DBMS > Dolt vs. etcd vs. Hypertable vs. Microsoft Azure Cosmos DB vs. TinkerGraph

System Properties Comparison Dolt vs. etcd vs. Hypertable vs. Microsoft Azure Cosmos DB vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

A distributed reliable key-value store

An open source BigTable implementation based on distributed file systems such as Hadoop

Globally distributed, horizontally scalable, multi-model database service

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Relational DBMS

Key-value store

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

github.com/dolthub/dolt
www.dolthub.com

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

azure.microsoft.com/services/cosmos-db

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

docs.dolthub.com

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

learn.microsoft.com/azure/cosmos-db

Developer

DoltHub Inc

Hypertable Inc.

Microsoft

Initial release

2018

2009

2014

2009

Current release

3.4, August 2019

0.9.8.11, March 2016

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux
macOS
Windows

FreeBSD
Linux
Windows

experimental

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST

gRPC
JSON over HTTP

C++ API
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

TinkerPop 3

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

C++
Java
Perl
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Groovy
Java

Server-side scripts

Stored procedures

yes

currently in alpha release

JavaScript

Triggers

yes

yes, watching key changes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

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

selectable replication factor on file system level

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

In-memory capabilities

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

yes

User concepts

Access control

Only one user is configurable, and must be specified in the config file at startup

Access rights can be defined down to the item level

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