DBMS > Apache IoTDB vs. GigaSpaces vs. Riak KV vs. Sadas Engine vs. Snowflake

System Properties Comparison Apache IoTDB vs. GigaSpaces vs. Riak KV vs. Sadas Engine vs. Snowflake

Editorial information provided by DB-Engines

Name

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

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Distributed, fault tolerant key-value store

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

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

docs.gigaspaces.com/latest/landing.html

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

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Gigaspaces Technologies

OpenSource, formerly Basho Technologies

SADAS s.r.l.

Snowflake Computing Inc.

Initial release

2018

2000

2009

2006

2014

Current release

1.1.0, April 2023

15.5, September 2020

3.2.0, December 2022

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version available

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

Java, C++, .Net

Erlang

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
macOS
Solaris
Windows

Linux
OS X

AIX
Linux
Windows

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL-like query language

SQL-99 for query and DML statements

yes

APIs and other access methods

JDBC
Native API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C++
Java
Python
Scala

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#
C++
Groovy
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Erlang

user defined functions

Triggers

yes

yes, event driven architecture

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

no "single point of failure"

horizontal partitioning

yes

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

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

selectable replication factor

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

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

managed by 'Learn by Usage'

User concepts

Access control

yes

Role-based access control

yes, using Riak Security

Access rights for users, groups and roles according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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