DBMS > Badger vs. HEAVY.AI vs. Microsoft SQL Server vs. QuestDB

System Properties Comparison Badger vs. HEAVY.AI vs. Microsoft SQL Server vs. QuestDB

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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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Microsofts flagship relational DBMS

A high performance open source SQL database for time series data

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Website

github.com/dgraph-io/badger

github.com/heavyai/heavydb
www.heavy.ai

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

questdb.io

Technical documentation

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

docs.heavy.ai

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

questdb.io/docs

Developer

DGraph Labs

HEAVY.AI, Inc.

Microsoft

QuestDB Technology Inc

Initial release

2017

2016

1989

2014

Current release

5.10, January 2022

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

Open Source

Apache 2.0

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++ and CUDA

C++

Java (Zero-GC), C++, Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free via InfluxDB Line Protocol

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

yes

SQL with time-series extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

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

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

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

Sharding

Round robin

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

horizontal partitioning (by timestamps)

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes, but depending on the SQL-Server Edition

Source-replica replication with eventual consistency

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

ACID for single-table writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

through memory mapped files

User concepts

Access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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