DBMS > BoltDB vs. eXtremeDB vs. PlanetScale vs. RocksDB vs. Solr

System Properties Comparison BoltDB vs. eXtremeDB vs. PlanetScale vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Natively in-memory DBMS with options for persistency, high-availability and clustering

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Key-value store

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

github.com/boltdb/bolt

Technical documentation

www.mcobject.com/docs/extremedb.htm

planetscale.com/docs

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

McObject

PlanetScale

Facebook, Inc.

Apache Software Foundation

Initial release

2013

2001

2020

2013

2006

Current release

8.2, 2021

8.11.4, April 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

BSD

Open Source

Apache Version 2

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 and C++

C++

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
macOS
Solaris
Windows

Docker
Linux
macOS

Linux

All OS with a Java VM

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

Data scheme

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic 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.

support of XML interfaces available

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
Java
Lua
Python
Scala

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

C
C++
Go
Java
Perl
Python
Ruby

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

Server-side scripts

Stored procedures

yes

proprietary syntax

Java plugins

Triggers

yes

by defining events

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning / sharding

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication
Source-replica replication

yes

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

none

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

ACID at shard level

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

yes

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more

Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more

Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
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