DBMS > atoti vs. etcd vs. FatDB vs. HugeGraph

System Properties Comparison atoti vs. etcd vs. FatDB vs. HugeGraph

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A distributed reliable key-value store

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A fast-speed and highly-scalable Graph DBMS

Primary database model

Object oriented DBMS

Key-value store

Document store
Key-value store

Graph DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

atoti.io

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

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.atoti.io

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

hugegraph.apache.org/docs

Developer

ActiveViam

FatCloud

Baidu

Initial release

2012

2018

Current release

3.4, August 2019

0.9

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 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

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

Windows

Linux
macOS
Unix

Data scheme

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

also supports composite index and range index

SQL

Support of SQL

Multidimensional Expressions (MDX)

Via inetgration in SQL Server

APIs and other access methods

gRPC
JSON over HTTP

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

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

Groovy
Java
Python

Server-side scripts

Stored procedures

Python

yes

via applications

asynchronous Gremlin script jobs

Triggers

yes, watching key changes

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Can implement custom security layer via applications

Users, roles and permissions

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