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DBMS > etcd vs. Faircom EDGE vs. GeoMesa vs. Solr

System Properties Comparison etcd vs. Faircom EDGE vs. GeoMesa vs. Solr

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Editorial information provided by DB-Engines
Nameetcd  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonGeoMesa  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA distributed reliable key-value storeFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelKey-value storeKey-value store
Relational DBMS
Spatial DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.03
Rank#54  Overall
#5  Key-value stores
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score0.86
Rank#205  Overall
#4  Spatial DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteetcd.io
github.com/­etcd-io/­etcd
www.faircom.com/­products/­faircom-edgewww.geomesa.orgsolr.apache.org
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
docs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlwww.geomesa.org/­documentation/­stable/­user/­index.htmlsolr.apache.org/­resources.html
DeveloperFairCom CorporationCCRi and othersApache Software Foundation
Initial release197920142006
Current release3.4, August 2019V3, October 20205.0.0, May 20249.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoRestricted, free version availableOpen Source infoApache License 2.0Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoANSI C, C++ScalaJava
Server operating systemsFreeBSD
Linux
Windows infoexperimental
Android
Linux infoARM, x86
Raspbian
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or datenoyes, ANSI Standard SQL Typesyesyes infosupports customizable data types and automatic typing
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyesnoyes
Secondary indexesnoyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyes infoANSI SQL queriesnoSolr Parallel SQL Interface
APIs and other access methodsgRPC
JSON over HTTP
ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
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++
Java
JavaScript
PHP
Python
VB.Net
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnoyes info.Net, JavaScript, C/C++noJava plugins
Triggersyes, watching key changesyesnoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesFile partitioning infoCustomizable business rules for partitioningdepending on storage layerSharding
Replication methods infoMethods for redundantly storing data on multiple nodesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.yes infoSynchronous and asynchronous realtime replication based on transaction logsdepending on storage layeryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Tunable Consistency
depending on storage layerEventual Consistency
Foreign keys infoReferential integritynoyes infowhen using SQLnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes infoacross SQL and NoSQLyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesdepending on storage layeryes
User concepts infoAccess controlnoFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.yes infodepending on the DBMS used for storageyes

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