Latest Posts (20 found)
Shayon Mukherjee 1 months ago

Building a tiny FUSE filesystem

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. Your terminal and random number generator are device files you can open and read (/dev/tty, /dev/urandom), and even network sockets, which are created with their own system call rather than opened by path, are read and written through the same interface afterwards.

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Shayon Mukherjee 3 months ago

I am not leaving GitHub any time soon

I created my GitHub account in October 2011. User 1,100,970. My first repository was a small project for an openSUSE summit, pushed in February 2012. I’ve opened GitHub almost every day since then for nearly fifteen years. It’s where most of my professional life has happened. So when Mitchell Hashimoto wrote last week that GitHub “is no longer a place for serious work” and that he’s moving Ghostty off the platform, I paid attention.

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Shayon Mukherjee 6 months ago

Software engineering when machine writes the code

In 1968, a group of computer scientists gathered at a NATO conference in Garmisch, Germany, and coined the term “software crisis.” The problem they identified wasn’t that computers were bad or unreliable. It was that computers had become too powerful for the existing methods of programming to handle. Edsger Dijkstra later put it memorably: “As long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem.

machine-learning Machine Learning programming Programming ai AI
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Shayon Mukherjee 8 months ago

A hypothetical search engine on S3 with Tantivy and warm cache on NVMe

I’ve been curious about how far you can push object storage as a foundation for database-like systems. In previous posts, I explored moving JSON data from PostgreSQL to Parquet on S3 and building MVCC-style tables with constant-time deletes using S3’s conditional writes. These experiments showed that decoupling storage from compute unlocks interesting trade-offs while lowering costs and simpler operations in exchange for higher cold query latency. Search engines traditionally don’t fit this model.

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Shayon Mukherjee 9 months ago

Diwali

Every year, I use Diwali as a moment to pause and reflect. Not in any formal way, not tied to ritual or ceremony but just a natural checkpoint (a database intended pun) where the lights come on and I look back at the year behind me, and forward at what’s ahead. It’s one of my favorite festivals, and I’d be lying if I said the sheer diversity and volume of sweets I consume during this period doesn’t have something to do with it.

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Shayon Mukherjee 9 months ago

Mutable atomic deletes with Parquet backed columnar tables on S3

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. This post extends that design. The focus is in file delete operations where we replace a Parquet row group and publish a new footer using S3 Multipart Upload (MPU) and UploadPartCopy without having to download and rebuild unchanged bytes. We preserve the same system model with immutable data files and a manifest pointer that serializes visibility.

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Shayon Mukherjee 9 months ago

An MVCC-like columnar table on S3 with constant-time deletes

Parquet is excellent for analytical workloads. Columnar layout, aggressive compression, predicate pushdown, but deletes require rewriting entire files. Systems like Apache Iceberg and Delta Lake solve this by adding metadata layers that track delete files separately from data files. But what if, for fun, we built something (arguably) simpler? S3 now has conditional writes (If-Match, If-None-Match) that enable atomic operations without external coordination. Let’s explore how we might build a columnar table format on S3 that gets most of Parquet’s benefits while supporting constant-time deletes.

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Shayon Mukherjee 9 months ago

Exploring PostgreSQL to Parquet archival for JSON data with S3 range reads

PostgreSQL handles large JSON payloads reasonably well until you start updating or deleting them frequently. Once payloads cross the 8 KB TOAST threshold and churn becomes high, autovacuum can dominate your I/O budget and cause other issues. I have been exploring the idea of moving older JSON data (read: cold data) to Parquet on S3 while keeping recent data hot in PostgreSQL daily partitions, then simply dropping those partitions instead of running expensive DELETE operations and subsequent vacuum cycles.

database Database performance Performance json JSON cloud Cloud
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Shayon Mukherjee 11 months ago

Bypass PostgreSQL catalog overhead with direct partition hash calculations

PostgreSQL’s hash partitioning distributes rows across partitions using deterministic hash functions. When you query through the parent table, PostgreSQL must perform catalog lookups to route each query to the correct partition. This results in measurable overhead for high-throughput applications, especially if you decide to use multi-level partitioning schemes where PostgreSQL must traverse deeper catalog structures to identify the target partition. Let’s take a look at some findings on speeding up the part where you already know the partition key values.

database Database
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Shayon Mukherjee 1 years ago

Is AGI paradoxical?

A developer types implement user authentication and watches as Cursor generates 50 lines of secure, production-ready code in seconds. It’s remarkable—the AI understands context, follows best practices, even adds appropriate error handling. But here’s what’s fascinating: every pattern it used was learned from millions of human-written codebases. The AI didn’t invent authentication; it synthesized decades of human security knowledge at unprecedented speed. This same pattern plays out across today’s most impressive AI systems as we pursue Artificial General Intelligence.

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Shayon Mukherjee 1 years ago

Pitfalls of premature closure with LLM assisted coding

A 51-year-old man walked into the emergency room with chest pain. The symptoms seemed clear enough: elevated blood pressure, chest discomfort, some cardiac irregularities. The emergency physician, attending doctor, and cardiologist all converged on the same diagnosis—acute coronary syndrome or accelerated hypertension. The classic signs of anything more serious simply weren’t there. But one hospitalist wasn’t satisfied. Despite multiple colleagues ruling out aortic dissection as unlikely, something felt incomplete. The pieces fit the common diagnosis, but not perfectly.

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Shayon Mukherjee 1 years ago

Another look into PostgreSQL CTE materialization and non-idempotent subqueries

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. That post clearly only scratched the surface of a deeper issue that I’ve since spent way too many hours exploring. So here are some more formed thoughts and findings. The core issue revisited Let’s quickly recap: when using a query like the following, the planner might execute your query in ways you don’t expect.

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Shayon Mukherjee 1 years ago

A PostgreSQL planner gotcha with CTEs DELETE and LIMIT

I recently discovered an unexpected behavior in PostgreSQL involving a pattern of using a Common Table Expression (CTE) with DELETE ... RETURNING and LIMIT to process a batch of items from a queue-like table. What seemed straightforward turned out to have a surprising interaction with the query planner. The scenario Let’s say you have a task_queue table and want to pull exactly one task for a specific queue_group_id. A common approach is using a CTE:

database Database
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Shayon Mukherjee 1 years ago

Selective asynchronous commits in PostgreSQL - balancing durability and performance

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). It can be very tempting to turn this off globally because the performance gains in terms of I/O, CPU, and TPS (transactions per second) are very hard to overlook. I noticed I/O completely gone, CPU down 20% (at peak), and a 30% increase in TPS.

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Shayon Mukherjee 1 years ago

Challenging AI generated code from first principles

Prototyping new features and fixing bugs has become so much faster now that we have coding copilots like Cursor and LLMs (Large Language Models). We can generate boilerplate code in no time, saving time that would have gone into repetitive tasks. AI suggestions often feel like magic—type a quick prompt, accept a snippet, and plug it in. However, this speed comes with a downside: it’s too easy to bypass deep thinking.

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Shayon Mukherjee 1 years ago

Scaling with PostgreSQL without boiling the ocean

“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. Scaling PostgreSQL successfully doesn’t always require a full team of DBAs and experts. The beauty of PostgreSQL is that solutions often lie within the database itself - by rethinking your data access patterns from first principles, you can solve many business problems at scale.

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Shayon Mukherjee 1 years ago

Database mocks are just not worth it

It’s tempting to rely on mocks for database calls. Mocking is faster and often feels more straightforward. However, testing against a real database uncovers hidden pitfalls that can appear as the application matures. Issues like unique constraint violations, default value handling, or even performance bottlenecks may only surface when the code is exercised against actual data. The importance of real database testing Consider a simple example where you create a user in your application.

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Shayon Mukherjee 1 years ago

Using CTID Based Pagination for Data Cleanups in PostgreSQL

When dealing with very large PostgreSQL tables (we’re talking 15TB+), sometimes routine maintenance like archiving very old data can become surprisingly challenging. Despite having good indexes. I recently faced this issue when trying to clean up very old data on a very large and legacy table. The Problem Initial approach used standard ID-based pagination. Imagine a query like this: DELETE FROM large_table WHERE id IN ( SELECT id FROM large_table WHERE created_at < '2023-04-01' ORDER BY id LIMIT 10000 ); While totally harmless on the surface, this kept timing out (10 min statement_timeout 😬).

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Shayon Mukherjee 1 years ago

pg_easy_replicate Supports Schema Change Tracking During Logical Replication

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. This new capability addresses one of the limitations of PostgreSQL’s native logical replication, bringing more flexibility to database migrations and replication through pg_easy_replicate. What is pg_easy_replicate? For those new to the project, pg_easy_replicate is a CLI orchestrator tool that simplifies the process of setting up and managing logical replication between PostgreSQL databases.

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Shayon Mukherjee 1 years ago

Stop Relying on IF NOT EXISTS for Concurrent Index Creation in PostgreSQL

As a developer, you might have encountered situations where creating an index in PostgreSQL fails due to lock timeouts. In such scenarios, it’s tempting to use the IF NOT EXISTS as a quick fix and move on. However, this approach can lead to subtle and hard-to-debug issues in production environments. Let’s understand how PostgreSQL handles concurrent index creation When we initiate CREATE INDEX CONCURRENTLY, PostgreSQL first creates an entry for the index in the system catalogs (specifically in pg_index) and marks it as invalid.

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