SharePoint 2016 Search Vs Solr Search - A comparison

By: Ayyappan Printer Friendly Format    

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. There are alos Solr Indexing search providers such as Smart Source who can help you plan, architect, develop, implement and maintain your Enterprise Solr Search Deployment.



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