DBMS > Ingres vs. Oracle Berkeley DB vs. ScyllaDB vs. STSdb

System Properties Comparison Ingres vs. Oracle Berkeley DB vs. ScyllaDB vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Well established RDBMS

Widely used in-process key-value store

Cassandra and DynamoDB compatible wide column store

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Wide column store

Key-value store

Secondary database models

Key-value store

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

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	4.15
Rank	#69	Overall
	#5	Wide column stores

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

www.actian.com/databases/ingres

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

www.scylladb.com

github.com/STSSoft/STSdb4

Technical documentation

docs.actian.com/ingres

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

docs.scylladb.com

Developer

Actian Corporation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

ScyllaDB

STS Soft SC

Initial release

1974

originally developed at University Berkely in early 1970s

1994

2015

2011

Current release

12.0, July 2024

18.1.40, May 2020

ScyllaDB Open Source 6.0.2, July 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

Open Source (AGPL), commercial license available

Open Source

GPLv2, commercial license 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.

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++

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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.

but tools for importing/exporting data from/to XML-files available

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)

APIs and other access methods

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Proprietary protocol (CQL)

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

.NET Client API

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#
Java

Server-side scripts

Stored procedures

yes

yes, Lua

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

yes

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

MVCC

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

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

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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