DBMS > Apache Druid vs. Cloudflare Workers KV vs. EventStoreDB vs. Firebolt vs. SQLite

System Properties Comparison Apache Druid vs. Cloudflare Workers KV vs. EventStoreDB vs. Firebolt vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS
Time Series DBMS

Key-value store

Event Store

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

druid.apache.org

www.cloudflare.com/developer-platform/workers-kv

www.eventstore.com

www.firebolt.io

www.sqlite.org

Technical documentation

druid.apache.org/docs/latest/design

developers.cloudflare.com/kv/api

developers.eventstore.com

docs.firebolt.io

www.sqlite.org/docs.html

Developer

Apache Software Foundation and contributors

Cloudflare

Event Store Limited

Firebolt Analytics Inc.

Dwayne Richard Hipp

Initial release

2012

2018

2012

2020

2000

Current release

29.0.1, April 2024

21.2, February 2021

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

Linux
OS X
Unix

hosted

Linux
Windows

hosted

server-less

Data scheme

yes

schema-less columns are supported

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP REST
Proprietary protocol

.Net
ODBC
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

Go
JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.