Several factors affect database performance, and one of the most critical is how efficiently your application manages database connections. When multiple clients connect to PostgreSQL simultaneously, creating a new
connection for each request can be resource-intensive and slow. This is where connection pooling comes into play. Connection pooling allows connections to be reused instead of creating a new one every time, reducing overhead and improving performance. In this blog, we’ll explore PgBouncer, a lightweight PostgreSQL connection pooler, and how to set it up for your environment. Continue reading “Complete Guide to Fixing PostgreSQL Performance with PgBouncer Connection Pooling”
Tag: PostgreSQL
The Ultimate Guide to Postgres MCP for Claude Desktop
Connecting PostgreSQL to a cloud desktop via the Model Completion Protocol (MCP) opens up new possibilities for using the powerful features of the cloud with your database information.
Introduction
If you’ve ever been stuck waiting for your data team to run a quick SQL query or worse, had to wrestle with writing your own you already know how frustrating traditional analytics workflows can be. But what if you could simply ask your database questions in plain English and get instant insights?
That’s exactly what Postgres MCP (Model Context Protocol for PostgreSQL) brings to the table when integrated with Claude Desktop. This setup not only saves valuable time but also ensures your database remains secure while supercharging team productivity.
In this post, we’ll break down what Postgres MCP is, why it matters, real-world applications, and a step-by-step guide to setting it up. Continue reading “The Ultimate Guide to Postgres MCP for Claude Desktop”
The Software Environment Types: Death by a Thousand Deployments
“Your code doesn’t just ship — it survives a gauntlet of digital Darwinism where only the fittest features reach users.”
How One PostgreSQL Version Mismatch Cost a Fortune 500 Company $4.7 Million
TL; DR — When Simple Becomes Catastrophic
Last month, two digits in a database version number brought at a Fortune 500 company a production outage that cost $4.7 million in lost revenue. The root cause? Their staging environment was running on PostgreSQL 13 while production was on PostgreSQL 15. A simple version mismatch became a career-ending incident.
This isn’t just another “environments matter” story. This is about the invisible architecture of trust that separates unicorn startups from digital graveyards.
Continue reading “The Software Environment Types: Death by a Thousand Deployments”
Stream and Analyze PostgreSQL Data from S3 Using Kafka and ksqlDB: Part 2
Introduction
In Part 1, we set up a real-time data pipeline that streams PostgreSQL changes to Amazon S3 using Kafka Connect. Here’s what we accomplished:
- Configured PostgreSQL for CDC (using logical decoding/WAL)
- Deployed Kafka Connect with JDBC Source Connector (to capture PostgreSQL changes)
- Set up an S3 Sink Connector (to persist data in S3 in Avro/Parquet format)
In Part 2 of our journey, we dive deeper into the process of streaming data from PostgreSQL to S3 via Kafka. This time, we explore how to set up connectors, create a sample PostgreSQL table with large datasets, and leverage ksqlDB for real-time data analysis. Additionally, we’ll cover the steps to configure AWS IAM policies for secure S3 access. Whether you’re building a data pipeline or experimenting with Kafka integrations, this guide will help you navigate the essentials with ease.
Continue reading “Stream and Analyze PostgreSQL Data from S3 Using Kafka and ksqlDB: Part 2”
Stream PostgreSQL Data to S3 via Kafka Using JDBC and S3 Sink Connectors : Part 1
Step 1: Set up PostgreSQL with Sample Data
Before you can source data from PostgreSQL into Kafka, you need a running instance of PostgreSQL with some data in it. This step involves:
- Setting up PostgreSQL: You spin up a PostgreSQL container (using Docker) to simulate a production database. PostgreSQL is a popular relational database, and in this case, it serves as the source of your data.
- Create a database and tables: You define a schema with a table (e.g., users) to hold some sample data. The table contains columns like id, name, and email. In a real-world scenario, your tables could be more complex, but this serves as a simple example.
- Populate the table with sample data: By inserting some rows into the users table, you simulate real data that will be ingested into Kafka.
Continue reading “Stream PostgreSQL Data to S3 via Kafka Using JDBC and S3 Sink Connectors : Part 1”