DBMS > EsgynDB vs. Google BigQuery vs. PouchDB vs. Splice Machine vs. ToroDB

System Properties Comparison EsgynDB vs. Google BigQuery vs. PouchDB vs. Splice Machine vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Large scale data warehouse service with append-only tables

JavaScript DBMS with an API inspired by CouchDB

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/bigquery

pouchdb.com

splicemachine.com

github.com/torodb/server

Technical documentation

cloud.google.com/bigquery/docs

pouchdb.com/guides

splicemachine.com/how-it-works

Developer

Esgyn

Google

Apache Software Foundation

Splice Machine

8Kdata

Initial release

2015

2010

2012

2014

2016

Current release

7.1.1, June 2019

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

AGPL-V3

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++, Java

JavaScript

Java

Server operating systems

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

via views

yes

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

View functions in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

Access rights for users and roles

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