DBMS > Apache Impala vs. Drizzle vs. Microsoft SQL Server vs. Quasardb vs. Titan

System Properties Comparison Apache Impala vs. Drizzle vs. Microsoft SQL Server vs. Quasardb vs. Titan

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Analytic DBMS for Hadoop

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Microsofts flagship relational DBMS

Distributed, high-performance timeseries database

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Time Series DBMS

Graph DBMS

Secondary database models

Document store

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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

impala.apache.org

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

quasar.ai

github.com/thinkaurelius/titan

Technical documentation

impala.apache.org/impala-docs.html

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

doc.quasar.ai/master

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

Microsoft

quasardb

Aurelius, owned by DataStax

Initial release

2013

2008

1989

2009

2012

Current release

4.1.0, June 2022

7.2.4, September 2012

SQL Server 2022, November 2022

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
Windows

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC

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

HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

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

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

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

consistent hashing

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

User authentification and security via Rexster Graph Server

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