DBMS > Apache Impala vs. Cloudflare Workers KV vs. Microsoft Azure Cosmos DB vs. openGauss

System Properties Comparison Apache Impala vs. Cloudflare Workers KV vs. Microsoft Azure Cosmos DB vs. openGauss

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

Globally distributed, horizontally scalable, multi-model database service

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Primary database model

Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Spatial DBMS

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.32
Rank	#283	Overall
	#40	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Website

impala.apache.org

www.cloudflare.com/developer-platform/workers-kv

azure.microsoft.com/services/cosmos-db

gitee.com/opengauss
opengauss.org

Technical documentation

impala.apache.org/impala-docs.html

developers.cloudflare.com/kv/api

learn.microsoft.com/azure/cosmos-db

docs.opengauss.org/en
gitee.com/opengauss/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cloudflare

Microsoft

Huawei and openGauss community

Initial release

2013

2018

2014

2019

Current release

4.1.0, June 2022

3.0, March 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

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

C++

C, C++, Java

Server operating systems

Linux

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

ANSI SQL 2011

APIs and other access methods

JDBC
ODBC

HTTP REST
Proprietary protocol

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

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

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

C
C++
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning (by range, list and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

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