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DBMS > Apache Impala vs. Faircom EDGE vs. Realm vs. Solr

System Properties Comparison Apache Impala vs. Faircom EDGE vs. Realm vs. Solr

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonRealm  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSKey-value store
Relational DBMS
Document storeSearch engine
Secondary database modelsDocument storeSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score7.41
Rank#52  Overall
#8  Document stores
Score41.02
Rank#24  Overall
#3  Search engines
Websiteimpala.apache.orgwww.faircom.com/­products/­faircom-edgerealm.iosolr.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlrealm.io/­docssolr.apache.org/­resources.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaFairCom CorporationRealm, acquired by MongoDB in May 2019Apache Software Foundation
Initial release2013197920142006
Current release4.1.0, June 2022V3, October 20209.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infoRestricted, free version availableOpen SourceOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ANSI C, C++Java
Server operating systemsLinuxAndroid
Linux infoARM, x86
Raspbian
Windows
Android
Backend: server-less
iOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesFlexible 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 dateyesyes, 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 indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infoANSI SQL queriesnoSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
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 languagesAll languages supporting JDBC/ODBCC
C#
C++
Java
JavaScript
PHP
Python
VB.Net
.Net
Java infowith Android only
Objective-C
React Native
Swift
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes info.Net, JavaScript, C/C++no inforuns within the applications so server-side scripts are unnecessaryJava plugins
Triggersnoyesyes infoChange Listenersyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingFile partitioning infoCustomizable business rules for partitioningnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoSynchronous and asynchronous realtime replication based on transaction logsnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Tunable Consistency
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infowhen using SQLnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes infoacross SQL and NoSQLyes
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.noyesyes infoIn-Memory realmyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.yesyes

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