DBMS > Drizzle vs. Hive vs. openGemini vs. XTDB

System Properties Comparison Drizzle vs. Hive vs. openGemini vs. XTDB

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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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

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

Primary database model

Relational DBMS

Time Series DBMS

Document store

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

hive.apache.org

www.opengemini.org
github.com/openGemini

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

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

docs.opengemini.org/guide

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Apache Software Foundation

initially developed by Facebook

Huawei and openGemini community

Juxt Ltd.

Initial release

2008

2012

2022

2019

Current release

7.2.4, September 2012

3.1.3, April 2022

1.1, July 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2

Open Source

Apache Version 2.0

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

Clojure

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

Linux
Windows

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

Integer, Float, Boolean, String

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 DML and DDL statements

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JDBC
ODBC
Thrift

HTTP REST

HTTP REST
JDBC

Supported programming languages

C
C++
Java
PHP

C++
Java
PHP
Python

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

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

hooks for callbacks inside the server can be used.

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

selectable replication factor

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Access rights for users, groups and roles

Administrators and common users accounts

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More resources

DB-Engines blog posts

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