DBMS > Couchbase vs. Ignite vs. Memcached vs. Spark SQL vs. Yanza

System Properties Comparison Couchbase vs. Ignite vs. Memcached vs. Spark SQL vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Time Series DBMS for IoT Applications

Primary database model

Document store

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

yanza.com

Technical documentation

docs.couchbase.com

apacheignite.readme.io/docs

github.com/memcached/memcached/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Couchbase, Inc.

Apache Software Foundation

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Apache Software Foundation

Yanza

Initial release

2011

2015

2003

2014

2015

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

Apache Ignite 2.6

1.6.25, March 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache 2.0

Open Source

BSD license

Open Source

Apache 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++, Go and Erlang

C++, Java, .Net

Scala

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Proprietary protocol

JDBC
ODBC

HTTP API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

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

Java
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

via the TAP protocol

yes (cache interceptors and events)

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes (replicated cache)

none

Repcached, a Memcached patch, provides this functionallity

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Security Hooks for custom implementations

yes

using SASL (Simple Authentication and Security Layer) protocol

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