DBMS > Google Cloud Spanner vs. KeyDB vs. Newts vs. Riak KV vs. SiteWhere

System Properties Comparison Google Cloud Spanner vs. KeyDB vs. Newts vs. Riak KV vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

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.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Time Series DBMS based on Cassandra

Distributed, fault tolerant key-value store

M2M integration platform for persisting/querying time series data

Primary database model

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

Time Series DBMS

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

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

cloud.google.com/spanner

github.com/Snapchat/KeyDB
keydb.dev

opennms.github.io/newts

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/spanner/docs

docs.keydb.dev

github.com/OpenNMS/newts/wiki

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

sitewhere1.sitewhere.io/index.html

Developer

Google

EQ Alpha Technology Ltd.

OpenNMS Group

OpenSource, formerly Basho Technologies

SiteWhere

Initial release

2017

2019

2014

2009

2010

Current release

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

BSD-3

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

Common Public Attribution License Version 1.0

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

Erlang

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
OS X

Linux
OS X
Windows

Data scheme

yes

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

restricted

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

APIs and other access methods

gRPC (using protocol buffers) API
JDBC

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

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP REST
Java API

HTTP API
Native Erlang Interface

HTTP REST

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Java

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

Lua

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

no "single point of failure"

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

links between data sets can be stored

Transaction concepts

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

ACID

Strict serializable isolation

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

yes, using Riak Security

Users with fine-grained authorization concept

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More resources

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