DBMS > etcd vs. HBase vs. Microsoft SQL Server vs. RavenDB vs. Trino

System Properties Comparison etcd vs. HBase vs. Microsoft SQL Server vs. RavenDB vs. Trino

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Wide-column store based on Apache Hadoop and on concepts of BigTable

Microsofts flagship relational DBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	5.00
Rank	#66	Overall
	#36	Relational DBMS

Website

etcd.io
github.com/etcd-io/etcd

hbase.apache.org

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

ravendb.net

trino.io

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

hbase.apache.org/book.html

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

ravendb.net/docs

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Hibernating Rhinos

Trino Software Foundation

Initial release

2008

1989

2010

2012

2020 rebranded from PrestoSQL

Current release

3.4, August 2019

2.3.4, January 2021

SQL Server 2022, November 2022

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

AGPL version 3, commercial license available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Starburst Galaxy offers a feature-rich user interface to connect all your data sources, manage your Trino clusters, and query your data.

Implementation language

Java

C++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux
Unix
Windows

using Cygwin

Linux
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
macOS

for devlopment

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

depending on connected data-source

SQL

Support of SQL

yes

SQL-like query language (RQL)

yes

APIs and other access methods

gRPC
JSON over HTTP

Java API
RESTful HTTP API
Thrift

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

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

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

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

yes

yes, depending on connected data-source

Triggers

yes, watching key changes

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

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

ACID, Cluster-wide transaction available

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

In-memory capabilities

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

yes

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

SQL standard access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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