DBMS > Google Cloud Datastore vs. HEAVY.AI vs. KairosDB vs. MySQL

System Properties Comparison Google Cloud Datastore vs. HEAVY.AI vs. KairosDB vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed Time Series DBMS based on Cassandra or H2

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Website

cloud.google.com/datastore

github.com/heavyai/heavydb
www.heavy.ai

github.com/kairosdb/kairosdb

www.mysql.com

Technical documentation

cloud.google.com/datastore/docs

docs.heavy.ai

kairosdb.github.io

dev.mysql.com/doc

Developer

Google

HEAVY.AI, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2008

2016

2013

1995

Current release

5.10, January 2022

1.2.2, November 2018

9.0.0, July 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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++ and CUDA

Java

C and C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

with proprietary extensions

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

Java
JavaScript

Node.js
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

using Google App Engine

yes

proprietary syntax

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

based on Cassandra

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Multi-source replication

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

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.

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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

fine grained access rights according to SQL-standard

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

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