DBMS > CrateDB vs. LevelDB vs. Microsoft Azure Cosmos DB vs. Tkrzw

System Properties Comparison CrateDB vs. LevelDB vs. Microsoft Azure Cosmos DB vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Distributed Date Store based on Lucene

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

Globally distributed, horizontally scalable, multi-model database service

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS
Search engine

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Document store
Time Series DBMS

Spatial DBMS

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

Score	0.97
Rank	#178	Overall
	#86	Relational DBMS
	#13	Search engines

Score	3.06
Rank	#101	Overall
	#18	Key-value stores

Score	40.22
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#354	Overall
	#54	Key-value stores

Website

crate.io

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

dbmx.net/tkrzw

Technical documentation

crate.io/docs

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

docs.microsoft.com/azure/cosmos-db

Developer

Crate

Google

Microsoft

Mikio Hirabayashi

Initial release

2013

2011

2014

2020

Current release

1.23, February 2021

0.9.3, August 2020

License

Commercial or Open Source

Open Source

BSD

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

All OS with a Java 7 (and higher) VM

Illumos
Linux
OS X
Windows

hosted

Linux
macOS

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

SQL

Support of SQL

yes, but no triggers and constraints

SQL-like query language

APIs and other access methods

ADO.NET

community maintained client
JDBC
MQTT (Message Queue Telemetry Transport)
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

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

Supported programming languages

Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
Ruby

community maintained client
Scala

community maintained client

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Implicit feature of the cloud service

none

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
Read Your Own Writes Consistency

for primary key lookups

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

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

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

rights management via user accounts

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

CrateDB is an open source distributed SQL database that spreads data and processing...
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Competitive advantages

CrateDB is typically used in operational analytics applications, particularly those...
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Typical application scenarios

CrateDB is ideal for integrating and analyzing streams of data--often machine-generated--in...
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Key customers

CrateDB customers include McAfee, Qualtrics , Nokia, NBC Golfnow , Digital Domain...
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Market metrics

The CrateDB open source project was started in 2014. CrateDB was the winner of TechCrunch...
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Licensing and pricing models

CrateDB pricing CrateDB Community Edition is open source and free to use under the...
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