DBMS > openGauss vs. RocksDB vs. Spark SQL

System Properties Comparison openGauss vs. RocksDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

gitee.com/opengauss
opengauss.org

rocksdb.org

spark.apache.org/sql

Technical documentation

docs.opengauss.org/en
gitee.com/opengauss/docs

github.com/facebook/rocksdb/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Huawei and openGauss community

Facebook, Inc.

Apache Software Foundation

Initial release

2019

2013

2014

Current release

3.0, March 2022

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

C++

Scala

Server operating systems

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

SQL

Support of SQL

ANSI SQL 2011

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

C
C++
Java

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash)

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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

Access rights for users, groups and roles according to SQL-standard

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