USER360_CHART_CONFIGS = {
"user_unoptimized_query": [
# Bar chart: Top inefficient users by weighted score
{
"chart_type": "bar",
"x_axis": "USER_NAME",
"y_axis": "WEIGHTED_SCORE",
"title": "Top 20 Inefficient Users by Weighted Score",
"color_field": "COST_STATUS",
"colors": {
"High Cost": "rgba(255, 99, 132, 0.6)",
"Normal": "rgba(54, 162, 235, 0.6)"
},
"tooltip_fields": [
"TOTAL_QUERIES",
"TOTAL_CREDITS",
"FAILURE_CANCELLATION_RATE_PCT",
"RECOMMENDATIONS"
],
# Insight: Identifies the top 20 users causing inefficiencies, with cost status highlighting high-cost users. Tooltips suggest actions like "Optimize joins to reduce failures."
},
# Heatmap: Correlation between inefficiency types and cost impact
{
"chart_type": "heatmap",
"x_axis": "INEFFICIENCY_TYPE",
"y_axis": "COST_IMPACT",
"title": "Correlation of Inefficiency Types and Cost Impact",
"color_scale": "RdBu_r",
"tooltip_fields": ["TOTAL_CREDITS", "RECOMMENDATIONS"],
# Insight: Shows which inefficiency types (e.g., spills, unpartitioned scans) correlate most with cost, guiding targeted fixes like "Use partitioning for scans."
},
# Treemap: Cost impact by inefficiency type
{
"chart_type": "treemap",
"path": ["INEFFICIENCY_TYPE"],
"values": "COST_IMPACT",
"title": "Cost Impact by Inefficiency Type",
"color_field": "INEFFICIENCY_TYPE",
"colors": [
"rgba(255, 99, 132, 0.6)",
"rgba(54, 162, 235, 0.6)",
"rgba(255, 206, 86, 0.6)",
"rgba(75, 192, 192, 0.6)",
"rgba(153, 102, 255, 0.6)",
"rgba(255, 159, 64, 0.6)",
"rgba(199, 199, 199, 0.6)",
"rgba(83, 102, 255, 0.6)",
"rgba(255, 99, 71, 0.6)",
"rgba(144, 238, 144, 0.6)"
],
"tooltip_fields": ["COST_IMPACT", "RECOMMENDATIONS"],
# Insight: Visualizes the relative cost of each inefficiency type (e.g., SELECT * queries), with larger areas indicating higher impact. Tooltips suggest "Reduce SELECT * usage."
},
# Table: Detailed summary
{
"chart_type": "table",
"title": "Unoptimized Query Summary for All Users",
},
],
"single_user_analysis": [
# Radar chart: Multi-metric user performance
{
"chart_type": "radar",
"categories": ["QUERY_EFFICIENCY", "COST_EFFICIENCY", "FAILURE_RATE", "SPILLAGE_RATE"],
"values": ["QUERY_EFFICIENCY_SCORE", "COST_EFFICIENCY_SCORE", "FAILURE_RATE_PCT", "SPILLAGE_RATE_PCT"],
"title": "User Performance Across Key Metrics",
"tooltip_fields": ["RECOMMENDATIONS"],
# Insight: Offers a holistic view of a user's efficiency, highlighting weak areas (e.g., high spillage rate) with suggestions like "Increase memory allocation."
},
# Waterfall chart: Incremental cost of bad practices
{
"chart_type": "waterfall",
"x_axis": "INEFFICIENCY_TYPE",
"y_axis": "COST_IMPACT",
"title": "Cost Impact Breakdown by Inefficiency",
"colors": ["rgba(255, 99, 132, 0.6)", "rgba(54, 162, 235, 0.6)", "rgba(255, 206, 86, 0.6)"],
"tooltip_fields": ["COST_IMPACT", "RECOMMENDATIONS"],
# Insight: Shows how each inefficiency adds to the total cost, pinpointing the biggest contributors (e.g., complex joins) with actions like "Simplify JOINs."
},
# Table: Detailed summary
{
"chart_type": "table",
"title": "Single User Analysis Summary",
},
],
"bad_practice_impact_all": [
# Treemap: Cost impact of bad practices across users
{
"chart_type": "treemap",
"path": ["ISSUE_DESCRIPTION"],
"values": "TOTAL_COST_IMPACT",
"title": "Cost Impact of Bad Practices Across All Users",
"color_field": "ISSUE_DESCRIPTION",
"colors": [
"rgba(255, 99, 132, 0.6)",
"rgba(54, 162, 235, 0.6)",
"rgba(255, 206, 86, 0.6)",
"rgba(75, 192, 192, 0.6)",
"rgba(153, 102, 255, 0.6)"
],
"tooltip_fields": ["TOTAL_QUERIES", "RECOMMENDATIONS"],
# Insight: Highlights the most costly bad practices (e.g., unpartitioned scans) across all users, suggesting fixes like "Implement partitioning."
},
],
"single_user_bad_practice_details": [
# Donut chart: Cost distribution with total cost context
{
"chart_type": "donut",
"names": "ISSUE_TYPE",
"values": "COST_IMPACT",
"title": "Cost Distribution by Bad Practice Type",
"colors": [
"rgba(255, 99, 132, 0.6)",
"rgba(54, 162, 235, 0.6)",
"rgba(255, 206, 86, 0.6)",
"rgba(75, 192, 192, 0.6)"
],
"tooltip_fields": ["RECOMMENDATIONS", "EXAMPLE_QUERY"],
"center_text": "Total Cost: ${total_cost_impact:,.2f}",
# Insight: Shows the proportion of cost per bad practice, with total cost in the center. Tooltips provide examples and fixes like "Cache repeated queries."
},
],
"user_value_vs_cost_quadrant": [
# Scatter with quadrants: Efficiency vs. cost segmentation
{
"chart_type": "scatter",
"x_axis": "EFFICIENCY_SCORE",
"y_axis": "COST_IMPACT",
"size_field": "QUERY_COUNT",
"color_field": "USER_SEGMENT",
"title": "User Value vs. Cost Quadrant",
"colors": {
"High Efficiency - High Cost": "rgba(75, 192, 192, 0.6)",
"Low Efficiency - High Cost (OPTIMIZE)": "rgba(255, 99, 132, 0.6)",
"High Efficiency - Low Cost (IDEAL)": "rgba(54, 162, 235, 0.6)",
"Low Efficiency - Low Cost (MONITOR)": "rgba(255, 206, 86, 0.6)"
},
"tooltip_fields": ["USER_NAME", "EFFICIENCY_SCORE", "COST_IMPACT", "QUERY_COUNT"],
"quadrants": {
"lines": [50, 50], # Thresholds for efficiency and cost
"labels": ["Monitor", "Ideal", "Optimize", "High Value"]
},
# Insight: Segments users into actionable categories (e.g., "Optimize" for low efficiency, high cost), guiding prioritization.
},
],
"user_efficiency_matrix": [
# Scatter with reference lines: Cost vs. performance efficiency
{
"chart_type": "scatter",
"x_axis": "COST_EFFICIENCY_SCORE",
"y_axis": "PERFORMANCE_EFFICIENCY_SCORE",
"size_field": "TOTAL_COST",
"color_field": "RISK_LEVEL",
"title": "User Efficiency Matrix",
"colors": {
"HIGH_RISK": "rgba(255, 99, 132, 0.6)",
"MEDIUM_RISK": "rgba(255, 206, 86, 0.6)",
"OPTIMIZED": "rgba(54, 162, 235, 0.6)"
},
"tooltip_fields": ["USER_NAME", "RECOMMENDATIONS", "TOTAL_COST"],
"reference_lines": {
"x": 50, # Cost efficiency threshold
"y": 50 # Performance efficiency threshold
},
# Insight: Maps users by cost and performance efficiency, with risk levels and suggestions like "Adjust warehouse size" for high-risk users.
},
],
"single_user_critical_bad_practices_pie": [
# Donut chart: Critical bad practices with total cost
{
"chart_type": "donut",
"names": "ISSUE_TYPE",
"values": "COST_IMPACT",
"title": "Cost of Critical Bad Practices",
"colors": [
"rgba(255, 99, 132, 0.6)",
"rgba(54, 162, 235, 0.6)",
"rgba(255, 206, 86, 0.6)",
"rgba(75, 192, 192, 0.6)"
],
"tooltip_fields": ["COST_IMPACT", "RECOMMENDATIONS"],
"center_text": "Total Cost: ${total_cost_impact:,.2f}",
# Insight: Highlights critical bad practices by cost, with total impact in the center and fixes like "Debug failing queries."
},
],
"bytes_scanned_vs_rows_returned": [
# Scatter with regression: Data efficiency analysis
{
"chart_type": "scatter",
"x_axis": "MB_SCANNED",
"y_axis": "ROWS_RETURNED",
"color_field": "EFFICIENCY_STATUS",
"title": "Bytes Scanned vs. Rows Returned",
"colors": {
"Inefficient": "rgba(255, 99, 132, 0.6)",
"Efficient": "rgba(54, 162, 235, 0.6)"
},
"tooltip_fields": ["QUERY_ID", "MB_PER_ROW", "RECOMMENDATIONS"],
"regression_line": True,
# Insight: Identifies inefficient queries (outliers above the line), suggesting "Use clustering to reduce scans."
},
],
"credits_query_efficiency_trend": [
# Dual-axis line: Daily credits vs. query efficiency
{
"chart_type Prune": "dual_axis",
"x_axis": "USAGE_DATE",
"y_axis": ["DAILY_CREDITS", "AVG_CREDITS_PER_QUERY"],
"color_field": "DAILY_USAGE_STATUS",
"title": "Daily Credits and Query Efficiency Trend",
"colors": {
"Very High": "rgba(255, 99, 132, 0.6)",
"High": "rgba(255, 206, 86, 0.6)",
"Normal": "rgba(54, 162, 235, 0.6)"
},
"tooltip_fields": ["QUERY_COUNT", "AVG_CREDITS_PER_QUERY", "RECOMMENDATIONS"],
# Insight: Tracks daily credit usage alongside query cost, pinpointing inefficient days with suggestions like "Review peak usage queries."
},
],
"query_inefficiency_bar_all": [
# Grouped bar: Inefficiency vs. query volume
{
"chart_type": "grouped_bar",
"x_axis": "USER_NAME",
"y_axis": ["AVG_MB_SCANNED_PER_ROW", "TOTAL_QUERIES"],
"title": "Query Inefficiency and Volume by User",
"colors": ["rgba(255, 99, 132, 0.6)", "rgba(54, 162, 235, 0.6)"],
"tooltip_fields": ["EFFICIENCY_STATUS", "TOTAL_CREDITS_USED", "RECOMMENDATIONS"],
# Insight: Compares inefficiency (MB/row) with query volume, identifying users needing optimization (e.g., "Reduce scan-heavy queries").
},
],
"failure_spillage_summary": [
# Bubble chart: Failure vs. spillage risk
{
"chart_type": "bubble",
"x_axis": "FAILURE_CANCELLATION_RATE_PCT",
"y_axis": "SPILLAGE_RATE_PCT",
"size_field": "TOTAL_CREDITS",
"color_field": "RISK_LEVEL",
"title": "Failure & Spillage Risk by User",
"colors": {
"High Risk": "rgba(255, 99, 132, 0.6)",
"Moderate Risk": "rgba(255, 206, 86, 0.6)",
"Low Risk": "rgba(54, 162, 235, 0.6)"
},
"tooltip_fields": ["USER_NAME", "TOTAL_CREDITS", "RECOMMENDATIONS"],
# Insight: Highlights users with high failure/spillage rates and costs, suggesting "Stabilize queries" or "Optimize memory."
},
],
}
Armaan Khan
Posted on
Nkw
For further actions, you may consider blocking this person and/or reporting abuse
Top comments (0)