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
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)
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
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()),
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 = []
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
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
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
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',
- fields=regex_fields,
- bases=(revdata_type,))
+ # make_dataclass is a function that defines a new dataclass type.
+ # here we extend the type we have already chosen and add the regular expression types
+ 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
# 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,
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()
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)
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:
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
projects / mediawiki_dump_tools.git / commitdiff