DBMS > Apache Solr vs. Graph Engine vs. OpenMLDB

System Properties Comparison Apache Solr vs. Graph Engine vs. OpenMLDB

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Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

An open-source machine learning database that provides a feature platform for training and inference

Primary database model

Search engine

Graph DBMS
Key-value store

Time Series DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	32.79
Rank	#23	Overall
	#3	Search engines

Score	0.58
Rank	#239	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.05
Rank	#358	Overall
	#38	Time Series DBMS

Website

solr.apache.org

www.graphengine.io

openmldb.ai

Technical documentation

solr.apache.org/resources.html

www.graphengine.io/docs/manual

openmldb.ai/docs/zh/main

Developer

Apache Software Foundation

Microsoft

4 Paradigm Inc.

Initial release

2006

2010

2020

Current release

9.6.1, May 2024

2024-2 February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

Open Source

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

.NET and C

C++, Java, Scala

Server operating systems

All OS with a Java VM

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

.NET

Linux

Data scheme

yes

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

yes

Fixed schema

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

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP API

JDBC
SQLAlchemy

Supported programming languages

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

C#
C++
F#
Visual Basic

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

Java plugins

yes

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

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