DBMS > LevelDB vs. Netezza vs. OpenSearch vs. Riak KV vs. Sqrrl

System Properties Comparison LevelDB vs. Netezza vs. OpenSearch vs. Riak KV 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

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

Data warehouse and analytics appliance part of IBM PureSystems

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Distributed, fault tolerant key-value store

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Vector DBMS

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

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

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	16.03
Rank	#37	Overall
	#4	Search engines

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

github.com/google/leveldb

www.ibm.com/products/netezza

github.com/opensearch-project
opensearch.org

sqrrl.com

Technical documentation

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

opensearch.org/docs/latest

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

IBM

Amazon Web Services

OpenSource, formerly Basho Technologies

Amazon

originally Sqrrl Data, Inc.

Initial release

2011

2000

2021

2009

2012

Current release

1.23, February 2021

2.5.0, January 2023

3.2.0, December 2022

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

C++

Java

Erlang

Java

Server operating systems

Illumos
Linux
OS X
Windows

Linux

included in appliance

All OS with a Java VM

Linux
OS X

Linux

Data scheme

schema-free

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

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.

Secondary indexes

yes

All search fields are automatically indexed

restricted

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
OLE DB

Java API
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Fortran
Java
Lua
Perl
Python
R

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Actionscript
C

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

Server-side scripts

Stored procedures

yes

Erlang

Triggers

yes

by using the 'percolation' feature

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

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

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

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

links between data sets can be stored

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

User concepts

Access control

Users with fine-grained authorization concept

yes, using Riak Security

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

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

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