def setUpClass(cls):
"""Set up the database once for the test run."""
cls.sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(cls.sc.parallelize(raw_pings),
num_reducers=10)
submit_aggregates(aggregates)
def setup_module():
global aggregates
global sc
sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(sc.parallelize(raw_pings))
submit_aggregates(aggregates)
def setup_module():
global aggregates
global sc
sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(sc.parallelize(raw_pings), num_reducers=10)
submit_aggregates(aggregates)
def setUpClass(cls):
"""Set up the database once for the test run."""
clear_db()
cls.sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(cls.sc.parallelize(raw_pings), num_reducers=10)
submit_aggregates(aggregates)
def setup_module():
global aggregates
global sc
logger = logging.getLogger("py4j")
logger.setLevel(logging.ERROR)
sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(sc.parallelize(raw_pings))
def setup_module():
global aggregates
global sc
logger = logging.getLogger("py4j")
logger.setLevel(logging.ERROR)
sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(sc.parallelize(raw_pings))
def setup_module():
global build_id_aggregates
global submission_date_aggregates
logger = logging.getLogger("py4j")
logger.setLevel(logging.ERROR)
sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(d.generate_pings())
build_id_aggregates, submission_date_aggregates = _aggregate_metrics(sc.parallelize(raw_pings), num_reducers=10)
build_id_aggregates = build_id_aggregates.collect()
submission_date_aggregates = submission_date_aggregates.collect()
# Note: most tests are based on the build-id aggregates as the aggregation
# code is the same for both scenarios.
sc.stop()
def setup_module():
global build_id_aggregates
global submission_date_aggregates
logger = logging.getLogger("py4j")
logger.setLevel(logging.ERROR)
sc = pyspark.SparkContext(master="local[*]")
raw_pings = list(d.generate_pings())
build_id_aggregates, submission_date_aggregates = _aggregate_metrics(
sc.parallelize(raw_pings), num_reducers=10)
build_id_aggregates = build_id_aggregates.collect()
submission_date_aggregates = submission_date_aggregates.collect()
# Note: most tests are based on the build-id aggregates as the aggregation
# code is the same for both scenarios.
sc.stop()
def aggregates(sc):
logger = logging.getLogger("py4j")
logger.setLevel(logging.ERROR)
raw_pings = list(d.generate_pings())
return _aggregate_metrics(sc.parallelize(raw_pings), num_reducers=10)
def aggregates(sc):
raw_pings = list(generate_pings())
aggregates = _aggregate_metrics(sc.parallelize(raw_pings), num_reducers=10)
submit_aggregates(aggregates)
return aggregates
