DBMS > Apache IoTDB vs. Geode vs. Google Cloud Spanner vs. Riak TS

System Properties Comparison Apache IoTDB vs. Geode vs. Google Cloud Spanner vs. Riak TS

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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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

Primary database model

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	1.51
Rank	#147	Overall
	#25	Key-value stores

Score	2.69
Rank	#101	Overall
	#51	Relational DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

iotdb.apache.org

geode.apache.org

cloud.google.com/spanner

Technical documentation

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

geode.apache.org/docs

cloud.google.com/spanner/docs

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

Developer

Apache Software Foundation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Open Source, formerly Basho Technologies

Initial release

2018

2002

2017

2015

Current release

1.1.0, April 2023

1.1, February 2017

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like query language

SQL-like query language (OQL)

yes

Query statements complying to ANSI 2011

yes, limited

APIs and other access methods

JDBC
Native API

Java Client API
Memcached protocol
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

Go
Java
JavaScript (Node.js)
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

Server-side scripts

Stored procedures

yes

user defined functions

Erlang

Triggers

yes

Cache Event Listeners

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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

Multi-source replication with 3 replicas for regional instances.

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

links between datasets can be stored

Transaction concepts

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

yes, on a single node

ACID

Strict serializable isolation

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

yes

Access rights per client and object definable

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

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