use super::{OptimizationRule, RuleContext, codes};
use crate::analyzer::k8s_optimize::config::K8sOptimizeConfig;
use crate::analyzer::k8s_optimize::parser::parse_memory_to_bytes;
use crate::analyzer::k8s_optimize::types::{
OptimizationIssue, ResourceRecommendation, ResourceSpec, RuleCode, Severity, WorkloadType,
};
pub struct HighMemoryRequestRule;
impl OptimizationRule for HighMemoryRequestRule {
fn code(&self) -> &'static str {
codes::HIGH_MEMORY_REQUEST
}
fn description(&self) -> &'static str {
"Memory request exceeds threshold for workload type"
}
fn default_severity(&self) -> Severity {
Severity::High
}
fn check(
&self,
ctx: &RuleContext,
config: &K8sOptimizeConfig,
) -> Option<ResourceRecommendation> {
if matches!(
ctx.workload_type,
WorkloadType::Database | WorkloadType::MachineLearning
) {
return None;
}
let memory_request = ctx.current.memory_request.as_ref()?;
let bytes = parse_memory_to_bytes(memory_request)?;
let mi = bytes / (1024 * 1024);
if mi <= config.max_memory_request_mi as u64 {
return None;
}
let defaults = ctx.workload_type.default_resources();
let recommended = ResourceSpec {
cpu_request: None,
cpu_limit: None,
memory_request: Some(defaults.memory_request.to_string()),
memory_limit: Some(defaults.memory_limit.to_string()),
};
Some(ResourceRecommendation {
resource_kind: ctx.resource_kind.clone(),
resource_name: ctx.resource_name.clone(),
namespace: ctx.namespace.clone(),
container: ctx.container_name.clone(),
file_path: ctx.file_path.clone(),
line: ctx.line,
issue: OptimizationIssue::OverProvisioned,
severity: self.default_severity(),
message: format!(
"Memory request ({}) exceeds {}Mi threshold for {} workload. This is likely over-provisioned.",
memory_request, config.max_memory_request_mi, ctx.workload_type
),
workload_type: ctx.workload_type,
current: ctx.current.clone(),
actual_usage: None,
recommended: recommended.clone(),
savings: None,
fix_yaml: recommended.to_yaml(),
rule_code: RuleCode::new(self.code()),
})
}
}