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

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

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

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 fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Document store
Graph DBMS

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

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

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

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

github.com/boltdb/bolt

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

rocksdb.org

spark.apache.org/sql

surrealdb.com

Technical documentation

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

github.com/facebook/rocksdb/wiki

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

surrealdb.com/docs

Developer

Oracle, originally developed by Digital Equipment Corporation (DEC)

Facebook, Inc.

Apache Software Foundation

SurrealDB Ltd

Initial release

2013

1984

2013

2014

2022

Current release

7.4.1.1, 2021

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

BSD

Open Source

Apache 2.0

Open Source

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

Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

HP Open VMS

Linux

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

schema-free

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

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

C++ API
Java API

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

Triggers

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

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

yes, on a single node

yes

ACID

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

yes, based on authentication and database rules

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