DBMS > Solr vs. VelocityDB vs. Vertica vs. Yaacomo

System Properties Comparison Solr vs. VelocityDB vs. Vertica vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Search engine

Graph DBMS
Object oriented DBMS

Relational DBMS

Column oriented

Relational DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	0.11
Rank	#347	Overall
	#35	Graph DBMS
	#15	Object oriented DBMS

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

solr.apache.org

velocitydb.com

www.vertica.com

yaacomo.com

Technical documentation

solr.apache.org/resources.html

velocitydb.com/UserGuide

vertica.com/documentation

Developer

Apache Software Foundation

VelocityDB Inc

OpenText

previously Micro Focus and Hewlett Packard

Q2WEB GmbH

Initial release

2006

2011

2005

2009

Current release

9.5.0, February 2024

7.x

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

All OS with a Java VM

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

Any that supports .NET

Linux

Android
Linux
Windows

Data scheme

yes

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

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

Solr Parallel SQL Interface

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

.Net

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

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

.Net

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Java plugins

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

User configurable commands triggered on index changes

Callbacks are triggered when data changes

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, hierarchical partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

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

Bi-directional Spark integration

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

Based on Windows Authentication

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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More resources

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