DBMS > Microsoft SQL Server vs. RocksDB vs. ScyllaDB vs. Trafodion vs. VoltDB

System Properties Comparison Microsoft SQL Server vs. RocksDB vs. ScyllaDB vs. Trafodion vs. VoltDB

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Microsofts flagship relational DBMS

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

Cassandra and DynamoDB compatible wide column store

Transactional SQL-on-Hadoop DBMS

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Key-value store

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.microsoft.com/en-us/sql-server

Technical documentation

learn.microsoft.com/en-US/sql/sql-server

github.com/facebook/rocksdb/wiki

docs.scylladb.com

trafodion.apache.org/documentation.html

docs.voltdb.com

Developer

Microsoft

Facebook, Inc.

ScyllaDB

Apache Software Foundation, originally developed by HP

VoltDB Inc.

Initial release

1989

2013

2015

2014

2010

Current release

SQL Server 2022, November 2022

8.11.4, April 2024

ScyllaDB Open Source 5.4.1, January 2024

2.3.0, February 2019

11.3, April 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

BSD

Open Source

Open Source (AGPL), commercial license available

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C++, Java

Java, C++

Server operating systems

Linux
Windows

Linux

Linux
OS X

for development

Data scheme

yes

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.

yes

Secondary indexes

yes

cluster global secondary indices

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements (CQL)

yes

only a subset of SQL 99

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

C++ API
Java API

Proprietary protocol (CQL)

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

ADO.NET
JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Go
Java
Perl
Python
Ruby

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

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes, Lua

Java Stored Procedures

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

yes

selectable replication factor

Representation of geographical distribution of servers is possible

yes, via HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

yes

Atomicity and isolation are supported for single operations

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

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