DBMS > Apache Phoenix vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. OpenMLDB

System Properties Comparison Apache Phoenix vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. OpenMLDB

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Multi-storage database for search, including full-text search.

Globally distributed, horizontally scalable, multi-model database service

An open-source machine learning database that provides a feature platform for training and inference

Primary database model

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Spatial DBMS

Relational DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

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

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Website

phoenix.apache.org

manticoresearch.com

azure.microsoft.com/services/cosmos-db

openmldb.ai

Technical documentation

phoenix.apache.org

manual.manticoresearch.com

learn.microsoft.com/azure/cosmos-db

openmldb.ai/docs/zh/main

Developer

Apache Software Foundation

Manticore Software

Microsoft

4 Paradigm Inc.

Initial release

2014

2017

2014

2020

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

6.0, February 2023

2024-2 February 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C++, Java, Scala

Server operating systems

Linux
Unix
Windows

FreeBSD
Linux
macOS
Windows

hosted

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

Fixed schema

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

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

JDBC
SQLAlchemy

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Synchronous replication based on Galera library

yes

Implicit feature of the cloud service

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual 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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

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

The original contents of fields are not stored in the Manticore index.

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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