DBMS > Alibaba Cloud TSDB vs. EJDB vs. GBase vs. Ignite vs. IRONdb

System Properties Comparison Alibaba Cloud TSDB vs. EJDB vs. GBase vs. Ignite vs. IRONdb

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A stable, reliable, and cost-effective online high-performance time series database service

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Primary database model

Time Series DBMS

Document store

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

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

Score	0.24
Rank	#306	Overall
	#26	Time Series DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Website

www.alibabacloud.com/product/hitsdb

github.com/Softmotions/ejdb

www.gbase.cn

ignite.apache.org

www.circonus.com/solutions/time-series-database/

Technical documentation

www.alibabacloud.com/help/en/time-series-database/latest/what-is-tsdb

github.com/Softmotions/ejdb/blob/master/README.md

apacheignite.readme.io/docs

docs.circonus.com/irondb/category/getting-started

Developer

Alibaba

Softmotions

General Data Technology Co., Ltd.

Apache Software Foundation

Circonus LLC.

Initial release

2012

2004

2015

2017

Current release

GBase 8a, GBase 8s, GBase 8c

Apache Ignite 2.6

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

Open Source

Apache 2.0

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

C, Java, Python

C++, Java, .Net

C and C++

Server operating systems

hosted

server-less

Linux

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

Numeric data and Strings

yes

string, integer, double, bool, date, object_id

yes

text, numeric, histograms

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

SQL

Support of SQL

Standard with numerous extensions

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

HTTP REST

in-process shared library

ADO.NET
C API
JDBC
ODBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API

Supported programming languages

Java

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes (compute grid and cache interceptors can be used instead)

yes, in Lua

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

yes (replicated cache)

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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 managed on TSDB instance level

yes

Security Hooks for custom implementations

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