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      Overview of Documents, Fields, and Schema Design

      The fundamental premise of Solr is simple. You give it a lot of information, then later you can ask it questions and find the piece of information you want. The part where you feed in all the information is called indexing or updating. When you ask a question, it’s called a query.

      One way to understand how Solr works is to think of a loose-leaf book of recipes. Every time you add a recipe to the book, you update the index at the back. You list each ingredient and the page number of the recipe you just added. Suppose you add one hundred recipes. Using the index, you can very quickly find all the recipes that use garbanzo beans, or artichokes, or coffee, as an ingredient. Using the index is much faster than looking through each recipe one by one. Imagine a book of one thousand recipes, or one million.

      Solr allows you to build an index with many different fields, or types of entries. The example above shows how to build an index with just one field, ingredients. You could have other fields in the index for the recipe’s cooking style, like Asian, Cajun, or vegan, and you could have an index field for preparation times. Solr can answer questions like "What Cajun-style recipes that have blood oranges as an ingredient can be prepared in fewer than 30 minutes?"

      The schema is the place where you tell Solr how it should build indexes from input documents.

      How Solr Sees the World

      Solr’s basic unit of information is a document, which is a set of data that describes something. A recipe document would contain the ingredients, the instructions, the preparation time, the cooking time, the tools needed, and so on. A document about a person, for example, might contain the person’s name, biography, favorite color, and shoe size. A document about a book could contain the title, author, year of publication, number of pages, and so on.

      In the Solr universe, documents are composed of fields, which are more specific pieces of information. Shoe size could be a field. First name and last name could be fields.

      Fields can contain different kinds of data. A name field, for example, is text (character data). A shoe size field might be a floating point number so that it could contain values like 6 and 9.5. Obviously, the definition of fields is flexible (you could define a shoe size field as a text field rather than a floating point number, for example), but if you define your fields correctly, Solr will be able to interpret them correctly and your users will get better results when they perform a query.

      You can tell Solr about the kind of data a field contains by specifying its field type. The field type tells Solr how to interpret the field and how it can be queried.

      When you add a document, Solr takes the information in the document’s fields and adds that information to an index. When you perform a query, Solr can quickly consult the index and return the matching documents.

      Field Analysis

      Field analysis tells Solr what to do with incoming data when building an index. A more accurate name for this process would be processing or even digestion, but the official name is analysis.

      Consider, for example, a biography field in a person document. Every word of the biography must be indexed so that you can quickly find people whose lives have had anything to do with ketchup, or dragonflies, or cryptography.

      However, a biography will likely contains lots of words you don’t care about and don’t want clogging up your index—words like "the", "a", "to", and so forth. Furthermore, suppose the biography contains the word "Ketchup", capitalized at the beginning of a sentence. If a user makes a query for "ketchup", you want Solr to tell you about the person even though the biography contains the capitalized word.

      The solution to both these problems is field analysis. For the biography field, you can tell Solr how to break apart the biography into words. You can tell Solr that you want to make all the words lower case, and you can tell Solr to remove accents marks.

      Field analysis is an important part of a field type. Understanding Analyzers, Tokenizers, and Filters is a detailed description of field analysis.

      Solr’s Schema File

      Solr stores details about the field types and fields it is expected to understand in a schema file. The name and location of this file may vary depending on how you initially configured Solr or if you modified it later.

      • managed-schema is the name for the schema file Solr uses by default to support making Schema changes at runtime via the Schema API, or Schemaless Mode features. You may explicitly configure the managed schema features to use an alternative filename if you choose, but the contents of the files are still updated automatically by Solr.

      • schema.xml is the traditional name for a schema file which can be edited manually by users who use the ClassicIndexSchemaFactory.

      • If you are using SolrCloud you may not be able to find any file by these names on the local filesystem. You will only be able to see the schema through the Schema API (if enabled) or through the Solr Admin UI’s Cloud Screens.

      Whichever name of the file in use in your installation, the structure of the file is not changed. However, the way you interact with the file will change. If you are using the managed schema, it is expected that you only interact with the file with the Schema API, and never make manual edits. If you do not use the managed schema, you will only be able to make manual edits to the file, the Schema API will not support any modifications.

      Note that if you are not using the Schema API yet you do use SolrCloud, you will need to interact with schema.xml through ZooKeeper using upconfig and downconfig commands to make a local copy and upload your changes. The options for doing this are described in Solr Control Script Reference and Using ZooKeeper to Manage Configuration Files.