DBMS > Elasticsearch vs. Google Cloud Datastore vs. Microsoft SQL Server vs. Sphinx vs. STSdb

System Properties Comparison Elasticsearch vs. Google Cloud Datastore vs. Microsoft SQL Server vs. Sphinx vs. STSdb

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Microsofts flagship relational DBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	128.79
Rank	#8	Overall
	#1	Search engines

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

www.elastic.co/elasticsearch

cloud.google.com/datastore

www.microsoft.com/en-us/sql-server

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

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

cloud.google.com/datastore/docs

learn.microsoft.com/en-US/sql/sql-server

sphinxsearch.com/docs

Developer

Elastic

Google

Microsoft

Sphinx Technologies Inc.

STS Soft SC

Initial release

2010

2008

1989

2001

2011

Current release

8.6, January 2023

SQL Server 2022, November 2022

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Elastic License

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

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

All OS with a Java VM

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

schema-free

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

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Proprietary protocol

.NET Client API

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

yes

using Google App Engine

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

by using the 'percolation' feature

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

using Google Cloud Dataflow

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 or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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