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Scoring fixes for scale down and type fixes in scoring methods #508

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merged 4 commits into from
May 5, 2024
Merged

Scoring fixes for scale down and type fixes in scoring methods #508

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7dd9e04
Adjust computation of minimum CPU util
geoffxy May 5, 2024
b08e8f1
Fix type issues in the AuroraProvisioningScore class
geoffxy May 5, 2024
3b469a7
Fix type issues in RedshiftProvisioningScore
geoffxy May 5, 2024
eb032ec
Fix formatting
geoffxy May 5, 2024
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16 changes: 9 additions & 7 deletions src/brad/planner/scoring/performance/unified_aurora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,8 +114,10 @@ def predict_loads(
current_has_replicas = curr_prov.num_nodes() > 1
next_has_replicas = next_prov.num_nodes() > 1

curr_writer_cpu_util = ctx.metrics.aurora_writer_cpu_avg / 100
curr_writer_cpu_util_denorm = curr_writer_cpu_util * aurora_num_cpus(curr_prov)
curr_writer_cpu_util = float(ctx.metrics.aurora_writer_cpu_avg / 100)
curr_writer_cpu_util_denorm = float(
curr_writer_cpu_util * aurora_num_cpus(curr_prov)
)

# We take a very conservative approach to query movement. If new queries
# are added onto Aurora, we increase the load. But if queries are
Expand Down Expand Up @@ -209,7 +211,7 @@ def predict_loads(
# We currently have read replicas.
curr_num_read_replicas = curr_prov.num_nodes() - 1
total_reader_cpu_denorm = (
(ctx.metrics.aurora_reader_cpu_avg / 100)
float(ctx.metrics.aurora_reader_cpu_avg / 100)
* aurora_num_cpus(curr_prov)
* curr_num_read_replicas
)
Expand Down Expand Up @@ -277,11 +279,11 @@ def compute_direct_cpu_denorm(
per_query_cpu_denorm = np.clip(
query_run_times * alpha, a_min=0.0, a_max=load_max
)
total_denorm = np.dot(per_query_cpu_denorm, arrival_weights)
max_query_cpu_denorm = per_query_cpu_denorm.max()
total_denorm = np.dot(per_query_cpu_denorm, arrival_weights).item()
max_query_cpu_denorm = (per_query_cpu_denorm * arrival_weights).max().item()
else:
# Edge case: Query with 0 arrival count (used as a constraint).
total_denorm = np.zeros_like(query_run_times)
total_denorm = 0.0
max_query_cpu_denorm = 0.0
if debug_dict is not None:
debug_dict["aurora_total_cpu_denorm"] = total_denorm
Expand Down Expand Up @@ -309,7 +311,7 @@ def query_movement_factor(
total_next_latency = np.dot(
curr_query_run_times, workload.get_arrival_counts_batch(query_indices)
)
return total_next_latency / norm_factor
return total_next_latency.item() / norm_factor

@classmethod
def predict_query_latency_load_resources(
Expand Down
14 changes: 7 additions & 7 deletions src/brad/planner/scoring/performance/unified_redshift.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,10 +53,10 @@ def compute(
ctx.metrics.redshift_cpu_list is not None
and ctx.metrics.redshift_cpu_list.shape[0] > 0
):
avg_cpu = ctx.metrics.redshift_cpu_list.mean()
avg_cpu: float = ctx.metrics.redshift_cpu_list.mean().item()
else:
# This won't be used. This is actually max.
avg_cpu = ctx.metrics.redshift_cpu_avg
avg_cpu = float(ctx.metrics.redshift_cpu_avg)

gamma_norm_factor = HotConfig.instance().get_value(
"query_lat_p90", default=30.0
Expand Down Expand Up @@ -180,7 +180,7 @@ def predict_max_node_cpu_util(
curr_cpu_util *= gamma

curr_cpu_denorm = curr_cpu_util * redshift_num_cpus(curr_prov)
curr_max_cpu_denorm = curr_cpu_denorm.max()
curr_max_cpu_denorm = curr_cpu_denorm.max().item()

(
peak_load,
Expand Down Expand Up @@ -262,11 +262,11 @@ def compute_direct_cpu_denorm(
per_query_cpu_denorm = np.clip(
query_run_times * alpha, a_min=0.0, a_max=load_max
)
total_denorm = np.dot(per_query_cpu_denorm, arrival_weights)
max_query_cpu_denorm = per_query_cpu_denorm.max()
total_denorm = np.dot(per_query_cpu_denorm, arrival_weights).item()
max_query_cpu_denorm = (per_query_cpu_denorm * arrival_weights).max().item()
else:
# Edge case: Query with 0 arrival count (used as a constraint).
total_denorm = np.zeros_like(query_run_times)
total_denorm = 0.0
max_query_cpu_denorm = 0.0
if debug_dict is not None:
debug_dict["redshift_total_cpu_denorm"] = total_denorm
Expand Down Expand Up @@ -294,7 +294,7 @@ def query_movement_factor(
total_next_latency = np.dot(
curr_query_run_times, workload.get_arrival_counts_batch(query_indices)
)
return total_next_latency / norm_factor
return total_next_latency.item() / norm_factor

@staticmethod
def predict_query_latency_load_resources(
Expand Down