DBMS > Apache Phoenix vs. Kinetica vs. Manticore Search vs. SAP SQL Anywhere vs. SQLite

System Properties Comparison Apache Phoenix vs. Kinetica vs. Manticore Search vs. SAP SQL Anywhere vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

GPU-accelerated database for real-time analysis of large and streaming datasets

Open source search engine for searching in data from different sources. Forked of and compatible with Sphinx.

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Search engine

Relational DBMS

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

Score	3.39
Rank	#94	Overall
	#47	Relational DBMS

Score	0.76
Rank	#193	Overall
	#97	Relational DBMS

Score	0.04
Rank	#318	Overall
	#15	Search engines

Score	6.30
Rank	#61	Overall
	#35	Relational DBMS

Score	121.89
Rank	#9	Overall
	#6	Relational DBMS

Website

phoenix.apache.org

www.kinetica.com

manticoresearch.com

www.sap.com/products/sql-anywhere.html

www.sqlite.org

Technical documentation

phoenix.apache.org

www.kinetica.com/docs

docs.manticoresearch.com/latest/html
github.com/manticoresoftware/manticoresearch

help.sap.com/sql-anywhere

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Kinetica

Manticore Software

SAP

formerly Sybase

Dwayne Richard Hipp

Initial release

2014

2012

2017

1992

2000

Current release

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

6.0

17, July 2015

3.34.0 (December 1, 2020), December 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, C++

C++

Server operating systems

Linux
Unix
Windows

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

late-bound, schema-on-read capabilities

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

yes, in C/C++, Java, .Net or Perl

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

none

Source-replica replication

Database mirroring

none

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

GPU vRAM or System RAM

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 and roles on table level

fine grained access rights according to SQL-standard

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