DBMS > LevelDB vs. NSDb vs. RethinkDB vs. Sequoiadb vs. SwayDB

System Properties Comparison LevelDB vs. NSDb vs. RethinkDB vs. Sequoiadb vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

NewSQL database with distributed OLTP and SQL

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Time Series DBMS

Document store

Document store
Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

github.com/google/leveldb

Technical documentation

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

nsdb.io/Architecture

rethinkdb.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Google

The Linux Foundation

since July 2017

Sequoiadb Ltd.

Simer Plaha

Initial release

2011

2017

2009

2013

2018

Current release

1.23, February 2021

2.4.1, August 2020

License

Commercial or Open Source

Open Source

BSD

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

Server: AGPL; Client: Apache V2

Open Source

GNU Affero GPL V3.0

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, Scala

C++

Scala

Server operating systems

Illumos
Linux
OS X
Windows

Linux
macOS

Linux
OS X
Windows

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

yes

string, binary, float, bool, date, geometry

yes

oid, date, timestamp, binary, regex

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

gRPC
HTTP REST
WebSocket

proprietary protocol using JSON

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

.Net
C++
Java
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

JavaScript

Triggers

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

range based

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-document operations

Document is locked during a transaction

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

Using Apache Lucene

yes

In-memory capabilities

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

yes

User concepts

Access control

yes

users and table-level permissions

simple password-based access control

More information provided by the system vendor

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More resources

DB-Engines blog posts

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