DBMS > Cloudflare Workers KV vs. Firebolt vs. H2 vs. Redis vs. SQream DB

System Properties Comparison Cloudflare Workers KV vs. Firebolt vs. H2 vs. Redis vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

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

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Key-value store

Relational DBMS

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Relational DBMS

Secondary database models

Spatial DBMS

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

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

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	155.94
Rank	#6	Overall
	#1	Key-value stores

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

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

Technical documentation

developers.cloudflare.com/kv/api

docs.firebolt.io

www.h2database.com/html/main.html

docs.redis.com/latest/index.html
redis.io/docs

docs.sqream.com

Developer

Cloudflare

Firebolt Analytics Inc.

Thomas Mueller

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

SQream Technologies

Initial release

2018

2020

2005

2009

2017

Current release

2.2.220, July 2023

7.2.5, May 2024

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

commercial

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.

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

All OS with a Java VM

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes, ANSI Standard SQL Types

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

with RediSearch module

SQL

Support of SQL

yes

with RediSQL module

yes

APIs and other access methods

HTTP REST
Proprietary protocol

.Net
ODBC
RESTful HTTP API

JDBC
ODBC

proprietary protocol

RESP - REdis Serialization Protocol

.Net
JDBC
ODBC

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

Go
JavaScript (Node.js)
Python

Java

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Lua; Redis Functions coming in Redis 7 (slides and Github)

user defined functions in Python

Triggers

yes

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

none

MapReduce

Offers an API for user-defined Map/Reduce methods

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts and optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

fine grained access rights according to SQL-standard

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

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.