DBMS > Badger vs. Firebolt vs. Microsoft SQL Server vs. XTDB

System Properties Comparison Badger vs. Firebolt vs. Microsoft SQL Server vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Microsofts flagship relational DBMS

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

Primary database model

Key-value store

Relational DBMS

Document store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.90
Rank	#134	Overall
	#62	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/dgraph-io/badger

www.firebolt.io

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

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

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.firebolt.io

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

www.xtdb.com/docs

Developer

DGraph Labs

Firebolt Analytics Inc.

Microsoft

Juxt Ltd.

Initial release

2017

2020

1989

2019

Current release

SQL Server 2022, November 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
Windows

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

Data scheme

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

.Net
ODBC
RESTful HTTP API

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

HTTP REST
JDBC

Supported programming languages

Go
JavaScript (Node.js)
Python

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

Clojure
Java

Server-side scripts

Stored procedures

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

yes, but depending on the SQL-Server Edition

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

fine grained access rights according to SQL-standard

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