DBMS > Derby vs. Drizzle vs. FatDB vs. GreptimeDB vs. TimescaleDB

System Properties Comparison Derby vs. Drizzle vs. FatDB vs. GreptimeDB vs. TimescaleDB

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.

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	0.12
Rank	#351	Overall
	#34	Time Series DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

db.apache.org/derby

greptime.com

www.timescale.com

Technical documentation

db.apache.org/derby/manuals/index.html

docs.greptime.com

docs.timescale.com

Developer

Apache Software Foundation

Drizzle project, originally started by Brian Aker

FatCloud

Greptime Inc.

Timescale

Initial release

1997

2008

2012

2022

2017

Current release

10.17.1.0, November 2023

7.2.4, September 2012

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

GNU GPL

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Java

C++

Rust

Server operating systems

All OS with a Java VM

FreeBSD
Linux
OS X

Windows

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Via inetgration in SQL Server

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC
HTTP API
JDBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Java

C
C++
Java
PHP

C++
Erlang
Go
Java
JavaScript

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

yes

via applications

Python

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

hooks for callbacks inside the server can be used.

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate 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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Can implement custom security layer via applications

Simple rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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