DBMS > Apache Druid vs. Badger vs. LevelDB vs. Machbase Neo vs. RocksDB

System Properties Comparison Apache Druid vs. Badger vs. LevelDB vs. Machbase Neo vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

TimeSeries DBMS for AIoT and BigData

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS
Time Series DBMS

Key-value store

Time Series DBMS

Key-value store

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

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

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	0.22
Rank	#324	Overall
	#29	Time Series DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Website

druid.apache.org

github.com/dgraph-io/badger

github.com/google/leveldb

machbase.com

rocksdb.org

Technical documentation

druid.apache.org/docs/latest/design

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

github.com/google/leveldb/blob/main/doc/index.md

machbase.com/dbms

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation and contributors

DGraph Labs

Google

Machbase

Facebook, Inc.

Initial release

2012

2017

2011

2013

Current release

29.0.1, April 2024

1.23, February 2021

V8.0, August 2023

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

BSD

commercial

free test version available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

BSD
Linux
OS X
Solaris
Windows

Illumos
Linux
OS X
Windows

Linux
macOS
Windows

Linux

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

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 for querying

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

C++ API
Java API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate 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

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

volatile and lookup table

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

simple password-based access control

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