v1.3.0: Dynamic Columns

v1.3.0: Dynamic Columns

Improvements

• Syncing now handles dynamic columns.
  Syncing a pipe with new columns will trigger an ALTER TABLE query to append the columns to your table:

  import meerschaum as mrsm
  pipe = mrsm.Pipe('foo', 'bar', instance='sql:memory')
  
  pipe.sync([{'a': 1}])
  print(pipe.get_data())
  #    a
  # 0  1
  
  pipe.sync([{'b': 1}])
  print(pipe.get_data())
  #       a     b
  # 0     1  <NA>
  # 1  <NA>     1

  If you've specified index columns, you can use this feature to fill in NULL values in your table:

  import meerschaum as mrsm
  pipe = mrsm.Pipe(
      'foo', 'bar',
      columns = {'id': 'id_col'},
      instance = 'sql:memory',
  )
  
  pipe.sync([{'id_col': 1, 'a': 10.0}])
  pipe.sync([{'id_col': 1, 'b': 20.0}])
  
  print(pipe.get_data())
  #    id_col     a     b
  # 0       1  10.0  20.0

• Add as many indices as you like.
  In addition to the special index column labels datetime, id, and value, the values of all keys within the Pipe.columns dictionary will be treated as indices when creating and updating tables:

  import meerschaum as mrsm
  indices = {'micro': 'station', 'macro': 'country'}
  pipe = mrsm.Pipe('demo', 'weather', columns=indices, instance='sql:memory')
  
  docs = [{'station': 1, 'country': 'USA', 'temp_f': 80.6}]
  pipe.sync(docs)
  
  docs = [{'station': 1, 'country': 'USA', 'temp_c': 27.0}]
  pipe.sync(docs)
  
  print(pipe.get_data())
  #    station  country  temp_f  temp_c
  # 0        1      USA    80.6    27.0

• Added a default 60-second timeout for pipe attributes.
  All parameter properties (e.g. Pipe.columns, Pipe.target, Pipe.dtypes, etc.) will sync with the instance every 60 seconds. The in-memory attributes will be patched on top of the database values, so your unsaved state won't be lost (persist your state with Pipe.edit()). You can change the timeout duration with mrsm edit config pipes under the keys attributes:local_cache_timeout_seconds. To disable this caching behavior, set the value to null.

• Added custom indices and Pandas data types to the Web UI.