DBMS > Firebird vs. FoundationDB vs. Google Cloud Datastore vs. SiriDB vs. Sqrrl

System Properties Comparison Firebird vs. FoundationDB vs. Google Cloud Datastore vs. SiriDB vs. Sqrrl

Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

Ordered key-value store. Core features are complimented by layers.

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

Open Source Time Series DBMS

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Document store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	1.03
Rank	#190	Overall
	#31	Document stores
	#28	Key-value stores
	#89	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.firebirdsql.org

github.com/apple/foundationdb

cloud.google.com/datastore

siridb.com

sqrrl.com

Technical documentation

www.firebirdsql.org/en/reference-manuals

apple.github.io/foundationdb

cloud.google.com/datastore/docs

docs.siridb.com

Developer

Firebird Foundation

FoundationDB

Google

Cesbit

Amazon

originally Sqrrl Data, Inc.

Initial release

2000

As fork of Borland's InterBase

2013

2008

2017

2012

Current release

5.0.0, January 2024

6.2.28, November 2020

License

Commercial or Open Source

Open Source

Initial Developer's Public License

Open Source

Apache 2.0

commercial

Open Source

MIT License

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.

Implementation language

C and C++

C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux
OS X
Windows

hosted

Linux

Data scheme

yes

schema-free

some layers support schemas

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

some layers support typing

yes, details here

yes

Numeric data

yes

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

yes

supported in specific SQL layer only

SQL-like query language (GQL)

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

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

HTTP API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

PSQL

in SQL-layer only

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication using Paxos

yes

selectable replication factor

making use of Hadoop

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

Linearizable consistency

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.

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

in SQL-layer only

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

Atomic updates per row, document, or graph entity

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

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 rights management via user accounts

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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