DBMS > BigchainDB vs. Hive vs. MarkLogic vs. Memcached vs. Snowflake

System Properties Comparison BigchainDB vs. Hive vs. MarkLogic vs. Memcached vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Operational and transactional Enterprise NoSQL database

In-memory key-value store, originally intended for caching

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

Primary database model

Document store

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

Relational DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

bigchaindb.readthedocs.io/en/latest

cwiki.apache.org/confluence/display/Hive/Home

docs.marklogic.com

github.com/memcached/memcached/wiki

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

initially developed by Facebook

MarkLogic Corp.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Snowflake Computing Inc.

Initial release

2016

2012

2001

2003

2014

Current release

3.1.3, April 2022

11.0, December 2022

1.6.25, March 2024

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

BSD license

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

C++

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

hosted

Data scheme

schema-free

yes

schema-free

Schema can be enforced

schema-free

yes

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

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL92

yes

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC
ODBC
Thrift

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Proprietary protocol

CLI Client
JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C++
Java
PHP
Python

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

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

via XQuery or JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Repcached, a Memcached patch, provides this functionallity

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

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, with Range Indexes

User concepts

Access control

yes

Access rights for users, groups and roles

Role-based access control at the document and subdocument levels

yes

using SASL (Simple Authentication and Security Layer) protocol

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

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