DBMS > EJDB vs. FoundationDB vs. Google BigQuery vs. Postgres-XL vs. SiriDB

System Properties Comparison EJDB vs. FoundationDB vs. Google BigQuery vs. Postgres-XL vs. SiriDB

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Large scale data warehouse service with append-only tables

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open Source Time Series DBMS

Primary database model

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

github.com/Softmotions/ejdb

github.com/apple/foundationdb

cloud.google.com/bigquery

www.postgres-xl.org

siridb.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

apple.github.io/foundationdb

cloud.google.com/bigquery/docs

www.postgres-xl.org/documentation

docs.siridb.com

Developer

Softmotions

FoundationDB

Google

Cesbit

Initial release

2012

2013

2010

2014

since 2012, originally named StormDB

2017

Current release

6.2.28, November 2020

10 R1, October 2018

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

commercial

Open Source

Mozilla public license

Open Source

MIT License

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++

Server operating systems

server-less

Linux
OS X
Windows

hosted

Linux
macOS

Linux

Data scheme

schema-free

some layers support schemas

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

some layers support typing

yes

Numeric data

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

yes

distributed, parallel query execution

APIs and other access methods

in-process shared library

RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Server-side scripts

Stored procedures

in SQL-layer only

user defined functions

in JavaScript

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

typically not needed, however similar functionality with collection joins possible

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

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

simple rights management via user accounts

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