DBMS > Axibase vs. BigchainDB vs. Google Cloud Datastore vs. SiteWhere vs. TimesTen

System Properties Comparison Axibase vs. BigchainDB vs. Google Cloud Datastore vs. SiteWhere vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

M2M integration platform for persisting/querying time series data

In-Memory RDBMS compatible to Oracle

Primary database model

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

Score	0.32
Rank	#288	Overall
	#25	Time Series DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.bigchaindb.com

cloud.google.com/datastore

github.com/sitewhere/sitewhere

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

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/datastore/docs

sitewhere1.sitewhere.io/index.html

docs.oracle.com/database/timesten-18.1

Developer

Axibase Corporation

Google

SiteWhere

Oracle, TimesTen Performance Software, HP

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

Initial release

2013

2016

2008

2010

1998

Current release

15585

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

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

Open Source

AGPL v3

commercial

Open Source

Common Public Attribution License Version 1.0

commercial

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

Python

Java

Server operating systems

Linux

hosted

Linux
OS X
Windows

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

Data scheme

yes

schema-free

predefined scheme

yes

Typing

predefined data types such as float or date

yes

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

yes, details here

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

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

CLI Client
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP REST

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
PHP
Python
R
Ruby

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

using Google App Engine

PL/SQL

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Multi-source replication using Paxos

selectable replication factor

based on HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

yes

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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