def display_leader_imbalance(cluster_topologies):
"""Display leader count and weight imbalance statistics.
:param cluster_topologies: A dictionary mapping a string name to a
ClusterTopology object.
"""
broker_ids = list(next(six.itervalues(cluster_topologies)).brokers.keys())
assert all(
set(broker_ids) == set(cluster_topology.brokers.keys())
for cluster_topology in six.itervalues(cluster_topologies))
broker_leader_counts = [
stats.get_broker_leader_counts(cluster_topology.brokers[broker_id]
for broker_id in broker_ids)
for cluster_topology in six.itervalues(cluster_topologies)
]
broker_leader_weights = [
stats.get_broker_leader_weights(cluster_topology.brokers[broker_id]
for broker_id in broker_ids)
for cluster_topology in six.itervalues(cluster_topologies)
]
_display_table_title_multicolumn(
'Leader Count',
'Brokers',
broker_ids,
list(cluster_topologies.keys()),
broker_leader_counts,
)
print('')
_display_table_title_multicolumn(
'Leader weight',
'Brokers',
broker_ids,
list(cluster_topologies.keys()),
broker_leader_weights,
)
for name, blc, blw in zip(list(cluster_topologies.keys()),
broker_leader_counts, broker_leader_weights):
print('\n'
'{name}'
'Leader count imbalance: {net_imbalance}\n'
'Broker leader weight mean: {weight_mean}\n'
'Broker leader weight stdev: {weight_stdev}\n'
'Broker leader weight cv: {weight_cv}'.format(
name='' if len(cluster_topologies) == 1 else name + '\n',
net_imbalance=stats.get_net_imbalance(blc),
weight_mean=stats.mean(blw),
weight_stdev=stats.standard_deviation(blw),
weight_cv=stats.coefficient_of_variation(blw),
))
def broker_leader_weight_cv(self):
return coefficient_of_variation(self.broker_leader_weights)
def broker_partition_count_cv(self):
return coefficient_of_variation(self.broker_partition_counts)
def broker_weight_cv(self):
"""Return the coefficient of variation of the weight of the brokers."""
return coefficient_of_variation(self.broker_weights)
def test_coefficient_of_variation():
assert stats.coefficient_of_variation([1, 2, 3, 4, 5]) == sqrt(2) / 3
def test_coefficient_of_variation():
assert stats.coefficient_of_variation([1, 2, 3, 4, 5]) == sqrt(2) / 3
def display_leader_imbalance(cluster_topologies):
"""Display leader count and weight imbalance statistics.
:param cluster_topologies: A dictionary mapping a string name to a
ClusterTopology object.
"""
broker_ids = list(next(six.itervalues(cluster_topologies)).brokers.keys())
assert all(
set(broker_ids) == set(cluster_topology.brokers.keys())
for cluster_topology in six.itervalues(cluster_topologies)
)
broker_leader_counts = [
stats.get_broker_leader_counts(
cluster_topology.brokers[broker_id]
for broker_id in broker_ids
)
for cluster_topology in six.itervalues(cluster_topologies)
]
broker_leader_weights = [
stats.get_broker_leader_weights(
cluster_topology.brokers[broker_id]
for broker_id in broker_ids
)
for cluster_topology in six.itervalues(cluster_topologies)
]
_display_table_title_multicolumn(
'Leader Count',
'Brokers',
broker_ids,
list(cluster_topologies.keys()),
broker_leader_counts,
)
print('')
_display_table_title_multicolumn(
'Leader weight',
'Brokers',
broker_ids,
list(cluster_topologies.keys()),
broker_leader_weights,
)
for name, blc, blw in zip(
list(cluster_topologies.keys()),
broker_leader_counts,
broker_leader_weights
):
print(
'\n'
'{name}'
'Leader count imbalance: {net_imbalance}\n'
'Broker leader weight mean: {weight_mean}\n'
'Broker leader weight stdev: {weight_stdev}\n'
'Broker leader weight cv: {weight_cv}'
.format(
name='' if len(cluster_topologies) == 1 else name + '\n',
net_imbalance=stats.get_net_imbalance(blc),
weight_mean=stats.mean(blw),
weight_stdev=stats.stdevp(blw),
weight_cv=stats.coefficient_of_variation(blw),
)
)
