DBMS > Drizzle vs. Google Cloud Bigtable vs. H2GIS vs. HarperDB vs. Hyprcubd

System Properties Comparison Drizzle vs. Google Cloud Bigtable vs. H2GIS vs. HarperDB vs. Hyprcubd

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.

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Spatial extension of H2

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.

Serverless Time Series DBMS

Primary database model

Relational DBMS

Key-value store
Wide column store

Spatial DBMS

Document store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Website

cloud.google.com/bigtable

www.h2gis.org

www.harperdb.io

hyprcubd.com (offline)

Technical documentation

cloud.google.com/bigtable/docs

www.h2gis.org/docs/home

docs.harperdb.io/docs

Developer

Drizzle project, originally started by Brian Aker

Google

CNRS

HarperDB

Hyprcubd, Inc.

Initial release

2008

2015

2013

2017

Current release

7.2.4, September 2012

3.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

LGPL 3.0

commercial

free community edition available

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++

Java

Node.js

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X

hosted

Data scheme

yes

schema-free

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

time, int, uint, float, string

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

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

gRPC (https)

Supported programming languages

C
C++
Java
PHP

C#
C++
Go
Java
JavaScript (Node.js)
Python

Java

.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

Server-side scripts

Stored procedures

yes

based on H2

Custom Functions

since release 3.1

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

based on H2

yes

the nodes on which a table resides can be defined

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

yes

based on H2

Access rights for users and roles

token access

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More resources

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