Add some descriptive comments.

wikiq

@@ -26,7 +26,7 @@ from deltas import SequenceMatcher
 from deltas import SegmentMatcher
 
 import dataclasses as dc
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass
 import pyarrow as pa
 import pyarrow.parquet as pq
 
@@ -133,6 +133,11 @@ class WikiqPage():
         return next(self.__revisions)
 
 
+"""
+A RegexPair is defined by a regular expression (pattern) and a label.
+The pattern can include capture groups.  If it does then each capture group will have a resulting column in the output.
+If the pattern does not include a capture group, then only one output column will result.
+"""
 class RegexPair(object):
     def __init__(self, pattern, label):
         self.pattern = re.compile(pattern)
@@ -201,6 +206,22 @@ class RegexPair(object):
 
         return rev_data
 
+"""
+
+We used to use a dictionary to collect fields for the output. 
+Now we use dataclasses. Compared to a dictionary, this should help:
+- prevent some bugs
+- make it easier to output parquet data. 
+- use class attribute '.' syntax instead of dictionary syntax. 
+- improve support for tooling (autocomplete, type hints)
+- use type information to define formatting rules
+
+Depending on the parameters passed into Wikiq, the output schema can be different. 
+Therefore, we need to end up constructing a dataclass with the correct output schema. 
+It also needs to have the correct pyarrow schema so we can write parquet files.
+
+The RevDataBase type has all the fields that will be output no matter how wikiq is invoked.
+"""
 @dataclass()
 class RevDataBase():
     revid: int 
@@ -218,10 +239,16 @@ class RevDataBase():
     editor: str = None
     anon: bool = None
 
+    # toggles url encoding. this isn't a dataclass field since it doesn't have a type annotation
     urlencode = False
+
+    # defines pyarrow schema.
+    # each field in the data class needs an entry in this array.
+    # the names should match and be in the same order.
+    # this isn't a dataclass field since it doesn't have a type annotation
     pa_schema_fields = [
         pa.field("revid", pa.int64()),
-        pa.field("date_time",pa.timestamp('ms')),
+        pa.field("date_time", pa.timestamp('ms')),
         pa.field("articleid",pa.int64()),
         pa.field("editorid",pa.int64()),
         pa.field("title",pa.string()),
@@ -236,9 +263,11 @@ class RevDataBase():
         pa.field("anon",pa.bool_())
     ]
 
+    # pyarrow is a columnar format, so most of the work happens in the flush_parquet_buffer function
     def to_pyarrow(self):
         return dc.astuple(self)
 
+    # logic to convert each field into the wikiq tsv format goes here.
     def to_tsv_row(self):
         
         row = []
@@ -275,12 +304,26 @@ class RevDataBase():
     def header_row(self):
         return '\t'.join(map(lambda f: f.name, dc.fields(self)))
 
+"""
+
+If collapse=True we'll use a RevDataCollapse dataclass.
+This class inherits from RevDataBase. This means that it has all the same fields and functions. 
+
+It just adds a new field and updates the pyarrow schema.
+
+"""
 @dataclass()
 class RevDataCollapse(RevDataBase):
     collapsed_revs:int = None
+
     pa_collapsed_revs_schema = pa.field('collapsed_revs',pa.int64())
     pa_schema_fields = RevDataBase.pa_schema_fields + [pa_collapsed_revs_schema]
 
+"""
+
+If persistence data is to be computed we'll need the fields added by RevDataPersistence. 
+
+"""
 @dataclass()
 class RevDataPersistence(RevDataBase):
     token_revs:int = None
@@ -296,6 +339,10 @@ class RevDataPersistence(RevDataBase):
         
     pa_schema_fields = RevDataBase.pa_schema_fields  + pa_persistence_schema_fields
 
+"""
+class RevDataCollapsePersistence uses multiple inheritence to make a class that has both persistence and collapse fields.
+
+"""
 @dataclass()
 class RevDataCollapsePersistence(RevDataCollapse, RevDataPersistence):
     pa_schema_fields = RevDataCollapse.pa_schema_fields + RevDataPersistence.pa_persistence_schema_fields
@@ -323,6 +370,9 @@ class WikiqParser():
         self.regex_revision_pairs = self.make_matchmake_pairs(regex_match_revision, regex_revision_label)
         self.regex_comment_pairs = self.make_matchmake_pairs(regex_match_comment, regex_comment_label)
 
+
+        # This is where we set the type for revdata.
+        
         if self.collapse_user is True:
             if self.persist == PersistMethod.none:
                 revdata_type = RevDataCollapse
@@ -333,16 +383,23 @@ class WikiqParser():
         else:
             revdata_type = RevDataBase
 
+        # if there are regex fields, we need to add them to the revdata type.
         regex_fields = [(field.name, list[str], dc.field(default=None)) for field in self.regex_schemas]
 
-        self.revdata_type = make_dataclass('RevData_Parser',
+        # make_dataclass is a function that defines a new dataclass type.
-                                           fields=regex_fields,
+        # here we extend the type we have already chosen and add the regular expression types 
-                                           bases=(revdata_type,))
+        self.revdata_type = dc.make_dataclass('RevData_Parser',
+                                              fields=regex_fields,
+                                              bases=(revdata_type,))
         
+        # we also need to make sure that we have the right pyarrow schema
         self.revdata_type.pa_schema_fields = revdata_type.pa_schema_fields + self.regex_schemas
                         
         self.revdata_type.urlencode = self.urlencode
 
+        self.schema = pa.schema(self.revdata_type.pa_schema_fields)
+
+        # here we initialize the variables we need for output.
         if output_parquet is True:
             self.output_parquet = True
             self.pq_writer = None
@@ -451,6 +508,7 @@ class WikiqParser():
             # Iterate through a page's revisions
             for rev in page:
                 
+                # create a new data object instead of a dictionary. 
                 rev_data = self.revdata_type(revid = rev.id,
                                              date_time = datetime.fromtimestamp(rev.timestamp.unix(), tz=timezone.utc),
                                              articleid = page.id,
@@ -556,6 +614,7 @@ class WikiqParser():
         print("Done: %s revisions and %s pages." % (rev_count, page_count),
               file=sys.stderr)
 
+        # remember to flush the parquet_buffer if we're done
         if self.output_parquet is True:
             self.flush_parquet_buffer()
             self.pq_writer.close()
@@ -564,6 +623,10 @@ class WikiqParser():
             self.output_file.close()
 
 
+    """
+    For performance reasons it's better to write parquet in batches instead of one row at a time.
+    So this function just puts the data on a buffer. If the buffer is full, then it gets flushed (written).
+    """
     def write_parquet_row(self, rev_data):
         padata = rev_data.to_pyarrow()
         self.parquet_buffer.append(padata)
@@ -572,10 +635,16 @@ class WikiqParser():
             self.flush_parquet_buffer()
 
 
+    """
+    Function that actually writes data to the parquet file. 
+    It needs to transpose the data from row-by-row to column-by-column
+    """
     def flush_parquet_buffer(self):
-        schema = pa.schema(self.revdata_type.pa_schema_fields)
 
-        def row_to_col(rg, types):
+        """
+        Returns the pyarrow table that we'll write
+        """
+        def rows_to_table(rg, schema):
             cols = []
             first = rg[0]
             for col in first:
@@ -586,17 +655,18 @@ class WikiqParser():
                     cols[j].append(row[j])
 
             arrays = []
-            for col, typ in zip(cols, types):
+            for col, typ in zip(cols, schema.types):
                 arrays.append(pa.array(col, typ))
-            return arrays
+            return pa.Table.from_arrays(arrays, schema=schema)
 
-        outtable = pa.Table.from_arrays(row_to_col(self.parquet_buffer, schema.types), schema=schema)
+        outtable = rows_to_table(self.parquet_buffer, self.schema)
         if self.pq_writer is None:
             self.pq_writer = pq.ParquetWriter(self.output_file, schema, flavor='spark')
 
         self.pq_writer.write_table(outtable)
         self.parquet_buffer = []
         
+    # depending on if we are configured to write tsv or parquet, we'll call a different function.
     def print_rev_data(self, rev_data):
         if self.output_parquet is False:
             printfunc = self.write_tsv_row