DBMS > Apache Drill vs. Elasticsearch vs. KeyDB vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Drill vs. Elasticsearch vs. KeyDB vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Relational DBMS

Search engine

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	128.79
Rank	#8	Overall
	#1	Search engines

Score	0.63
Rank	#232	Overall
	#32	Key-value stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

drill.apache.org

www.elastic.co/elasticsearch

github.com/Snapchat/KeyDB
keydb.dev

azure.microsoft.com/services/cosmos-db

Technical documentation

drill.apache.org/docs

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

docs.keydb.dev

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Elastic

EQ Alpha Technology Ltd.

Microsoft

Initial release

2012

2010

2019

2014

Current release

1.20.3, January 2023

8.6, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Elastic License

Open Source

BSD-3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux

hosted

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes

JSON types

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

All search fields are automatically indexed

yes

by using the Redis Search module

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

C++

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

user defined functions

yes

Lua

JavaScript

Triggers

yes

by using the 'percolation' feature

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency
Strong eventual consistency with CRDTs

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

Depending on the underlying data source

Memcached and Redis integration

yes

User concepts

Access control

Depending on the underlying data source

simple password-based access control and ACL

Access rights can be defined down to the item level

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