Goals:

1. Improve Metadata Search: This requires redesign of how we store metadata. Design proposed below.
   
   • Fix the bug in Metadata Search for tags which works only for the first entry and not other tags in the list
   • Support tokenized search where user can search with any word from the value
2. Schema Search:
   • CDAP Schema for Datasets, Streams and Views should be stored as metadata and searchable through fieldnames.
3. Metadata Search Results:
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   • In addition to the above we will also like to return some other relevant info. Please see details below.
     
     
4. Minor: We will also take Bhoosan's work on System Metadata to completion.
   • Work done by Bhooshan Mogal:
     
     • Doc: Metadata and Data Discovery 3.3
     • PR: https://github.com/caskdata/cdap/pull/4683
   • Things to do:
     • Emit more metadata from system entities
     • Merge: https://github.com/caskdata/cdap/pull/4683

Checklist

• User stories documented (Rohit/Poorna)
• User stories reviewed (Nitin)
• Design documented (Rohit/Poorna)
• Design reviewed (Andreas)
• Feature merged (Rohit)
• Examples and guides (Rohit)
• Integration tests (Rohit) 
• Documentation for feature (Rohit)
• Blog post 

User Stories: 

 Goal 1 and 2: 

1. User should be able to search key-value metadata with the following or with its prefix:
   • key-value 
   • key with part of value
   • value
   • Individual words in the value
     
     Example:
     User stores a key-value metadata with key = "Codename" and value = "Alpha Tango Charlie" for an entity
   • User should be able to search for 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. User should be able to search tags metadata with the following or with its prefix:
   • tags key and a tag value
   • a tag value
     
     Example:
     User tags an entity with the following tags "Tag1, Tag22"
   • User should be able to search for 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. User should be able search for entities (datasets, streams, views) through field-names in schema with the following or with its prefix:
   1. fieldname
   2. fieldname scoped with schema - this should limit the search to just schema fields and not other metadata

   3. User should be able to search for all entities with which has a schema
      
      Example:
      A dataset has the following schema: 

      Nested Schema

      {
        "EmpName": "String",
        "EmpContact": {
          "EmpTel": "Integer",
          "EmpAddr": "String"
        }
      }

      User should be able to search for this dataset 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*
      • search for all entities with a schema
        1. schema:* This will return this dataset entity and also all the other entities which have schema stored as their metadata
           
           
      Note:
      
      • 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)
          • Maybe We can index its fields with input and output schema and we expect an user to specify whether they are looking for something in input schema or output schema. 
        • What about entities which have more than one schema?
          • Maybe we can store them either as input output with identifier.
        • How will an user search for a fieldName across input and output schema ?
          • One way is to besides indexing the fields as input and output schema we also index every field as just schema so that we can perform such queries.
            

 Goal 3:

1. User should be able to see all metadata of an entity in search result of a metadata search
2. User should be able to see other relevant information of entity.

New Proposed Design:

Storage:

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

1. Value Row: This row will store the entity id with key and value in the value column
2. Index Row: This row will store the entity id with key (like above) appended by the index which is also stored in the index column. The index column will be used for indexing.

Metadata Storage Format:

Index Storage Format:

This table data represents key-value, tags and schema example discussed above to show how we plan to store the data. Index Column contains all the possibilities of search queries. 

We will be using the indexedTable like before but now our keys which store values will be prefixed with a special VRPrefix (ValueRowPrefix) and we will store the value in the value column. The indexes will also be stored in the same table and the key will be prefixes with IRPrefix (IndexRowPrefix), the value column for such rows will be empty and the index column will have the index value which will be indexed for search.

Another possibility was to store the real key value in a separate table and the indexes in the indexedTable which will avoid the empty column values for a row but this will lead to 6 tables on total (3 for system and business each) hence we have decided against it.

Search Result:

Metadata search will return Entities with the following details depending upon the type of the Entity.

Design Decision:

• In the search result of entity we will return all the metadata for that entity too. 

Open Question: 

• Please suggest other things which we can add to different search result entities ? 
  
  

System Metadata: 

Here is a list of System Metadata which we are planning to emit from different entities. If you have any suggestions as what other info can be useful as system metadata please comment below.

Artifacts

• Version

Applications

• ArtifactId
• Plugins
  • Plugin Type
  • Plugin Name
• Schedule
• Programs

Programs

• Type: Flow, MapReduce etc
  • Workflow
    • Nodes under this workflow
• Mode: Batch, Realtime

Datasets

• Schema
• RecordScannable/BatchWritable/RecordWritable/BatchReadable
• Type: KVTable, FileSet etc
• ttl

Streams

• Schema
• ttl

Views

• Schema

Open Questions:

• Please suggest other things which we can add to different system metadata entries
• Nitin Motgi: Can we call "business metadata" "user metadata" and also the table which stores it userMetadata table rather than business to keep it consistent with other stuff  like metrics etc.