DBMS > Apache Solr vs. GridGain vs. OpenTenBase

System Properties Comparison Apache Solr vs. GridGain vs. OpenTenBase

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Editorial information provided by DB-Engines

Name

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

An enterprise-level distributed HTAP open source database based on PostgreSQL

Primary database model

Search engine

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	34.15
Rank	#24	Overall
	#3	Search engines

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Website

solr.apache.org

www.gridgain.com

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

Technical documentation

solr.apache.org/resources.html

www.gridgain.com/docs/index.html

docs.opentenbase.org
docs.opentenbase.org/en

Developer

Apache Software Foundation

GridGain Systems, Inc.

OpenAtom Foundation, previously Tencent

Initial release

2006

2007

Current release

9.6.1, May 2024

GridGain 8.5.1

2.5, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial, open source

Open Source

BSD-3

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

Java, C++, .Net, Python, REST, SQL

Server operating systems

All OS with a Java VM

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

Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

Solr Parallel SQL Interface

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

C
Go
Java
PHP
Python

Server-side scripts

Stored procedures

Java plugins

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

User configurable commands triggered on index changes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

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

yes

Role-based access control
Security Hooks for custom implementations

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

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