Crux as General-Purpose Database

Crux is an innovative database in the Clojure world. What follows is an experience report.

Crux is open source, bitemporal, highly scalable and built on solid foundations with Kafka and RocksDB as default storage. It is very flexible and provides low-level building blocks to allow you to build exactly the database you need for your use-case.

I believe that having time as first class concept in your database should be the default. It is much easier to have the concept of time built-in from the beginning and only build specialized solutions for the parts where performance becomes a problem. Nowadays computers are powerful enough that in most cases performance is not the issue and the focus should be on simplicity and correctness first.

By coincidence I started a toy project, letsdo.events, to write more Clojure in the spring of this year. Just after I started, Crux announced its public beta. Naturally I had to give it a try.

Since letsdo.events is mostly a toy project to play with different ideas. I took this opportunity to test Crux for a normal, standard application. The goal was to see if Crux hides the overhead of bitemporal storage well enough to make it viable as a default approach even when it is not an initial requirement of the application.

To start out I chose to only persist using RocksDB which allows to pack the whole system in a single .jar file and deploy it as a single process. Once the storage needs to scale, it would be trivial to switch to Kafka as storage backend. Since all the data is simply a log of transactions and documents, replaying them into a new storage would be straight forward.

Because of the flexibility Crux gives you, it does not necessarily have all the tools you would expect from a database built-in. Surely there will be more tools and patterns in the ecosystem of Crux once it has been around for a bit longer. But for now, here are a few things I built in the application’s database layer:

Single Transactor

Update: By now Crux officially supports transaction functions which allow you to achieve the same things you would use a single transactor for, but in a distributed and potentially more performant way.

Enforcing constraints such as uniqueness is built into many databases. The simplest way to achieve this outside the database is to only allow a single write at a time. Reads can still happen in parallel.

I created a tx helper which is used for all writes to the database:

( let [ ctx ( init { :config config })] ( tx ctx ( if ( exists-by-attribute :user/email mail ) :email-taken ( create! { :user/email mail } ctx ))) ( close ctx ))

:id key

Crux expects the ID it uses internally under the namespaced key :crux.db/id . This is a good pattern to not conflict with any domain-specific data. However in my business logic I always ended up wanting to use this same ID without coupling the business logic to Crux.

So in letsdo.events all database functions convert the ID to :id transparently using these helpers:

( defn- crux->id [ x ] ( rename-keys x { :crux.db/id :id })) ( defn- id->crux [ x ] ( rename-keys x { :id :crux.db/id }))

Working with document time

It is totally fine in Crux to have normal :created and :updated as part of your documents. And in most cases that is probably a good idea for performance. But you don’t need to have these timestamps since Crux also tracks time in its history.

It might not always be a good idea to do this but I created a helper which takes a list of document IDs and returns the documents with additional :created and :updated keys:

( defn list-by-ids-with-timestamps [{ :keys [ ::crux ]} ids ] ( let [ db ( crux/db crux )] ( ->> ids ( map # ( let [ h ( crux/history crux % )] ( assoc ( crux->id ( crux/entity db % )) :created ( -> h last :crux.db/valid-time ) :updated ( -> h first :crux.db/valid-time )))))))

Common database operations

The primitives Crux provides for transactions and the datalog language for querying are simple yet powerful. Still, for an application to interact with the database I prefer to hide the details of the how the database works behind plain functions.

The lde.core.db namespace contains helpers for operations such as create, update, delete, list, get by id, get by attribute, count, exists.

However, the database layer still allows to do plain datalog queries which is very useful for join-like queries likes this one:

( defn get-organizer-names-by-event-id [ ctx event-id ] ( ->> ( db/q ctx { :find [ '?name ] :where ' [[ o :organizer/event e ] [ o :organizer/user u ] [ u :user/name ?name ]] :args [{ 'e event-id }]}) ( map first ) not-empty ))

Global queries

Another great development feature of Crux has been the ability to easily query across all your data.

In relational databases this would involve writing out all tables and joining them together in a gigantic query, but with Crux and datalog there is no strict separation between data types.

You can easily query attributes across all documents which makes it very simple in development to understand the data in your system:

; All data in db ( ->> ( crux/q ( crux/db ( :lde.core.db/crux @ ctx )) { :find ' [ id ] :where ' [[ id :crux.db/id _ ]]}) ( map first ) ( map # ( crux/entity ( crux/db ( :lde.core.db/crux @ ctx )) % )))

This is very handy for all sorts of global statistics:

; All attributes in db ( ->> ( crux/q ( crux/db ( :lde.core.db/crux @ ctx )) { :find ' [ id ] :where ' [[ id :crux.db/id _ ]]}) ( map first ) ( map # ( crux/entity ( crux/db ( :lde.core.db/crux @ ctx )) % )) ( mapcat keys ) set sort )

Wrap up

It has been fun to work with Crux for this project.

Getting started with a new application with a schemaless database also made things easier. I can imagine that for mature projects it would be helpful to enforce more constraints on write in the database or via clojure.spec.

So far I haven’t tested out Crux at scale but the existing examples look very promising.

The pluggable architecture is one of biggest strong points of Crux and makes it very flexible to switch to a different storage backend or index implementation.

The biggest challenge will be to integrate with the world people are familiar with since Clojure, EDN and datalog are still far from mainstream at this point. But with the HTTP API, integrations such as Kafka Connect, potential plans for an SQL-like frontend and detailed documentation, the future looks bright.

I do believe having time built-in from the start is a huge advantage for any new project and will become the norm in the future.