DBMS > CouchDB vs. Google BigQuery vs. LevelDB vs. OrigoDB vs. Sqrrl

System Properties Comparison CouchDB vs. Google BigQuery vs. LevelDB vs. OrigoDB vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A fully ACID in-memory object graph database

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store

Relational DBMS

Key-value store

Document store
Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Website

couchdb.apache.org

cloud.google.com/bigquery

github.com/google/leveldb

origodb.com

sqrrl.com

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

github.com/google/leveldb/blob/main/doc/index.md

origodb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Robert Friberg et al

Amazon

originally Sqrrl Data, Inc.

Initial release

2005

2010

2011

2009

under the name LiveDB

2012

Current release

3.3.3, December 2023

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

BSD

Open Source

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

Erlang

C++

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Illumos
Linux
OS X
Windows

Linux
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
HTTP API
LINQ

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Source-replica replication

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

Write ahead log

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

Access rights for users can be defined per database

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role based authorization

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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