DBMS > Apache IoTDB vs. GBase vs. Hazelcast vs. OrigoDB vs. Sqrrl

System Properties Comparison Apache IoTDB vs. GBase vs. Hazelcast vs. OrigoDB vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

A widely adopted in-memory data grid

A fully ACID in-memory object graph database

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

Document store
Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

sqrrl.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

hazelcast.org/imdg/docs

origodb.com/docs

Developer

Apache Software Foundation

General Data Technology Co., Ltd.

Hazelcast

Robert Friberg et al

Amazon

originally Sqrrl Data, Inc.

Initial release

2018

2004

2008

2009

under the name LiveDB

2012

Current release

1.1.0, April 2023

GBase 8a, GBase 8s, GBase 8c

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, Java, Python

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Linux
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

yes

the object must implement a serialization strategy

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

SQL-like query language

APIs and other access methods

JDBC
Native API

ADO.NET
C API
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API
HTTP API
LINQ

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

user defined functions

yes

Event Listeners, Executor Services

yes

Triggers

yes

Events

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

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

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Replicated Map

Source-replica replication

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

yes

Role-based access control

Role based authorization

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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