docker VS PostgreSQL

Most developers first learn Docker, which helps run one container at a time. But Docker alone is low-level — you still have to manage containers manually. Kubernetes, on the other hand, provides high-level tools and abstractions so you can describe your apps in configuration files, and Kubernetes takes care of the rest.

Containerize your workflows: Tools like Docker and Podman aren't just for deployment - they make automation more reproducible.

The solution to this problem started with setting up different teams for both - and ClickOps was coined. As cloud technologies evolved, people realized that it was getting increasingly difficult to keep systems in sync given the room for human error. Naturally, it evolved to the adoption of scripting based pipelines, and it led to the birth of DevOps. This bridged the gap between development and operations quite a bit, and was based on the idea of you build it, you run it, which inadvertently caused shrinkage of operations teams. It was progress alright, but still left techies yearning for something more. Application developers were getting overloaded with the cognitive load of managing infrastructure, and it was getting in the way of product development. Besides this, the complexity of application development was shifting from monoliths to microservices. Upholding the need for separation of concern, technologies evolved and containerization paved the path for taking things to the next level. Docker became somewhat ubiquitous and with the advent of containers, came the need for container orchestration. Google looked over their shoulders and donated Borg to the world, which came to be better known as Kubernetes or k8s. This is where things started to get really spicy, and the cognitive load on developers quickly started to multiply exponentially.

Docker (to containerize your setup) https://www.docker.com

Docker (Engine) helps you download and deploy software packaged in lightweight, isolated containers.

We will use Docker and Ollama to run the Qwen3 Embedding model. Create a file named Dockerfile and put the following code inside it:

1. Docker

I’ve had a grudge against Docker for a long time.

Docker is the best-known and most popular container platform. It pioneered today's container landscape by making the Linux kernel technologies that underpin containers more accessible to developers.

Forge shines as a first-pass troubleshooter for environment and deployment issues. Whenever our CI/CD jobs break or a server misbehaves, I paste the error or describe the situation. For example, when a Docker build failed with a generic “permission denied” error, I asked Forge for help. It analyzed the problem and realized we were creating files as root without chown, then suggested the exact fix (use chown or run as non-root). Similarly, it caught a missing .env copy in our Dockerfile that was causing production errors. In general, I treat Forge as my AI debugger: it has “helped troubleshoot environment and deployment problems” by surfacing root causes whenever we prompt it. This saves us from long blind hunts in logs and configs.

You're minding your own business, managing AWS infrastructure for a client with a pretty standard e-commerce setup: a Medusa.js backend, a Next.js storefront, and most importantly for this story, a PostgreSQL RDS instance safely stashed away in a private subnet where nothing from the outside world can touch it. Exactly how the AWS gods intended.

Your Database: This is your system's memory. It can be a fast in-memory store like Redis for temporary data (perfect for velocity checks) or a persistent relational database like PostgreSQL for long-term data (like blacklists).

#! /bin/bash set -ex IMG=postgres/test-0.0.1 IMG_ID=`docker images ${IMG} -q` PG_TAG=REL_18_BETA1 if [ "${IMG_ID}" = "" ]; then if [ ! -d "postgres-${PG_TAG}" ]; then wget https://github.com/postgres/postgres/archive/refs/tags/${PG_TAG}.tar.gz && tar -xzf ${PG_TAG}.tar.gz fi ID=991 USR=postgres USR_HOME=/home/postgres cat > Dockerfile << EOF FROM ubuntu:latest RUN groupadd -g ${ID} ${USR} && useradd -r -u ${ID} -g ${USR} ${USR} ADD postgres-${PG_TAG} ${USR_HOME} WORKDIR ${USR_HOME} RUN chown -R ${USR}:${USR} ${USR_HOME} RUN apt-get update && apt-get install -y g++ zlib1g-dev make curl tar gzip perl liblz4-dev libreadline-dev flex bison libicu-dev liburing-dev RUN apt-get install --reinstall -y pkg-config && ./configure --with-liburing --enable-debug --with-lz4 && make -j4 && make all && make install RUN echo "export PATH=/usr/local/pgsql/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin" >> /etc/bash.bashrc && \ chown -R ${USR}:${USR} /usr/local/pgsql USER ${USR} EOF docker build -t ${IMG}:latest . rm Dockerfile rm -rf postgres-${PG_TAG} ${PG_TAG}.tar.gz else echo "Image ${IMG} already exists with ID ${IMG_ID}" fi

Choosing the database was straightforward: PostgreSQL. I have the most experience with it, very easy to spin up, has a lot of trust and is open-source (I guess the last two come hand-in-hand).

I was explaining why it is the way that it is. If you'd like your own version of a parser, here's Postgres' [0]. Personally, I really like SQL's syntax and find that it makes sense when reading it.

[0]: https://github.com/postgres/postgres/tree/master/src/backend...

If you’ve never tried PostgreSQL before, the official site is a great starting point: PostgreSQL: The world’s most advanced open source database.

/* https://github.com/postgres/postgres/blob/144ad723a4484927266a316d1c9550d56745ff67/src/backend/optimizer/path/costsize.c#L3375 */ void final_cost_nestloop(PlannerInfo *root, NestPath *path, JoinCostWorkspace *workspace, JoinPathExtraData *extra) { /* ... */ if (path->jpath.path.param_info) path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; else path->jpath.path.rows = path->jpath.path.parent->rows; /* ... */ } /* https://github.com/postgres/postgres/blob/144ad723a4484927266a316d1c9550d56745ff67/src/backend/optimizer/path/costsize.c#L3873 */ void final_cost_mergejoin(PlannerInfo *root, MergePath *path, JoinCostWorkspace *workspace, JoinPathExtraData *extra) { /* ... */ if (path->jpath.path.param_info) path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; else path->jpath.path.rows = path->jpath.path.parent->rows; /* ... */ } /* https://github.com/postgres/postgres/blob/144ad723a4484927266a316d1c9550d56745ff67/src/backend/optimizer/path/costsize.c#L4305 */ void final_cost_hashjoin(PlannerInfo *root, HashPath *path, JoinCostWorkspace *workspace, JoinPathExtraData *extra) { /* ... */ if (path->jpath.path.param_info) path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; else path->jpath.path.rows = path->jpath.path.parent->rows; /* ... */ }

PostgreSQL as database

> As introduced in the previous chapters, all identifers are hashed, thus we can also hash the known keywords at startup and make comparing them very fast.

One trick that postgres uses [1][2] is perfect hashing [3]. Since you know in advance what your keywords are, you can design such hashing functions that for each w(i) in list of i keywords W, h(w(i)) = i. It essentially means no collisions and it's O(i) for the memory requirement.

[1] https://github.com/postgres/postgres/blob/master/src/tools/P...

[2] https://github.com/postgres/postgres/blob/master/src/tools/g...

[3] https://en.wikipedia.org/wiki/Perfect_hash_function

Understanding a database starts with understanding its structure. For PostgreSQL users, one of the most effective ways to visualize and manage your schema is by using an Entity-Relationship Diagram (ERD). Either if you're working with a large legacy database or starting something new, an ER diagram shows how your tables are connected and how your data is organized.