#!/usr/bin/env luajit

local ffi = require('ffi')
local json = require('cjson')

-- AO Process Configuration
local AO = {
    name = "quantum-db-ao",
    metrics = {},
    config = {
        optimization_interval = 60,  -- seconds
        metric_collection_interval = 5,  -- seconds
        thresholds = {
            latency_ms = 100,
            error_rate = 0.01,
            memory_usage = 0.8  -- 80% threshold
        }
    }
}

-- FFI declarations for system metrics
ffi.cdef[[
    typedef struct {
        double cpu_usage;
        double memory_usage;
        double disk_io;
        double network_io;
    } system_metrics_t;
]]

-- Initialize shared metrics table
local shared = require('table.new')(0, 1024)

-- Metric collection function
function AO:collect_metrics()
    local metrics = {
        timestamp = os.time(),
        system = {
            cpu_usage = 0,
            memory_usage = 0,
            disk_io = 0,
            network_io = 0
        },
        database = {
            query_latency = {},
            throughput = 0,
            error_rate = 0,
            connection_pool = {
                active = 0,
                idle = 0,
                max = 100
            }
        }
    }

    -- Collect real metrics here
    -- This is a placeholder for actual metric collection

    self.metrics = metrics
    shared.metrics = json.encode(metrics)
end

-- Optimization function
function AO:optimize()
    local current_metrics = self.metrics
    if not current_metrics then return end

    -- Check thresholds
    if current_metrics.database.query_latency.avg > self.config.thresholds.latency_ms then
        self:optimize_query_performance()
    end

    if current_metrics.database.error_rate > self.config.thresholds.error_rate then
        self:handle_high_error_rate()
    end

    if current_metrics.system.memory_usage > self.config.thresholds.memory_usage then
        self:optimize_memory_usage()
    end
end

-- Query optimization
function AO:optimize_query_performance()
    -- Implement query optimization logic
    print(string.format("[%s] Optimizing query performance...", os.date()))
end

-- Error rate handling
function AO:handle_high_error_rate()
    -- Implement error handling logic
    print(string.format("[%s] Handling high error rate...", os.date()))
end

-- Memory optimization
function AO:optimize_memory_usage()
    -- Implement memory optimization logic
    print(string.format("[%s] Optimizing memory usage...", os.date()))
end

-- Main AO loop
function AO:run()
    print(string.format("[%s] Starting %s...", os.date(), self.name))

    while true do
        -- Collect metrics
        self:collect_metrics()

        -- Run optimization if needed
        self:optimize()

        -- Wait for next interval
        os.execute("sleep " .. self.config.metric_collection_interval)
    end
end

-- Start the AO process
AO:run()
