DBMS > EJDB vs. Google BigQuery vs. RavenDB vs. SWC-DB vs. Transbase

System Properties Comparison EJDB vs. Google BigQuery vs. RavenDB vs. SWC-DB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Open Source Operational and Transactional Enterprise NoSQL Document Database

A high performance, scalable Wide Column DBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Time Series DBMS

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

Score	0.30
Rank	#294	Overall
	#44	Document stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	3.01
Rank	#101	Overall
	#17	Document stores

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

github.com/Softmotions/ejdb

cloud.google.com/bigquery

ravendb.net

github.com/kashirin-alex/swc-db
www.swcdb.org

www.transaction.de/en/products/transbase.html

Technical documentation

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

cloud.google.com/bigquery/docs

ravendb.net/docs

www.transaction.de/en/products/transbase/features.html

Developer

Softmotions

Google

Hibernating Rhinos

Alex Kashirin

Transaction Software GmbH

Initial release

2012

2010

2020

1987

Current release

5.4, July 2022

0.5, April 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

AGPL version 3, commercial license available

Open Source

GPL V3

commercial

free development 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++

C and C++

Server operating systems

server-less

hosted

Linux
macOS
Raspberry Pi
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL-like query language (RQL)

SQL-like query language

yes

APIs and other access methods

in-process shared library

RESTful HTTP/JSON API

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

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C++

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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.

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID, Cluster-wide transaction available

yes

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.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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