DBMS > Apache Drill vs. Badger vs. DolphinDB vs. IBM Db2 Event Store

System Properties Comparison Apache Drill vs. Badger vs. DolphinDB vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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.

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store
Relational DBMS

Key-value store

Time Series DBMS

Event Store
Time Series DBMS

Secondary database models

Relational DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

drill.apache.org

github.com/dgraph-io/badger

www.dolphindb.com

www.ibm.com/products/db2-event-store

Technical documentation

drill.apache.org/docs

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

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

www.ibm.com/docs/en/db2-event-store

Developer

Apache Software Foundation

DGraph Labs

DolphinDB, Inc

IBM

Initial release

2012

2017

2018

2017

Current release

1.20.3, January 2023

v2.00.4, January 2022

2.0

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

free community version available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Server operating systems

Linux
OS X
Windows

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C++

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

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

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

Depending on the underlying data source

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Administrators, Users, Groups

fine grained access rights according to SQL-standard

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