MyDistributed.Systems

Log-structured storage is a powerful approach to organizing data that prioritizes sequential writes for efficiency and durability. In this post, I delve into the inner workings of log-structured storage, focusing on LSM-trees. We'll explore their write and read paths, the mechanics of compaction, and compare leveled and tiered compaction strategies. Additionally, I'll discuss the differences between LSM-trees and B-trees, and talk about variations like Jungle and Bitcask, highlighting their unique optimizations and trade-offs.  Basics Write Path Write are first appended to a Write-Ahead-Log (WAL) for durability, then they are written to a MemTable which is basically an in-memory sorted data structure such as a balanced tree (e.g. red-black or AVL tree) or a skip list (I have a detailed post about skip list on this blog). After writing a write to WAL and the MemTable, we can return success to the client. We can have multiple MemTables, but at any given time, ...

I this blog post, we talk about the various levels of abstraction available for working with Kubernetes (K8s) in Go. We start with fundamental building blocks for interacting with Kubernetes resources, starting with the REST API, and move through the Go API Library, API Machinery, the high-level client-go library,  controller-runtime, and kubebuilder. Each section provides insights into the utilities and patterns for resource creation, updates, and management, as well as details on working with custom resources and building operators.

In this post, I share some notes on Go. Check out part 1 .

In this post, I share some notes on Go.

Ray is a framework for scaling Python applications. Ray allows you to execute the same Python program that you would run on your laptop on a cluster of computers with minimum effort. All you need is to tell Ray what part of your program needs to be scalable by decorating your Python functions and classes, and Ray takes care of the rest for you. Ray has been used by several companies including OpenAI  for building ChatGPT.

ByteGraph is a distributed graph database developed by ByteDance, the company behind the popular social network TikTok. As a large social media platform, ByteDance handles an enormous amount of graph data, and using a specialized graph database like ByteGraph is essential for efficiently managing and processing this data. In 2022, ByteDance published a paper on ByteGraph at the VLDB conference. In this post, I will provide an overview of my understanding of ByteGraph and highlight some of the key points from the paper that I found interesting.

Ten years after the public availability of DynamoDB in 2012, and fifteen years after the publication of the original Dynamo paper in 2007, the DynamoDB team published a paper in Usenix ATC 2022 on how DynamoDB has evolved over its ten years of operation. The paper covers several aspects of DynamoDB such as adaptation to customer access patterns, smarter capacity allocation, durability, correctness, and availability. In this post, I share some of points that I found interesting in the paper.