Goals:

1. Improve Metadata Search: This requires redesign of how we store metadata. Design proposed below.
   
   • Make search for tags work for all the 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 fieldname

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: 

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 for all entities with a schema

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. 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: 

   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*
   • 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:
   
   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)
        • 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. 

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.

In addition to above goals we also plan to do the following:

1. Metadata Search Results:

   • CDAP-4274 - Metadata search should returns the metadata of matching entities ( Open)
   • Also return some other relevant info. Please see details below.

   Search Result 

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

   Entity Type	Search Details	Note
   Application	Type
   	Name
   	Metadata: Tags and Properties
   	App Description
   Program	Type	If Type=Workflow then also show all program under the workflow
   	Name
   	Metadata: Tags and Properties
   	App it belongs to
   Artifact	Type
   	Name
   Dataset	Type
   	Name
   Stream	Name
   	Type
   View	Name
   	Type
   	Stream Name

   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 ? 

2. Emit more metadata from system entities:

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.