Class: RailsPulse::Queries::Cards::ExecutionRate

Inherits:
Cards::Base
  • Object
show all
Defined in:
app/models/rails_pulse/queries/cards/execution_rate.rb

Instance Method Summary collapse

Constructor Details

#initialize(query: nil, disabled_tags: [], show_non_tagged: true, period: 7, period_type: "day") ⇒ ExecutionRate

Returns a new instance of ExecutionRate.



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# File 'app/models/rails_pulse/queries/cards/execution_rate.rb', line 5

def initialize(query: nil, disabled_tags: [], show_non_tagged: true, period: 7, period_type: "day")
  @query = query
  @disabled_tags = disabled_tags
  @show_non_tagged = show_non_tagged
  @period = period
  @period_type = period_type
end

Instance Method Details

#to_metric_card ⇒ Object 



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# File 'app/models/rails_pulse/queries/cards/execution_rate.rb', line 13

def to_metric_card
  # Use base class helper for query construction
  base_query = base_summary_query("RailsPulse::Query")

  metrics = base_query.select(
    "SUM(count) AS total_count",
    "SUM(CASE WHEN period_start >= #{quote(current_window_start)} THEN count ELSE 0 END) AS current_count",
    "SUM(CASE WHEN period_start >= #{quote(range_start)} AND period_start < #{quote(current_window_start)} THEN count ELSE 0 END) AS previous_count"
  ).take

  # Calculate metrics from single query result
  total_execution_count = metrics.total_count || 0
  current_period_count = metrics.current_count || 0
  previous_period_count = metrics.previous_count || 0

  # Use base class trend calculation
  trend_icon, trend_amount = trend_for(current_period_count, previous_period_count) if show_trend?

  # Create a query for sparkline data using only the current period
  sparkline_query = RailsPulse::Summary
    .with_tag_filters(@disabled_tags, @show_non_tagged)
    .where(
      summarizable_type: "RailsPulse::Query",
      period_type: @period_type,
      period_start: current_window_start..now
    )
  sparkline_query = sparkline_query.where(summarizable_id: @query.id) if @query

  if period_type_hours?
    grouped_data = sparkline_query.group_by_hour(:period_start).sum(:count)
  else
    grouped_data = sparkline_query.group_by_date(:period_start).sum(:count)
  end

  # Use base class sparkline generation (handles hour vs day automatically)
  sparkline_data = sparkline_from(grouped_data)

  # Calculate appropriate rate display based on frequency
  total_minutes = (period_type_hours? ? (@period * 24).hours : (@period * 2).days) / 1.minute.to_f
  executions_per_minute = total_execution_count.to_f / total_minutes

  # Choose appropriate time unit for display
  if executions_per_minute >= 1
    summary = "#{executions_per_minute.round(2)} / min"
  elsif executions_per_minute * 60 >= 1
    executions_per_hour = executions_per_minute * 60
    summary = "#{executions_per_hour.round(2)} / hour"
  else
    executions_per_day = executions_per_minute * 60 * 24
    summary = "#{executions_per_day.round(2)} / day"
  end

  {
    id: "execution_rate",
    context: "queries",
    title: "Execution Rate",
    summary: summary,
    chart_data: sparkline_data,
    trend_icon: trend_icon,
    trend_amount: trend_amount,
    trend_text: (show_trend? ? comparison_period_text : nil),
    period_stat: total_execution_count > 0 ? "#{format_number(total_execution_count)} executions" : period_date_range,
    help_heading: "Query Execution Rate",
    help_text: "Total database queries executed over the last 14 days, expressed as an average rate. Spikes may indicate N+1 queries or inefficient data access patterns."
  }
end