DBMS > FoundationDB vs. GBase vs. Google BigQuery vs. InfluxDB vs. Oracle Berkeley DB

System Properties Comparison FoundationDB vs. GBase vs. Google BigQuery vs. InfluxDB vs. Oracle Berkeley DB

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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.

Description

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Large scale data warehouse service with append-only tables

DBMS for storing time series, events and metrics

Widely used in-process key-value store

Primary database model

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

with GEO package

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

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

github.com/apple/foundationdb

www.gbase.cn

cloud.google.com/bigquery

www.influxdata.com/products/influxdb-overview

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

apple.github.io/foundationdb

cloud.google.com/bigquery/docs

docs.influxdata.com/influxdb

docs.oracle.com/cd/E17076_05/html/index.html

Developer

FoundationDB

General Data Technology Co., Ltd.

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2013

2004

2010

2013

1994

Current release

6.2.28, November 2020

GBase 8a, GBase 8s, GBase 8c

2.7.6, April 2024

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

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++

C, Java, Python

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux
OS X

through Homebrew

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

some layers support schemas

yes

schema-free

Typing

predefined data types such as float or date

some layers support typing

yes

Numeric data and Strings

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

Standard with numerous extensions

yes

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

RESTful HTTP/JSON API

HTTP API
JSON over UDP

Supported programming languages

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

in SQL-layer only

user defined functions

in JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

none

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

in enterprise version only

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

in SQL-layer only

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

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

Depending on used storage engine

yes

User concepts

Access control

yes

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

simple rights management via user accounts

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