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1. Key Value Metadata Search
   1. User should be able to search with key-value or its prefix
   2. User should be able to search with key and part of value or its prefix
   3. User should be able to search with just value or its prefix
   4. User should be able to search with individual words in the value 
2. Tag Metadata Search
   1. User should be able to search with tags key and a tag value or its prefix
   2. User should be able to search with just a tag value or its prefix.
3. Schema Search:
   1. User should be able search with fieldname or its prefix
   2. User should be able to search with fieldname or its prefix scoped just to schema 
   3. User should be able to search with fieldname and fieldtype (only for primitive types)
4. Search Filtering:
   1. User should be able to filter searches to a particular entity type for example app, program, dataset
5. Partial Searching:
   1. User should be able to see result for individual words in search query.

Design

Search Query Examples:

1. User stores a key-value metadata with key = "Codename" and value = "Alpha Tango Charlie" for an entity
   1. User can retrieve this entity with the following queries:
      • key-value
        1. Codename: Alpha Tango Charlie
        2. Codename: Alpha Tang*
      • key with part of value
        1. Codename: Alpha
        2. Codename: Tango
        3. Codename: Charlie
        4. Codename: Alp*
      • value
        1. Alpha Tango Charlie
        2. Alpha*
        3. Alpha Tan*
           Note:
           1. We have decided not to support searches for queries which have parts of value for example "Tango Charlie". You can either search for whole value or with prefix or single words (we plan to tokenize on whitespace) 
      • parts of value
        1. Alpha
        2. Tango
        3. Charlie
        4. Alph*
        5. Tan*
        6. Ch*
   2. Not supported:
      1. key* i.e. Codename*
2. User tags an entity with the following tags "Tag1, Tag22"
   • User can retrieve this entity with the following queries:
     • tag key and a tag value:
       1. tags: Tag1
       2. tags: Tag*
     • a tag value
       1. tag22 
       2. tag2*

3. A dataset has the following schema: 

   Code Block
   title Nested Schema
   { "EmpName": "String", "EmpContact": { "EmpTel": "Integer", "EmpAddr": "String" } }

   User can retrieve this dataset entity with the following queries:

   • fieldname:
     1. EmpName
     2. EmpContact
     3. EmpTel 
     4. EmpAddr
     5. Emp*
   • fieldname scoped to schema:
     1. schema: EmpName
     2. schema: EmpContact
     3. schema: EmpTel
     4. schema: EmpAddr 
     5. schema: Emp*
   • fieldname with fieldtype (only for primitive types)
     1. EmpName:String (only for java primitive types)
        
        
   Note:
   
   1. We don't plan to support schema searches with fieldType. If a user  searched with a query which is not scoped with schema by default it will search for schema fields besides the normal key-value and tags.
      Open questions:
      • What if an entity has multiple schema (ex: transform which has input and output schema)
        • We will index both schema
      • How will an user search for a fieldName across input and output schema ?
        • We do not support searches limited to input/output or just one schema 
4. Search Filtering:
   1. User wants to search only for 'dataset'
      1. dataset: Codename: Alpha
      2. dataset: tags: Tag1
      3. dataset: schema: EmpName
         Note: if not entity type is specified we will return all matched entities. 
5. Partial Searching:
   1. User searches for  "California USA" : Separate every search query on white space and search for every single word (or)
      Search result will contain:
      1. All entities tagged with  "California USA" followed by
      2. All entities tagged with "California" followed by
      3. All entities tagged with "USA"

Storage:

We are going to use the IndexedTable which we are using currently too. In the new storage design we will have two rows: 

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