DBMS > Faircom DB vs. PieCloudDB vs. Solr vs. TerarkDB

System Properties Comparison Faircom DB vs. PieCloudDB vs. Solr vs. TerarkDB

Please select another system to include it in the comparison.

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.

A cloud-native analytic database platform with new technologoy for elastic MPP

A widely used distributed, scalable search engine based on Apache Lucene

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Search engine

Key-value store

Secondary database models

Spatial 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	0.25
Rank	#304	Overall
	#138	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

www.faircom.com/products/faircom-db

www.openpie.com

solr.apache.org

github.com/bytedance/terarkdb

Technical documentation

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

solr.apache.org/resources.html

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

FairCom Corporation

OpenPie

Apache Software Foundation

ByteDance, originally Terark

Initial release

1979

2006

2016

Current release

V12, November 2020

2.1, January 2023

9.6.0, April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

Java

C++

Server operating systems

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

easily portable to other OSs

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

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

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

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

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

C++ API
Java API

Supported programming languages

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

Java
PL/SQL
Python
R

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

Sharding

none

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).

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency

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

ACID

optimistic locking

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

User Roles and pluggable authentication with full SQL Standard

yes

More information provided by the system vendor

Specific characteristics

PieCloudDB, OpenPie's flagship product, is a cutting-edge cloud-native data warehouse....
» more

Competitive advantages

Extreme Elastic: PieCloudDB utilizes a cutting-edge eMPP cloud-native architecture...
» more

Typical application scenarios

PieCloudDB is ideal for Data mining applications that require extreme scalability...
» more

Key customers

Sail-Cloud China Shipbuilding Group Haizhou System Soochow Securities etc.,
» more

Licensing and pricing models

PieCloudDB Community Edition: Community License, Free Download, Self-Hosted Deployment；...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Elasticsearch replaced Solr as the most popular search engine
12 January 2016, Paul Andlinger

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

FairCom kicks off new era of database technology USA - English
10 November 2020, PR Newswire

provided by Google News

SOLR-led walkout demands better conditions for Compass workers
27 February 2024, Daily Northwestern

(SOLR) Technical Pivots with Risk Controls
28 April 2024, news.stocktradersdaily.com

Best Practices from Rackspace for Modernizing a Legacy HBase/Solr Architecture Using AWS Services | Amazon Web ...
9 October 2023, AWS Blog

SOLR hosts teach-in of labor movements at Northwestern
28 January 2024, Daily Northwestern

Top 5 stock gainers and losers: SOLR.V, GRSL.V, ANON.C
21 November 2023, Equity.Guru

provided by Google News

Share this page