DBMS > Amazon CloudSearch vs. Apache Impala vs. Google Cloud Spanner vs. Microsoft SQL Server vs. Percona Server for MySQL

System Properties Comparison Amazon CloudSearch vs. Apache Impala vs. Google Cloud Spanner vs. Microsoft SQL Server vs. Percona Server for MySQL

Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

Analytic DBMS for Hadoop

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Microsofts flagship relational DBMS

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

Primary database model

Search engine

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	1.81
Rank	#137	Overall
	#12	Search engines

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.10
Rank	#119	Overall
	#57	Relational DBMS

Website

aws.amazon.com/cloudsearch

impala.apache.org

cloud.google.com/spanner

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

www.percona.com/software/mysql-database/percona-server

Technical documentation

docs.aws.amazon.com/cloudsearch

impala.apache.org/impala-docs.html

cloud.google.com/spanner/docs

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

www.percona.com/downloads/Percona-Server-LATEST

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Microsoft

Percona

Initial release

2012

2013

2017

1989

2008

Current release

4.1.0, June 2022

SQL Server 2022, November 2022

8.0.36-28, 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

GPL version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

Server operating systems

hosted

Linux

hosted

Linux
Windows

Linux

Data scheme

yes

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.

yes

Secondary indexes

yes

all search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

HTTP API

JDBC
ODBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

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

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python

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

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

selectable replication factor

Multi-source replication with 3 replicas for regional instances.

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication
XtraDB Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

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

authentication via encrypted signatures

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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