DBMS > Drizzle vs. Google Cloud Datastore vs. SwayDB vs. Tibero vs. XTDB

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. SwayDB vs. Tibero vs. XTDB

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.

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A secure RDBMS, designed for easy portability from Oracle

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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	#382	Overall
	#59	Key-value stores

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

cloud.google.com/datastore

swaydb.simer.au

us.tmaxsoft.com/products/tibero

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/datastore/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Google

Simer Plaha

TmaxSoft

Juxt Ltd.

Initial release

2008

2018

2003

2019

Current release

7.2.4, September 2012

6, April 2015

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

GNU Affero GPL V3.0

commercial

Open Source

MIT License

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++

Scala

C and Assembler

Clojure

Server operating systems

FreeBSD
Linux
OS X

hosted

AIX
HP-UX
Linux
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

yes, extensible-data-notation format

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

yes

with proprietary extensions

SQL-like query language (GQL)

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

HTTP REST
JDBC

Supported programming languages

C
C++
Java
PHP

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

Java
Kotlin
Scala

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Clojure
Java

Server-side scripts

Stored procedures

using Google App Engine

Persistent Stored Procedure (PSM)

Triggers

hooks for callbacks inside the server can be used.

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

by range, hash, list or composite

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

none

Multi-source replication
Source-replica replication

yes, each node contains all data

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 operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

planned for next version

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)

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

More information provided by the system vendor

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Related products and services

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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