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Training #1

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4ba8b82
Fixed first test on word count
ovidiu-eremia Feb 24, 2022
c73d68f
Fixed SA errors
ovidiu-eremia Feb 25, 2022
4e57245
Finished tests and app run
ovidiu-eremia Mar 3, 2022
3dbebdb
Corrections after evaluation
ovidiu-eremia Mar 4, 2022
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11 changes: 10 additions & 1 deletion data_transformations/citibike/distance_transformer.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,22 @@
from pyspark.sql import SparkSession, DataFrame
from pyspark.sql import functions as fn
from pyspark.sql import types as tp

METERS_PER_FOOT = 0.3048
FEET_PER_MILE = 5280
EARTH_RADIUS_IN_METERS = 6371e3
EARTH_RADIUS_IN_MILES = 3963.0
DISTANCE_SCALE = 2
METERS_PER_MILE = METERS_PER_FOOT * FEET_PER_MILE


def compute_distance(_spark: SparkSession, dataframe: DataFrame) -> DataFrame:
return dataframe
scale_factor = fn.pow(fn.lit(10), DISTANCE_SCALE).cast(tp.DoubleType())
return dataframe.withColumn("distance", fn.floor(fn.acos(
fn.sin(fn.radians("start_station_latitude")) * fn.sin(fn.radians("end_station_latitude")) +
fn.cos(fn.radians("start_station_latitude")) * fn.cos(fn.radians("end_station_latitude")) *
fn.cos(fn.radians("start_station_longitude") - fn.radians("end_station_longitude"))
) * fn.lit(EARTH_RADIUS_IN_MILES) * scale_factor) / scale_factor)


def run(spark: SparkSession, input_dataset_path: str, transformed_dataset_path: str) -> None:
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13 changes: 12 additions & 1 deletion data_transformations/wordcount/word_count_transformer.py
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@@ -1,12 +1,23 @@
import logging

from pyspark.sql import SparkSession
from pyspark.sql import functions as f


def run(spark: SparkSession, input_path: str, output_path: str) -> None:
logging.info("Reading text file from: %s", input_path)
input_df = spark.read.text(input_path)

is_not_none = lambda t: ((t != "") & (t != "'"))
words_df = (input_df.select(f.split(input_df.value, r"[^'a-zA-Z]").alias("tokens"))
.withColumn("words", f.filter("tokens", is_not_none))
.select(f.explode("words").alias("word"))
.filter(~ f.col("word").like("'%"))
.select(f.lower(f.col("word")).alias("word"))
.groupBy("word")
.agg(f.count("*").alias("count"))
.orderBy("word"))

logging.info("Writing csv to directory: %s", output_path)

input_df.coalesce(1).write.csv(output_path, header=True)
words_df.coalesce(1).write.csv(output_path, header=True, mode="overwrite")
6 changes: 3 additions & 3 deletions jobs/citibike_distance_calculation.py
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Expand Up @@ -12,12 +12,12 @@
logging.basicConfig(filename=LOG_FILENAME, level=logging.INFO)
arguments = sys.argv

if len(arguments) is not 3:
if len(arguments) != 3:
logging.warning("Dataset file path and output path not specified!")
sys.exit(1)

dataset_path = arguments[2]
output_path = arguments[3]
dataset_path = arguments[1]
output_path = arguments[2]

spark = SparkSession.builder.appName(APP_NAME).getOrCreate()
logging.info("Application Initialized: " + spark.sparkContext.appName)
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2 changes: 1 addition & 1 deletion jobs/citibike_ingest.py
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Expand Up @@ -12,7 +12,7 @@
logging.basicConfig(filename=LOG_FILENAME, level=logging.INFO)
logging.info(sys.argv)

if len(sys.argv) is not 3:
if len(sys.argv) != 3:
logging.warning("Input source and output path are required")
sys.exit(1)

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2 changes: 1 addition & 1 deletion jobs/word_count.py
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Expand Up @@ -12,7 +12,7 @@
logging.basicConfig(filename=LOG_FILENAME, level=logging.INFO)
logging.info(sys.argv)

if len(sys.argv) is not 3:
if len(sys.argv) != 3:
logging.warning("Input .txt file and output path are required")
sys.exit(1)

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17 changes: 14 additions & 3 deletions tests/integration/test_distance_transformer.py
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Expand Up @@ -4,6 +4,8 @@

import pytest
from pyspark.sql.types import StructField, DoubleType
from pyspark.sql import DataFrame
from pyspark.sql import functions as fn

from data_transformations.citibike import distance_transformer
from tests.integration import SPARK
Expand Down Expand Up @@ -92,11 +94,10 @@ def test_should_maintain_all_data_it_reads() -> None:
expected_columns = set(given_dataframe.columns)
expected_schema = set(given_dataframe.schema)

assert expected_columns == actual_columns
assert expected_columns.issubset(actual_columns)
assert expected_schema.issubset(actual_schema)


@pytest.mark.skip
def test_should_add_distance_column_with_calculated_distance() -> None:
given_ingest_folder, given_transform_folder = __create_ingest_and_transform_folders()
distance_transformer.run(SPARK, given_ingest_folder, given_transform_folder)
Expand All @@ -114,7 +115,7 @@ def test_should_add_distance_column_with_calculated_distance() -> None:
actual_distance_schema = actual_dataframe.schema['distance']

assert expected_distance_schema == actual_distance_schema
assert expected_dataframe.collect() == actual_dataframe.collect()
assert __check_dataframes_equality(expected_dataframe, actual_dataframe)


def __create_ingest_and_transform_folders() -> Tuple[str, str]:
Expand All @@ -124,3 +125,13 @@ def __create_ingest_and_transform_folders() -> Tuple[str, str]:
ingest_dataframe = SPARK.createDataFrame(SAMPLE_DATA, BASE_COLUMNS)
ingest_dataframe.write.parquet(ingest_folder, mode='overwrite')
return ingest_folder, transform_folder


def __check_dataframes_equality(df1: DataFrame, df2: DataFrame) -> bool:
df1_cols = sorted(df1.columns)
df2_cols = sorted(df2.columns)
df1_groupedby_cols = df1.groupby(df1_cols).agg(fn.count(df1_cols[1]))
df2_groupedby_cols = df2.groupby(df2_cols).agg(fn.count(df2_cols[1]))
if df1_groupedby_cols.subtract(df2_groupedby_cols).rdd.isEmpty():
return df2_groupedby_cols.subtract(df1_groupedby_cols).rdd.isEmpty()
return False
2 changes: 1 addition & 1 deletion tests/integration/test_ingest.py
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Expand Up @@ -38,7 +38,7 @@ def __create_ingest_and_transform_folders() -> Tuple[str, str]:


def __write_csv_file(file_path: str, content: List[List[str]]) -> None:
with open(file_path, 'w') as csv_file:
with open(file_path, 'w', encoding="utf8") as csv_file:
input_csv_writer = csv.writer(csv_file)
input_csv_writer.writerows(content)
csv_file.close()
3 changes: 1 addition & 2 deletions tests/integration/test_word_count.py
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Expand Up @@ -12,14 +12,13 @@ def _get_file_paths(input_file_lines: List[str]) -> Tuple[str, str]:
base_path = tempfile.mkdtemp()

input_text_path = "%s%sinput.txt" % (base_path, os.path.sep)
with open(input_text_path, 'w') as input_file:
with open(input_text_path, 'w', encoding="utf8") as input_file:
input_file.writelines(input_file_lines)

output_path = "%s%soutput" % (base_path, os.path.sep)
return input_text_path, output_path


@pytest.mark.skip
def test_should_tokenize_words_and_count_them() -> None:
lines = [
"In my younger and more vulnerable years my father gave me some advice that I've been "
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