DBMS > Amazon DynamoDB vs. Drizzle vs. Faircom DB vs. HugeGraph

System Properties Comparison Amazon DynamoDB vs. Drizzle vs. Faircom DB vs. HugeGraph

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A fast-speed and highly-scalable Graph DBMS

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Website

aws.amazon.com/dynamodb

www.faircom.com/products/faircom-db

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

hugegraph.apache.org/docs

Developer

Amazon

Drizzle project, originally started by Brian Aker

FairCom Corporation

Baidu

Initial release

2012

2008

1979

2018

Current release

7.2.4, September 2012

V12, November 2020

0.9

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

GNU GPL

commercial

Restricted, free version available

Open Source

Apache Version 2.0

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++

ANSI C, C++

Java

Server operating systems

hosted

FreeBSD
Linux
OS X

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
macOS
Unix

Data scheme

schema-free

yes

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes

with proprietary extensions

yes, ANSI SQL with proprietary extensions

APIs and other access methods

RESTful HTTP API

JDBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C++
Java
PHP

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Groovy
Java
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

asynchronous Gremlin script jobs

Triggers

yes

by integration with AWS Lambda

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Fine grained access rights according to SQL-standard with additional protections for files

Users, roles and permissions

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