def test_mixed_sql_and_udf(self):
df = self.data
w = self.unbounded_window
ow = self.ordered_window
max_udf = self.pandas_agg_max_udf
min_udf = self.pandas_agg_min_udf
result1 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min_udf(df['v']).over(w))
expected1 = df.withColumn('v_diff', max(df['v']).over(w) - min(df['v']).over(w))
# Test mixing sql window function and window udf in the same expression
result2 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min(df['v']).over(w))
expected2 = expected1
# Test chaining sql aggregate function and udf
result3 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
.withColumn('min_v', min(df['v']).over(w)) \
.withColumn('v_diff', col('max_v') - col('min_v')) \
.drop('max_v', 'min_v')
expected3 = expected1
# Test mixing sql window function and udf
result4 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
.withColumn('rank', rank().over(ow))
expected4 = df.withColumn('max_v', max(df['v']).over(w)) \
.withColumn('rank', rank().over(ow))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
self.assertPandasEqual(expected3.toPandas(), result3.toPandas())
self.assertPandasEqual(expected4.toPandas(), result4.toPandas())
def test_window_functions(self):
df = self.sqlCtx.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
w = Window.partitionBy("value").orderBy("key")
from pyspark.sql import functions as F
sel = df.select(
df.value,
df.key,
F.max("key").over(w.rowsBetween(0, 1)),
F.min("key").over(w.rowsBetween(0, 1)),
F.count("key").over(w.rowsBetween(float("-inf"), float("inf"))),
F.rowNumber().over(w),
F.rank().over(w),
F.denseRank().over(w),
F.ntile(2).over(w),
)
rs = sorted(sel.collect())
expected = [
("1", 1, 1, 1, 1, 1, 1, 1, 1),
("2", 1, 1, 1, 3, 1, 1, 1, 1),
("2", 1, 2, 1, 3, 2, 1, 1, 1),
("2", 2, 2, 2, 3, 3, 3, 2, 2),
]
for r, ex in zip(rs, expected):
self.assertEqual(tuple(r), ex[: len(r)])
def process_file(date_update):
"""Process downloaded MEDLINE folder to parquet file"""
print("Process MEDLINE file to parquet")
# remove if folder still exist
if glob(os.path.join(save_dir, 'medline_*.parquet')):
subprocess.call(['rm', '-rf', 'medline_*.parquet'])
date_update_str = date_update.strftime("%Y_%m_%d")
path_rdd = sc.parallelize(glob(os.path.join(download_dir, 'medline*.xml.gz')), numSlices=1000)
parse_results_rdd = path_rdd.\
flatMap(lambda x: [Row(file_name=os.path.basename(x), **publication_dict)
for publication_dict in pp.parse_medline_xml(x)])
medline_df = parse_results_rdd.toDF()
medline_df.write.parquet(os.path.join(save_dir, 'medline_raw_%s.parquet' % date_update_str),
mode='overwrite')
window = Window.partitionBy(['pmid']).orderBy(desc('file_name'))
windowed_df = medline_df.select(
max('delete').over(window).alias('is_deleted'),
rank().over(window).alias('pos'),
'*')
windowed_df.\
where('is_deleted = False and pos = 1').\
write.parquet(os.path.join(save_dir, 'medline_lastview_%s.parquet' % date_update_str),
mode='overwrite')
# parse grant database
parse_grant_rdd = path_rdd.flatMap(lambda x: pp.parse_medline_grant_id(x))\
.filter(lambda x: x is not None)\
.map(lambda x: Row(**x))
grant_df = parse_grant_rdd.toDF()
grant_df.write.parquet(os.path.join(save_dir, 'medline_grant_%s.parquet' % date_update_str),
mode='overwrite')
def extractor (df, min_count, output_path):
n_gram_df = make_ngrams (df)
n_gram_score = chi_square_procedur(n_gram_df, min_count)
window = Window.partitionBy(n_gram_score['category'])\
.orderBy(n_gram_score['aprx_chi_scr'].desc())
n_gram_score = n_gram_score.dropDuplicates(['n_gram', 'category'])
top_word_df = n_gram_score.select('*', F.rank().over(window).alias('rank'))\
.filter(F.col('rank')<=1000)
top_word_df = top_word_df.join(categories, on = ['category'], how='left')
top_words = top_word_df.orderBy(F.col('category'), F.col('count').desc()).select('n_gram','category',\
'count','distinct_user_count','aprx_chi_scr').toPandas()
top_words.to_csv(output_path)
return top_words
def test_window_functions_without_partitionBy(self):
df = self.sqlCtx.createDataFrame([(1, "1"), (2, "2"), (1, "2"),
(1, "2")], ["key", "value"])
w = Window.orderBy("key", df.value)
from pyspark.sql import functions as F
sel = df.select(
df.value, df.key,
F.max("key").over(w.rowsBetween(0, 1)),
F.min("key").over(w.rowsBetween(0, 1)),
F.count("key").over(w.rowsBetween(float('-inf'), float('inf'))),
F.rowNumber().over(w),
F.rank().over(w),
F.denseRank().over(w),
F.ntile(2).over(w))
rs = sorted(sel.collect())
expected = [
("1", 1, 1, 1, 4, 1, 1, 1, 1), ("2", 1, 1, 1, 4, 2, 2, 2, 1),
("2", 1, 2, 1, 4, 3, 2, 2, 2), ("2", 2, 2, 2, 4, 4, 4, 3, 2)
]
for r, ex in zip(rs, expected):
self.assertEqual(tuple(r), ex[:len(r)])
def retrieve_next_layer(self, f, e, topx, direction='out'):
if direction == 'out':
orig_node = 'src'
dest_node = 'dst'
else:
orig_node = 'dst'
dest_node = 'src'
df = f.select("id").join(e.drop('in_scope'), f.id == e[orig_node],
'inner').drop(orig_node)
window = Window.partitionBy(df['id']).orderBy(df['amount'].desc())
df = df.select(
'*',
F.rank().over(window).alias('rank')).filter(F.col('rank') <= topx)
dummy_tmp = self.create_dummy_edges(df, f, topx, direction)
df = dummy_tmp.union(df.select(dummy_tmp.columns))
df = df.withColumn("direction", F.lit(direction))
df = df.withColumnRenamed(dest_node, "adj")
return df
def spark_mysql(title_basics, title_ratings):
titles = title_basics.select('tconst', 'startYear', 'originalTitle',
'titleType')
titles = titles.withColumn('startYear', titles['startYear'].cast(IntegerType()))\
.where(titles['startYear'].isNotNull())
ratings = title_ratings.select('tconst', 'averageRating')
result = ratings.join(titles, on=['tconst'])
window = Window.partitionBy(['titleType',
'startYear']).orderBy(desc('averageRating'))
result = result.select(
'*',
rank().over(window).alias('rank')).filter(col('rank') <= 10)
result = result.orderBy('titleType', 'startYear', desc('averageRating'))
return result
def find_most_trees_address(self, df):
"""Find the adrress with most number of trees planted
:param df: Input DataFrame containing all details of trees
:return: DataFrame of the address with most number of trees
"""
# dataframe of address and corresponding tree_id(s) and find the total count of trees in each address
max_trees = df.select('address', 'tree_id').filter(
df.address.isNotNull()).groupBy(col('address')).count().sort(
desc("count"))
# rank the addresses based on decreasing order of number of trees planted and find top address, which is rank 1
max_trees_place = max_trees.withColumn(
"rank",
rank().over(Window.orderBy(col("count").desc()))).filter(
col("rank") == 1).select('address')
self.log.warn('Found the address with most number of trees planted')
return max_trees_place
def main(spark, model_file, data_file, K):
'''Main routine for Collaborative Filtering Model testing
Parameters
----------
spark: SparkSession object
model_file: string, path to store the model
data_file: string, path to the parquet file to load
K: int, evaluations are based on predictions of the top K items for each user
'''
testIdx = spark.read.parquet(data_file)
model = ALSModel.load(model_file)
users_val = testIdx.select("user_idx").distinct()
perUserPredictedItemsDF = model.recommendForUserSubset(users_val, K)
perUserPredictedItemsDF = perUserPredictedItemsDF.select(
"user_idx", "recommendations.track_idx").withColumnRenamed(
'user_idx', 'user').withColumnRenamed('recommendations.track_idx',
'items')
w2 = Window.partitionBy('user_idx').orderBy(col('count').desc())
perUserActualItemsDF = testIdx.select(
'user_idx', 'track_idx', 'count',
F.rank().over(w2).alias('rank')).where(
'rank <= {0}'.format(K)).groupBy('user_idx').agg(
expr('collect_list(track_idx) as items')).withColumnRenamed(
'user_idx', 'user')
perUserItemsRDD = perUserPredictedItemsDF.join(
perUserActualItemsDF, 'user').rdd.map(lambda row: (row[1], row[2]))
rankingMetrics = RankingMetrics(perUserItemsRDD)
print("============================================")
print("meanAveragePrecision = %.8f" % rankingMetrics.meanAveragePrecision)
print("precisionAt(K) = %.8f" % rankingMetrics.precisionAt(K))
print("ndcgAt(K) = %.8f" % rankingMetrics.ndcgAt(K))
def runOtherFunctions(spark, personDf):
df = spark.createDataFrame([("v1", "v2", "v3")], ["c1", "c2", "c3"])
# array
df.select(df.c1, df.c2, df.c3,
array("c1", "c2", "c3").alias("newCol")).show(truncate=False)
# desc, asc
personDf.show()
personDf.sort(functions.desc("age"), functions.asc("name")).show()
# pyspark 2.1.0 버전은 desc_nulls_first, desc_nulls_last, asc_nulls_first, asc_nulls_last 지원하지 않음
# split, length (pyspark에서 컬럼은 df["col"] 또는 df.col 형태로 사용 가능)
df2 = spark.createDataFrame([("Splits str around pattern", )], ['value'])
df2.select(df2.value, split(df2.value, " "),
length(df2.value)).show(truncate=False)
# rownum, rank
f1 = StructField("date", StringType(), True)
f2 = StructField("product", StringType(), True)
f3 = StructField("amount", IntegerType(), True)
schema = StructType([f1, f2, f3])
p1 = ("2017-12-25 12:01:00", "note", 1000)
p2 = ("2017-12-25 12:01:10", "pencil", 3500)
p3 = ("2017-12-25 12:03:20", "pencil", 23000)
p4 = ("2017-12-25 12:05:00", "note", 1500)
p5 = ("2017-12-25 12:05:07", "note", 2000)
p6 = ("2017-12-25 12:06:25", "note", 1000)
p7 = ("2017-12-25 12:08:00", "pencil", 500)
p8 = ("2017-12-25 12:09:45", "note", 30000)
dd = spark.createDataFrame([p1, p2, p3, p4, p5, p6, p7, p8], schema)
w1 = Window.partitionBy("product").orderBy("amount")
w2 = Window.orderBy("amount")
dd.select(dd.product, dd.amount,
functions.row_number().over(w1).alias("rownum"),
functions.rank().over(w2).alias("rank")).show()
def get_users_dataframe(complete_listens_df, metadata):
""" Prepare users dataframe
Args:
complete_listens_df (dataframe): Dataframe with all the columns/fields that a typical listen has.
Returns:
users_df (dataframe): Columns can be depicted as:
[
'user_name', 'user_id'
]
"""
# We use window function to give rank to distinct user_names
# Note that if user_names are not distinct rank would repeat and give unexpected results.
user_window = Window.orderBy('user_name')
users_df = complete_listens_df.select('user_name').distinct().withColumn(
'user_id',
rank().over(user_window))
metadata['users_count'] = users_df.count()
save_dataframe(users_df, path.USERS_DATAFRAME_PATH)
return users_df
def main(sc, out_file_name):
"""
Read GDELT data from S3, clean themes from taxonomy words, and
perform frequency count of cleaned themes. Pick top 1000 most
popular themes and write to out_file_name
"""
#Obtain list of taxonomy words for theme cleaning
tax_file = os.environ['TAX_LIST_FILE']
tax_list = f.read_tax_file(tax_file)
rdd_tax_list = sc.broadcast(tax_list)
#Read 'GKG" table from GDELT S3 bucket. Transform into RDD and clean taxonomy words
gkgRDD = sc.textFile('s3a://gdelt-open-data/v2/gkg/201[5-9]*000000.gkg.csv')
gkgRDD = gkgRDD.map(lambda x: x.encode("utf", "ignore"))
gkgRDD.cache()
gkgRDD = gkgRDD.map(lambda x: x.split('\t'))
gkgRDD = gkgRDD.filter(lambda x: len(x)==27)
gkgRDD = gkgRDD.filter(lambda x: f.is_not_empty(x[7]]))
gkgRowRDD = gkgRDD.map(lambda x : Row(themes = f.clean_taxonomy(x[7].split(';')[:-1], rdd_tax_list)))
sqlContext = SQLContext(sc)
#Transform RDDs to dataframes
gkgDF = sqlContext.createDataFrame(gkgRowRDD)
#Each document could contain multiple themes. Explode on the themes and make a new column
explodedDF = gkgDF.select(explode(gkgDF.themes).alias("theme"))
#Count the frequency of each theme
testDF = explodedDF.groupBy('theme').agg(count('*').alias('num_mentions'))
#Find top 1000 most popular themes, use Pandas to write to output file
window = Window.orderBy(testDF['num_mentions'].desc())
rankDF = testDF.select('*', rank().over(window).alias('rank')) .filter(col('rank') <= 1000).where(col('theme') != '')
pandasDF = rankDF.toPandas()
pandasDF.to_csv(out_file_name, columns = ["theme", "num_mentions", "rank"])
def transform_as4_invoice(self, sources: dict) -> DataFrame:
"""
Dim Location records and attributes from dataB Invoice Plant data
"""
spark = self.get_spark()
inv_loc_df = spark.read.orc(sources['dataB_invoice_extract']['path'])
df = (inv_loc_df.select(
col('plant').alias('location_id'), 'invoice_date', 'invoice',
col('plant_name').alias('description')))
window = Window.partitionBy('location_id').orderBy(
df['invoice_date'].desc(), df['invoice'].desc())
df = df.withColumn("rank", F.rank().over(window))
df_rank = df.filter("rank = 1").distinct()
df_final = (df_rank.select('location_id', 'invoice_date', 'invoice',
'description'))
return df_final
def top_payments_monthly(data_df):
payment_otc = spark.read.jdbc(mysql_url,
"source.payment_otc",
properties={
"user": mysql_un,
"password": mysql_password
})
payment_df = data_df.groupBy("payment_type", "month", "year").count()
window = Window.partitionBy("month", "year").orderBy(fn.desc("count"))
payment_df2 = payment_df.withColumn("rank", fn.rank().over(window))
payment_df3 = payment_df2.where("rank <= 3").select(
"month", "year", "payment_type", "count")
payment_df4 = payment_df3.join(payment_otc, ["payment_type"]).select(
"month", "year", "payment_name", "count")
payment_df4.orderBy("year", "month").write.orc(hdfs_output +
"top_payments_monthly",
mode="overwrite")
def obtener_topN_ciclistas_por_provincia_en_total_de_kilometros(
ciclistas_kilometros_df, N):
#obtiene el total de kilómetros por ciclista, agrupado por provincia, cedula y nombre_Completo
provincia_ciclistas_kilometros_total_df = ciclistas_kilometros_df.groupBy(
"provincia", "cedula", "nombre_Completo").sum("TotalKilometros")
#provincia_ciclistas_kilometros_total_df.show()
provincia_ciclistas_kilometros_total_df = \
provincia_ciclistas_kilometros_total_df.select(
col('provincia'),
col('cedula'),
col('nombre_Completo'),
col('sum(TotalKilometros)').alias('TotalKilometros'))
#provincia_ciclistas_kilometros_total_df.show()
#particiona los datos por provincia, ordenados por TotalKilometros descendente y cedula ascendente, y posteriormente crea columna para asignarles una "posición"
window = Window.partitionBy('provincia').orderBy(
col('TotalKilometros').desc(),
col('cedula').asc())
provincia_ciclistas_kilometros_total_df = provincia_ciclistas_kilometros_total_df.withColumn(
"Posicion_Por_Provincia",
rank().over(window))
provincia_ciclistas_kilometros_total_df = provincia_ciclistas_kilometros_total_df.withColumn(
"Tipo_Top_N_Ciclistas_Por_Provincia", lit("Total de Km"))
#obtiene el top N
provincia_ciclistas_kilometros_total_df = provincia_ciclistas_kilometros_total_df.filter(
provincia_ciclistas_kilometros_total_df.Posicion_Por_Provincia <= N)
provincia_ciclistas_kilometros_total_df = provincia_ciclistas_kilometros_total_df.select(
col('Tipo_Top_N_Ciclistas_Por_Provincia'), col('provincia'),
col('cedula'), col('nombre_Completo'),
col('TotalKilometros').alias('Valor'), col('Posicion_Por_Provincia'))
return provincia_ciclistas_kilometros_total_df
def transform(retail_df):
"""
:param retail_df:
:return:
"""
from pyspark.sql.window import Window
from pyspark.sql.functions import col, date_format, desc, dense_rank, rank, max
# convert date format on retail_df
transform_step1 = (retail_df.withColumn('InvoiceDate',
date_format(col("InvoiceDate"), "MM/dd/yyyy H:mm")))
# window function
window_function = (Window.partitionBy("CustomerId")
.orderBy(desc("Quantity"))
.rowsBetween(Window.unboundedPreceding, Window.currentRow))
# aggregate functions
max_purchase_quantity = max(col("Quantity")).over(window_function)
# rank functions
purchase_dense_rank = dense_rank().over(window_function)
purchase_rank = rank().over(window_function)
transformed_df = (retail_df.withColumn('InvoiceDate', date_format(col("InvoiceDate"), "MM/dd/yyyy H:mm"))
.where("CustomerId IS NOT NULL")
.orderBy("CustomerId")
.select(col("CustomerId"),
col("InvoiceDate"),
col("Quantity"),
purchase_rank.alias("quantityRank"),
purchase_dense_rank.alias("quantityDenseRank"),
max_purchase_quantity.alias("maxPurchaseQuantity")))
return transformed_df
def run_job(self, sc, sqlc):
input_data = sc.textFile(self.text_file, minPartitions=4)
output = input_data.mapPartitionsWithIndex(
self.process_warcs).reduce(add)
output_json = sc.parallelize(output)
self.create_db_connection()
self.reference_to_instagram_df = output_json.toDF() \
.orderBy("reference_link", "warc_date") \
window = Window.partitionBy("instagram_link",
"reference_link").orderBy(
"warc_date", 'tiebreak')
self.reference_to_instagram_df = (
self.reference_to_instagram_df.withColumn(
'tiebreak', monotonically_increasing_id()).withColumn(
'rank',
rank().over(window)).filter(col('rank') == 1).drop(
'rank', 'tiebreak'))
self.log_aggregators(sc)
self.prepare_csv(sc, sqlc)
try:
self.drop_outdated_references()
self.perform_aggregations()
self.conn.commit()
self.conn.close()
finally:
pass
def runOtherFunctions(spark, personDf):
df = spark.createDataFrame([("v1", "v2", "v3")], ["c1", "c2", "c3"]);
# array
df.select(df.c1, df.c2, df.c3, array("c1", "c2", "c3").alias("newCol")).show(truncate=False)
# desc, asc
personDf.show()
personDf.sort(functions.desc("age"), functions.asc("name")).show()
# pyspark 2.1.0 버전은 desc_nulls_first, desc_nulls_last, asc_nulls_first, asc_nulls_last 지원하지 않음
# split, length (pyspark에서 컬럼은 df["col"] 또는 df.col 형태로 사용 가능)
df2 = spark.createDataFrame([("Splits str around pattern",)], ['value'])
df2.select(df2.value, split(df2.value, " "), length(df2.value)).show(truncate=False)
# rownum, rank
f1 = StructField("date", StringType(), True)
f2 = StructField("product", StringType(), True)
f3 = StructField("amount", IntegerType(), True)
schema = StructType([f1, f2, f3])
p1 = ("2017-12-25 12:01:00", "note", 1000)
p2 = ("2017-12-25 12:01:10", "pencil", 3500)
p3 = ("2017-12-25 12:03:20", "pencil", 23000)
p4 = ("2017-12-25 12:05:00", "note", 1500)
p5 = ("2017-12-25 12:05:07", "note", 2000)
p6 = ("2017-12-25 12:06:25", "note", 1000)
p7 = ("2017-12-25 12:08:00", "pencil", 500)
p8 = ("2017-12-25 12:09:45", "note", 30000)
dd = spark.createDataFrame([p1, p2, p3, p4, p5, p6, p7, p8], schema)
w1 = Window.partitionBy("product").orderBy("amount")
w2 = Window.orderBy("amount")
dd.select(dd.product, dd.amount, functions.row_number().over(w1).alias("rownum"),
functions.rank().over(w2).alias("rank")).show()
def process_file(date_update):
"""Process downloaded MEDLINE folder to parquet file"""
print("Process MEDLINE file to parquet")
# remove if folder still exist
if glob(os.path.join(save_dir, 'medline_*.parquet')):
subprocess.call(['rm', '-rf', 'medline_*.parquet'])
date_update_str = date_update.strftime("%Y_%m_%d")
path_rdd = sc.parallelize(glob(
os.path.join(download_dir, 'medline*.xml.gz')),
numSlices=1000)
parse_results_rdd = path_rdd.\
flatMap(lambda x: [Row(file_name=os.path.basename(x), **publication_dict)
for publication_dict in pp.parse_medline_xml(x)])
medline_df = parse_results_rdd.toDF()
medline_df.write.parquet(os.path.join(
save_dir, 'medline_raw_%s.parquet' % date_update_str),
compression='gzip')
window = Window.partitionBy(['pmid']).orderBy(desc('file_name'))
windowed_df = medline_df.select(
max('delete').over(window).alias('is_deleted'),
rank().over(window).alias('pos'), '*')
windowed_df.\
where('is_deleted = False and pos = 1').\
write.parquet(os.path.join(save_dir, 'medline_lastview_%s.parquet' % date_update_str),
compression='gzip')
# parse grant database
parse_grant_rdd = path_rdd.flatMap(lambda x: pp.parse_medline_grant_id(x))\
.filter(lambda x: x is not None)\
.map(lambda x: Row(**x))
grant_df = parse_grant_rdd.toDF()
grant_df.write.parquet(os.path.join(
save_dir, 'medline_grant_%s.parquet' % date_update_str),
compression='gzip')
def __get_recent_items():
window = Window.partitionBy(date_format(
df.dateAdded, 'yyy-MM-dd')).orderBy(df['dateAdded'].desc())
recent_items = (
df.select('*',
rank().over(window).alias('rank')).filter(col('rank') <= 1).
dropDuplicates(
['dateAdded']
) # Sometimes, for a day, multiple products have been added at the recent time. Since the output has to be a single product, dropping the duplicates
.orderBy(df.dateAdded, ascending=False))
# removing unnecessary fields
recent_items = recent_items.select(
date_format(df.dateAdded, 'yyy-MM-dd').alias('dateAdded'), 'id',
'brand', 'colors')
# writing the results to redis (uses HASH data structure)
recent_items.write \
.format("org.apache.spark.sql.redis") \
.option("table", "recent") \
.option("key.column", "dateAdded") \
.mode("overwrite") \
.save()
def get_recordings_df(mapped_listens_df, metadata):
""" Prepare recordings dataframe.
Args:
mapped_listens_df (dataframe): listens mapped with msid_mbid_mapping.
Returns:
recordings_df: Dataframe containing distinct recordings and corresponding
mbids and names.
"""
recording_window = Window.orderBy('mb_recording_mbid')
recordings_df = mapped_listens_df.select('mb_artist_credit_id',
'mb_artist_credit_mbids',
'mb_recording_mbid',
'mb_release_mbid',
'msb_artist_credit_name_matchable',
'msb_recording_name_matchable') \
.distinct() \
.withColumn('recording_id', rank().over(recording_window))
metadata['recordings_count'] = recordings_df.count()
save_dataframe(recordings_df, path.RECORDINGS_DATAFRAME_PATH)
return recordings_df
def get_recordings_df(mapped_listens_df, metadata, save_path):
""" Prepare recordings dataframe.
Args:
mapped_listens_df (dataframe): listens mapped with msid_mbid_mapping.
save_path (str): path where recordings_df should be saved
Returns:
recordings_df: Dataframe containing distinct recordings and corresponding
mbids and names.
"""
recording_window = Window.orderBy('recording_mbid')
recordings_df = mapped_listens_df \
.select(
'artist_credit_id',
'recording_mbid',
) \
.distinct() \
.withColumn('recording_id', rank().over(recording_window))
metadata['recordings_count'] = recordings_df.count()
save_dataframe(recordings_df, save_path)
return recordings_df
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--posts', type=str, required=True)
parser.add_argument('--num_top', type=int, default=1)
parser.add_argument('--output_dir', type=str, required=True)
args = parser.parse_args()
conf = SparkConf().set("spark.driver.maxResultSize", "10G"). \
set("spark.hadoop.validateOutputSpecs", "false"). \
set('spark.default.parallelism', '400')
spark = SparkSession.builder.\
appName("SO Tag first usage date").\
config(conf=conf).\
getOrCreate()
sc = spark.sparkContext
in_rdd = sc.textFile(args.posts).filter(lambda x: get_field(x, 'Id') is not None).\
map(lambda x: (int(get_field(x, 'Id')), x))
in_rdd = in_rdd.filter(lambda x: get_field(x[1], 'Tags') is not None and get_field(x[1], 'CreationDate') is not None).\
map(lambda x: (datetime.strptime(get_field(x[1], 'CreationDate').decode('utf-8'), DT_FORMAT),
get_tags(get_field(x[1], 'Tags').decode('utf-8')))).\
flatMap(lambda x: [(x[0], y) for y in x[1]])
tag_date_df = in_rdd.toDF(['CreationDate', 'Tag'])
window = Window.partitionBy(tag_date_df['Tag']).orderBy(tag_date_df['CreationDate'].asc())
#tag_first_appearances = tag_date_df.groupBy('Tag').agg({'CreationDate': 'min'})
tag_first_appearances = tag_date_df.select('*', rank().over(window).alias('rank')).\
filter(col('rank') <= args.num_top)
tag_first_appearances_pd = tag_first_appearances.toPandas().drop(columns=['rank'])
make_sure_path_exists(args.output_dir)
with open(os.path.join(args.output_dir, 'tag_'+str(args.num_top)+'_earliest_appearance.csv'), 'w') as f:
tag_first_appearances_pd.to_csv(f)
def get_topN(df, group_by_columns, order_by_column, n=10):
window = Window.partitionBy(group_by_columns).orderBy(
order_by_column.desc())
return df.select('*',
f.rank().over(window).alias('rank')).filter(
f.col('rank') <= n).drop("rank")
from pyspark.sql import Window
spark = SparkSession.builder.enableHiveSupport().getOrCreate()
df1 = spark.sql(''' select * from app.app_saas_sfs_model_input where dt='2018-07-31' ''').select(['sku_code', 'sale_date', 'sale'])
df1.show()
day_len = 90
day_end = '2018-07-31'
day_start = (parse(day_end) - datetime.timedelta(day_len)).strftime('%Y-%m-%d')
df1_sum = df1.where(''' sale_date >= '{day_start}' and sale_date <= '{day_end}' '''.format(day_start=day_start, day_end=day_end)).groupBy('sku_code').agg(F.sum('sale').alias('sale_sum'))
# Temp rank of the sale, just to split into 5
windowspec_r = Window.orderBy(F.col('sale_sum').desc())
df1_rank = df1_sum.withColumn('rank', F.rank().over(windowspec_r))
# 16483
df1_cnt = df1_sum.select(F.countDistinct('sku_code').alias('sku_count'))
df1_rcnt = df1_rank.crossJoin(F.broadcast(df1_cnt))
df1_rcnt = df1_rcnt.withColumn('rank_rate', F.col('rank')/ F.col('sku_count'))
band_sql = '''
Case
When rank_rate < 0.2 Then 1
When rank_rate < 0.4 Then 2
When rank_rate < 0.6 Then 3
When rank_rate < 0.8 Then 4
else 5
end
as
fontsize=30)
graph2.set_xlabel("Players Age", fontsize=20)
graph2.set_ylabel("Player Overall ratings", fontsize=20)
graph2.set_xlim(15, 45)
graph2.set_ylim(50, 100)
graph2.xaxis.set_major_locator(tck.MultipleLocator(5))
graph2.yaxis.set_major_locator(tck.MultipleLocator(5))
plt.show()
# Finding top 10 best players with respect to each position considering their overall
# Adding a rank column to rank the best players in each position
window_for_highest_overall = Window.partitionBy(
cleaned_set['Position']).orderBy(cleaned_set['Overall'].desc())
top_worthy_players = cleaned_set.select(
'*',
rank().over(window_for_highest_overall).alias('rank')).filter(
col('rank') <= 10)
print(
"\nThe top 10 best players who are worthy based on their Overall for each position are\n"
)
print("The column rank indicates the rank of players")
top_worthy_players.show(1000)
# Plotting average market value of top 10 worthy players for each position with required plot properties
# Adding avg(Value) column containing average of market values for each position
positionvsavgvalue_dataset = top_worthy_players.groupBy('Position').agg(
{'Value': 'avg'})
g = positionvsavgvalue_dataset.toPandas()
graph3 = g.plot(x='Position',
y='avg(Value)',
kind='bar',
def createDataFile(start_date, end_date, spark_instance, jackknife_buckets,
sample_percent, output_path):
feature_data_phase1 = spark_instance.table(_TABLE_SOURCE).select([
_COL_ID.alias("id"),
_DATE_PARSED.alias("date"),
# TODO: Use MD5 instead of CRC32
(F.floor(F.crc32(_COL_ID) / 100) % jackknife_buckets).alias("bucket"),
lit(1).alias("is_active"),
F.when(_COL_URI_COUNT >= _NUM_ADAU_THRESHOLD, 1).otherwise(0).alias(
"is_active_active"),
F.to_date(_COL_PC_DATE).alias("profile_creation_date")
] + list(_MAP_NATURAL_DIMENSIONS.keys())).filter(
(_DATE_PARSED.between(start_date, end_date))
& (_COL_SAMPLE < sample_percent)).withColumn(
"young_profile",
F.when(
col("date") < F.date_add(col("profile_creation_date"), 14),
"TRUE").otherwise("FALSE"))
new_profile_window = Window.partitionBy(col("id")).orderBy(col("date"))
new_profile_data = feature_data_phase1.filter(
(col("date") >= col("profile_creation_date"))
& (col("date") <= F.date_add(col("profile_creation_date"), 6))).select(
"*",
F.rank().over(new_profile_window).alias('rank')).filter(
col('rank') == 1).withColumn("new_profile",
lit(1)).drop("date").withColumn(
"date",
col("profile_creation_date"))
feature_data = feature_data_phase1.alias("fd").join(
new_profile_data.alias("np"),
(col("fd.id") == col("np.id")) & (col("fd.date") == col("np.date")),
how='full',
).select(
[F.coalesce(col("np.new_profile"), lit(0)).alias("new_profile")] +
[F.coalesce(col("fd.is_active"), lit(0)).alias("is_active")] + [
F.coalesce(col("fd.is_active_active"), lit(0)).alias(
"is_active_active")
] + [
F.coalesce(col("fd.{}".format(c)), col("np.{}".format(c))).alias(c)
for c in feature_data_phase1.columns
if c not in ["is_active", "is_active_active"]
])
once_ever_profiles = feature_data.filter(
col("is_active") == 1).groupBy("id").count().filter(
col("count") == 1).select("id").withColumn("single_day_profile",
lit("1"))
feature_data = feature_data.alias("fd").join(
once_ever_profiles.alias("oep"), "id",
"outer").fillna({"single_day_profile": "0"})
ppi_profiles = spark_instance.table("main_summary").select(
col("client_id").alias("id"),
lit(1).alias("ppi")).filter('''submission_date_s3 >= '20190121'
AND scalar_parent_startup_profile_selection_reason IN (
'firstrun-skipped-default', 'restart-skipped-default'
)''')
feature_data = feature_data.alias("fd").join(ppi_profiles.alias("ppip"),
"id",
"left").fillna({"ppi": 0})
feature_data.write.partitionBy("date").mode('overwrite').parquet(
output_path)
# 1. human interfere
df_cleanning = human_interfere(spark, df_cleanning, df_interfere)
df_cleanning.persist()
# 2. 以MOLE_NAME为主键JOIN
df_result = similarity(spark, df_cleanning, df_standard)
df_result.persist()
df_result.show()
print(df_result.count())
# 3. 对每个需要匹配的值做猜想排序
# windowSpec = Window.partitionBy("id").orderBy(desc("SIMILARITY"))
windowSpec = Window.partitionBy("id").orderBy("SIMILARITY")
df_match = df_result.withColumn("RANK", rank().over(windowSpec))
df_match = df_match.where(df_match.RANK <= 5)
df_match.persist()
# df_match.printSchema()
df_match = df_match.withColumn("check", check_similarity(df_match.PACK_ID_CHECK, df_match.PACK_ID_STANDARD, df_match.SIMILARITY))
# df_match.show(5)
df_match = df_match.orderBy("id").drop("ORIGIN", "STANDARD")
df_match.persist()
df_match.repartition(1).write.format("parquet").mode("overwrite").save("s3a://ph-max-auto/2020-08-11/BPBatchDAG/refactor/azsanofi/0.0.4/all")
# df_match.repartition(1).write.format("parquet").mode("overwrite").save("s3a://ph-max-auto/2020-08-11/BPBatchDAG/refactor/0.0.15/all")
df_replace = df_match.filter(df_match.check == 1)
table + '/*.' +
fileFormat)
#load today's changes
logger.info("Load the latest changes")
latestChanges = spark.read.format(fileFormat).load(landingDir +
'/' + table +
'/today/*.' +
fileFormat)
#Combine the datasets
logger.info(
"Merge two datasets which will have duplicates for the records modified"
)
dataMerge = baseData.union(latestChanges)
#Filter the old data and save the files with latest changes
logger.info(
"Filter the old data and have the latest changes for the records modified"
)
dataMerge.select("*", rank().over(Window.partitionBy(partitionByColumn).orderBy(desc(lmtColumn))).alias("latestRecord"))\
.where("latestRecord == 1").drop("latestRecord").repartition(1).write.option('path',targetDir+'/'+table).mode(fileMode)\
.bucketBy(noBuckets,bucketByColumn).saveAsTable(destDB+'.'+table)
logger.info(
f"Latest changes merged with the base data and it is available in {targetDir}/{table}"
)
except Exception as error:
logger.exception(f"Failed with error- {error}")
else:
logger.info("Latest changes are merged successfully")
finally:
spark.stop()
# 7. Saving the end results
# you cannot save df directly as textfile should convet it to rdd
# dff=dff.withColumn('year',dff['_c1'].substr(8,4) <<postion,length
#Note : dff = sc.textfile(trainingdata+"part-00000").map(lambda x: x.replace('[','').replace(']','').split('|')).toDF()
# best combination of python and spark
#########################################################################################
# lines=Source.fromFile("E:\scala\spark-data\movie-description") scala
from pyspark.sql import Window
from pyspark.sql.functions import rank
dff = spark.read.csv("E:\scala\spark-data\movie-description", sep="|")
movie_year_df = dff.withColumn('year', dff['_c2'].substr(8, 4)).select(
"_c0", "_c1", "_c2", "year")
movie_number_df = spark.read.csv("E:\scala\spark-data\movie-data.data",
inferSchema=True,
header=False,
sep="\t").select("_c0", "_c3")
cond = movie_number_df._c0 == movie_year_df._c0
mv_fi_df = movie_year_df.join(
movie_number_df, cond,
'right').withColumnRenamed("_c1", "movie").withColumnRenamed(
"_c3", "gross").select("movie", "year", "gross")
windowSpec = Window.partitionBy("year").orderBy("gross")
mv_fi_df = mv_fi_df.withColumn("rank", rank().over(windowSpec))
mv_fi_df.filter(mv_fi_df.rank == 1).show()
"PROVINCE", regexp_replace(df_cleanning.PROVINCE, "市", ""))
df_cleanning = df_cleanning.withColumn(
"PROVINCE", regexp_replace(df_cleanning.PROVINCE, "自治区", ""))
df_cleanning = df_cleanning.withColumn(
"CITY", regexp_replace(df_cleanning.CITY, "市", ""))
# 2. Join 一下
df_cleanning = df_cleanning.join(broadcast(df_standard),
on=["PROVINCE", "CITY"],
how="left")
df_cleanning.persist()
df_not_match = df_cleanning.where(isnull(df_cleanning.STANDARD_NAME))
df_cleanning = df_cleanning.where(~isnull(df_cleanning.STANDARD_NAME))
df_cleanning = df_cleanning.repartition(800).withColumn(
"SIMILARITY",
efftiveness_with_jaro_winkler_similarity_in_hc_mapping(
df_cleanning.NAME, df_cleanning.STANDARD_NAME))
windowSpec = Window.partitionBy("ID").orderBy(desc("SIMILARITY"))
df_cleanning = df_cleanning.withColumn("RANK", rank().over(windowSpec))
df_cleanning = df_cleanning.where(df_cleanning.RANK == 1)
df_cleanning.repartition(1).write.mode("overwrite").option(
"header", "true"
).csv(
"s3a://ph-max-auto/2020-08-11/BPBatchDAG/refactor/alfred/tmp/chc_hc_cleanning/hc_result_2"
)
df_not_match.repartition(1).write.mode("overwrite").option(
"header", "true"
).csv(
"s3a://ph-max-auto/2020-08-11/BPBatchDAG/refactor/alfred/tmp/chc_hc_cleanning/hc_result_not_match"
)
def compile_rank(t, expr, scope, *, window, **kwargs):
return F.rank().over(window).astype('long') - F.lit(1)
train.select(['user_id']).distinct().count()
#5298
val_new.select(['user_id']).distinct().count()
#1751
test_new.select(['user_id']).distinct().count()
#1636
## there are some users in val_add and test_add but not in val_new and test_new, therefore, 8774> 5298+1751+1636=8685
## example of writing x% data
train_add_test.write.parquet("train01.parquet")
val_new.write.parquet("val01.parquet")
test_new.write.parquet("test01.parquet")
# create the true rank list (example of 1% data)
from pyspark.sql.window import Window
from pyspark.sql.functions import rank, col
window = Window.partitionBy(val['user_id']).orderBy(val['rating'].desc())
val_true_order = val.select('*', rank().over(window).alias('rank'))
val_true_list = val_true_order.select(
'user_id',
'book_id').groupBy('user_id').agg(expr('collect_list(book_id) as books'))
val_true_list.write.parquet("val01_true_list.parquet")
window = Window.partitionBy(test['user_id']).orderBy(test['rating'].desc())
test_true_order = test.select('*', rank().over(window).alias('rank'))
test_true_list = test_true_order.select(
'user_id',
'book_id').groupBy('user_id').agg(expr('collect_list(book_id) as books'))
test_true_list.write.parquet("test01_true_list.parquet")
def match_accidents_with_roads(spark, road_df, accident_df, use_cache=True):
cache_path = workdir + "data/matches_accident-road.parquet"
if isdir(cache_path) and use_cache:
print("Reading accident-road matches from cache...")
return spark.read.parquet(cache_path)
nb_top_road_center_preselected = 5
max_distance_accepted = 10 # in meters
# Compute distance between accident and road centers to identify the
# top nb_top_road_center_preselected closest roads
road_centers = road_df.select(
["street_id", "center_long", "center_lat"]
).drop_duplicates()
acc_window = Window.partitionBy("accident_id").orderBy("distance_measure")
accidents_top_k_roads = (
accident_df.select("loc_lat", "loc_long", "accident_id")
.crossJoin(road_centers)
.withColumn(
"distance_inter",
distance_intermediate_formula(
"loc_lat", "loc_long", "center_lat", "center_long"
),
)
.withColumn("distance_measure", distance_measure())
.select(
"accident_id",
"street_id",
"distance_measure",
"loc_lat",
"loc_long",
rank().over(acc_window).alias("distance_rank"),
)
.filter(col("distance_rank") <= nb_top_road_center_preselected)
.drop("distance_measure", "distance_rank")
.persist()
)
# For each accident identify road point closest
accidents_roads_first_match = (
accidents_top_k_roads.join(road_df, "street_id")
.withColumn(
"distance_inter",
distance_intermediate_formula(
"loc_lat", "loc_long", "coord_lat", "coord_long"
),
)
.withColumn("distance_measure", distance_measure())
.select(
"accident_id",
"loc_lat",
"loc_long",
"coord_lat",
"coord_long",
"street_id",
"street_name",
row_number().over(acc_window).alias("distance_rank"),
"distance_measure",
)
.filter(col("distance_rank") == 1)
.withColumn("distance", col("distance_measure") * (6371 * 2 * 1000))
.drop("distance_rank", "distance_measure", "coord_lat", "coord_long")
.persist()
)
# If the distance is lower than max_distance_accepted we keep the
# accident/street matches
accidents_road_correct_match = accidents_roads_first_match.filter(
col("distance") < max_distance_accepted
).select("accident_id", "street_id")
# If not, we try to get a better match by adding intermediate points on
# the preselected streets
# For unsatisfying matches, recompute the k closests roads
# Recomputing is probably faster than reading from disk
# cache + joining on accident_ids
accidents_close_streets_coords = (
accidents_roads_first_match.filter(col("distance") >= max_distance_accepted)
.select("accident_id", "loc_lat", "loc_long")
.crossJoin(road_centers)
.withColumn(
"distance_inter",
distance_intermediate_formula(
"loc_lat", "loc_long", "center_lat", "center_long"
),
)
.withColumn("distance_measure", distance_measure())
.select(
"accident_id",
"street_id",
"distance_measure",
"loc_lat",
"loc_long",
rank().over(acc_window).alias("distance_rank"),
)
.filter(col("distance_rank") <= nb_top_road_center_preselected)
.drop("distance_measure", "distance_rank")
.join(road_df.select("street_id", "coord_lat", "coord_long"), "street_id")
)
# Add the intermediate points
street_rolling_window = (
Window.partitionBy("street_id").orderBy("coord_long").rowsBetween(0, +1)
)
accidents_close_streets_with_additional_coords = (
accidents_close_streets_coords.select(
"accident_id",
"street_id",
"loc_lat",
"loc_long",
avg("coord_long").over(street_rolling_window).alias("coord_long"),
avg("coord_lat").over(street_rolling_window).alias("coord_lat"),
)
.union(accidents_close_streets_coords)
.dropDuplicates()
)
accidents_close_streets_coords.unpersist()
# Recompute distances between accident and new set of points
# and use closest point to identify street
accidents_roads_first_match_with_additional_coords = (
accidents_close_streets_with_additional_coords.withColumn(
"distance_inter",
distance_intermediate_formula(
"loc_lat", "loc_long", "coord_lat", "coord_long"
),
)
.withColumn("distance_measure", distance_measure())
.select(
"accident_id",
"street_id",
"loc_lat",
"loc_long",
"coord_lat",
"coord_long",
row_number().over(acc_window).alias("distance_rank"),
)
.filter(col("distance_rank") == 1)
.drop("distance_rank", "loc_lat", "loc_long", "coord_lat", "coord_long")
)
# Union accidents matched correctly with first method with the accidents
# for which we used more street points
final_match = accidents_road_correct_match.union(
accidents_roads_first_match_with_additional_coords
)
# Make sure there is only one road per accident
final_match = (
final_match.join(road_centers, "street_id")
.join(accident_df.select("loc_lat", "loc_long", "accident_id"), "accident_id")
.withColumn(
"distance_inter",
distance_intermediate_formula(
"loc_lat", "loc_long", "center_lat", "center_long"
),
)
.withColumn("distance_measure", distance_measure())
.withColumn("dist_rank", row_number().over(acc_window))
.filter(col("dist_rank") == 1)
.select("accident_id", "street_id")
)
return final_match
.partitionBy("CustomerId", "date")\
.orderBy(desc("Quantity"))\
.rowsBetween(Window.unboundedPreceding, Window.currentRow)
# COMMAND ----------
from pyspark.sql.functions import max
maxPurchaseQuantity = max(col("Quantity")).over(windowSpec)
# COMMAND ----------
from pyspark.sql.functions import dense_rank, rank
purchaseDenseRank = dense_rank().over(windowSpec)
purchaseRank = rank().over(windowSpec)
# COMMAND ----------
from pyspark.sql.functions import col
dfWithDate.where("CustomerId IS NOT NULL").orderBy("CustomerId")\
.select(
col("CustomerId"),
col("date"),
col("Quantity"),
purchaseRank.alias("quantityRank"),
purchaseDenseRank.alias("quantityDenseRank"),
maxPurchaseQuantity.alias("maxPurchaseQuantity")).show()
