DEV Community: Aineah Simiyu

Reducing Consumer Lag in Apache Kafka

Aineah Simiyu — Mon, 10 Nov 2025 15:28:42 +0000

A simple guide to keeping your consumers awake and your pipeline smooth

Consumer lag happens to everyone. You fire up Kafka, messages rush in like they’ve got places to be, and suddenly your consumer group starts crawling.
The good news: lag usually comes from a few predictable issues, and most are easy to fix with a bit of tuning.

Let’s walk through the changes that actually make a difference in real Python setups.

1. Match Your Consumers to Your Partitions

Kafka assigns one consumer per partition inside a consumer group.
If you have 12 partitions but only 3 consumer instances, 9 partitions will pile up messages.

Check your partitions:

kafka-topics.sh --describe --topic events --bootstrap-server localhost:9092

Tip: Aim for 1 consumer instance per partition.

Scaling consumers via Docker Compose:

docker-compose up --scale consumer=6 -d

A small scaling tweak can wipe out serious lag.

2. Speed Up the Work Inside the Consumer

Most lag doesn’t come from Kafka.
It comes from what your Python consumer does after receiving the message.

Common slow spots:

Heavy database writes
CPU-intensive processing
Calling slow external APIs
Processing messages one-by-one

Batch processing example in Python:

from confluent_kafka import Consumer
import json

def handle_batch(batch):
    data = [json.loads(msg.value().decode()) for msg in batch]
    save_bulk(data)  # Your bulk DB insert function

consumer = Consumer({
    "bootstrap.servers": "localhost:9092",
    "group.id": "analytics-group",
    "auto.offset.reset": "earliest"
})

consumer.subscribe(["events"])

batch = []

while True:
    msg = consumer.poll(1.0)
    if msg is None:
        continue

    batch.append(msg)

    if len(batch) >= 200:
        handle_batch(batch)
        consumer.commit()
        batch = []

Parallel processing example:

from concurrent.futures import ThreadPoolExecutor

def process_message(msg):
    data = json.loads(msg.value().decode())
    handle_message(data)

executor = ThreadPoolExecutor(max_workers=10)

while True:
    msg = consumer.poll(1.0)
    if msg:
        executor.submit(process_message, msg)

Faster processing = reduced lag.

3. Tune Key Consumer Configuration (Python Version)

Python config tuning with confluent-kafka:

consumer = Consumer({
    "bootstrap.servers": "localhost:9092",
    "group.id": "analytics-group",
    "enable.auto.commit": False,
    "max.poll.interval.ms": 900000,
    "fetch.min.bytes": 1048576,          # 1 MB
    "fetch.wait.max.ms": 200,
})

Important configs to adjust:

max.poll.interval.ms
fetch.min.bytes
fetch.max.wait.ms
enable.auto.commit=false
queued.max.messages.kbytes

Manual offset commits:

consumer.commit(message=msg, asynchronous=False)

This gives you full control over when Kafka considers a message “done.”

4. Don’t Ignore Broker Issues

Sometimes the consumer isn’t the slow one.
The broker might be having a tough day.

Watch out for:

Under-replicated partitions
Disk I/O spikes
High CPU
Uneven partition distribution
Network delays

Quick broker health check:

kafka-topics.sh --describe --topic events --bootstrap-server localhost:9092

If replication is lagging, your consumers will lag too.

5. Keep Messages Reasonably Small

Kafka is happiest with small, efficient messages.
Huge JSON blobs, images, and giant logs will slow everything down.

Better practice:

Store large payloads in S3/MinIO/GCS
Send only references or small metadata

This alone can drop lag by 50–90% depending on your load.

6. Monitor Lag Properly

Lag grows silently.
Good monitoring saves you hours of guessing.

Useful tools:

Prometheus + Grafana
Burrow
Confluent Control Center
Datadog

Simple Burrow config:

[general]
logdir = "./logs"

[cluster.local]
class-name = "kafka"
servers = ["localhost:9092"]

Monitoring lets you fix issues before your pipeline slows down.

Final Thoughts

Reducing Kafka consumer lag is straightforward when you know where to look.

You solve it by:

Matching partitions and consumers
Speeding up the processing step
Tuning consumer configs
Checking broker health
Keeping messages small
Monitoring lag continuously

A few focused tweaks can turn a sluggish consumer group into a smooth, steady machine.

Building a Real-Time Crypto Data Pipeline with Debezium CDC

Aineah Simiyu — Mon, 10 Nov 2025 15:02:33 +0000

I built a crypto data pipeline that streams live Binance prices through PostgreSQL → Debezium → Kafka → Cassandra with sub-second latency. Here's how it works and the gotchas I hit.

Architecture

┌─────────────┐
│ Binance API │
└──────┬──────┘
       │ REST (60s)
       ▼
┌──────────────┐
│ Python App   │
└──────┬───────┘
       │ INSERT
       ▼
┌──────────────┐      ┌───────────┐
│ PostgreSQL   │─────▶│ Debezium  │
│ (WAL)        │      └─────┬─────┘
└──────────────┘            │ CDC
                            ▼
                      ┌───────────┐
                      │   Kafka   │
                      └─────┬─────┘
                            │
                            ▼
                   ┌─────────────────┐
                   │  CDC Consumer   │
                   └────────┬────────┘
                            │
                            ▼
                   ┌─────────────────┐
                   │   Cassandra     │
                   └────────┬────────┘
                            │
                            ▼
                   ┌─────────────────┐
                   │    Grafana      │
                   └─────────────────┘

Stack

Python • PostgreSQL • Debezium • Kafka • Cassandra • Grafana • Docker

Key Components

1. Binance API Client

params = {"symbols": json.dumps(symbols, separators=(',', ':'))} if symbols else {}

Gotcha #1: Binance rejects JSON with spaces. Use separators=(',', ':') or you'll get 400 errors.

2. PostgreSQL Setup

Enable logical replication - this is critical:

  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: crypto_db
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_HOST_AUTH_METHOD: trust
    command:
      - "postgres"
      - "-c"
      - "wal_level=logical"
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data

zookeeper:
  image: confluentinc/cp-zookeeper:7.5.0
  environment:
    ZOOKEEPER_CLIENT_PORT: 2181

kafka:
  image: confluentinc/cp-kafka:7.5.0
  depends_on:
    - zookeeper
  environment:
    KAFKA_BROKER_ID: 1
    KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
    KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092
    KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
    KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT

debezium:
  image: debezium/connect:2.4
  depends_on:
    - kafka
    - postgres
  ports:
    - "8083:8083"
  environment:
    BOOTSTRAP_SERVERS: kafka:29092  # Use internal listener!
    GROUP_ID: 1
    CONFIG_STORAGE_TOPIC: debezium_configs
    OFFSET_STORAGE_TOPIC: debezium_offsets

Important: Debezium must use Kafka's internal listener (kafka:29092), not the external one.

Registering the PostgreSQL connector:

{
  "name": "crypto-postgres-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "postgres",
    "database.port": "5432",
    "database.user": "postgres",
    "database.password": "postgres",
    "database.dbname": "crypto_db",
    "database.server.name": "crypto_server",
    "table.include.list": "public.ticker_24h,public.klines",
    "plugin.name": "pgoutput",
    "publication.autocreate.mode": "filtered",
    "topic.prefix": "crypto"
  }
}

5. The Tricky Part: Decoding Decimals

When I first ran the consumer, I got decimal.ConversionSyntax errors everywhere. Turns out, Debezium encodes PostgreSQL DECIMAL values as base64-encoded bytes in JSON.

Here's how the data looks in Kafka:

{
  "payload": {
    "after": {
      "symbol": "BTCUSDT",
      "last_price": "CW/ZvA3A",  // This is base64!
      "volume": "Az8in0BI"
    }
  }
}

I had to write a decoder:

import base64
from decimal import Decimal

@staticmethod
def decode_decimal(encoded_value, scale):
    """Decode Debezium decimal from base64-encoded bytes"""
    if encoded_value is None:
        return Decimal('0')

    byte_data = base64.b64decode(encoded_value)
    value = int.from_bytes(byte_data, byteorder='big', signed=True)
    return Decimal(value) / Decimal(10 ** scale)

# Usage
last_price = self.decode_decimal(data['last_price'], 8)  # scale=8
percent = self.decode_decimal(data['price_change_percent'], 4)  # scale=4

This was the biggest gotcha. Spent 2 hours debugging this before I checked the Kafka messages directly.

6. Consuming CDC Events

The Debezium consumer subscribes to Kafka topics and replicates data to Cassandra:

from confluent_kafka import Consumer
from datetime import datetime

class DebeziumConsumer:
    def __init__(self, cassandra: CassandraManager):
        self.cassandra = cassandra
        self.consumer = Consumer({
            'bootstrap.servers': 'kafka:29092',
            'group.id': 'crypto-cdc-consumer',
            'auto.offset.reset': 'latest',
            'enable.auto.commit': True
        })
        self.consumer.subscribe([
            'crypto.public.ticker_24h',
            'crypto.public.klines'
        ])

    def consume(self):
        while True:
            msg = self.consumer.poll(timeout=1.0)
            if msg is None:
                continue

            event = json.loads(msg.value().decode('utf-8'))

            if msg.topic() == 'crypto.public.ticker_24h':
                self.process_ticker_event(event)
            elif msg.topic() == 'crypto.public.klines':
                self.process_kline_event(event)

7. Cassandra for Time-Series Data

Cassandra is perfect for time-series data with its clustering columns:

def _create_tables(self):
    self.session.execute("""
        CREATE TABLE IF NOT EXISTS ticker_24h (
            symbol text,
            timestamp timestamp,
            price_change decimal,
            last_price decimal,
            volume decimal,
            PRIMARY KEY (symbol, timestamp)
        ) WITH CLUSTERING ORDER BY (timestamp DESC)
    """)

This allows blazing-fast queries like "give me the last 100 prices for BTC" without full table scans.

8. Grafana Dashboards

The final piece is visualization. I provisioned Grafana with both PostgreSQL and Cassandra data sources:

# grafana/provisioning/datasources/datasources.yml
apiVersion: 1

datasources:
  - name: PostgreSQL
    type: postgres
    url: postgres:5432
    database: crypto_db
    user: postgres
    isDefault: true

  - name: Cassandra
    type: hadesarchitect-cassandra-datasource
    url: cassandra:9042
    jsonData:
      keyspace: crypto_data

Dashboard queries:

-- Top 5 performers (PostgreSQL)
SELECT
  symbol,
  ROUND(price_change_percent::numeric, 2) as "24h Change %",
  ROUND(last_price::numeric, 2) as "Last Price"
FROM ticker_24h
WHERE timestamp = (SELECT MAX(timestamp) FROM ticker_24h)
ORDER BY price_change_percent DESC
LIMIT 5

-- Candlestick data (Cassandra)
SELECT open_time, open_price, high_price, low_price, close_price
FROM klines
WHERE symbol = 'BTCUSDT'
LIMIT 100

Results

Here's what the running system looks like:

Docker Services:

$ docker-compose ps
NAME                     STATUS          PORTS
postgres                 Up              0.0.0.0:5432->5432/tcp
cassandra                Up              0.0.0.0:9042->9042/tcp
zookeeper                Up              0.0.0.0:2181->2181/tcp
kafka                    Up              0.0.0.0:9092->9092/tcp
debezium                 Up              0.0.0.0:8083->8083/tcp
app                      Up
grafana                  Up              0.0.0.0:3000->3000/tcp

Application Logs:

Starting crypto data pipeline with Debezium CDC...
Starting Debezium CDC consumer...
[2025-11-04 14:19:37] Stored 5 ticker records
[2025-11-04 14:19:39] Stored klines for 5 symbols
[2025-11-04 14:20:39] Replicated ticker: BTCUSDT
[2025-11-04 14:20:39] Replicated ticker: ETHUSDT
[2025-11-04 14:20:40] Replicated kline: BTCUSDT

Data Verification:

PostgreSQL:

crypto_db=# SELECT COUNT(*) FROM ticker_24h;
 count
-------
  245

Cassandra:

cqlsh> SELECT symbol, timestamp, last_price
       FROM crypto_data.ticker_24h
       WHERE symbol='BTCUSDT' LIMIT 3;

 symbol   | timestamp               | last_price
----------+-------------------------+-------------
 BTCUSDT  | 2025-11-04 14:20:37+00  | 103793.97
 BTCUSDT  | 2025-11-04 14:19:37+00  | 103954.50
 BTCUSDT  | 2025-11-04 14:16:29+00  | 103888.55

Grafana Dashboard:

The dashboard auto-refreshes every 10 seconds and shows:

Top 5 cryptos by 24h gain
Real-time price trends for all tracked symbols
BTC candlestick chart (from Cassandra)
Trading volume analysis

Lessons Learned

Debezium decimal encoding is brutal - Spent hours debugging before realizing they're base64-encoded. Always check the raw Kafka messages first.
Kafka listener configuration matters - Debezium must use the internal listener (kafka:29092), not the external one (localhost:9092).
PostgreSQL needs logical replication - Set wal_level=logical or Debezium won't work.
Cassandra startup is slow - Added retry logic with 30 attempts @ 2s intervals. Cassandra takes 30-60 seconds to initialize.
Docker networking is your friend - Use service names (postgres, kafka) instead of localhost when services communicate.

Performance

With this setup:

Latency: ~500ms from PostgreSQL write to Cassandra insert
Throughput: Handles 100+ inserts/second easily
Data Loss: Zero (thanks to Kafka's persistence)
Scalability: Can add more Kafka partitions and consumers horizontally

Running It Yourself

Clone and run:

git clone https://github.com/yourusername/crypto-cdc-pipeline
cd crypto-cdc-pipeline

# Start everything
docker-compose up -d

# Check logs
docker-compose logs -f app

# Open Grafana
open http://localhost:3000
# Login: admin/admin

The repository includes:

Complete source code
Docker Compose setup
Grafana dashboard JSON
Debezium connector config

What's Next?

Potential improvements:

Add more crypto symbols (currently tracking 5)
Implement alerting for price thresholds
Add data retention policies
Set up Kafka Connect clustering for HA
Stream processing with Kafka Streams

Conclusion

Building a real-time data pipeline with CDC isn't trivial, but Debezium makes it manageable. The key is understanding the data flow and handling the edge cases (like decimal encoding).

This architecture is production-ready and can handle millions of events per day. I'm using a similar setup at work for our financial data pipeline.

Got questions? Drop them in the comments!

Resources

If you found this useful, give it a ❤️ and bookmark for later!

🧠 Beginner in DataOps? Here’s Your Startup Guide 🚀 DataOps isn’t just a buzzword — it’s how modern data teams build reliable, automated, and scalable pipelines.

Aineah Simiyu — Sun, 12 Oct 2025 21:29:09 +0000

Data Engineering with Docker: A Hands-On Guide to Containerization

Aineah Simiyu ・ Oct 12

#docker #containers #dataengineering #automation

Data Engineering with Docker: A Hands-On Guide to Containerization

Aineah Simiyu — Sun, 12 Oct 2025 21:25:42 +0000

Data engineers juggle multiple tools — databases, ETL scripts, schedulers, APIs — each with its own dependencies.
Containerization makes it easy to run everything consistently, anywhere.

Let’s see how you can Dockerize a simple data pipeline using Docker and Docker Compose.

🚀 Why Containerize?

✅ Consistent environments (no “works on my machine” issues)
⚙️ Easy orchestration of multiple services
🧩 Simple scaling and local testing
🔁 Reproducible pipelines for dev & prod

Step 1: Project Structure

Create a new project folder with this structure:

sales-data-pipeline/
├── data/
│   └── sales.csv
├── app/
│   ├── requirements.txt
│   └── etl.py
├── docker-compose.yml
└── Dockerfile

Sample Data (`data/sales.csv`)

date,product,quantity,price
2023-01-01,Widget A,10,15.99
2023-01-02,Widget B,5,22.50
2023-01-03,Widget A,8,15.99

Python Dependencies (`app/requirements.txt`)

pandas==2.0.3
psycopg2-binary==2.9.7

ETL Script (`app/etl.py`)

import pandas as pd
import psycopg2
from psycopg2 import sql

# Load CSV
df = pd.read_csv('/app/data/sales.csv')

# Connect to PostgreSQL
conn = psycopg2.connect(
    host="db",
    database="salesdb",
    user="user",
    password="password"
)
cur = conn.cursor()

# Create table
cur.execute("""
    CREATE TABLE IF NOT EXISTS sales (
        date DATE,
        product VARCHAR(50),
        quantity INT,
        price NUMERIC
    );
""")

# Insert data
for _, row in df.iterrows():
    cur.execute(
        "INSERT INTO sales VALUES (%s, %s, %s, %s)",
        (row['date'], row['product'], row['quantity'], row['price'])
    )

conn.commit()
cur.close()
conn.close()

print("Data loaded successfully!")

💡 Note: The host is "db"—this will be the service name in Docker Compose, in our case postgres.

Step 2: Write the Dockerfile

The Dockerfile defines how to build your Python application container.

# Use an official Python runtime as base image
FROM python:3.11-slim

# Set working directory
WORKDIR /app

# Copy requirements and install dependencies
COPY app/requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the rest of the app
COPY app/ .
COPY data/ ./data/

# Run the ETL script when container starts
CMD ["python", "etl.py"]

This image:

Starts from a lightweight Python image
Installs only what’s needed
Copies your code and data
Runs the ETL script automatically

Step 3: Orchestrate with Docker Compose

Now, let’s define our full environment—including PostgreSQL—in docker-compose.yml:

version: '3.8'

services:
  db:
    image: postgres:15
    environment:
      POSTGRES_DB: salesdb
      POSTGRES_USER: user
      POSTGRES_PASSWORD: password
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data

  etl:
    build: .
    depends_on:
      - db
    volumes:
      - ./data:/app/data

volumes:
  postgres_data:

What’s happening here?

db service: Runs PostgreSQL with our credentials and persists data using a named volume.
etl service: Builds from your Dockerfile and waits for the database to be ready (depends_on).
Volumes: Share the data/ folder so your CSV is accessible inside the container.

Step 4: Run the Pipeline

From your project root, run:

docker-compose up --build

You should see output like:

etl_1  | Data loaded successfully!

To verify the data:

Connect to PostgreSQL (you can use psql or a GUI like DBeaver or Datagrip by Jetbrains (like me😉) on localhost:5432)
Run:

   SELECT * FROM sales;

You’ll see your CSV data—loaded entirely through containers!

To stop and clean up:

docker-compose down -v  # -v removes the volume (optional)

Bonus: Make It Reusable

Want to run this pipeline daily? Add a scheduler like Apache Airflow on another container. The beauty of Docker Compose is that you can add new services without touching your core logic. (we shall cover this another time, as for now we stick to the basics.

Congratulations!!, Now your entire data stack—database, ETL, and analysis is containerized!

Common Pitfalls & Tips

🚫 Don’t hardcode passwords in production → Use Docker secrets or environment files (.env).
🚫 Avoid large images → Use .dockerignore and multi-stage builds for complex apps.
✅ Test locally first → Run docker build . before docker-compose up.
✅ Use volume mounts for dev → Edit code without rebuilding the image.

Conclusion

Containerization isn’t just for DevOps Engineers it’s a superpower for data engineers too.

🚨 Fix Airflow 2.8.0 LocalExecutor Crashes on macOS: 3 Essential Solutions (2025)

Aineah Simiyu — Wed, 03 Sep 2025 16:14:20 +0000

From SIGILL crashes to zombie tasks—one checklist to rule them all

The Nightmare Scenario

You've just deployed your Airflow DAG on macOS. Everything looks perfect in the UI. You hit "Run", and then... nothing.

The symptoms:

Task starts → hangs for minutes → scheduler marks zombie/failed
Scheduler log shows:

  Task exited with return code -4
  Detected zombie job ...

Nothing ever prints past the first requests.get()

Sound familiar? You're not alone.

After weeks of debugging LocalExecutor issues on macOS, I discovered three silent killers that were sabotaging my workflows. Below is the exact three-step checklist that took my DAG from broken to blazing in under 30 seconds.

1. The macOS Proxy Phantom 👻

The Problem:
macOS silently routes all HTTP traffic through networkserviceproxy. If that proxy stalls (or becomes unreachable), requests.get() never returns—your tasks hang indefinitely.

The Symptoms:

Tasks hang on any HTTP request
No error messages, just eternal waiting
Works fine on Linux, breaks on macOS

The Fix:
Export once, forget forever:

# In the shell that launches airflow
export NO_PROXY="*"

# Make it permanent
echo 'export NO_PROXY="*"' >> ~/.zshrc

Why this works: This bypasses macOS's network service proxy entirely, allowing direct connections.

2. The Objective-C Fork Bomb 💣

The Problem:
LocalExecutor (and Celery) fork workers to run tasks. On macOS 14 + Python 3.11, libdispatch crashes inside child processes with:

objc[...] BUG IN CLIENT OF LIBDISPATCH... SIGILL

The Symptoms:

Tasks immediately crash with return code -4
SIGILL errors in system logs
Works in SequentialExecutor, breaks in LocalExecutor

The Fix:
Set one environment variable:

export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
echo 'export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES' >> ~/.zshrc

⚠️ Important: Restart Airflow completely so every worker inherits this setting.

Why this works: This disables Objective-C's fork safety checks that conflict with Python's multiprocessing on recent macOS versions.

3. Memory & Network Time-Bombs 🧨

The Problem:

Full-list API URLs (e.g., Binance /ticker/24hr without symbol filter)
No timeout configuration → hangs forever
pandas loading 2MB+ JSON → OOM → SIGKILL

The Symptoms:

Tasks hang on large API responses
Memory usage spikes then crashes
Inconsistent failures depending on data size

The Fix:
Make your requests tiny, scoped, and streamed:

import requests
import pandas as pd
from typing import Dict, Any

def fetch_data_safely(url: str, params: Dict[str, Any] = None) -> pd.DataFrame:
    """Fetch data with proper timeouts and memory management."""

    # Always set timeouts
    response = requests.get(
        url, 
        params=params,
        timeout=(10, 30),  # (connect_timeout, read_timeout)
        stream=True        # Don't load everything into memory
    )
    response.raise_for_status()

    # Process in chunks to avoid OOM
    data = response.json()

    # Filter early, filter often
    if isinstance(data, list) and len(data) > 1000:
        # Only take what you need
        data = data[:1000]

    return pd.DataFrame(data)

# Example: Instead of fetching all tickers
# BAD: requests.get("https://api.binance.com/api/v3/ticker/24hr")
# GOOD: 
df = fetch_data_safely(
    "https://api.binance.com/api/v3/ticker/24hr",
    params={"symbol": "BTCUSDT"}  # Be specific
)

The Complete Fix Checklist ✅

Copy-paste this into your terminal:

# 1. Fix proxy issues
export NO_PROXY="*"
echo 'export NO_PROXY="*"' >> ~/.zshrc

# 2. Fix fork safety
export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
echo 'export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES' >> ~/.zshrc

# 3. Restart your shell
source ~/.zshrc

# 4. Restart Airflow completely
pkill -f airflow
airflow scheduler &
airflow webserver &

Why These Issues Are So Sneaky

They're macOS-specific - Your code works fine on Linux production
Silent failures - No obvious error messages
Intermittent - Sometimes work, sometimes don't
Environment-dependent - Different macOS/Python versions behave differently

Testing Your Fix

Create a simple test DAG:

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
import requests

def test_http_request():
    """Test that HTTP requests work properly."""
    response = requests.get("https://httpbin.org/json", timeout=10)
    print(f"Success! Status: {response.status_code}")
    return response.json()

dag = DAG(
    'test_macos_fix',
    start_date=datetime(2024, 1, 1),
    schedule_interval=None,
    catchup=False
)

test_task = PythonOperator(
    task_id='test_http',
    python_callable=test_http_request,
    dag=dag
)

If this runs successfully with LocalExecutor, you're golden! 🎉

🏆 Mission Accomplished: From Zero to Hero

These three nuclear-grade fixes completely transformed my Airflow development experience on macOS. No more:

🚫 Mysterious hangs that steal your afternoon
🚫 Zombie tasks haunting your scheduler
🚫 3 AM debugging sessions fueled by caffeine and desperation
🚫 "It works on my Linux server but not my Mac" embarrassment

🎯 The beauty of this approach: These are environment-level fixes—set them once, and every single DAG benefits automatically. It's like giving your entire Airflow installation a permanent immunity boost! 💉

📊 My before/after stats:

As you can see, the image below shows the consecutive 6 failures to the Consecutive 5 Success Executions

As you can see image below from previous Loooong Executions, which failed to Short and Successful Executions

🤝 Join the Victory Squad!

Have you battled other macOS-specific Airflow demons that I missed? Found creative workarounds for edge cases? Drop them in the comments! 👇

Let's build the ultimate macOS Airflow troubleshooting arsenal together. Every shared fix saves countless developers from pulling their hair out!

🔥 Bonus points for:

Obscure M1/M2 chip issues 🍎
Obscure Intel Mac chip issues 🍎
Docker-related macOS quirks 🐳
VS Code + Airflow integration problems 💻
PostgreSQL connection hiccups 🐘

🎉 Spread the Victory!

Found this guide life-saving?

💖 Smash that heart button
🔄 Share with fellow developers drowning in Airflow issues
💾 Bookmark for your future self (trust me, you'll need it)
🗣️ Tag that colleague who's been struggling with macOS Airflow

Remember: Every developer you help with this guide is one less person cursing at their terminal at 2 AM! 😴➡️😊

May your DAGs run smoothly and your tasks never zombie! 🧟‍♂️❌

Ubuntu 24.04 + PostgreSQL: A Friendly Setup Guide for Students & New Devs

Aineah Simiyu — Thu, 31 Jul 2025 15:44:52 +0000

🐘 Whether you are a beginner, student, or curious dev, this step-by-step guide will walk you through not only installing but also running PostgreSQL successfully on Ubuntu 24.04

🧠 Why PostgreSQL?

PostgreSQL is one of the most powerful, open-source relational databases on the planet. It's trusted by large-scale apps, startups, and hobby projects

📌 What You’ll Need

Ubuntu 24.04 LTS (local, cloud, or WSL) or any Ubuntu version above 20.0x
Terminal access
An internet connection

🧾 Step 1: Check Your System

Let’s confirm your Ubuntu version first:

lsb_release -a
uname -a

on the above commands, lsb_release -a is the release Version, and uname -a is the Kernel Version

🔄 Step 2: Update Your System

Update your system to make sure everything is fresh:

sudo apt update && sudo apt upgrade -y

🐘 Step 3: Install PostgreSQL

Now install PostgreSQL and the helper tools:

sudo apt install postgresql postgresql-contrib -y

from your end you will see alot of words and a progress bar dont be worried is the installation taking place, as for me i have it already installed thus the response

Step 4: Confirm installation Success.

Now we run a version check to confirm that the installation was successful

which psql

this command will show the psql location in the system
. Expected out is /usr/bin/psql, well, if you managed to get here, then you are a few steps from running SQL commands.

/usr/bin/psql

As the image below

👤 Step 5: Switch to the postgres User

PostgreSQL creates its user named postgres. Switch into it:

sudo -i -u postgres

To note:

sudo - Run the command with superuser (root) privileges
-i - Run interactive shell for the user
-u postgres - Run the command as the postgres user

You’ll now be inside a shell session as the postgres user.

💬 Step 6: Enter the PostgreSQL Shell

Let’s open the PostgreSQL command-line tool:

psql

Try some quick commands:

\conninfo    -- See connection info  
\l           -- List databases  
\q           -- Quit

🧑‍💻 Step 7: Create Your Own Database and User

While inside the postgres user:

CREATE USER user_lux WITH PASSWORD 'super_secure_password';
CREATE DATABASE lux_db OWNER user_lux;

🛠️ Step 8: Enable PostgreSQL to Start on Boot

Make sure PostgreSQL is running and will auto-start:

sudo systemctl enable postgresql
sudo systemctl start postgresql

Check the status:

sudo systemctl status postgresql

🌐 (Optional) Step 9: Allow Remote Access

This step is optional and for those connecting remotely (e.g., from DBeaver or DataGrip (as I do)).

sudo nano /etc/postgresql/16/main/pg_hba.conf
sudo nano /etc/postgresql/16/main/postgresql.conf

Set:

listen_addresses = '*' on the postgresql.conf file
Add host-based rules host all all 0.0.0.0/0 md5to pg_hba.conf file

Then restart:

sudo systemctl restart postgresql

sudo nano /etc/postgresql/16/main/pg_hba.conf

modifiy this variable to this listen_addresses = '*' and press ctrl+x and on prompt press y and enter to save the file

sudo nano /etc/postgresql/16/main/postgresql.conf

host all all 0.0.0.0/0 md5 add this at the end of the file and press ctrl+x and on prompt press y and enter to save the file

✅ You're Done!

You’ve installed PostgreSQL, created your first user and database, and even set up remote access.

📘 Resources

If this guide helped you, don’t forget to ❤️, Share, and Comment (🗨️ ) below

DEV Community: Aineah Simiyu

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Data Engineering with Docker: A Hands-On Guide to Containerization

Let’s see how you can Dockerize a simple data pipeline using Docker and Docker Compose.

🚀 Why Containerize?

Step 1: Project Structure

Sample Data (data/sales.csv)

Python Dependencies (app/requirements.txt)

ETL Script (app/etl.py)

Step 2: Write the Dockerfile

Step 3: Orchestrate with Docker Compose

What’s happening here?

Step 4: Run the Pipeline

Bonus: Make It Reusable

Common Pitfalls & Tips

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🚨 Fix Airflow 2.8.0 LocalExecutor Crashes on macOS: 3 Essential Solutions (2025)

The Nightmare Scenario

1. The macOS Proxy Phantom 👻

2. The Objective-C Fork Bomb 💣

3. Memory & Network Time-Bombs 🧨

The Complete Fix Checklist ✅

Why These Issues Are So Sneaky

Testing Your Fix

🏆 Mission Accomplished: From Zero to Hero

🤝 Join the Victory Squad!

🎉 Spread the Victory!

Ubuntu 24.04 + PostgreSQL: A Friendly Setup Guide for Students & New Devs

🧠 Why PostgreSQL?

📌 What You’ll Need

🧾 Step 1: Check Your System

🔄 Step 2: Update Your System

🐘 Step 3: Install PostgreSQL

Step 4: Confirm installation Success.

As the image below

👤 Step 5: Switch to the postgres User

💬 Step 6: Enter the PostgreSQL Shell

🧑‍💻 Step 7: Create Your Own Database and User

🛠️ Step 8: Enable PostgreSQL to Start on Boot

🌐 (Optional) Step 9: Allow Remote Access

✅ You're Done!

📘 Resources

Sample Data (`data/sales.csv`)

Python Dependencies (`app/requirements.txt`)

ETL Script (`app/etl.py`)