DBMS > Apache Phoenix vs. InfinityDB vs. Memcached vs. Microsoft SQL Server vs. Transwarp Hippo

System Properties Comparison Apache Phoenix vs. InfinityDB vs. Memcached vs. Microsoft SQL Server vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

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

A Java embedded Key-Value Store which extends the Java Map interface

In-memory key-value store, originally intended for caching

Microsofts flagship relational DBMS

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

phoenix.apache.org

boilerbay.com

www.memcached.org

www.microsoft.com/en-us/sql-server

www.transwarp.cn/en/subproduct/hippo

Technical documentation

phoenix.apache.org

boilerbay.com/infinitydb/manual

github.com/memcached/memcached/wiki

learn.microsoft.com/en-US/sql/sql-server

Developer

Apache Software Foundation

Boiler Bay Inc.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Initial release

2014

2002

2003

1989

2023

Current release

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

4.0

1.6.25, March 2024

SQL Server 2022, November 2022

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

BSD license

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Unix
Windows

Linux
Windows

Linux
macOS

Data scheme

yes

late-bound, schema-on-read capabilities

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

Vector, Numeric and String

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API

Supported programming languages

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

Java

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++
Java
Python

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Repcached, a Memcached patch, provides this functionallity

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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.

yes

User concepts

Access control

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

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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