage values, are handled via
StateFun's type system.
# StateFun ships built-in primitive types that handles de-/serialization
of messages across
# functions:
if message.is_bool():
val = message.as_bool()
elif message.is_int():
# 32 bit, signed integer
val = message.as_int()
elif message.is_long():
# 64 bit signed integer
val = message.as_long()
elif message.is_float():
val = message.as_float()
elif message.is_string():
val = message.as_string()
elif message.is_double():
val = message.as_double()
elif message.is_type(GREET_JSON_TYPE):
# You can also define your own types using the type system, such as
a JSON message ...
val = message.as_type(GREET_JSON_TYPE)
elif message.is_type(USER_PROFILE_PROTOBUF_TYPE):
# Or protobuf
val = message.as_type(USER_PROFILE_PROTOBUF_TYPE)
else:
val = None
print(f"I've got a message with {val} as payload!")
```
### Messaging Primitives
This function demonstrates how to send messages to other functions.
```
@functions.bind("showcase/messaging")
def messaging(context: Context, message: Message):
# You send messages to functions simply by specifying the target
function's typename
# and the target instance id for that for function; StateFun handles the
routing for you,
# without the need of any means for service discovery.
target = "showcase/types"
# you can directly send primitive type values as messages, ...
context.send(message_builder(target_typename=target, target_id="0",
bool_value=True))
context.send(message_builder(target_typename=target, target_id="1",
int_value=123))
context.send(message_builder(target_typename=target, target_id="2",
float_value=3.14159e+11))
context.send(message_builder(target_typename=target, target_id="3",
str_value="Hello world"))
context.send(message_builder(target_typename=target, target_id="4",
double_value=1.23))
context.send(message_builder(target_typename=target, target_id="5",
long_value=123456789))
# ... or, in general, a value of any custom defined type.
context.send(
message_builder(target_typename=target, target_id="6",
value={"name": "Joe"}, value_type=GREET_JSON_TYPE))
# or extract the payload directly
print(f"The payload's type is {message.value_typename()} and the raw
bytes are {message.raw_value()}")
# You can send messages to any function, including yourself!
me = context.address
context.send(message_builder(target_typename=me.typename,
target_id=me.id, str_value="hello!"))
# Additionally, you may ask StateFun to send out a message after a
specified delay.
# A common usage pattern is to send delayed messages to yourself to
model timer triggers.
context.send_after(timedelta(minutes=10),
message_builder(target_typename=target,
target_id="7", str_value="Hello from the future"))
```
### Sending messages to egresses
To let your StateFun application interact with the outside world, functions
may write messages
to egresses. This function demonstrates sending messages to an Apache Kafka
or AWS Kinesis
egress, which is currently our most commonly used egresses that are natively
supported by
StateFun.
```
@functions.bind("showcase/egress")
def egress(context: Context, message: Message):
# there is a specific builder for messages to be sent to a Kafka egress
...
context.send_egress(
kafka_egress_message(typename="showcase/kafka-egress",
topic="my-kafka-topic", key="my-key",
value="my-utf8-value"))
# and a builder for kinesis
context.send(
kinesis_egress_message(typename="showcase/kinesis-egress",
stream="a-stream",
partition_key="key",
explicit_hash_key="hash-key",
value="a value"))
# if you've implemented your own egress (it is currently only possible
to do with Java)
# then you can send it a message like this:
context.send_egress(egress_message_builder(target_typename="showecase/my-custom-egress",
value={"name": "Bob"},
value_type=GREET_JSON_TYPE))
```
### Function state storage
```
@functions.bind("showcase/storage", specs=[
ValueSpec(name="an_int", type=IntType),
ValueSpec(name="an_expiring_str", type=StringType,
expire_after_write=timedelta(days=7)),
ValueSpec(name="greet_json", type=GREET_JSON_TYPE)
])
def storage(context, message):
# each function invocation gets access to storage that is scoped to the
current function
# instance's address, i.e. (function typename, instance id). For
example, if (UserFn, "Gordon")
# was invoked, the values you get access to belongs specifically to user
Gordon.
storage = context.storage
# each value spec defined above, will appear as a property on the
storage.
print(storage.an_int)
storage.an_int = 42
# a value can be None if it has expired, or never set before.
if storage.an_expiring_str:
print(storage.an_expiring_str)
else:
print("Oh no, the str has expiried (or wasn't set before)")
# a value can also be deleted
storage.greet_json = {"name": "Anton"}
del storage.greet_json
```
### Asynchronous operations
This function demonstrates performing asynchronous operations during a
function invocation. It
is a common scenario for functions to have external dependencies in order
for it to complete its
work, such as fetching enrichment information from remote databases.
```
@functions.bind("showcase/async", specs=[ValueSpec(name="user_profile",
type=USER_PROFILE_PROTOBUF_TYPE)])
async def async_ops(context, message):
profile = context.storage.user_profile
if not profile:
user_name = context.address.id
profile = UserProfile()
profile.name = user_name
profile.favorite_ice_cream = await
call_favorite_ice_cream_service(user_name)
profile.favorite_tv_show = await
call_faviorite_show_service(user_name)
context.storage.user_profile = profile
# reply to our caller with the computed user profile!
caller: SdkAddress = context.caller
context.send(message_builder(target_typename=caller.typename,
target_id=caller.id,
value=profile,
value_type=USER_PROFILE_PROTOBUF_TYPE))
async def call_favorite_ice_cream_service(user_name: str) -> str:
await asyncio.sleep(0.2)
return "mint chocolate chip"
async def call_faviorite_show_service(user_name: str) -> str:
await asyncio.sleep(0.5)
return "The Office"
```
### Serving
* First, lets define a simple function that computes personalized greetings
messages based on the number of times
That this function was invoked.
For demonstration purposes, this function prints to console the generated
greetings
messages.
```
@functions.bind("showcase/serving", specs=[ValueSpec(name="seen",
type=IntType)])
async def serving(context, message: Message):
seen = context.storage.seen or 0
seen += 1
context.storage.seen = seen
greet_request = message.as_type(GREET_JSON_TYPE)
name = greet_request["name"]
# in this example, the id part of the currently executing function
# will be also equal to that name (context.address.id == name).
print(f"hello {name}! I've seen you {seen} times!", flush=True)
```
* Create a request-reply handler for the registered functions, which
understands how to
decode invocation requests dispatched from StateFun cluster, dispatch to the
correct function,
and encode side-effects (e.g. storage updates, or invoking other functions)
as responses to be handled by StateFun.
```
statefun_handler = RequestReplyHandler(functions)
```
* Exposing the handler via HTTP
The code below handles the physical HTTP serving.
In this case we chose aiohttp, although any other HTTP serving framework
will do.
The only thing that the HTTP handler needs to do, is to pass the raw (bytes)
request body
to StateFun's handler (defined above), and return the raw (bytes) StateFun's
handler provided as a response.
```
async def handle(request):
req = await request.read()
res = await statefun_handler.handle_async(req)
return web.Response(body=res, content_type="application/octet-stream")
app = web.Application()
app.add_routes([web.post('/statefun', handle)])
if __name__ == '__main__':
web.run_app(app, port=8000)
```
To actually start serving run from one terminal:
```python3 -m showcase```
And from another:
```docker-compose up```
# Next Steps
The setup you executed in the last part of this tutorial is not how you'd
normally deploy StateFun processes
and functions. It's a rather simplified setup to allow you to explore the
interaction between
functions and the StateFun processes by setting debugger breakpoints in the
function code in your IDE.
We recommend now to take a look at a slightly more realistic setup, using
Docker Compose, in the
[Greeter Docker Compose Example](../greeter).
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