DBMS > Firebird vs. Google Cloud Datastore vs. KeyDB vs. RavenDB

System Properties Comparison Firebird vs. Google Cloud Datastore vs. KeyDB vs. RavenDB

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Editorial information provided by DB-Engines

Name

Description

Firebird is an open source RDBMS forked from Borland's InterBase

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

www.firebirdsql.org

cloud.google.com/datastore

github.com/Snapchat/KeyDB
keydb.dev

ravendb.net

Technical documentation

www.firebirdsql.org/en/reference-manuals

cloud.google.com/datastore/docs

docs.keydb.dev

ravendb.net/docs

Developer

Firebird Foundation

Google

EQ Alpha Technology Ltd.

Hibernating Rhinos

Initial release

2000

As fork of Borland's InterBase

2008

2019

2010

Current release

5.0.0, January 2024

5.4, July 2022

License

Commercial or Open Source

Open Source

Initial Developer's Public License

commercial

Open Source

BSD-3

Open Source

AGPL version 3, commercial license available

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

C and C++

C++

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

Linux

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

partial

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

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

SQL-like query language (RQL)

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

PSQL

using Google App Engine

Lua

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency
Strong eventual consistency with CRDTs

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Optimistic locking, atomic execution of commands blocks and scripts

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

Users with fine-grained authorization concept

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control and ACL

Authorization levels configured per client per database

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