DBMS > Apache Impala vs. Brytlyt vs. Drizzle vs. Microsoft SQL Server vs. OrigoDB

System Properties Comparison Apache Impala vs. Brytlyt vs. Drizzle vs. Microsoft SQL Server vs. OrigoDB

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.

Description

Analytic DBMS for Hadoop

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Microsofts flagship relational DBMS

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Document store
Object oriented 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	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.38
Rank	#279	Overall
	#126	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Website

impala.apache.org

brytlyt.io

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

origodb.com

Technical documentation

impala.apache.org/impala-docs.html

docs.brytlyt.io

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

origodb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Brytlyt

Drizzle project, originally started by Brian Aker

Microsoft

Robert Friberg et al

Initial release

2013

2016

2008

1989

2009

under the name LiveDB

Current release

4.1.0, June 2022

5.0, August 2023

7.2.4, September 2012

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GNU GPL

commercial

restricted free version is available

Open Source

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.

Implementation language

C++

C, C++ and CUDA

C++

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

specific XML-type available, but no XML query functionality.

yes

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

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

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C
C++
Java
PHP

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

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in PL/pgSQL

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

yes

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

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

depending on model

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

Write ahead log

In-memory capabilities

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

yes

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Role based authorization

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