DBMS > Apache Druid vs. Cassandra vs. Datomic vs. etcd

System Properties Comparison Apache Druid vs. Cassandra vs. Datomic vs. etcd

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Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

A distributed reliable key-value store

Primary database model

Relational DBMS
Time Series DBMS

Wide column store

Relational DBMS

Key-value store

Secondary database models

Vector DBMS

starting with release V5

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Website

druid.apache.org

cassandra.apache.org

www.datomic.com

etcd.io
github.com/etcd-io/etcd

Technical documentation

druid.apache.org/docs/latest/design

cassandra.apache.org/doc/latest

docs.datomic.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top level project, originally developped by Facebook

Cognitect

Initial release

2012

2008

2012

Current release

29.0.1, April 2024

4.1.3, July 2023

1.0.6735, June 2023

3.4, August 2019

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache version 2

commercial

limited edition free

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Java, Clojure

Server operating systems

Linux
OS X
Unix

BSD
Linux
OS X
Windows

All OS with a Java VM

FreeBSD
Linux
Windows

experimental

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

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.

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL for querying

SQL-like SELECT, DML and DDL statements (CQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

RESTful HTTP API

gRPC
JSON over HTTP

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Clojure
Java

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

Server-side scripts

Stored procedures

yes

Transaction Functions

Triggers

yes

By using transaction functions

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

no "single point of failure"

none

But extensive use of caching in the application peers

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

Representation of geographical distribution of servers is possible

none

But extensive use of caching in the application peers

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

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

User concepts

Access control

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

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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