DBMS > Databend vs. EsgynDB vs. Greenplum vs. RocksDB

System Properties Comparison Databend vs. EsgynDB vs. Greenplum vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

github.com/datafuselabs/databend
www.databend.com

www.esgyn.cn

greenplum.org

rocksdb.org

Technical documentation

docs.databend.com

docs.greenplum.org

github.com/facebook/rocksdb/wiki

Developer

Databend Labs

Esgyn

Pivotal Software Inc.

Facebook, Inc.

Initial release

2021

2015

2005

2013

Current release

1.0.59, April 2023

7.0.0, September 2023

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

BSD

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

Rust

C++, Java

C++

Server operating systems

hosted
Linux
macOS

Linux

Data scheme

yes

schema-free

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.

yes

since Version 4.2

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

All languages supporting JDBC/ODBC/ADO.Net

C
Java
Perl
Python
R

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

yes

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

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

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