DBMS > BigchainDB vs. HarperDB vs. Memcached vs. Microsoft SQL Server vs. Rockset

System Properties Comparison BigchainDB vs. HarperDB vs. Memcached vs. Microsoft SQL Server vs. Rockset

Editorial information provided by DB-Engines

Name

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Microsofts flagship relational DBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Website

www.bigchaindb.com

www.harperdb.io

www.memcached.org

www.microsoft.com/en-us/sql-server

rockset.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.harperdb.io/docs

github.com/memcached/memcached/wiki

learn.microsoft.com/en-US/sql/sql-server

docs.rockset.com

Developer

HarperDB

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Rockset

Initial release

2016

2017

2003

1989

2019

Current release

3.1, August 2021

1.6.27, May 2024

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

AGPL v3

commercial

free community edition available

Open Source

BSD license

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Node.js

C++

Server operating systems

Linux

Linux
OS X

FreeBSD
Linux
OS X
Unix
Windows

Linux
Windows

hosted

Data scheme

schema-free

dynamic schema

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like data manipulation statements

yes

Read-only SQL queries, including JOINs

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

the nodes on which a table resides can be defined

none

Repcached, a Memcached patch, provides this functionallity

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes, using LMDB

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

Access rights for users and roles

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

Access rights for users and organizations can be defined via Rockset console

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