Add an example reading from Reddit

python/docs/source/examples/index.md

@@ -2,6 +2,8 @@
 
 The [Time-Centric Calculations example](./time_centric.ipynb) shows how to work with time and produce past training examples and recent results for applying models.
 
+The [Reddit example](./reddit.md) shows how to read and aggregate live messages from Reddit.
+
 The [Bluesky Firehose example](./bluesky.md) shows how to read and aggregate messages from the Bluesky firehose.
 This demonstrates how to use Kaskada to connect in real-time and parse messages as part of the query.
 
@@ -10,5 +12,6 @@ This demonstrates how to use Kaskada to connect in real-time and parse messages
 :maxdepth: 2
 
 time_centric
+reddit
 bluesky
 ```

python/docs/source/examples/reddit.md

@@ -0,0 +1,73 @@
+# Reddit Live Example
+
+In this example, we'll show how you can receive and process Reddit comments using Kaskada.
+
+You can see the full example in the file [reddit.py](https://github.com/kaskada-ai/kaskada/blob/main/python/docs/source/examples/reddit.py).
+
+## Setup Reddit credentials
+
+Follow Reddit's [First Steps](https://github.com/reddit-archive/reddit/wiki/OAuth2-Quick-Start-Example#first-steps) guide to create an App and obtain a client ID and secret.
+The "script" type application is sufficient for this example.
+
+## Setup the event data source
+
+Before we can receive events from Reddit, we need to create a data source to tell Kaskada how to handle the events.
+We'll provide a schema and configure the time and entity fields.
+
+```{literalinclude} reddit.py
+:language: python
+:start-after: "[start_setup]"
+:end-before: "[end_setup]"
+:linenos:
+:lineno-match:
+:dedent: 4
+```
+
+## Define the incoming event handler
+
+The `asyncpraw` python library takes care of requesting and receiving events from Reddit, all you need to do is create a handler to configure what to do with each event.
+This handler converts [Comment](https://praw.readthedocs.io/en/stable/code_overview/models/comment.html#praw.models.Comment) messages into a dict, and passes the dict to Kaskada.
+
+```{literalinclude} reddit.py
+:language: python
+:start-after: "[start_incoming]"
+:end-before: "[end_incoming]"
+:linenos:
+:lineno-match:
+:dedent: 4
+```
+
+## Construct a real-time query and result handler
+
+Now we can use Kaskada to transform the events as they arrive.
+First we'll use `with_key` to regroup events by author, then we'll apply a simple `count` aggregation.
+Finally, we create a handler for the transformed results - here just printing them out.
+
+
+```{literalinclude} reddit.py
+:language: python
+:start-after: "[start_result]"
+:end-before: "[end_result]"
+:linenos:
+:lineno-match:
+:dedent: 4
+```
+
+## Final touches
+
+Now we just need to kick it all off by calling `asyncio.gather` on the two handler coroutines. This kicks off all the async processing.
+
+```{literalinclude} reddit.py
+:start-after: "[start_run]"
+:end-before: "[end_run]"
+:language: python
+:linenos:
+:lineno-match:
+:dedent: 4
+```
+
+Try running it yourself and playing different transformations!
+
+```bash
+python reddit.py
+```

python/docs/source/examples/reddit.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+#
+# Reddit Kaskada Consumer
+#
+# This script demonstrates the use of Kaskada to consume and compute over
+# Reddit updates.
+
+import os
+import asyncio
+import time
+import kaskada as kd
+import pyarrow as pa
+
+import asyncpraw
+
+
+async def main():
+    # Initialize the Kaskada session so we can use it for stream processing
+    kd.init_session()
+
+    # Create the Reddit client.
+    reddit = asyncpraw.Reddit(
+        client_id=os.getenv('REDDIT_CLIENT_ID'),
+        client_secret=os.getenv('REDDIT_CLIENT_SECRET'),
+        user_agent=os.getenv('REDDIT_USER_AGENT', 'kaskada-demo'),
+    )
+
+    # [start_setup]
+    # Setup the data source.
+    # This defintes (most of) the schema of the events we'll receive,
+    # and tells Kaskada which fields to use for time and initial entity.
+    #
+    # We'll push events into this source as they arrive in real-time.
+    comments = kd.sources.PyDict(
+        schema=pa.schema([
+            pa.field("author", pa.string()),
+            pa.field("body", pa.string()),
+            pa.field("permalink", pa.string()),
+            pa.field("submission_id", pa.string()),
+            pa.field("subreddit", pa.string()),
+            pa.field("ts", pa.float64()),
+        ]),
+        time_column="ts",
+        key_column="submission_id",
+        time_unit="s",
+    )
+    # [end_setup]
+
+    # [start_incoming]
+    # Handler to receive new comments as they're created
+    async def receive_comments():
+        # Creat the subreddit handle
+        sr = await reddit.subreddit(os.getenv("SUBREDDIT", "all"))
+
+        # Consume the stream of new comments
+        async for comment in sr.stream.comments():
+            # Add each comment to the Kaskada data source
+            await comments.add_rows({
+                "author": comment.author.name,
+                "body": comment.body,
+                "permalink": comment.permalink,
+                "submission_id": comment.submission.id,
+                "subreddit_id": comment.subreddit.display_name,
+                "ts": time.time(),
+            })
+    # [end_incoming]
+
+    # [start_result]
+    # Handler for values emitted by Kaskada.
+    async def receive_outputs():
+
+        # We'll perform a very simple aggregation - key by author and count.
+        comments_by_author = comments.with_key(comments.col("author"))
+
+        # Consume outputs as they're generated and print to STDOUT.
+        async for row in comments_by_author.count().run_iter(kind="row", mode="live"):
+            print(f"{row['_key']} has posted {row['result']} times since startup")
+    # [end_result]
+
+    # [start_run]
+    # Kickoff the two async processes concurrently.
+    await asyncio.gather(receive_comments(), receive_outputs())
+    # [end_run]
+
+if __name__ == "__main__":
+    asyncio.run(main())