DBMS > Google Cloud Datastore vs. HarperDB vs. MonetDB vs. RocksDB

System Properties Comparison Google Cloud Datastore vs. HarperDB vs. MonetDB vs. RocksDB

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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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

A relational database management system that stores data in columns

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

cloud.google.com/datastore

www.harperdb.io

www.monetdb.org

rocksdb.org

Technical documentation

cloud.google.com/datastore/docs

docs.harperdb.io/docs

www.monetdb.org/Documentation

github.com/facebook/rocksdb/wiki

Developer

Google

HarperDB

MonetDB BV

Facebook, Inc.

Initial release

2008

2017

2004

2013

Current release

3.1, August 2021

Dec2023 (11.49), December 2023

9.2.1, May 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Mozilla Public License 2.0

Open Source

BSD

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

Node.js

C++

Server operating systems

hosted

Linux
OS X

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

JSON data types

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)

SQL-like data manipulation statements

yes

SQL 2003 with some extensions

APIs and other access methods

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

C++ API
Java API

Supported programming languages

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

Custom Functions

since release 3.1

yes, in SQL, C, R

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding via remote tables

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

the nodes on which a table resides can be defined

none

Source-replica replication available in experimental status

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.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Atomic execution of specific operations

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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)

Access rights for users and roles

fine grained access rights according to SQL-standard

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