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

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

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Editorial information provided by DB-Engines

Name

Description

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

DBMS for structured and unstructured content wrapped with an application server

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

Primary database model

Key-value store

Native XML DBMS
Search engine

Relational DBMS

Key-value store

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

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	0.01
Rank	#386	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#377	Overall
	#58	Key-value stores

Website

rocksdb.org

www.searchxml.net/category/products

spark.apache.org/sql

github.com/bytedance/terarkdb

Technical documentation

github.com/facebook/rocksdb/wiki

www.searchxml.net/support/handouts

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

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Facebook, Inc.

informationpartners gmbh

Apache Software Foundation

ByteDance, originally Terark

Initial release

2013

2015

2014

2016

Current release

8.11.4, April 2024

1.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

Apache 2.0

commercial

restricted open source version available

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++

Scala

C++

Server operating systems

Linux

Windows

Linux
OS X
Windows

Data scheme

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

C++ API
Java API

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC

C++ API
Java API

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

C++

most other programming languages supported via APIs

Java
Python
R
Scala

C++
Java

Server-side scripts

Stored procedures

yes

on the application server

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

sychronisation to multiple collections

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

Transaction concepts

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

yes

multiple readers, single writer

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

Domain, group and role-based access control at the document level and for application services

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