DBMS > BigObject vs. Drizzle vs. Hazelcast vs. SwayDB vs. XTDB

System Properties Comparison BigObject vs. Drizzle vs. Hazelcast 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

Analytic DBMS for real-time computations and queries

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

A widely adopted in-memory data grid

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

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store

Document store

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

bigobject.io

hazelcast.com

swaydb.simer.au

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

Technical documentation

docs.bigobject.io

hazelcast.org/imdg/docs

www.xtdb.com/docs

Developer

BigObject, Inc.

Drizzle project, originally started by Brian Aker

Hazelcast

Simer Plaha

Juxt Ltd.

Initial release

2015

2008

2018

2019

Current release

7.2.4, September 2012

5.3.6, November 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU GPL

Open Source

Apache Version 2; commercial licenses available

Open Source

GNU Affero GPL V3.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Scala

Clojure

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

All OS with a Java VM

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, 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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

C
C++
Java
PHP

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

Java
Kotlin
Scala

Clojure
Java

Server-side scripts

Stored procedures

Lua

yes

Event Listeners, Executor Services

Triggers

hooks for callbacks inside the server can be used.

yes

Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

Replicated Map

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

Atomic execution of operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Role-based access control

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More resources

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