def run(spark, args):
"""Write features w/ churn status in first column"""
users = join.spark_job.run(spark, args)
features = list(users.columns)
# churn must be the first column
features.remove('churned')
features = ['churned'] + features
users.select(features).write.csv(dt_path(args['output_path'], args['dt']),
compression='gzip',
mode='overwrite')
def run(spark, args):
query_dt = args['dt']
dt_start = utils.dt_start(query_dt, days_back=28)
sessions = spark.read.load(args['input_path']).where(
"dt between '{dt_start}' and '{query_dt}'".format(query_dt=query_dt,
dt_start=dt_start))
sessions.createOrReplaceTempView('sessions')
user_activity = spark.sql(query.activity_sql)
user_activity.write.parquet(utils.dt_path(args['output_path'], query_dt),
mode='overwrite')
def run(spark, args):
query_dt = args['dt']
data = spark.read.option('header', 'true').csv(args['input_path'])
data.createOrReplaceTempView('conviva_strings')
data_recast = spark.sql(query.user_cast_sql)
data_recast.createOrReplaceTempView('conviva_streams')
user_perf_features = spark.sql(query.get_perf_sql(query_dt, days_back=28))
user_perf_features.write.parquet(
utils.dt_path(args['output_path'], query_dt), mode='overwrite')
def run(spark, args):
query_dt = args['dt']
next_month_date = get_next_month(query_dt)
all_subs = spark.read.load(args['input_path'])
all_subs.createOrReplaceTempView("sub_table")
sub_sql = get_sub_sql(query_dt=query_dt,
month_later_dt=next_month_date.strftime('%Y-%m-%d'))
subs = spark.sql(sub_sql)
subs.write.parquet(utils.dt_path(args['output_path'], query_dt),
mode='overwrite')
def run(spark, args):
dt = args['dt']
users = spark.read.load(dt_path(args['input_subscription'], dt))
predictions = spark.read.json(dt_path(args['input_prediction'], dt))
# creating a row number is the only way to horizontally concatenate
users = users.withColumn('row_num', monotonically_increasing_id())
predictions = predictions.withColumn('row_num',
monotonically_increasing_id())
user_prediction = users.select(['user_id',
'row_num']).join(predictions, 'row_num')
user_prediction = user_prediction.withColumnRenamed(
'predicted_label', 'predicted_churn')
user_prediction = user_prediction.withColumnRenamed(
'score', 'churn_risk_score')
user_prediction.write.parquet(dt_path(args['output_path'], dt),
mode='overwrite')
def setup_predictions(spark, args):
records = [{
'predicted_label': 1.0,
'score': .55
}, {
'predicted_label': 0.0,
'score': .05
}]
rows = [Row(**record) for record in records]
data = spark.createDataFrame(rows)
sample_input_path = dt_path(args['input_prediction'], args['dt'])
data.write.json(sample_input_path, compression='gzip', mode='overwrite')
def run(spark, args):
dt = args['dt']
user_subscription = spark.read.load(dt_path(args['input_subscription'],
dt))
user_performance = spark.read.load(dt_path(args['input_performance'], dt))
user_activity = spark.read.load(dt_path(args['input_activity'], dt))
users = user_subscription.join(user_performance, 'user_id',
'left_outer').join(user_activity, 'user_id',
'left_outer')
performance_features = user_performance.columns
performance_features.remove('user_id')
session_features = ['avg_session_length', 'median_session_length']
for feature in performance_features + session_features:
median_expr = 'percentile_approx({}, 0.5)'.format(feature)
median_rows = users.agg(expr(median_expr)).collect()
median = median_rows[0][median_expr]
users = users.fillna({feature: median})
for feature in ['total_timespent', 'num_sessions', 'active_days']:
users = users.fillna({feature: 0})
users.cache()
features = list(users.columns)
features.remove('user_id')
features.remove('churned')
# write data with no churn status
users.select(features).write.csv(dt_path(args['output_path'], dt),
compression='gzip',
mode='overwrite')
return users
def run(spark, args):
query_dt = args['dt']
dt_start = utils.dt_start(query_dt, days_back=1)
# off-the-bat 'where' for optimization
segment = spark.read.load(args['input_path']).where(
"dt in ('{dt_start}', '{query_dt}')".format(
dt_start=dt_start, query_dt=query_dt))
events = spark.createDataFrame(segment.rdd.map(parse), samplingRatio=.4)
events.createOrReplaceTempView('events')
sessions = spark.sql(query.get_session_sql(query_dt))
# Other jobs will use daily session roll-up
sessions.write.parquet(utils.dt_path(args['output_path'], query_dt), mode='overwrite')
def run(spark, args):
query_dt = args['dt']
dt_start = utils.dt_start(query_dt, days_back=28)
# Job dependent on 28 days of successful session job runs
sessions = spark.read.load(args['input_path']).where(
"dt between '{dt_start}' and '{query_dt}'".format(
query_dt=query_dt, dt_start=dt_start))
sessions.createOrReplaceTempView('sessions')
user_device_timespent = spark.sql(query.device_sql)
user_device_timespent_pivoted = user_device_timespent.groupby('user_id') \
.pivot('device_code').agg(sum('device_minutes'))
user_device_timespent_pivoted.cache()
user_device_timespent_pivoted.write.parquet(
utils.dt_path(args['output_path'], query_dt), mode='overwrite')
def test_dt_path():
assert utils.dt_path('s3://mypath', '2019-01-09') == 's3://mypath/dt=2019-01-09'
