DBMS > BigchainDB vs. Ingres vs. Snowflake vs. Solr vs. Valentina Server

System Properties Comparison BigchainDB vs. Ingres vs. Snowflake vs. Solr vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Well established RDBMS

Cloud-based data warehousing service for structured and semi-structured data

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

Object-relational database and reports server

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

www.bigchaindb.com

www.actian.com/databases/ingres

www.snowflake.com

solr.apache.org

www.valentina-db.net

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.actian.com/ingres

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Actian Corporation

Snowflake Computing Inc.

Apache Software Foundation

Paradigma Software

Initial release

2016

1974

originally developed at University Berkely in early 1970s

2014

2006

1999

Current release

11.2, May 2022

9.6.0, April 2024

5.7.5

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

Apache Version 2

commercial

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

Python

Java

Server operating systems

Linux

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

All OS with a Java VM

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

Linux
OS X
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

CLI Client
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

JavaScript (Node.js)
Python

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

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

user defined functions

Java plugins

yes

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Ingres Replicator

yes

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

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

fine grained access rights according to SQL-standard

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