#Data Structures

Articles about Data Structures — exploring patterns, best practices, and real-world implementations in production systems.

9 posts tagged with data structures. ← All posts

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Pratik Dhanave · ·6 min read

Collections: Vec, String, and HashMap

Module 1's arrays and tuples were fixed-size and stack-bound. Real programs need growable, heap-backed collections — and Rust's three workhorses, Vec, String, and HashMap, are where ownership and borrowing stop being abstract rules and become the everyday texture of writing Rust. This opens Module 2: the data structures and abstractions you actually build with.

Real programs need growable, heap-backed collections — and Rust's three workhorses, Vec, String, and HashMap, are where ownership and borrowing stop being abstract rules and become the everyday texture of writing Rust.

#Rust#Systems Programming#Programming Languages#Data Structures
Pratik Dhanave · ·6 min read

HNSW: Navigable Small World Graphs

HNSW is the algorithm behind most modern vector databases, and its idea is borrowed from the "six degrees of separation" that connects any two people through a short chain of acquaintances. Build the right graph of vectors, and you can walk from a random entry point to a query's nearest neighbors in a handful of hops — searching millions of vectors while touching only a few hundred.

HNSW is behind most modern vector databases, and its idea comes from the 'six degrees of separation' that connects any two people through a short chain — build the right graph and you can walk from a random entry to a query's nearest neighbors in a handful of hops.

#Vector Search#RAG#AI Engineering#Data Structures#Vector Databases#Databases
Pratik Dhanave · ·6 min read

IVF: The Inverted File Index

The simplest way to beat brute force is to avoid searching most of your data — cluster the vectors into regions, and at query time only look inside the few regions nearest the query. That's IVF, and its one tuning knob, how many regions to probe, is a clean, visible dial on the recall-versus-speed trade at the heart of the whole field.

The simplest way to beat brute force is to avoid searching most of your data — cluster the vectors into regions, and at query time only look inside the few nearest the query. That's IVF, and its one knob (nprobe) is a clean dial on the recall-versus-speed trade.

#Vector Search#RAG#AI Engineering#Data Structures#Vector Databases
Pratik Dhanave · ·6 min read

Distance Metrics and the Curse of Dimensionality

"Nearest" is meaningless until you define "distance," and the metric you choose — cosine, dot product, or Euclidean — must match how your embedding model was trained or your search is quietly wrong. And in high dimensions, distance itself behaves so strangely that the naive intuitions you'd bring from 2D geometry actively mislead you.

'Nearest' is meaningless until you define 'distance,' and the metric you choose must match how your embedding model was trained or your search is quietly wrong — and in high dimensions, distance itself behaves so strangely that 2D intuitions mislead you.

#Vector Search#RAG#AI Engineering#Data Structures#Embeddings#Vector Databases
Pratik Dhanave · ·6 min read

B-Trees vs LSM-Trees

Almost every database on earth stores its data in one of two structures: a B-tree that updates in place, or an LSM-tree that only ever appends. This one choice ripples through everything — read speed, write speed, space usage, and latency predictability — so knowing which your database uses tells you more about its behavior than almost anything else.

Almost every database stores data in one of two structures: a B-tree that updates in place, or an LSM-tree that only appends. This one choice ripples through read speed, write speed, space, and latency predictability.

#Databases#Backend Engineering#Data Structures#System Design
Pratik Dhanave · ·7 min read

Structs, Unions, and Data Structures

Structs let you bundle related data into a single named type — the closest C gets to an object. Combined with pointers and heap allocation, they're how you build every data structure C is famous for: linked lists, trees, hash tables. This post covers structs, their cousins unions and enums, the memory-layout details that bite (padding), and puts it all together to build a linked list from scratch.

Structs let you bundle related data into a single named type — the closest C gets to an object. Combined with pointers and heap allocation, they're how you build every data structure C is famous for: linked lists, trees, hash tables. This post covers structs, unions, enums, the padding that bites, and builds a linked list from scratch.

#C#Data Structures#Systems Programming#Computer Science#Programming Languages
Pratik Dhanave · ·13 min read

Structs, Embedding, and Tags

How Go builds aggregate types from value semantics up — why a struct is a copy, when it stops being comparable, what embedding actually promotes (and what it deliberately doesn't), and how a backtick string in a field definition ends up steering `encoding/json`.

How Go builds aggregate types from value semantics up — why a struct is a copy, when it stops being comparable, what embedding actually promotes (and what it deliberately doesn't), and how a backtick string...

#Go#Structs#Data Structures#Backend Engineering#Embeddings
Pratik Dhanave · ·13 min read

Maps

How Go's built-in hash table really behaves — reference semantics, the nil-write panic, comma-ok, randomized iteration, why `&m[k]` is illegal, and the presizing and concurrency rules that separate correct map code from the code that bites you at 2 a.m.

How Go's built-in hash table really behaves — reference semantics, the nil-write panic, comma-ok, randomized iteration, why `&m[k]` is illegal, and the presizing and concurrency rules that separate correct...

#Go#Maps#Data Structures#Backend Engineering#Concurrency
Pratik Dhanave · ·12 min read

Arrays and Slices

Why an array is a value and a slice is a view — the three-word header, how `append` really grows, the aliasing trap that silently corrupts data, and the small habits (three-index slices, `copy`, pre-sizing) that keep it from biting you.

Why an array is a value and a slice is a view — the three-word header, how `append` really grows, the aliasing trap that silently corrupts data, and the small habits (three-index slices, `copy`, pre-sizing)...

#Go#Slices#Data Structures#Backend Engineering

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.