DBMS > BoltDB vs. ClickHouse vs. Datomic vs. Hazelcast vs. MaxDB

System Properties Comparison BoltDB vs. ClickHouse vs. Datomic vs. Hazelcast vs. MaxDB

Editorial information provided by DB-Engines

Name

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.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A widely adopted in-memory data grid

A robust and reliable RDBMS optimized to run all major SAP solutions

Primary database model

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Website

github.com/boltdb/bolt

Technical documentation

clickhouse.com/docs

docs.datomic.com

hazelcast.org/imdg/docs

maxdb.sap.com/documentation

Developer

Clickhouse Inc.

Cognitect

Hazelcast

SAP, acquired from Software AG (Adabas-D) in 1997

Initial release

2013

2016

2012

2008

1984

Current release

v24.4.1.2088-stable, May 2024

1.0.7075, December 2023

5.3.6, November 2023

7.9.10.12, February 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

commercial

limited edition free

Open Source

Apache Version 2; commercial licenses available

commercial

Limited community edition free

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.

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.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

Java, Clojure

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

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

Clojure
Java

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C#
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Event Listeners, Executor Services

yes

Triggers

By using transaction functions

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

none

But extensive use of caching in the application peers

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

none

But extensive use of caching in the application peers

yes

Replicated Map

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

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.

Role-based access control

fine grained access rights according to SQL-standard

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