DBMS > Amazon SimpleDB vs. Axibase vs. HugeGraph vs. Riak TS vs. TimescaleDB

System Properties Comparison Amazon SimpleDB vs. Axibase vs. HugeGraph vs. Riak TS vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

A fast-speed and highly-scalable Graph DBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

aws.amazon.com/simpledb

axibase.com/docs/atsd/finance

github.com/hugegraph
hugegraph.apache.org

www.timescale.com

Technical documentation

docs.aws.amazon.com/simpledb

hugegraph.apache.org/docs

www.tiot.jp/riak-docs/riak/ts/latest

docs.timescale.com

Developer

Amazon

Axibase Corporation

Baidu

Open Source, formerly Basho Technologies

Timescale

Initial release

2007

2013

2018

2015

2017

Current release

15585

0.9

3.0.0, September 2022

2.15.0, May 2024

License

Commercial or Open Source

commercial

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

Open Source

Apache Version 2.0

Open Source

Apache 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

Java

Erlang

Server operating systems

hosted

Linux

Linux
macOS
Unix

Linux
OS X

Linux
OS X
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

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

All columns are indexed automatically

yes

also supports composite index and range index

restricted

yes

SQL

Support of SQL

SQL-like query language

yes, limited

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API
Native Erlang Interface

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Go
Java
PHP
Python
R
Ruby

Groovy
Java
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Erlang

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

yes

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

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

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

selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

links between datasets can be stored

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Concurrency

Support for concurrent manipulation of data

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

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

Users, roles and permissions

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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