This article is attractively presented and seems to be well written, but I disagree with the framing:

> Databases were made to solve one problem:

> How do we store data persistently and then efficiently look it up later?

There are indeed things described as "databases" which are made to solve that problem, but more commonly such things are instead called "file formats" or, to old IBMers, "access methods".

As I see it, the much more interesting problem that databases solve, the one that usually distinguishes what we call "databases" from what we call "file formats", is query evaluation:

> How do we organize a large body of information such that we can easily answer questions efficiently from it?

Prolog, Datalog, QBE, QUEL, and SQL are approaches to expressing the questions, and indexing, materialized views, query planning, resolution, the WAM, and tabled resolution are approaches to answering them efficiently.

dbm is not a database. ISAM is not a database. But SQLite in :memory: is still a database.

I more or less built my own database in Erlang a few months ago. I say "more or less" because I did use Bitcask as the underlying store, and I used the riak_core libraries initially, but I did handle replication and different fault tolerance techniques on my own.

It was actually very fun; a key-value database is something that can be any level of difficulty that you want. If you want a simple KV "database", you could just serialize and deserialize a JSON string all the time, or write a protobuf, but there is of course no limit to the level of complexity.

I use the JSON example because that was actually how I started; I was constantly serializing and deserializing JSON with base64 binary encoded strings, just because it was easy and good enough, and over the course of building the project I ended up making a proper replicated database. I even had a very basic "query language" to handle some basic searches.

I love the design and examples in this post. Easy to read for sure.

Exercises like this also seem fun in general. It's a real test of how much you know to start anything from scratch.

I clicked through a couple of the articles in the OP, and I must say, the design and animations are extremely pretty!

Kudos for that!

I also recommend this free online book to build a database https://build-your-own.org/database/

“LSM trees are the underlying data structure used for [..] DynamoDB, and they have proven to perform really well at scale [..] 80 million requests per second!”

This is a tad bit misleading, as the LSM is used for the node-level storage engine, but doesn’t explain how the overall distributed system scales to 80 million rps.

iirc the original Dynamo paper used BerkeleyDB (b-tree or LSM), but the 2012 paper shifted to a fully LSM-based engine.

Part of the reason why I'm not a "maker" is because my mind gets ahead of me with all the things that I would need to do in order to do things properly. So the article starts out interesting and then gets more and more, well, not exactly stressful but I get a bit weary by it.

Not that I would aspire to implement a general-purpose database. But even smaller tasks can make my mind spin too much.

A very nice explanation with visual interactions of how database works internally. author is a great teacher.

I absolutely love this "first principles" approach of explaining a topic. You can really go through this and at each time understand what problem needs to be solved and what other problems this introduces, until you get at a reasonably satisfying solution.

if author is reading, can you add an rss feed to your site? i want to add to feedly.

One of our final projects during university was to design and program a basic database in C. Even after 20 years I think that was one of the most one I've had in a project.

>Problem. How do we store data persistently and then efficiently look it up later?"

I would say without transactions it is not a database yet from a practical standpoint.

If you want to store key values in a file, one fun and ridiculous technique is to use xattrs :)

I made a pointless program to help w/ this on macOS for kicks: https://github.com/radiofreejohn/xattrkv

When I made it I also found a bug in the xattr implementation for Darwin and submitted it to Apple, they eventually fixed it.

The first example in the "Sorting in Practice" section appears to be broken. The text makes it seem like the list should be sorted in-memory and then written to disk sorted, but the example un-sorts the list when it's written to disk.

Edit: the flush example (2nd one) in the recap section does the same thing, when the text says that the records are supposed to be written to the file in sorted order.

I have spending the last ~4 weeks writing a triple store!

I wish this came out earlier, there are a few insights in there that took me me a while to understand :)

Great read. I’ve been modeling developer activity as a time series key value system where each developer is a key and commits are values. Faced the same issues: logs grow fast, indexes get heavy, range queries slow down. How do you decide what to drop when compacting segments? Balancing freshness and retention is tricky.

Great post and beautiful website. I got a bit confused by the flush operation that happens when the memtable is full. A quick note that a new on-disk segment is created would help. In the recap at the end, segmentation is also not mentioned.

This gets fuzzy around the end - indexes are depicted as separate (partial) entities. Do we store all of those separated in different files? If so, do we need to open them all to search for a record?

Here's a simple key-value store inspired by D.B. Cooper:

  ~/bin/cooper-db-set
  ===================
  #! /bin/bash

  key="$1"
  value="$2"

  echo "${key}:${value}" >> /dev/null


  ~/bin/cooper-db-get
  ===================
  #! /bin/bash

  key="$1"
  </dev/null awk -F: -v key="$key" '$1 == key {result = $2} END {print result}'

It looks like it got hugged to death already.

thanks for share

am i the only one who IS a huge fan of this blogpost layout

Nice interactivity, but this is taken straight from the Designing Data-Intensive Applications. Literally all the content here is an interactive version of chapter 3.

Maybe give credit?

> Databases were made to solve one problem:

>

> "How do we store data persistently and then efficiently look it up later?"

Isn't that two problems?

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