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.
| SN | Feature | SharePoint 2016 Search | Solr |
| 1 | Full-text, boolean, range search, sorting, sub-second, facets, did-you-mean, synonyms, faceting | Yes | Yes |
| 2 | Integration | SharePoint 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. |
| 3 | sacling for document volume | add columns | add shards |
| 4 | Boolean Query Language | Yes (FQL) | Yes (lucene or Dismax) |
| 5 | APIs | HTTP, Java, .NET, C++, PHP | HTTP, Java, .NET, Ruby, Python, PHP, Perl, JS |
| 6 | Processes Running | Many Process (C++, Java, Python). Multiple points of failure | Single Process (Java) One war file in clustered HA environment |
| 7 | Navigators / Facets | index-time | query-time (dynamic) |
| 8 | Did-You-Mean | dictionary Based | Dictionary or index based |
| 9 | Feeding | API only | API or HTTP Post |
| 10 | Document Processing | Pipeline (py) | Simple pipeline (Java, JS, Jython, Jruby, Groovy…) |
| 11 | Multified Querying | Composite Fields | DisMax handler |
| 12 | Relevancy Tuning | Rank Profiles, term boosting | Reranking and built-in analytics engine for continuous learning and reranking |
| 13 | Pluggability | Docprocs, Clients | Everything is pluggable. Request Handlers, Query Parsers, Docprocs, Rank, Spell, tokenizer +++ |
| 14 | Resource Consuming | Resource intensive | least resource consuming in terms of memory and CPU cores. Therefore minimal hardware required. |
| 15 | Ditributed Search | No sharding | Sharding distributes index into multiple shards of core to enhance the performance |
| 16 | Platform Interoperability | Not available | All platforms |
| 17 | Office 365 | Integrates easily with Office 365 | Need external connector for office 365 |
| 18 | Big Data | Not 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 |
| 19 | Speed | Good | Lightening fast due to disributed search. The more shards the faster results. |
| 20 | Geo Spatial Search | minimal support | Full Support |
| 21 | Frontend Support | Works well with sharepoint sites and .NET frontends | Easily integrates with any frontend application using standard APIs |
| 22 | Thirdparty tool integration | Limited extensibility | Can be extended with many open source plugins this providing additional capabilities. |
| 23 | New Features Release | Depends on Microsoft | Apache Foundation and active open source contribution enables new features available continuously |
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.