Cleanup Examples and Tutorials

docs/build/README.md

@@ -3,9 +3,16 @@
 Generate API docs for `quixstreams` module using [Pydoc Markdown](https://niklasrosenstein.github.io/pydoc-markdown/just-generate-me-some-markdown/). 
 
 
-To generate new API docs
+## Generate new API docs
 
 - Go to `docs/build`
 - Install requirements via `python -m pip install -r requirements.txt`
 - do `./build.sh`
 - Check the generated docs in `docs/` folder
+
+
+## Render/View Docs:
+- Go to `docs/build`
+- `python -m pip install mkdocs mkdocs-material mkdocs-material-extensions`
+- `mkdocs serve -f ../../mkdocs.yml`
+- [navigate to `localhost:8000` in browser](`http://localhost:8000`)

docs/connectors/sources/custom-sources.md

@@ -8,7 +8,7 @@ Quix Streams also provides a set of classes to help users implement custom sourc
 
 ## Source
 
-The recomended parent class to create a new source. It handles configuring, starting and stopping the source, as well as implementing a series of helpers.
+The recommended parent class to create a new source. It handles configuring, starting and stopping the source, as well as implementing a series of helpers.
 
 To get started, implement the [`run`](../../api-reference/sources.md#sourcerun) method and return when `self.running` is `False`.
 

docs/introduction.md

@@ -27,3 +27,4 @@ Check out Quix Streams tutorials for more in-depth examples:
 - [Tutorial - Word Count](tutorials/word-count/tutorial.md)
 - [Tutorial - Anomaly Detection](tutorials/anomaly-detection/tutorial.md)
 - [Tutorial - Purchase Filtering](tutorials/purchase-filtering/tutorial.md) 
+- [Tutorial - Websocket Source](tutorials/websocket-source/tutorial.md) 

docs/tutorials/README.md

@@ -13,7 +13,7 @@ Besides a Kafka instance to connect to, most of them likely fit the pattern of:
 
 `python ./path/to/producer.py`
 
-`python ./path/to/application.py`
+`python ./path/to/tutorial_app.py`
 
 
 ## Running Kafka Locally

docs/tutorials/anomaly-detection/tutorial.md

@@ -1,12 +1,20 @@
 # Tutorial: Anomaly Detection
 
-You will learn how to build a simple anomaly detection system, a common use case of stateful streaming applications. This will show how to use a Quix Streams application to:
+We will build a simple anomaly detection system, a common use case of stateful 
+streaming applications. 
 
-- Create a topic
+
+
+## What You Will Learn
+
+This example will show how to use a Quix Streams `Application` to:
+
+- Ingest a non-Kafka data source
 - Use stateful windowed operations
 - Do simple event alterations
 - Do simple event filtering
-- Produce the result to a topic
+- Create a Kafka topic 
+- Produce results to a Kafka topic
 
 
 
@@ -23,12 +31,11 @@ When this occurs, we want to send alerts as soon as possible so appropriate acti
 
 ## Our Example
 
-We will use a producer to generate mock temperature events for 3 machines (MACHINE_IDs '0', '1', or '2'); ID's 0 and 1 are functioning normally, 2 is malfunctioning (overheating).
-
-These events will be processed by our new Anomaly Detector application.
-
-NOTE: our example uses JSON formatting for Kafka message values.
+We will use a [Quix Streams `Source`](../../connectors/sources/README.md) to generate mock temperature events for 
+3 machines (MACHINE_IDs '0', '1', or '2'); ID's 0 and 1 are functioning normally, 
+2 is malfunctioning (overheating).
 
+These events will be processed by our new Anomaly Detector `Application`.
 
 
 ## Alerting Approach (Windowing)
@@ -44,21 +51,30 @@ This approach is desirable since temperatures fluctuate quickly; it enables more
 - allows more time for the machine to cool back down (as part of normal operation)
 
 
+
 ## Before Getting Started
 
-- You will see links scattered throughout this tutorial.
+1. You will see links scattered throughout this tutorial.
     - Tutorial code links are marked **>>> LIKE THIS <<<** .
     - ***All other links provided are completely optional***. 
     - They are great ways to learn more about various concepts if you need it!
 
+2. This tutorial uses a [`Source`](../../connectors/sources/README.md) rather than a Kafka [`Topic`]() to ingest data.
+    - `Source` connectors enable reading data from a non-Kafka origin (typically to get it into Kafka). 
+    - This approach circumvents users having to run a [producer](../../producer.md) alongside the `Application`.
+    - A `Source` is easily replaced with an actual Kafka topic (just pass a `Topic` instead of a `Source`).
+
+
 
 ## Generating Temperature Data
 
-Without going into much detail, we have this [**>>> Temperature Readings Producer <<<**](producer.py) to pair up nicely with our Anomaly Detector.
+Our [**>>> Anomaly Detector Application <<<**](tutorial_app.py) uses a `Source` called 
+`PurchaseGenerator` to generate temperature events.
 
-It cycles through MACHINE_ID's 0-2 (using the ID as the Kafka key), and produces a (-1, 0, +1) temperature change for each machine a few times a second, along with the time. 
+It cycles through MACHINE_ID's 0-2 (using the ID as the Kafka key), and produces a 
+(-1, 0, +1) temperature change for each machine a few times a second, along with the time. 
 
-So the kafka messages look like:
+So the incoming messages look like:
 
 ```python
 # ...
@@ -77,57 +93,93 @@ Feel free to inspect it further, but it's just to get some data flowing. Our foc
 
 
 
+## Anomaly Detector Application
 
+Now let's go over the `main()` portion of our 
+[**>>> Anomaly Detector Application <<<**](tutorial_app.py) in detail!
 
-## Anomaly Detector Application
 
-Now let's go over our [**>>> Anomaly Detector Application <<<**](application.py) line-by-line!
 
-### Create Application
+### Create an Application
+
+Create a [Quix Streams Application](../../configuration.md), which is our constructor for everything! 
+
+We provide it our connection settings, consumer group (ideally unique per Application), 
+and where the consumer group should start from on the (internal) Source topic.
+
+!!! TIP
+
+    Once you are more familiar with Kafka, we recommend 
+    [learning more about auto_offset_reset](https://www.quix.io/blog/kafka-auto-offset-reset-use-cases-and-pitfalls).
+
+#### Our Application
 
 ```python
 import os
 from quixstreams import Application
 
 app = Application(
-    broker_address=os.environ.get("BROKER_ADDRESS", "localhost:9092"),
+    broker_address=os.getenv("BROKER_ADDRESS", "localhost:9092"),
     consumer_group="temperature_alerter",
-    auto_offset_reset="earliest"
+    auto_offset_reset="earliest",
 )
 ```
 
-First, create the [Quix Streams Application](../../configuration.md), which is our constructor for everything! We provide it our connection settings, consumer group (ideally unique per Application), and where the consumer group should start from on our topic. 
 
-NOTE: Once you are more familiar with Kafka, we recommend [learning more about auto_offset_reset](https://www.quix.io/blog/kafka-auto-offset-reset-use-cases-and-pitfalls).
+
+### Specify Topics
+
+`Application.topic()` returns [`Topic`](../../api-reference/topics.md) objects which are used by `StreamingDataFrame`.
+
+Create one for each topic used by your `Application`.
+
+!!! NOTE
+
+    Any missing topics will be automatically created for you upon running an `Application`.
 
 
-### Define Topics
+#### Our Topics
+We have one output topic, named `temperature_alerts`:
 
 ```python
-temperature_readings_topic = app.topic(name="temperature_readings")
-alerts_topic = app.topic(name="alerts")
+alerts_topic = app.topic("temperature_alerts")
 ```
 
-Next we define our input/output topics, named `temperature_readings_topic` and `alerts_topic`, respectively. 
 
-They each return [`Topic`](../../api-reference/topics.md) objects, used later on.
 
-NOTE: the topics will automatically be created for you in Kafka when you run the application should they not exist.
+### The StreamingDataFrame (SDF)
 
+Now for the fun part: building our [StreamingDataFrame](../../processing.md#introduction-to-streamingdataframe), often shorthanded to "SDF".
 
-### The StreamingDataFrame (SDF)
+SDF allows manipulating the message value in a dataframe-like fashion using various operations.
+
+After initializing with either a `Topic` or `Source`, we continue reassigning to the 
+same `sdf` variable as we add operations.
+
+!!! NOTE
+
+    A few `StreamingDataFrame` operations are 
+    ["in-place"](../../advanced/dataframe-assignments.md#valid-in-place-operations), 
+    like `.print()`.
+
+#### Initializing our SDF
 
 ```python
-sdf = app.dataframe(topic=temperature_readings_topic)
+sdf = app.dataframe(source=TemperatureGenerator())
 ```
 
-Now for the fun part: building our [StreamingDataFrame](../../processing.md#introduction-to-streamingdataframe), often shorthanded to "SDF".  
+First, we initialize our SDF with our `TemperatureGenerator` `Source`, 
+which means we will be consuming data from a non-Kafka origin.
 
-SDF allows manipulating the message value in a dataframe-like fashion using various operations.
 
-After initializing, we continue re-assigning to the same `sdf` variable as we add operations.
+!!! TIP
+
+    You can consume from a Kafka topic instead by passing a `Topic` object
+    with `app.dataframe(topic=<Topic>)`.
+
+Let's go over the SDF operations in this example in detail.
+
 
-(Also: notice that we pass our input `Topic` from the previous step to it.)
 
 ### Prep Data for Windowing
 
@@ -148,6 +200,7 @@ So we'll perform a generic SDF transformation using [`SDF.apply(F)`](../../proce
 our `F` is a simple `lambda`, in this case.
 
 
+
 ### Windowing
 
 ```python
@@ -164,6 +217,8 @@ Now we do a (5 second) windowing operation on our temperature value. A few very
 
 - The event's windowing timestamp comes from the "Timestamp" (case-sensitive!!!) field, which SDF looks for in the message value when first receiving it. If it doesn't exist, the kafka message timestamp is used. [A custom function can also be used](../../windowing.md#extracting-timestamps-from-messages).
 
+
+
 ### Using Window Result
 
 ```python
@@ -180,15 +235,21 @@ Now we get a window result (mean) along with its start/end timestamp:
 
 `>>> {"value": 67.49478585, "start": 1234567890, "end": 1234567895}`
 
-We don't particularly care about the window itself in our case, just the result...so we extract the "value" with `SDF.apply()` and [`SDF.filter(F)`](../../processing.md#streamingdataframefilter), where `F` is our "should_alert" function. 
+We don't particularly care about the window itself in our case, just the result...
+so we extract the "value" with `SDF.apply()` and [`SDF.filter(F)`](../../processing.md#streamingdataframefilter), where 
+`F` is our "should_alert" function. 
+
+In our case, this example event would then stop since `bool(None)` is `False`.
 
-For `SDF.filter(F)`, if the (_**boolean**_-ed) return value of `F` is: 
+!!! INFO "SDF filtering behavior"
 
-- `True` -> continue processing this event
+    For `SDF.filter(F)`, if the (_**boolean**_-ed) return value of `F` is: 
+
+    - `True` -> continue processing this event
+
+    - `False` -> stop ALL further processing of this event (including produces!)
 
-- `False` -> stop ALL further processing of this event (including produces!)
 
-In our case, this example event would then stop since `bool(None)` is `False`.
 
 ### Producing an Alert
 
@@ -199,27 +260,53 @@ sdf = sdf.to_topic(alerts_topic)
 However, if the value ended up >= 90....we finally finish by producing our alert to our downstream topic via [`SDF.to_topic(T)`](../../processing.md#writing-data-to-kafka-topics), where `T`
 is our previously defined `Topic` (not the topic name!).
 
-NOTE: because we use "Current" windowing, we may produce a lot of "duplicate" alerts once triggered...you could solve this in numerous ways downstream. What we care about is alerting as soon as possible!
+!!! NOTE
+
+    Because we use "Current" windowing, we may produce a lot of "duplicate" alerts once 
+    triggered...you could solve this in numerous ways downstream. What we care about is 
+    alerting as soon as possible!
+
+
+
+
+### Running the Application
+
+Running a `Source`-based `Application` requires calling `Application.run()` within a
+`if __name__ == "__main__"` block.
+
+#### Our Application Run Block 
+
+Our entire `Application` (and all its spawned objects) resides within a 
+`main()` function, executed as required:
+
+```python
+if __name__ == "__main__":
+    main()
+```
+
+
+
 
-## Try it yourself!
+## Try it Yourself!
 
 ### 1. Run Kafka
 First, have a running Kafka cluster. 
 
-To conveniently follow along with this tutorial, just [run this simple one-liner](../README.md#running-kafka-locally).
+To easily run a broker locally with Docker, just [run this simple one-liner](../README.md#running-kafka-locally).
 
-### 2. Install Quix Streams
-In your python environment, run `pip install quixstreams`
+### 2. Download files
+- [tutorial_app.py](tutorial_app.py)
 
-### 3. Run the Producer and Application
-Just call `python producer.py` and `python application.py` in separate windows.
+### 3. Install Quix Streams
+In your desired python environment, execute: `pip install quixstreams`
 
-You'll note that the Application does not print any output beyond initialization: it will only print alerts being fired (and thus when it's producing a downstream message).
+### 4. Run the application
+In your desired python environment, execute: `python tutorial_app.py`.
 
-### 4. Check out the results!
+### 5. Check out the results!
 
 Eventually, you should see an alert will look something like: 
 
 `>>> Alerting for MID b'2': Average Temperature 98.57`
 
-NICE!
+NICE!