DBMS > BoltDB vs. ClickHouse vs. IRONdb vs. PouchDB vs. Splice Machine

System Properties Comparison BoltDB vs. ClickHouse vs. IRONdb vs. PouchDB vs. Splice Machine

Editorial information provided by DB-Engines

Name

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

Description

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

JavaScript DBMS with an API inspired by CouchDB

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Time Series DBMS

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

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

github.com/boltdb/bolt

clickhouse.com

www.circonus.com/solutions/time-series-database/

pouchdb.com

splicemachine.com

Technical documentation

clickhouse.com/docs

docs.circonus.com/irondb/category/getting-started

pouchdb.com/guides

splicemachine.com/how-it-works

Developer

Clickhouse Inc.

Circonus LLC.

Apache Software Foundation

Splice Machine

Initial release

2013

2016

2017

2012

2014

Current release

v24.4.1.2088-stable, May 2024

V0.10.20, January 2018

7.1.1, June 2019

3.1, March 2021

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0, commercial license available

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.

DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

C and C++

JavaScript

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

via views

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

yes, in Lua

View functions in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

Automatic, metric affinity per node

Sharding

with a proxy-based framework, named couchdb-lounge

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

configurable replication factor, datacenter aware

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.