PHPMem v2.0.1

Version
1.6.45
Uptime
7 days 6 hours 30 minutes 40 seconds

Memory

Total
512MB
Used
13,32MB (2.6%)
Free
498,68MB

Keys

Current
10 162
Total (since start)
11 092
Evictions
0
Reclaimed
157
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
4 / 1 024 max
Total
61 788
Rejected
0
llm:8c37a71422a4b1132b06a5c016c50156f9c825370a54a1adb8a5d9ee19bfd077
TTL 4 days 5 hours 36 minutes 53 seconds Size 1,76KB Export
Edit
I need to compute distribution statistics for all numeric columns across the dataset. From the evidence, I can see the numeric columns are: **In b2b_sales_funnel_pipeline:** - close_value (BIGINT) **In accounts:** - year_established (BIGINT) - revenue (DOUBLE) - employees (BIGINT) Let me compute comprehensive distribution statistics for all these columns in a single query. ```json { "action": "tool_call", "reasoning": "Computing distribution statistics (min, max, mean, median, stddev) for all numeric columns across both tables in one query. This will provide the complete statistical profile requested.", "tool": "query_sql", "args": { "sql": "SELECT 'b2b_sales_funnel_pipeline.close_value' as column_name, COUNT(*) as total_rows, COUNT(close_value) as non_null_count, MIN(close_value) as min_value, MAX(close_value) as max_value, AVG(close_value) as mean_value, MEDIAN(close_value) as median_value, STDDEV(close_value) as std_dev FROM raw.b2b_sales_funnel_pipeline UNION ALL SELECT 'accounts.year_established', COUNT(*), COUNT(year_established), MIN(year_established), MAX(year_established), AVG(year_established), MEDIAN(year_established), STDDEV(year_established) FROM raw.accounts UNION ALL SELECT 'accounts.revenue', COUNT(*), COUNT(revenue), MIN(revenue), MAX(revenue), AVG(revenue), MEDIAN(revenue), STDDEV(revenue) FROM raw.accounts UNION ALL SELECT 'accounts.employees', COUNT(*), COUNT(employees), MIN(employees), MAX(employees), AVG(employees), MEDIAN(employees), STDDEV(employees) FROM raw.accounts", "purpose": "Calculate distribution statistics (min, max, mean, median, standard deviation) for all numeric columns in the dataset" } } ```