PHPMem v2.0.1
Version
1.6.45
Uptime
16 days 23 hours 20 minutes 42 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
14 / 1 024 max
Total
193 418
Rejected
0
llm:82a67fc4aa2c70d74ec318877c70d6b427ef81cbdf895adf09228b943a4f63ae
Edit
{
"action": "tool_call",
"reasoning": "No query results are in evidence for the distinct counts I drafted, so I will compute them now with one UNION ALL query over the five monthly tables.",
"tool": "query_sql",
"args": {
"sql": "SELECT 'January' AS month, COUNT(*) AS row_count, COUNT(DISTINCT \"Company Name\") AS company_name, COUNT(DISTINCT Location) AS location, COUNT(DISTINCT Sector) AS sector, COUNT(DISTINCT Stage) AS stage, COUNT(DISTINCT Investor) AS investor FROM raw.\"Indian Startups _ Funding Investors Data January 2022\" UNION ALL SELECT 'February', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data February 2022\" UNION ALL SELECT 'March', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data March 2022\" UNION ALL SELECT 'April', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data April 2022\" UNION ALL SELECT 'May', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding Investors Data May 2022\"",
"purpose": "Distinct counts per categorical column for each monthly table"
}
}