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import itertools
import csv
import logging
from robusta_krr.core.abstract import formatters
from robusta_krr.core.models.allocations import RecommendationValue
from robusta_krr.core.models.result import ResourceScan, ResourceType, Result
from robusta_krr.utils import resource_units
import datetime
logger = logging.getLogger("krr")
NONE_LITERAL = "unset"
NAN_LITERAL = "?"
def _format(value: RecommendationValue) -> str:
if value is None:
return NONE_LITERAL
elif isinstance(value, str):
return NAN_LITERAL
else:
return resource_units.format(value)
def __calc_diff(allocated, recommended, selector, multiplier=1) -> str:
if recommended is None or isinstance(recommended.value, str) or selector != "requests":
return ""
else:
reccomended_val = recommended.value if isinstance(recommended.value, (int, float)) else 0
allocated_val = allocated if isinstance(allocated, (int, float)) else 0
diff_val = reccomended_val - allocated_val
diff_sign = "+" if diff_val >= 0 else "-"
return f"{diff_sign}{_format(abs(diff_val) * multiplier)}"
def _format_request_str(item: ResourceScan, resource: ResourceType, selector: str) -> str:
allocated = getattr(item.object.allocations, selector)[resource]
recommended = getattr(item.recommended, selector)[resource]
if allocated is None and recommended.value is None:
return f"{NONE_LITERAL}"
diff = __calc_diff(allocated, recommended, selector)
if diff != "":
diff = f"({diff}) "
return (
diff
+ _format(allocated)
+ " -> "
+ _format(recommended.value)
)
def _format_total_diff(item: ResourceScan, resource: ResourceType, pods_current: int) -> str:
selector = "requests"
allocated = getattr(item.object.allocations, selector)[resource]
recommended = getattr(item.recommended, selector)[resource]
return __calc_diff(allocated, recommended, selector, pods_current)
@formatters.register()
def csv_export(result: Result) -> str:
current_datetime = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
file_path = f"krr-{current_datetime}.csv"
# We need to order the resource columns so that they are in the format of Namespace,Name,Pods,Old Pods,Type,Container,CPU Diff,CPU Requests,CPU Limits,Memory Diff,Memory Requests,Memory Limits
resource_columns = []
for resource in ResourceType:
resource_columns.append(f"{resource.name} Diff")
resource_columns.append(f"{resource.name} Requests")
resource_columns.append(f"{resource.name} Limits")
with open(file_path, 'w+', newline='') as csvfile:
csv_writer = csv.writer(csvfile)
csv_writer.writerow([
"Namespace", "Name", "Pods", "Old Pods", "Type", "Container",
*resource_columns
])
for _, group in itertools.groupby(
enumerate(result.scans), key=lambda x: (x[1].object.cluster, x[1].object.namespace, x[1].object.name)
):
group_items = list(group)
for j, (i, item) in enumerate(group_items):
full_info_row = j == 0
row = [
item.object.namespace if full_info_row else "",
item.object.name if full_info_row else "",
f"{item.object.current_pods_count}" if full_info_row else "",
f"{item.object.deleted_pods_count}" if full_info_row else "",
item.object.kind if full_info_row else "",
item.object.container,
]
for resource in ResourceType:
row.append(_format_total_diff(item, resource, item.object.current_pods_count))
row += [_format_request_str(item, resource, selector) for selector in ["requests", "limits"]]
csv_writer.writerow(row)
logger.info("CSV File: %s", file_path)
return ""
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