DBMS > BoltDB vs. Dolt vs. Oracle Rdb vs. RocksDB vs. Spark SQL

System Properties Comparison BoltDB vs. Dolt vs. Oracle Rdb vs. RocksDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

A MySQL compatible DBMS with Git-like versioning of data and schema

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

Secondary database models

Document store

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/boltdb/bolt

github.com/dolthub/dolt
www.dolthub.com

www.oracle.com/database/technologies/related/rdb.html

rocksdb.org

spark.apache.org/sql

Technical documentation

docs.dolthub.com

www.oracle.com/database/technologies/related/rdb-doc.html

github.com/facebook/rocksdb/wiki

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

Developer

DoltHub Inc

Oracle, originally developed by Digital Equipment Corporation (DEC)

Facebook, Inc.

Apache Software Foundation

Initial release

2013

2018

1984

2013

2014

Current release

7.4.1.1, 2021

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

commercial

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

HP Open VMS

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

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

yes

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
HTTP REST

C++ API
Java API

JDBC
ODBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

currently in alpha release

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

yes, on a single node

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

Only one user is configurable, and must be specified in the config file at startup

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