DBMS > Amazon DynamoDB vs. Axibase vs. BigchainDB vs. HugeGraph vs. TimesTen

System Properties Comparison Amazon DynamoDB vs. Axibase vs. BigchainDB vs. HugeGraph vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

A fast-speed and highly-scalable Graph DBMS

In-Memory RDBMS compatible to Oracle

Primary database model

Document store
Key-value store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

aws.amazon.com/dynamodb

axibase.com/docs/atsd/finance

www.bigchaindb.com

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.aws.amazon.com/dynamodb

bigchaindb.readthedocs.io/en/latest

hugegraph.apache.org/docs

docs.oracle.com/database/timesten-18.1

Developer

Amazon

Axibase Corporation

Baidu

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

2013

2016

2018

1998

Current release

15585

0.9

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

AGPL v3

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Python

Java

Server operating systems

hosted

Linux

Linux
macOS
Unix

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

CLI Client
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Go
Java
PHP
Python
R
Ruby

Go
Haskell
Java
JavaScript
Python
Ruby

Groovy
Java
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

PL/SQL

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

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 or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

by means of logfiles and checkpoints

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)

yes

Users, roles and permissions

fine grained access rights according to SQL-standard

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