DBMS > BigchainDB vs. Quasardb vs. Snowflake vs. Solr vs. Splice Machine

System Properties Comparison BigchainDB vs. Quasardb vs. Snowflake vs. Solr vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

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

Distributed, high-performance timeseries database

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

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

Technical documentation

bigchaindb.readthedocs.io/en/latest

doc.quasar.ai/master

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

splicemachine.com/how-it-works

Developer

quasardb

Snowflake Computing Inc.

Apache Software Foundation

Splice Machine

Initial release

2016

2009

2014

2006

2014

Current release

3.14.1, January 2024

9.6.0, April 2024

3.1, March 2021

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

Apache Version 2

Open Source

AGPL 3.0, commercial license 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

Python

C++

Java

Server operating systems

Linux

BSD
Linux
OS X
Windows

hosted

All OS with a Java VM

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

Linux
OS X
Solaris
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

integer and binary

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

with tags

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

CLI Client
RESTful HTTP API

HTTP API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

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

JavaScript (Node.js)
Python

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Java plugins

yes

Java

Triggers

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

consistent hashing

yes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication with selectable replication factor

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

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

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

yes

Cryptographically strong user authentication and audit trail

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

yes

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

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