DBMS > Google Cloud Datastore vs. Kinetica vs. NSDb vs. Riak TS vs. TimesTen

System Properties Comparison Google Cloud Datastore vs. Kinetica vs. NSDb vs. Riak TS vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Fully vectorized database across both GPUs and CPUs

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

cloud.google.com/datastore

www.kinetica.com

nsdb.io

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/datastore/docs

docs.kinetica.com

nsdb.io/Architecture

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

docs.oracle.com/database/timesten-18.1

Developer

Google

Kinetica

Open Source, formerly Basho Technologies

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2008

2012

2017

2015

1998

Current release

7.1, August 2021

3.0.0, September 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

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, C++

Java, Scala

Erlang

Server operating systems

hosted

Linux

Linux
macOS

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

restricted

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like DML and DDL statements

SQL-like query language

yes, limited

yes

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Java
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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Erlang

PL/SQL

Triggers

Callbacks using the Google Apps Engine

yes

triggers when inserted values for one or more columns fall within a specified range

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

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 or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

links between datasets can be stored

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

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

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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