DBMS > Oracle Berkeley DB vs. ScyllaDB vs. SpaceTime vs. Spark SQL vs. SWC-DB

System Properties Comparison Oracle Berkeley DB vs. ScyllaDB vs. SpaceTime vs. Spark SQL vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

Cassandra and DynamoDB compatible wide column store

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Key-value store

Relational DBMS

Time Series DBMS

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

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

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

www.scylladb.com

www.mireo.com/spacetime

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.oracle.com/cd/E17076_05/html/index.html

docs.scylladb.com

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

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

ScyllaDB

Mireo

Apache Software Foundation

Alex Kashirin

Initial release

1994

2015

2020

2014

2020

Current release

18.1.40, May 2020

ScyllaDB Open Source 5.4.1, January 2024

3.5.0 ( 2.13), September 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

commercial license available

Open Source

Open Source (AGPL), commercial license available

commercial

Open Source

Apache 2.0

Open Source

GPL V3

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.

Scylla Cloud: Create real-time applications that run at global scale with Scylla Cloud, the industry’s most powerful NoSQL DBaaS

Implementation language

C, Java, C++ (depending on the Berkeley DB edition)

C++

Scala

C++

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Linux
OS X
Windows

Linux

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

only with the Berkeley DB XML edition

Secondary indexes

yes

cluster global secondary indices

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements (CQL)

A subset of ANSI SQL is implemented

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

RESTful HTTP API

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

C#
C++
Python

Java
Python
R
Scala

C++

Server-side scripts

Stored procedures

yes, Lua

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Fixed-grid hypercubes

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

Real-time block device replication (DRBD)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

in-memory tables

User concepts

Access control

Access rights for users can be defined per object

yes

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more

Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
» more

Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
» more

Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more

Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more

Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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