DBMS > Apache Phoenix vs. Hive vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage

System Properties Comparison Apache Phoenix vs. Hive vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Website

phoenix.apache.org

hive.apache.org

manticoresearch.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

phoenix.apache.org

cwiki.apache.org/confluence/display/Hive/Home

manual.manticoresearch.com

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

initially developed by Facebook

Manticore Software

Microsoft

Initial release

2014

2012

2017

2014

2012

Current release

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

3.1.3, April 2022

6.0, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

GPL version 2

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

FreeBSD
Linux
macOS
Windows

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Fixed schema

schema-free

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC

JDBC
ODBC
Thrift

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

RESTful HTTP API

Supported programming languages

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

C++
Java
PHP
Python

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

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

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

Synchronous replication based on Galera library

yes

Implicit feature of the cloud service

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

Hadoop integration

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

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

optimistic locking

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 for users, groups and roles

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

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