DBMS > Elasticsearch vs. etcd vs. Google BigQuery vs. Microsoft Azure SQL Database vs. TimesTen

System Properties Comparison Elasticsearch vs. etcd vs. Google BigQuery vs. Microsoft Azure SQL Database vs. TimesTen

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

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

Database as a Service offering with high compatibility to Microsoft SQL Server

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Search engine

Key-value store

Relational DBMS

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	7.05
Rank	#53	Overall
	#5	Key-value stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.elastic.co/elasticsearch

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

cloud.google.com/bigquery

azure.microsoft.com/en-us/products/azure-sql/database

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

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

cloud.google.com/bigquery/docs

docs.microsoft.com/en-us/azure/azure-sql

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Elastic

Google

Microsoft

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2010

1998

Current release

8.6, January 2023

3.4, August 2019

V12

Release 22.1

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2.0

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

C++

Server operating systems

All OS with a Java VM

FreeBSD
Linux
Windows

experimental

hosted

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

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

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 query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

gRPC
JSON over HTTP

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

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

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Transact SQL

PL/SQL

Triggers

yes

by using the 'percolation' feature

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes, with always 3 replicas available

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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