DBMS > Apache Impala vs. Brytlyt vs. Microsoft SQL Server vs. VoltDB vs. XTDB

System Properties Comparison Apache Impala vs. Brytlyt vs. Microsoft SQL Server vs. VoltDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Microsofts flagship relational 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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store

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	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

impala.apache.org

brytlyt.io

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

www.voltdb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

impala.apache.org/impala-docs.html

docs.brytlyt.io

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

docs.voltdb.com

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Brytlyt

Microsoft

VoltDB Inc.

Juxt Ltd.

Initial release

2013

2016

1989

2010

2019

Current release

4.1.0, June 2022

5.0, August 2023

SQL Server 2022, November 2022

11.3, April 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

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

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C, C++ and CUDA

C++

Java, C++

Clojure

Server operating systems

Linux

Linux
OS X
Windows

Linux
Windows

Linux
OS X

for development

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

only a subset of SQL 99

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

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

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

Java API
JDBC
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Clojure
Java

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

Java

Triggers

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

yes, each node contains all data

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

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

ACID

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Snapshots and command logging

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Users and roles with access to stored procedures

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