DBMS > Amazon CloudSearch vs. Apache Impala vs. CockroachDB vs. Microsoft SQL Server vs. TDengine

System Properties Comparison Amazon CloudSearch vs. Apache Impala vs. CockroachDB vs. Microsoft SQL Server vs. TDengine

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

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Microsofts flagship relational DBMS

Time Series DBMS and big data platform

Primary database model

Search engine

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

Relational 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	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Website

aws.amazon.com/cloudsearch

impala.apache.org

www.cockroachlabs.com

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

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.aws.amazon.com/cloudsearch

impala.apache.org/impala-docs.html

www.cockroachlabs.com/docs

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

docs.tdengine.com

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cockroach Labs

Microsoft

TDEngine, previously Taos Data

Initial release

2012

2013

2015

1989

2019

Current release

4.1.0, June 2022

24.1.0, May 2024

SQL Server 2022, November 2022

3.0, August 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0, commercial license available

commercial

restricted free version is available

Open Source

AGPL V3, also commercial editions 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

C++

Server operating systems

hosted

Linux

Linux
macOS
Windows

Linux
Windows

Data scheme

yes

dynamic schema

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, wire compatible with PostgreSQL

yes

Standard SQL with extensions for time-series applications

APIs and other access methods

HTTP API

JDBC
ODBC

JDBC

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

JDBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

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

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

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

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

selectable replication factor

Multi-source replication using RAFT

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Role-based access control

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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