DBMS > Badger vs. DolphinDB vs. Microsoft Azure Synapse Analytics vs. XTDB

System Properties Comparison Badger vs. DolphinDB vs. Microsoft Azure Synapse Analytics 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.

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

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

Primary database model

Secondary database models

Relational DBMS

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

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

Score	4.23
Rank	#82	Overall
	#6	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.09
Rank	#351	Overall
	#47	Document stores

Website

github.com/dgraph-io/badger

www.dolphindb.com

azure.microsoft.com/services/synapse-analytics

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

Technical documentation

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

docs.dolphindb.cn/en/help200/index.html

docs.microsoft.com/azure/synapse-analytics

www.xtdb.com/docs

Developer

DGraph Labs

DolphinDB, Inc

Microsoft

Juxt Ltd.

Initial release

2017

2018

2016

2019

Current release

v2.00.4, January 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community version available

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

hosted

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

ADO.NET
JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C#
Java
PHP

Clojure
Java

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

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

Administrators, Users, Groups

yes

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