DBMS > Blueflood vs. LevelDB vs. LMDB vs. Microsoft Azure Cosmos DB vs. NSDb

System Properties Comparison Blueflood vs. LevelDB vs. LMDB vs. Microsoft Azure Cosmos DB vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

A high performant, light-weight, embedded key-value database library

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Time Series DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

blueflood.io

github.com/google/leveldb

www.symas.com/symas-embedded-database-lmdb

azure.microsoft.com/services/cosmos-db

nsdb.io

Technical documentation

github.com/rax-maas/blueflood/wiki

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

www.lmdb.tech/doc

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

Developer

Rackspace

Google

Symas

Microsoft

Initial release

2013

2011

2014

2017

Current release

1.23, February 2021

0.9.32, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD

Open Source

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Java, Scala

Server operating systems

Linux
OS X

Illumos
Linux
OS X
Windows

Linux
Unix
Windows

hosted

Linux
macOS

Data scheme

predefined scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

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

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

HTTP REST

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

gRPC
HTTP REST
WebSocket

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
Scala

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

Using Apache Lucene

In-memory capabilities

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

yes

User concepts

Access control

Access rights can be defined down to the item level

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