DBMS > Apache Doris vs. Badger vs. Bangdb vs. Microsoft Azure Synapse Analytics

System Properties Comparison Apache Doris vs. Badger vs. Bangdb vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

Converged and high performance database for device data, events, time series, document and graph

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Relational DBMS

Key-value store

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.69
Rank	#237	Overall
	#108	Relational DBMS

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

github.com/dgraph-io/badger

bangdb.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

github.com/apache/doris/wiki

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

docs.bangdb.com

docs.microsoft.com/azure/synapse-analytics

Developer

Apache Software Foundation, originally contributed from Baidu

DGraph Labs

Sachin Sinha, BangDB

Microsoft

Initial release

2017

2012

2016

Current release

1.2.2, February 2023

BangDB 2.0, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

BSD 3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

SQL like support with command line tool

yes

APIs and other access methods

JDBC
MySQL client

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Java

C
C#
C++
Java
Python

C#
Java
PHP

Server-side scripts

Stored procedures

user defined functions

Transact SQL

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor, Knob for CAP (enterprise version only)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

yes

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