DBMS > Badger vs. LevelDB vs. RocksDB vs. SiteWhere vs. Sphinx

System Properties Comparison Badger vs. LevelDB vs. RocksDB vs. SiteWhere vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

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

M2M integration platform for persisting/querying time series data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Key-value store

Time Series DBMS

Search engine

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

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

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

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

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

github.com/dgraph-io/badger

github.com/google/leveldb

rocksdb.org

github.com/sitewhere/sitewhere

sphinxsearch.com

Technical documentation

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

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

github.com/facebook/rocksdb/wiki

sitewhere1.sitewhere.io/index.html

sphinxsearch.com/docs

Developer

DGraph Labs

Google

Facebook, Inc.

SiteWhere

Sphinx Technologies Inc.

Initial release

2017

2011

2013

2010

2001

Current release

1.23, February 2021

8.11.4, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD

Open Source

BSD

Open Source

Common Public Attribution License Version 1.0

Open Source

GPL version 2, commercial licence available

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++

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Illumos
Linux
OS X
Windows

Linux

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

predefined scheme

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (SphinxQL)

APIs and other access methods

C++ API
Java API

HTTP REST

Proprietary protocol

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

based on HBase

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication factor

based on HBase

none

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

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

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept

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