使用 Python 建立即時 GitHub 統計儀表板

使用 GitHub Firehose API 即時視覺化最活躍的 GitHub 使用者

您曾經嘗試過 Google Analytics 中的即時儀表板嗎？看到人們與您的網站即時互動是一件很有趣的事情。您知道可以使用 GitHub 時間軸來建立類似的東西嗎？

在 Streamlit 中建立基本原型非常容易。我想向您展示如何建立一個非常簡單、最小化的 MVP 版本的即時儀表板。您可以使用 GitHub 事件的公共串流，即：GitHub Firehose。

您可以使用它來建立一個非常簡單的管道和儀表板，透過 GitHub 使用者名稱追蹤事件數量並顯示當前前 10 個事件。

這是您從 GithubFirehose API 獲得的有效負載的預覽。在本教程中，我們將僅使用其中的一小部分（“actor”物件）

{
    "id": "41339742815",
    "type": "WatchEvent",
    "actor": {
        "id": 12345678,
        "login": "someuser123",
        "display_login": "someuser123",
        "gravatar_id": "",
        "url": "https://api.github.com/users/someuser123",
        "avatar_url": "https://avatars.githubusercontent.com/u/12345678?",
    },
    "repo": {
        "id": 89012345,
        "name": "someorg/somerepo",
        "url": "https://api.github.com/repos/someorg/somerepo",
    },
    "payload": {
        "action": "started"
    },
    "public": true,
    "created_at": "2024-08-26T12:21:53Z"
}

但是我們不能只將 Firehose 連接到 Streamlit，我們首先需要一個後端來處理資料。為什麼？

在 Streamlit 中渲染即時資料可能是一個挑戰

Streamlit 是一個非常棒的工具，可以使用 Python 快速建立互動式應用程式原型。然而，當涉及到渲染即時資料時，Streamlit 可能有點棘手。您可以輕鬆地將 GitHub Firehose 串流傳輸到 Kafka（我的同事 Kris Jenkins 有一個關於此的優秀教程，名為 Python 中的高效能 Kafka Producers）

但是讓 Streamlit 渲染您直接從 Kafka 消費的資料可能有點繁瑣。 Streamlit 的設計初衷並不是為了處理每毫秒流動的連續資料流。無論如何，您實際上並不需要可視化以該頻率刷新。

即使您的來源資料流每毫秒更新一次（就像非常繁忙的伺服器日誌），您也可以每秒顯示資料快照。為此，您需要將該資料存入資料庫，並將其置於 API 後面，Streamlit 可以以更易於管理的速率進行輪詢。

透過將資料放在 API 後面，您不僅可以讓 Streamlit 更容易處理，還可以讓其他工具可以存取資料。這個方法需要一些後端開發，但不用擔心 — 我已經準備了一組 Docker 容器和一個 docker compose 檔案來幫助你在本地開始。

我保證，你可以成為一個完全的後端新手，但仍然了解它是如何運作的。

建築

以下是我們將使用的組件的快速概述：

是的，我知道它看起來很多，但我將專注於靜態資料層 ——即 API 服務和 Streamlit 儀表板。我不會在這裡深入研究流數據層的黑暗深處（但如果您想了解更多信息，我會給您一些鏈接）。

我添加了這一層，因為它可以很方便地了解流後端的一般工作原理，即使您不必自己維護它們。

以下是每項服務的功能的簡要說明：

Service	Description
Streamlit Service	Displays a Streamlit Dashboard which polls the API and renders the data in a chart and table.
Flask API	Serves a minimal REST API that can query a database and return the results as JSON.
Postgres Database	Stores the event count data.
Postgres Writer Service	Reads from a topic and continuously updates the database with new data.
Aggregation Service	Refines the raw event logs and continuously aggregates them into event counts broken down by GitHub display name.
Streaming Data Producer	Reads from a real-time public feed of activity on GitHub and streams the data to a topic in Redpanda (our local message broker).
Red Panda Server	Manages the flow of streaming data via topics (buffers for streaming data).

服務 描述 標題> 簡化服務 顯示 Streamlit 儀表板，該儀表板輪詢 API 並在圖表和表格中呈現資料。 Flask API 提供最小的 REST API，可以查詢資料庫並以 JSON 形式傳回結果。 Postgres 資料庫 儲存事件計數資料。 Postgres 寫入服務 從主題讀取並持續以新資料更新資料庫。 聚合服務 細化原始事件日誌並不斷將其聚合為按 GitHub 顯示名稱細分的事件計數。 流資料生產者 從 GitHub 上的即時公共活動來源讀取數據，並將數據串流傳輸到 Redpanda（我們的本地訊息代理）中的主題。 小熊貓伺服器 透過主題（流資料緩衝區）管理流資料流。 表>

You can inspect the code in the accompanying Github repo. Each service has its own subfolder, with code and a README:

Setting Up Your Environment

To get started, you’ll need Docker with Docker Compose installed (and Git of course). The easiest way is to install Docker Desktop. Once you have Docker set up, follow these steps:

1. Clone the repository:

git clone https://github.com/quixio/template-streamlit-rt-githubdash

2. Navigate to the repository:

cd template-streamlit-rt-githubdash

3. Spin up the Containers:

docker compose up

You should see some log entries that look like this (make sure Docker Desktop is running).

4. Visit the Streamlit URL

Open your browser and go to http://localhost:8031. You should see your dashboard running.

Code explanation

Let’s take a look at the two main components of the static data layer: the dashboard and the data API.

The Streamlit Dashboard

Now, let’s look at how the Streamlit app uses this API. It polls the API, gets the results back as JSON, turns the JSON into a dataframe, then caches the results. To refresh the results, the cache is cleared at a defined interval (currently 1 second) so that Streamlit needs to retrieve the data again.

import streamlit as st
import requests
import time
import pandas as pd
import os
import logging
from datetime import datetime
import plotly.express as px

from dotenv import load_dotenv
load_dotenv() ### for local dev, outside of docker, load env vars from a .env file

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# API endpoint URL
api_url = os.environ['API_URL']

## Function to get data from the API
def get_data():
   print(f"[{datetime.now()}] Fetching data from API...")
   response = requests.get(api_url)
   data = response.json()
   df = pd.DataFrame(data)

   try:
       # Reorder columns
       # df = df[['page_id', 'count']]
       df = df[['displayname', 'event_count']]
   except:
       logger.info("No data yet...")

   return df

# Function to get data and cache it
@st.cache_data
def get_cached_data():
   return get_data()

# Streamlit UI
st.title("Real-time Dashboard for GitHub Data using Streamlit,Flask and Quix")
st.markdown("This dashboard reads from a table via an API, which is being continuously updated by a Quix Streams sink process running in Docker. It then displays a dynamically updating Plotly chart and table underneath.")
st.markdown("In the backend, there are services that:\n * Read from the GitHub Firehose \n * Stream the event data to Redpanda\n * Read from Redpanda and aggregate the events per GitHub user\n * Sinking the page event counts (which are continuously updating) to PostGres\n\n ")
st.markdown("Take a closer a look at the [back-end code](https://github.com/quixio/template-streamlit-rt-githubdash), and learn how to read from a real-time source and apply some kind of transformation to the data before bringing it into Streamlit (using only Python)")

# Placeholder for the bar chart and table
chart_placeholder = st.empty()
table_placeholder = st.empty()

# Placeholder for countdown text
countdown_placeholder = st.empty()

# Main loop
while True:
   # Get the data
   df = get_cached_data()

   # Check that data is being retrieved and passed correctly
   if df.empty:
       st.error("No data found. Please check your data source.")
       break

   # Calculate dynamic min and max scales
   min_count = df['event_count'].min()
   max_count = df['event_count'].max()
   min_scale = min_count * 0.99
   max_scale = max_count * 1.01

   # Create a Plotly figure
   fig = px.bar(df, x='displayname', y='event_count', title="Current Top 10 active GitHub Users by event count",
                range_y=[min_scale, max_scale])

   # Style the chart
   fig.update_layout(
       xaxis_title="Display Name",
       yaxis_title="Event Count",
       xaxis=dict(tickangle=-90)  # Vertical label orientation
   )

   # Display the Plotly chart in Streamlit using st.plotly_chart
   chart_placeholder.plotly_chart(fig, use_container_width=True)

   # Display the dataframe as a table
   table_placeholder.table(df)

   # Countdown
   for i in range(1, 0, -1):
       countdown_placeholder.text(f"Refreshing in {i} seconds...")
       time.sleep(1)

   # Clear the countdown text
   countdown_placeholder.empty()

   # Clear the cache to fetch new data
   get_cached_data.clear()

This Streamlit app polls the API every second to fetch the latest data. It then displays this data in a dynamically updating bar chart and table. The @st.cache_data decorator is used to cache the data, reducing the load on the API.

The Data API

The API, built with Flask, serves as the gateway to our data. This code sets up a simple Flask API that queries a PostgreSQL database and returns the results as JSON. We’re using the psycopg2 library to interact with the database and Flask to define an API route.

import os
from flask import Flask, jsonify
from waitress import serve
import psycopg2
import logging

from dotenv import load_dotenv
load_dotenv() ### for local dev, outside of docker, load env vars from a .env file

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)

# Replace with your PostgreSQL connection details
pg_host = os.environ['PG_HOST']
pg_port = os.getenv('PG_PORT','5432')
pg_db = os.environ['PG_DATABASE']
pg_user = os.environ['PG_USER']
pg_password = os.environ['PG_PASSWORD']
pg_table = os.environ['PG_TABLE']

# Establish a connection to PostgreSQL
conn = psycopg2.connect(
   host=pg_host,
   port=pg_port,
   database=pg_db,
   user=pg_user,
   password=pg_password
)

@app.route('/events', methods=['GET'])
def get_user_events():
   query = f"SELECT * FROM {pg_table} ORDER BY event_count DESC LIMIT 10"

   logger.info(f"Running query: {query}")

   try:
       # Execute the query
       with conn.cursor() as cursor:
           cursor.execute(query)
           results = cursor.fetchall()
           columns = [desc[0] for desc in cursor.description]

       # Convert the result to a list of dictionaries
       results_list = [dict(zip(columns, row)) for row in results]
   except:
       logger.info(f"Error querying Postgres...")
       results_list = [{"error": "Database probably not ready yet."}]

   return jsonify(results_list)

if __name__ == '__main__':
   serve(app, host="0.0.0.0", port=80)

You can set up a much fancier API with query parameters and whatnot, but here I want to keep it simple. When an API request is received, just select the top 10 entries from the event_counts table and returns them as JSON. That’s it.

Why Use an API?

You might wonder why we use an API instead of querying the database directly from Streamlit. Here are a few reasons:

1. Decoupling: By separating the data access layer from the presentation layer, you can easily swap out or upgrade components without affecting the entire system.
2. Scalability: An API can handle multiple clients and can be scaled independently of the Streamlit app.
3. Reusability: Other applications or services can use the same API to access the data.

Actually, while we’re on the subject of APIs, it’s about time for a digression on why all this back end stuff is good to know in the first place.

An intro to Quix Streams — the library that powers the streaming data layer

I know I said I wasn’t going to talk too much about the streaming data layer, but I want to quickly show you how it works, because it’s really not as complicated as you might think.

It uses one Python library — Quix Streams — to produce to Redpanda (our Kafka stand-in), consume from it, then do our aggregations. If you know Pandas, you’ll quickly get the hang of it, because it uses the concept of “streaming data frames” to manipulate data.

Here are some code snippets that demonstrate the basics of Quix Streams.

Connecting to Kafka (or any other Kafka-compatible message broker) and initializing data source and destination.

# Initialize a connection by providing a broker address
app = Application(broker_address="localhost:19092") # This is a basic example, are many other optional arguments

# Define a topic to read from and/or to produce to
input_topic = app.topic("raw_data")
output_topic = app.topic("processed_data")

# turn the incoming data into a Streaming Dataframe
sdf = app.dataframe(input_topic)
All of the services in the streaming data layer (Streaming Data Producer, Aggregation service, and the Postgres Writer) use these basic conventions to interact with Redpanda.
The aggregation service uses the following code to manipulate the data on-the-fly.
sdf = app.dataframe(input_topic)

# Get just the "actor" data out of the larger JSON message and use it as the new streaming dataframe
sdf = sdf.apply(
   lambda data: {
       "displayname": data['actor']['display_login'],
       "id": data['actor']['id']
   }
)

# Group (aka "Re-key") the streaming data by displayname so we can count the events
sdf = sdf.group_by("displayname")

# Counts the number of events by displayname
def count_messages(value: dict, state: State):
   current_total = state.get('event_count', default=0)
   current_total += 1
   state.set('event_count', current_total)
   return current_total

# Adds the message key (displayname) to the message body too (not that necessary, it's just for convenience)
def add_key_to_payload(value, key, timestamp, headers):
   value['displayname'] = key
   return value

# Start manipulating the streaming dataframe
sdf["event_count"] = sdf.apply(count_messages, stateful=True) # Apply the count function and initialize a state store (acts as like mini database that lets us track the counts per displayname), then store the results in an "event_count" column

sdf = sdf[["event_count"]] # Cut down our streaming dataframe to be JUST the event_count column

sdf = sdf.apply(add_key_to_payload, metadata=True) # Add the message key "displayname" as a column so that its easier to see what user each event count belongs to.

sdf = sdf.filter(lambda row: row['displayname'] not in ['github-actions', 'direwolf-github', 'dependabot']) # Filter out what looks like bot accounts from the list of displaynames

sdf = sdf.update(lambda row: print(f"Received row: {row}")) # Print each row so that we can see what we're going to send to the downstream topic

# Produce our processed streaming dataframe to the downstream topic "event_counts"
sdf = sdf.to_topic(output_topic)

As you can see, it’s very similar to manipulating data in Pandas, it’s just that it’s data on-the-move, and we’re interacting with topics rather than databases or CSV files.

Running the Python files outside of Docker

With Docker, we’ve simplified the setup process, so you can see how the pipeline works straight away. But you can also tinker with the Python files outside of Docker.

In fact, I’ve included a stripped down docker-compose file ‘docker-compose-rp-pg.yaml’ with just the non-Python services included (it’s simpler to keep running the Repanda broker and the PostgreSQL DB in Docker).

That way, you can easily run Redpanda and Postgres while you tinker with the Python files.

docker-compose down

To continue working with the Python files outside of Docker:

• Create a fresh virtual environment.
• Install the requirements (this requirements file is at the root of the tutorial repo: *

pip install -r requirements.txt

• If your containers from the previous steps are still running, take them down with:.

docker-compose down

• Start the Redpanda and Postgres servers by running the “stripped down” docker compose file instead:

docker-compose -f docker-compose-rp-pg.yaml up

Now you’re ready to experiment with the Python files and run the code directly in an IDE such as PyCharm.

The next challenge

Here’s your next challenge. Why not update the services to create a real-time “trending repos” report by counting the most active repos in a 30-min time window?

You can use the Quix Streams Windowed Aggregations as a reference. After all, who wants to see the trending repos just for a whole day? Attention spans are getting shorter -and the world is going real-time — we need minute-by-minute updates! Life just feels more exciting when the numbers change before our eyes.

As a reminder, you can find all the code files in this repo: https://github.com/quixio/template-streamlit-rt-githubdash

Now, go forth and impress your colleagues with amazing real-time dashboards!

PS: If you run into trouble, feel free to drop me a line in the Quix Community slack channel and I’ll do my best to help.

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