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Ref Guide: really minor typos/grammar fixes

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Cassandra Targett 2018-03-08 12:03:18 -06:00
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solr/solr-ref-guide/src/a-quick-overview.adoc
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Solr is a search server built on top of Apache Lucene, an open source, Java-based, information retrieval library. It is designed to drive powerful document retrieval applications - wherever you need to serve data to users based on their queries, Solr can work for you. Solr is a search server built on top of Apache Lucene, an open source, Java-based, information retrieval library. It is designed to drive powerful document retrieval applications - wherever you need to serve data to users based on their queries, Solr can work for you.
Here is a example of how Solr might be integrated into an application: Here is a example of how Solr could integrate with an application:
.Solr integration with applications .Solr integration with applications
image::images/a-quick-overview/sample-client-app-arch.png[image,width=500,height=379] image::images/a-quick-overview/sample-client-app-arch.png[image,width=500,height=379]
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If Solr's capabilities are not impressive enough, its ability to handle very high-volume applications should do the trick. If Solr's capabilities are not impressive enough, its ability to handle very high-volume applications should do the trick.
A relatively common scenario is that you have so much data, or so many queries, that a single Solr server is unable to handle your entire workload. In this case, you can scale up the capabilities of your application using <<solrcloud.adoc#solrcloud,SolrCloud>> to better distribute the data, and the processing of requests, across many servers. Multiple options can be mixed and matched depending on the type of scalability you need. A relatively common scenario is that you have so much data, or so many queries, that a single Solr server is unable to handle your entire workload. In this case, you can scale up the capabilities of your application using <<solrcloud.adoc#solrcloud,SolrCloud>> to better distribute the data, and the processing of requests, across many servers. Multiple options can be mixed and matched depending on the scalability you need.
For example: "Sharding" is a scaling technique in which a collection is split into multiple logical pieces called "shards" in order to scale up the number of documents in a collection beyond what could physically fit on a single server. Incoming queries are distributed to every shard in the collection, which respond with merged results. Another technique available is to increase the "Replication Factor" of your collection, which allows you to add servers with additional copies of your collection to handle higher concurrent query load by spreading the requests around to multiple machines. Sharding and Replication are not mutually exclusive, and together make Solr an extremely powerful and scalable platform. For example: "Sharding" is a scaling technique in which a collection is split into multiple logical pieces called "shards" in order to scale up the number of documents in a collection beyond what could physically fit on a single server. Incoming queries are distributed to every shard in the collection, which respond with merged results. Another technique available is to increase the "Replication Factor" of your collection, which allows you to add servers with additional copies of your collection to handle higher concurrent query load by spreading the requests around to multiple machines. Sharding and replication are not mutually exclusive, and together make Solr an extremely powerful and scalable platform.
Best of all, this talk about high-volume applications is not just hypothetical: some of the famous Internet sites that use Solr today are Macy's, EBay, and Zappo's. For more examples, take a look at https://wiki.apache.org/solr/PublicServers. Best of all, this talk about high-volume applications is not just hypothetical: some of the famous Internet sites that use Solr today are Macy's, EBay, and Zappo's. For more examples, take a look at https://wiki.apache.org/solr/PublicServers.

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solr/solr-ref-guide/src/solr-tutorial.adoc
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:page-tocclass: right :page-tocclass: right
:experimental: :experimental:
This tutorial covers getting Solr up and running, ingesting a variety of data sources into multiple collections, This tutorial covers getting Solr up and running, ingesting a variety of data sources into Solr collections,
and getting a feel for the Solr administrative and search interfaces. and getting a feel for the Solr administrative and search interfaces.
It is organized into three sections that each build on the one before it. The <<exercise-1,first exercise>> will ask you to start Solr, create a collection, index some basic documents, and then perform a few searches. The tutorial is organized into three sections that each build on the one before it. The <<exercise-1,first exercise>> will ask you to start Solr, create a collection, index some basic documents, and then perform some searches.
The <<exercise-2,second exercise>> works with a different set of data, and explores requesting facets with the dataset. The <<exercise-2,second exercise>> works with a different set of data, and explores requesting facets with the dataset.