DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. STSdb vs. VoltDB vs. YottaDB

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. STSdb vs. VoltDB vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

A fast and solid embedded Key-value store

Primary database model

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

cloud.google.com/datastore

www.hawkular.org

github.com/STSSoft/STSdb4

www.voltdb.com

yottadb.com

Technical documentation

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.voltdb.com

yottadb.com/resources/documentation

Developer

Google

Community supported by Red Hat

STS Soft SC

VoltDB Inc.

YottaDB, LLC

Initial release

2008

2014

2011

2010

2001

Current release

4.0.8, September 2015

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Open Source

AGPL 3.0

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

Java

Java, C++

Server operating systems

hosted

Linux
OS X
Windows

Windows

Linux
OS X

for development

Docker
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

only a subset of SQL 99

by using the Octo plugin

APIs and other access methods

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

HTTP REST

.NET Client API

Java API
JDBC
RESTful HTTP/JSON API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

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

Go
Java
Python
Ruby

C#
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

using Google App Engine

Java

Triggers

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

none

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Transactions are executed single-threaded within stored procedures

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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)

Users and roles with access to stored procedures

Users and groups based on OS-security mechanisms

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