SharePoint vs Solr Search – A Comparison

Before jumping to any conclusions, it is important to note that SharePoint is more a content management and collaboration solution than a Search solution. ‘FAST Enterprise Search’ was acquired in 2008 by Microsoft and integrated to SharePoint to provide search capabilities for SharePoint rather than be used as a stand-alone search product.

Whereas Solr is a purpose-built Big Data enabled, highly available fault tolerant, lightening fast Search solution. So comparing SharePoint 2016 with Apache Solr is NOT an apple to apple comparison.

However, there are some user queries asking for comparison of these two technologies and hence most of the comparison points are listed below.  You may want to check your use case and decide accordingly before choosing the right enterprise search solution.

SNFeatureSharePoint 2016 SearchSolr
1Full-text, boolean, range search, sorting, sub-second, facets, did-you-mean, synonyms, facetingYesYes
2IntegrationSharePoint search may not be the best bet for heavy duty search applications with multiple sources, but within the SharePoint universe, it’s a pretty decent search platform and is tightly integrated with SharePoint.Integration with Backends: Solr can crawl websites, diverse data sources and other repositories, and supports ‘binary’ document formats such as Microsoft Office and PDF documents.
3sacling for document volumeadd columnsadd shards
4Boolean Query LanguageYes (FQL)Yes (lucene or Dismax)
5APIsHTTP, Java, .NET, C++, PHPHTTP, Java, .NET, Ruby, Python, PHP, Perl, JS
6Processes RunningMany Process (C++, Java, Python). Multiple points of failureSingle Process (Java) One war file in clustered HA environment
7Navigators / Facetsindex-timequery-time (dynamic)
8Did-You-Meandictionary BasedDictionary or index based
9FeedingAPI onlyAPI or HTTP Post
10Document ProcessingPipeline (py)Simple pipeline (Java, JS, Jython, Jruby, Groovy…)
11Multified QueryingComposite FieldsDisMax handler
12Relevancy TuningRank Profiles, term boostingReranking and built-in analytics engine for continuous learning and reranking
13PluggabilityDocprocs, ClientsEverything is pluggable. Request Handlers, Query Parsers, Docprocs, Rank, Spell, tokenizer +++
14Resource ConsumingResource intensiveleast resource consuming in terms of memory and CPU cores. Therefore minimal hardware required.
15Ditributed SearchNo shardingSharding distributes index into multiple shards of core to enhance the performance
16Platform InteroperabilityNot availableAll platforms
17Office 365Integrates easily with Office 365Need external connector for office 365
18Big DataNot suitable for Big Data.Built for the big data and many big data vendors bundle solr into their big data offerings such as hadoop etc
19SpeedGoodLightening fast due to disributed search. The more shards the faster results.
20Geo Spatial Searchminimal supportFull Support
21Frontend SupportWorks well with sharepoint sites and .NET frontendsEasily integrates with any frontend application using standard APIs
22Thirdparty tool integrationLimited extensibilityCan be extended with many open source plugins this providing additional capabilities.
23New Features ReleaseDepends on MicrosoftApache Foundation and active open source contribution enables new features available continuously
SharePoint Vs Solr Search

The open source community is very active and provides documentations and forums online freely. Smart Source can help you plan, architect, develop, implement and maintain your Enterprise Solr Search Deployment.

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