DBMS > Amazon DocumentDB vs. Apache Druid vs. ClickHouse vs. ToroDB

System Properties Comparison Amazon DocumentDB vs. Apache Druid vs. ClickHouse vs. ToroDB

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Editorial information provided by DB-Engines

Name

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

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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 MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Document store

Relational DBMS
Time Series DBMS

Relational DBMS

Document store

Secondary database models

Time Series DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Website

aws.amazon.com/documentdb

druid.apache.org

clickhouse.com

github.com/torodb/server

Technical documentation

aws.amazon.com/documentdb/resources

druid.apache.org/docs/latest/design

clickhouse.com/docs

Developer

Apache Software Foundation and contributors

Clickhouse Inc.

8Kdata

Initial release

2019

2012

2016

Current release

30.0.0, June 2024

v24.6.2.17-stable, July 2024

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

AGPL-V3

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.

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.

Implementation language

Java

C++

Java

Server operating systems

hosted

Linux
OS X
Unix

FreeBSD
Linux
macOS

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-less columns are supported

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

SQL

Support of SQL

SQL for querying

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
RESTful HTTP/JSON API

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

manual/auto, time-based

key based and custom

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes, via HDFS, S3 or other storage engines

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

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

User concepts

Access control

Access rights for users and roles

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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 and roles

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