Shayon Mukherjee

Every storage engine has to decide what must finish before it tells a client that a write succeeded.

Lately I have been working around sandboxing, storage, and networking, and a lot of that work keeps coming back to files, which makes sense since Unix has organized itself around everything is a file for over fifty years.

I created my GitHub account in October 2011.

I recently went down a rabbit hole trying to understand how Linux handles page faults, what mmap actually does at the physical page level, and how userfaultfd lets userspace take over that fault handling.

There is a lot of energy right now around sandboxing untrusted code.

I have been spending time learning about model training infrastructure lately, and something that stood out to me was GIL contention when saving training checkpoints in PyTorch.

In 1968, a group of computer scientists gathered at a NATO conference in Garmisch, Germany, and coined the term “software crisis.”

I’ve been curious about how far you can push object storage as a foundation for database-like systems.

Every year, I use Diwali as a moment to pause and reflect.

In the previous post, I explored a Parquet on S3 design with tombstones for constant time deletes and a CAS updated manifest for snapshot isolation.

Parquet is excellent for analytical workloads.

PostgreSQL handles large JSON payloads reasonably well until you start updating or deleting them frequently.

PostgreSQL’s hash partitioning distributes rows across partitions using deterministic hash functions.

A developer types implement user authentication and watches as Cursor generates 50 lines of secure, production-ready code in seconds.

A 51-year-old man walked into the emergency room with chest pain.

A few days ago, I wrote about a surprising planner behavior with CTEs, DELETE, and LIMIT in PostgreSQL, a piece I hastily put together on a bus ride.

I recently discovered an unexpected behavior in PostgreSQL involving a pattern of using a Common Table Expression (CTE) with DELETE ...

I was recently looking into some workloads that generate a lot of I/O and CPU contention on some very high-write code paths and came across synchronous_commit (https://www.postgresql.org/docs/current/wal-async-commit.html).

Prototyping new features and fixing bugs has become so much faster now that we have coding copilots like Cursor and LLMs (Large Language Models).

“Postgres was great when we started but now that our service is being used heavily we are running into a lot of ‘weird’ issues” This sentiment is frequently echoed by CTOs and senior engineers at high-growth startups when I speak with them.

It’s tempting to rely on mocks for database calls.

When dealing with very large PostgreSQL tables (we’re talking 15TB+), sometimes routine maintenance like archiving very old data can become surprisingly challenging.

I have been meaning to support common DDLs (Data Definition Language) for pg_easy_replicate for quite some time now and I am super stoked that it is now finally out.

As a developer, you might have encountered situations where creating an index in PostgreSQL fails due to lock timeouts.

The New York Times recently reported on how a group of tech elites helped J.D.

Logical replication is a powerful feature in PostgreSQL that allows for real-time data replication between databases.

Let’s say you need to fetch a lot of data from an upstream API, then you want to manipulate that data, maybe even enrich it, and then send it downstream to a database or another API.

Have you ever had a brilliant idea that you wanted to act on immediately?

Speed is key, except when its not.

We often hear the mantra “move fast and break things,” but what if I told you that “moving fast” also requires diving deep — getting into the weeds of every discussion, every idea, every potential innovation as much as possible.

Recently I have been working with some queries in Postgres where I noticed either it has decided not to use an index and perform a sequential scan, or it decided to use an alternative index over a composite partial index.

In the fast-moving world of startups, shipping code is a crucial competitive advantage.

It’s exciting to share that pg_easy_replicate, a project I’ve been maintaining, has hit a new milestone with its 2.0 release!

Before we dive in, let’s set the stage with a few pointers: YMMV.

Schema changes are usually critical operations to perform on a high volume database.

While I enjoy working with MySQL (first database) both as a product and infrastructure engineer, lately, I have come to appreciate PostgreSQL as well.

Scaling Postgres Without Boiling the Ocean — Postgres Meetup for All (2026) PGConf NYC 2024 - Using Logical Replication for Major Version Upgrades and Tenant Migrations RailsConf 2023 (Video): Beyond CRUD: the PostgreSQL techniques your Rails app is missi…

Originally wrote this as a part of a submission (Dec 2019) for an upcoming O’Reilly Book in 2021.

books current Einstein in Bohemia backlog Designing Data-Intensive Applications (Refresher) Code: The Hidden Language of Computer Hardware and Software The Manager’s Path Napoleon: A Life Sapiens: A Brief History of Humankind The Systems Bible The Pragmat…

rollback scale down / up turn off feature flags keep looking a deploy went out, but unlikely related rollback turned on a feature flag few hours ago, not likely it turn it off error rate isn’t high, not the last deploy rollback none of the code paths touc…

I am a Software Engineer with Product and Infrastructure engineering experience.

Lately I have been very interested about Signal handling.