DBMS > BigObject vs. Drizzle vs. GreptimeDB vs. Infobright

System Properties Comparison BigObject vs. Drizzle vs. GreptimeDB vs. Infobright

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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

Analytic DBMS for real-time computations and queries

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

An open source Time Series DBMS built for increased scalability, high performance and efficiency

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	0.09
Rank	#343	Overall
	#31	Time Series DBMS

Score	0.88
Rank	#198	Overall
	#92	Relational DBMS

Website

bigobject.io

greptime.com

ignitetech.com/softwarelibrary/infobrightdb

Technical documentation

docs.bigobject.io

docs.greptime.com

Developer

BigObject, Inc.

Drizzle project, originally started by Brian Aker

Greptime Inc.

Ignite Technologies Inc.; formerly InfoBright Inc.

Initial release

2015

2008

2022

2005

Current release

7.2.4, September 2012

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU GPL

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

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++

Rust

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

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux
Windows

Data scheme

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

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

Knowledge Grid Technology used instead

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC

gRPC
HTTP API
JDBC

ADO.NET
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

C++
Erlang
Go
Java
JavaScript

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Lua

Python

Triggers

hooks for callbacks inside the server can be used.

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

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

Simple rights management via user accounts

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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