DBMS > Elasticsearch vs. HarperDB vs. Microsoft Azure Cosmos DB vs. SQream DB

System Properties Comparison Elasticsearch vs. HarperDB vs. Microsoft Azure Cosmos DB vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Globally distributed, horizontally scalable, multi-model database service

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Search engine

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

www.elastic.co/elasticsearch

www.harperdb.io

azure.microsoft.com/services/cosmos-db

sqream.com

Technical documentation

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

docs.harperdb.io/docs

learn.microsoft.com/azure/cosmos-db

docs.sqream.com

Developer

Elastic

HarperDB

Microsoft

SQream Technologies

Initial release

2010

2017

2014

2017

Current release

8.6, January 2023

3.1, August 2021

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Elastic License

commercial

free community edition available

commercial

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.

Implementation language

Java

Node.js

C++, CUDA, Haskell, Java, Scala

Server operating systems

All OS with a Java VM

Linux
OS X

hosted

Linux

Data scheme

schema-free

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

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

JSON types

yes, ANSI Standard SQL 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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

SQL-like data manipulation statements

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

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

.Net
JDBC
ODBC

Supported programming languages

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

JavaScript

user defined functions in Python

Triggers

yes

by using the 'percolation' feature

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users and roles

Access rights can be defined down to the item level

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