def add_send_order_column(df):
return (df.withColumn(
"min_slot_date", F.array_min("action_slots")).withColumn(
"num_avail_slots", F.size("action_slots")).select(
"*",
F.dense_rank().over(
Window.partitionBy(F.col("prim_party_id")).orderBy(
F.col("prim_party_id"),
F.col("min_slot_date").asc(),
F.col("num_avail_slots").asc(),
F.col("rank").asc())).alias('send_order')))
def parse_col(name):
col = f.col(name)
col = f.when(col == '-', None).otherwise(col)
if name in dtypes:
dtype = dtypes[name]
if isinstance(dtype, t.ArrayType):
col = f.split(col, ",")
col = col.cast(dtypes[name])
if name in needsMinVal:
col = f.array_min(col)
return col.alias(name)
def Calculate_CCF(graph):
iteration = 0
done = False
while not done:
iteration += 1
startPair = newPair.value
# CCF-Iterate MAP
ccf_iterate_map = graph.union(graph.select(col("value").alias("key"), col("key").alias("value")))
# CCF-Iterate REDUCE
ccf_iterate_reduce_pair = ccf_iterate_map.groupBy(col("key")).agg(collect_set("value").alias("value"))\
.withColumn("min", least(col("key"), array_min("value")))\
.filter((col('key')!=col('min')))
newPair += ccf_iterate_reduce_pair.withColumn("count", size("value")-1).select(sum("count")).collect()[0][0]
ccf_iterate_reduce = ccf_iterate_reduce_pair.select(col("min").alias("a_min"), concat(array(col("key")), col("value")).alias("valueList"))\
.withColumn("valueList", explode("valueList"))\
.filter((col('a_min')!=col('valueList')))\
.select(col('a_min').alias("key"), col('valueList').alias("value"))
# CFF-Dedup MAP & REDUCE
ccf_dedup_reduce = ccf_iterate_reduce.distinct()
graph = ccf_dedup_reduce
if startPair == newPair.value:
done = True
print("Itération : ", iteration, "Number of newPair : ", newPair.value)
return graph
# MAIN #
if __name__ == "__main__":
sc = pyspark.SparkContext(appName="Spark_RDD")
spark = SparkSession.builder.getOrCreate()
newPair = sc.accumulator(0)
dataset_path = "/user/user335/dataset/ccf"
dataset = sc.textFile(dataset_path + "/web-Google.txt", use_unicode="False")
graph = prepare_dataset(dataset)
t1 = time.perf_counter()
graph = Calculate_CCF(graph)
t2 = time.perf_counter()
print("calculation time (s) :", t2 - t1)
def min_element_column(parquetFiles):
parquetFiles = parquetFiles.withColumn('bars_confidence_min', F.array_min(col('bars_confidence')))
parquetFiles = parquetFiles.withColumn('bars_start_min', F.array_min(col('bars_start')))
parquetFiles = parquetFiles.withColumn('beats_confidence_min', F.array_min(col('beats_confidence')))
parquetFiles = parquetFiles.withColumn('segments_confidence_min', F.array_min(col('segments_confidence')))
parquetFiles = parquetFiles.withColumn('segments_loudness_max_time_min',
F.array_min(col('segments_loudness_max_time')))
parquetFiles = parquetFiles.withColumn('tatums_confidence_min', F.array_min(col('tatums_confidence')))
return parquetFiles
def assoc_fn(df: DataFrame, group_by_cols):
gbc = [col(x) for x in group_by_cols]
h_fn = partial(harmonic_fn,
partition_cols=group_by_cols,
over_col="evs_score",
output_col=harmonic_col)
assoc_df = (df.withColumn(
"evs_score", array_min(array(col("evidence_score") / 10.0, lit(1.0)))
).transform(h_fn).groupBy(*gbc).agg(
countDistinct(col("pmid")).alias("f"),
mean(col("evidence_score")).alias("mean"),
stddev(col("evidence_score")).alias("std"),
max(col("evidence_score")).alias("max"),
min(col("evidence_score")).alias("min"),
expr("approx_percentile(evidence_score, array(0.25, 0.5, 0.75))").
alias("q"),
count(col("pmid")).alias("N"),
first(col(harmonic_col)).alias(harmonic_col)).withColumn(
"median", element_at(col("q"), 2)).withColumn(
"q1", element_at(col("q"),
1)).withColumn("q3", element_at(col("q"),
3)).drop("q"))
return assoc_df
def filter_slots_group_adjacency_days(df):
udf_action_slots = SpaceEmails.udf_action_slots()
return (
df.withColumn(
"min_slot_date", F.array_min("action_slots")).withColumn(
"last_group_adjacency_days",
F.datediff(F.col("min_slot_date"),
F.col("last_group_delivery_date"))).withColumn(
"days_to_add",
F.col("group_adjacency_days") -
F.col("last_group_adjacency_days")).
withColumn(
"p_start_group_adjacency_days",
F.expr("date_add(min_slot_date, days_to_add)").cast(
T.TimestampType())).withColumn(
"action_slots_",
udf_action_slots(
F.col("p_start_group_adjacency_days"),
F.col("p_end_date"),
F.col("action_slots"))).drop(*[
"days_to_add", "action_slots",
"p_start_group_adjacency_days", "min_slot_date"
]).withColumnRenamed("action_slots_",
"action_slots"))
def main(args):
sparkConf = (SparkConf().set("spark.driver.memory", "10g").set(
"spark.executor.memory",
"10g").set("spark.driver.maxResultSize",
"0").set("spark.debug.maxToStringFields", "2000").set(
"spark.sql.execution.arrow.maxRecordsPerBatch",
"500000"))
if args.local:
spark = (SparkSession.builder.config(
conf=sparkConf).master('local[*]').getOrCreate())
else:
spark = (SparkSession.builder.config(conf=sparkConf).getOrCreate())
print('args: ', args)
print('Spark version: ', spark.version)
start_time = time()
# load co-occurrences from parquet dataset coming from path
coocs = (spark.read.parquet(args.in_cooccurrences))
# we need some filtering; not all data is ready to be used
# 1. at least 2 data points per month
# 2. there must be data for the year 2020
w2 = Window.partitionBy(*predictions_grouped_keys)
# curry function to pass to transform with the keys to group by
tfn = partial(assoc_fn, group_by_cols=grouped_keys)
aggregated = (coocs.withColumn("year", year(
coocs.pubDate)).withColumn("month", month(coocs.pubDate)).withColumn(
"day",
lit(1)).filter((coocs.isMapped == True) & (coocs.type == "GP-DS")
& col("year").isNotNull()
& col("month").isNotNull()).selectExpr(
*coocs_columns).transform(tfn).withColumn(
"ds",
to_date(
concat_ws("-",
col("year"), col("month"),
col("day")))).withColumn(
"y", col(harmonic_col)).
dropna(subset=predictions_selection_keys).withColumn(
"years",
collect_set(col("year")).over(w2)).withColumn(
"nYears", array_size(col("years"))).withColumn(
"minYear", array_min(col("years"))).withColumn(
"maxYear",
array_max(col("years"))).withColumn(
"dtCount",
count(col("y")).over(w2)).withColumn(
"dtMaxYear",
max(col("year")).over(w2)).
filter((col("maxYear") >= 2019) & (col("nYears") >= 3)
& (col("dtCount") >= 12)).select(
*predictions_selection_keys).repartition(
*predictions_grouped_keys).persist())
aggregated.write.parquet(f"{args.out_prefix}/associationsFromCoocsTS")
print('Completed aggregated data in {:.1f} secs'.format(time() -
start_time))
# generate the models
start_time = time()
fbp = (aggregated.groupBy(*predictions_grouped_keys).applyInPandas(
make_predictions, prediction_schema).persist())
# fbp.show(20, False)
fbp.write.parquet(f"{args.out_prefix}/associationsFromCoocsTSPredictions")
print('Completed TS analysis (FB Prophet) data in {:.1f} secs'.format(
time() - start_time))
# clean all up just in case
spark.stop()
return 0
#Okay now we have a list of outages, restore_times, locations, core_ids
#First let's calculate some high level metrics
#size of outages
pw_finalized_outages = pw_finalized_outages.withColumn("cluster_size", F.size(F.array_distinct("core_id")))
#standard deviation outage times
pw_finalized_outages = pw_finalized_outages.withColumn("outage_times_stddev", F.explode("outage_times"))
#this expression essentially takes the first value of each column (which should all be the same after the explode)
exprs = [F.first(x).alias(x) for x in pw_finalized_outages.columns if x != 'outage_times_stddev' and x != 'outage_time']
pw_finalized_outages = pw_finalized_outages.groupBy("outage_time").agg(F.stddev_pop("outage_times_stddev").alias("outage_times_stddev"),*exprs)
#range of outage times
pw_finalized_outages = pw_finalized_outages.withColumn("outage_times_range", F.array_max("outage_times") - F.array_min("outage_times"))
#standard deviation and range of restore times
pw_finalized_outages = pw_finalized_outages.withColumn("restore_times", col("restore_time"))
pw_finalized_outages = pw_finalized_outages.withColumn("restore_time", F.explode("restore_time"))
#this expression essentially takes the first value of each column (which should all be the same after the explode)
exprs = [F.first(x).alias(x) for x in pw_finalized_outages.columns if x != 'restore_time' and x != 'outage_time']
pw_finalized_outages = pw_finalized_outages.groupBy("outage_time").agg(F.avg("restore_time").alias("restore_times_mean"),*exprs)
pw_finalized_outages = pw_finalized_outages.withColumn("restore_times_stddev", F.explode("restore_times"))
#this expression essentially takes the first value of each column (which should all be the same after the explode)
exprs = [F.first(x).alias(x) for x in pw_finalized_outages.columns if x != 'restore_times_stddev' and x != 'outage_time']
pw_finalized_outages = pw_finalized_outages.groupBy("outage_time").agg(F.stddev_pop("restore_times_stddev").alias("restore_times_stddev"),*exprs)
pw_finalized_outages = pw_finalized_outages.withColumn("restore_times_range", F.array_max("restore_times") - F.array_min("restore_times"))
def foreach_jdbc_writer(df, epoch_id):
df.write.\
jdbc(url="jdbc:mysql://localhost/world",table="amazon_products",mode='append',properties={"driver":"com.mysql.cj.jdbc.Driver","user":"******"})
spark = SparkSession.builder.master('local[2]').appName(
'StreamingDemo').getOrCreate()
df = spark.readStream.format('kafka')\
.option('kafka.bootstrap.servers','localhost:9092')\
.option('subscribe','amazon')\
.load()
deser = udf(lambda x: pickle.loads(x), MapType(StringType(), StringType()))
deserlizedDF = df.withColumn('map', deser(df['value']))
parsedDF = deserlizedDF.withColumn('title',element_at('map','productTitle'))\
.withColumn('Categories',element_at('map','productCategories'))\
.withColumn('Rating',element_at('map','productRating'))\
.withColumn('Description',element_at('map','productDescription'))\
.withColumn('Prices',element_at('map','productPrices'))\
.withColumn('Min_Price',array_min(split(element_at('map','productPrices'),r'#*\$').cast(ArrayType(FloatType()))))\
.withColumn('Max_Price',array_max(split(element_at('map','productPrices'),r'#*\$').cast(ArrayType(FloatType()))))
projectedDF = parsedDF.select('title', 'Categories', 'Rating', 'Prices',
'Min_Price', 'Max_Price')
result = projectedDF.writeStream.foreachBatch(foreach_jdbc_writer).start()
result.awaitTermination()
#standard deviation outage times
pw_finalized_outages = pw_finalized_outages.withColumn(
"outage_times_stddev", F.explode("outage_times"))
#this expression essentially takes the first value of each column (which should all be the same after the explode)
exprs = [
F.first(x).alias(x) for x in pw_finalized_outages.columns
if x != 'outage_times_stddev' and x != 'outage_time'
]
pw_finalized_outages = pw_finalized_outages.groupBy("outage_time").agg(
F.stddev_pop("outage_times_stddev").alias("outage_times_stddev"), *exprs)
#range of outage times
pw_finalized_outages = pw_finalized_outages.withColumn(
"outage_times_range",
F.array_max("outage_times") - F.array_min("outage_times"))
#Okay now to effectively calculate SAIDI/SAIFI we need to know the sensor population
#join the number of sensors reporting metric above with our outage groupings
#then we can calculate the relative SAIDI/SAIFI contribution of each outage
pw_finalized_outages = pw_finalized_outages.join(
pw_distinct_user_id,
F.date_trunc("day", F.from_unixtime(
pw_finalized_outages["outage_time"])) == F.date_trunc(
"day", pw_distinct_user_id["window_mid_point"]))
pw_finalized_outages = pw_finalized_outages.select(
"outage_time", "cluster_size", "phones_reporting", "user_id",
"outage_times", "outage_times_range", "outage_times_stddev")
pw_finalized_outages = pw_finalized_outages.withColumn(
"relative_cluster_size",
