DBMS > Faircom DB vs. Hive vs. OpenSearch vs. Splunk

System Properties Comparison Faircom DB vs. Hive vs. OpenSearch vs. Splunk

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Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Analytics Platform for Big Data

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Search engine

Secondary database models

Vector DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

www.faircom.com/products/faircom-db

hive.apache.org

github.com/opensearch-project
opensearch.org

www.splunk.com

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

opensearch.org/docs/latest

docs.splunk.com/Documentation/Splunk

Developer

FairCom Corporation

Apache Software Foundation

initially developed by Facebook

Amazon Web Services

Splunk Inc.

Initial release

1979

2012

2021

2003

Current release

V12, November 2020

3.1.3, April 2022

2.5.0, January 2023

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Limited free edition and free developer edition available

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.

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Implementation language

ANSI C, C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

All OS with a Java VM

Linux
OS X
Solaris
Windows

Data scheme

schema free, schema optional, schema required, partial schema,

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like DML and DDL statements

SQL-like query language

Splunk Search Processing Language for search commands

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC
Thrift

Java API
RESTful HTTP/JSON API

HTTP REST

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C++
Java
PHP
Python

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users, groups and roles

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

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