DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Amazon DocumentDB vs. ClickHouse vs. HBase vs. Microsoft Azure Data Explorer

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Amazon DocumentDB vs. ClickHouse vs. HBase vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Fully managed big data interactive analytics platform

Primary database model

column oriented

Secondary database models

Time Series DBMS

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

aws.amazon.com/documentdb

clickhouse.com

hbase.apache.org

azure.microsoft.com/services/data-explorer

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

aws.amazon.com/documentdb/resources

clickhouse.com/docs

hbase.apache.org/book.html

docs.microsoft.com/en-us/azure/data-explorer

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Clickhouse Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Initial release

2016

2019

2016

2008

2019

Current release

v24.4.1.2088-stable, May 2024

2.3.4, January 2021

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

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.

DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

C++

Java

Server operating systems

hosted

FreeBSD
Linux
macOS

Linux
Unix
Windows

using Cygwin

hosted

Data scheme

yes

schema-free

yes

schema-free, schema definition possible

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-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.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

proprietary protocol using JSON (MongoDB compatible)

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

Java API
RESTful HTTP API
Thrift

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

Supported programming languages

C
Java
Perl
Python
R

Go
Java
JavaScript (Node.js)
PHP
Python

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Yes, possible languages: KQL, Python, R

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

key based and custom

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

ACID

Atomic single-document operations

Single row ACID (across millions of columns)

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Azure Active Directory Authentication

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