DBMS > RocksDB vs. Spark SQL vs. TerarkDB vs. TerminusDB

System Properties Comparison RocksDB vs. Spark SQL vs. TerarkDB vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store
RDF store

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

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

rocksdb.org

spark.apache.org/sql

github.com/bytedance/terarkdb

terminusdb.com

Technical documentation

github.com/facebook/rocksdb/wiki

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

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

terminusdb.github.io/terminusdb/#

Developer

Facebook, Inc.

Apache Software Foundation

ByteDance, originally Terark

DataChemist Ltd.

Initial release

2013

2014

2016

2018

Current release

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

BSD

Open Source

Apache 2.0

commercial

restricted open source version available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Scala

C++

Prolog, Rust

Server operating systems

Linux

Linux
OS X
Windows

Linux

Data scheme

schema-free

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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (WOQL)

APIs and other access methods

C++ API
Java API

JDBC
ODBC

C++ API
Java API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

C++
Java

JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

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