DBMS > Apache Impala vs. Datomic vs. IBM Db2 vs. Microsoft SQL Server vs. NuoDB

System Properties Comparison Apache Impala vs. Datomic vs. IBM Db2 vs. Microsoft SQL Server vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Common in IBM host environments, 2 different versions for host and Windows/Linux

Microsofts flagship relational DBMS

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Graph DBMS
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

impala.apache.org

www.datomic.com

www.ibm.com/products/db2

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

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

www.ibm.com/docs/en/db2

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

doc.nuodb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

IBM

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2013

2012

1983

host version

1989

2013

Current release

4.1.0, June 2022

1.0.6735, June 2023

12.1, October 2016

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

commercial

free version is available

commercial

restricted free version is available

commercial

limited edition free

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java, Clojure

C and C++

C++

Server operating systems

Linux

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

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

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

yes

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

Java, SQL

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

only with Windows/Unix/Linux Version

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

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

yes

with separate tools (MQ, InfoSphere)

yes, but depending on the SQL-Server Edition

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

Temporary table

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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