DBMS > EsgynDB vs. etcd vs. Graph Engine vs. Lovefield

System Properties Comparison EsgynDB vs. etcd vs. Graph Engine vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS

Key-value store

Graph DBMS
Key-value store

Relational DBMS

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

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	7.05
Rank	#53	Overall
	#5	Key-value stores

Score	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Website

www.esgyn.cn

etcd.io
github.com/etcd-io/etcd

www.graphengine.io

google.github.io/lovefield

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.graphengine.io/docs/manual

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Esgyn

Microsoft

Google

Initial release

2015

2010

2014

Current release

3.4, August 2019

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

.NET and C

JavaScript

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

.NET

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

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

via JavaScript builder pattern

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC
JSON over HTTP

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

C#
C++
F#
Visual Basic

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes, watching key changes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

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