DBMS > EXASOL vs. FatDB vs. Hawkular Metrics vs. Lovefield vs. Manticore Search

System Properties Comparison EXASOL vs. FatDB vs. Hawkular Metrics vs. Lovefield vs. Manticore Search

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Embeddable relational database for web apps written in pure JavaScript

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

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Relational DBMS

Search engine

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Website

www.exasol.com

www.hawkular.org

google.github.io/lovefield

manticoresearch.com

Technical documentation

www.exasol.com/resources

www.hawkular.org/hawkular-metrics/docs/user-guide

github.com/google/lovefield/blob/master/docs/spec_index.md

manual.manticoresearch.com

Developer

Exasol

FatCloud

Community supported by Red Hat

Google

Manticore Software

Initial release

2000

2012

2014

2017

Current release

2.1.12, February 2017

6.0, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL version 2

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

JavaScript

C++

Server operating systems

Windows

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
macOS
Windows

Data scheme

yes

schema-free

yes

Fixed schema

Typing

predefined data types such as float or date

yes

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

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

SQL

Support of SQL

yes

Via inetgration in SQL Server

SQL-like query language

via JavaScript builder pattern

SQL-like query language

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP REST

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

Supported programming languages

Java
Lua
Python
R

Go
Java
Python
Ruby

JavaScript

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

Server-side scripts

Stored procedures

user defined functions

yes

via applications

user defined functions

Triggers

yes

via applications

yes

via Hawkular Alerting

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

Synchronous replication based on Galera library

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

Can implement custom security layer via applications

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