DBMS > Drizzle vs. Firebase Realtime Database vs. Google Cloud Datastore vs. SwayDB vs. XTDB

System Properties Comparison Drizzle vs. Firebase Realtime Database vs. Google Cloud Datastore vs. SwayDB 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.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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 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	14.29
Rank	#39	Overall
	#6	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

firebase.google.com/products/realtime-database

cloud.google.com/datastore

swaydb.simer.au

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

Technical documentation

firebase.google.com/docs/database

cloud.google.com/datastore/docs

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Google

acquired by Google 2014

Google

Simer Plaha

Juxt Ltd.

Initial release

2008

2012

2008

2018

2019

Current release

7.2.4, September 2012

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

GNU Affero GPL V3.0

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

Clojure

Server operating systems

FreeBSD
Linux
OS X

hosted

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

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (GQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

Android
iOS
JavaScript API
RESTful HTTP API

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

HTTP REST
JDBC

Supported programming languages

C
C++
Java
PHP

Java
JavaScript
Objective-C

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

Java
Kotlin
Scala

Clojure
Java

Server-side scripts

Stored procedures

limited functionality with using 'rules'

using Google App Engine

Triggers

hooks for callbacks inside the server can be used.

Callbacks are triggered when data changes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

none

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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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

Transaction concepts

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

ACID

yes

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

yes, based on authentication and database rules

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

More information provided by the system vendor

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More resources

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