DBMS > PlanetScale vs. Sequoiadb vs. Solr vs. Tkrzw vs. Yaacomo

System Properties Comparison PlanetScale vs. Sequoiadb vs. Solr vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Scalable, distributed, serverless MySQL database platform built on top of Vitess

NewSQL database with distributed OLTP and SQL

A widely used distributed, scalable search engine based on Apache Lucene

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Document store
Relational DBMS

Search engine

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

yaacomo.com

Technical documentation

planetscale.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

solr.apache.org/resources.html

Developer

PlanetScale

Sequoiadb Ltd.

Apache Software Foundation

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2020

2013

2006

2020

2009

Current release

9.6.1, May 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2

Open Source

Apache Version 2.0

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

C++

Java

C++

Server operating systems

Docker
Linux
macOS

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

Solr Parallel SQL Interface

yes

APIs and other access methods

ADO.NET
JDBC
MySQL protocol
ODBC

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C++
Java
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax

JavaScript

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency across shards
Immediate Consistency within a shard

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID at shard level

Document is locked during a transaction

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

using specific database classes

yes

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

simple password-based access control

yes

fine grained access rights according to SQL-standard

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