DBMS > Drizzle vs. Google Cloud Spanner vs. HarperDB vs. Oracle NoSQL

System Properties Comparison Drizzle vs. Google Cloud Spanner vs. HarperDB vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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.

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Relational DBMS

Document store

Document store
Key-value store
Relational DBMS

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

Score	2.69
Rank	#101	Overall
	#51	Relational DBMS

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Website

cloud.google.com/spanner

www.harperdb.io

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

cloud.google.com/spanner/docs

docs.harperdb.io/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Drizzle project, originally started by Brian Aker

Google

HarperDB

Oracle

Initial release

2008

2017

2011

Current release

7.2.4, September 2012

3.1, August 2021

24.1, May 2024

License

Commercial or Open Source

Open Source

GNU GPL

commercial

free community edition available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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++

Node.js

Java

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X

Linux
Solaris SPARC/x86

Data scheme

yes

dynamic schema

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

JSON data types

optional

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

yes

with proprietary extensions

yes

Query statements complying to ANSI 2011

SQL-like data manipulation statements

SQL-like DML and DDL statements

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
JDBC

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

Go
Java
JavaScript (Node.js)
Python

.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#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Triggers

hooks for callbacks inside the server can be used.

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication with 3 replicas for regional instances.

yes

the nodes on which a table resides can be defined

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

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

Transaction concepts

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

ACID

Strict serializable isolation

Atomic execution of specific operations

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Access rights for users and roles

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