local sqlite = require('lsqlite3')
local metrics = require('metrics')
local M = {}

-- Tuning parameters
local parameters = {
    quantum = {
        optimization_level = 1,
        max_depth = 100,
        error_threshold = 0.001,
        sync_interval = 300  -- 5 minutes
    },
    game = {
        cache_size = 1000,
        sync_to_arweave = true,
        sync_interval = 600  -- 10 minutes
    },
    user = {
        cache_size = 500,
        session_timeout = 3600,
        sync_interval = 900  -- 15 minutes
    },
    hdr = {
        optimization_level = 1,
        max_depth = 100,
        error_threshold = 0.001,
        sync_interval = 300  -- 5 minutes
    }
}

-- Initialize tuning database
local tuning_db = nil

function M.init()
    tuning_db = sqlite.open('tuning.db')
    tuning_db:exec[[
        CREATE TABLE IF NOT EXISTS optimizations (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            db_name TEXT,
            parameter TEXT,
            old_value REAL,
            new_value REAL,
            timestamp INTEGER,
            success_metric REAL
        );
    ]]
end

-- Optimize database based on metrics
function M.optimize_db(db_name)
    local perf = metrics.get_performance_summary(db_name)
    local params = parameters[db_name]
    
    if not params then
        return false
    end

    -- Analyze performance metrics
    if perf.query_time and perf.query_time.avg > 0.1 then
        -- Increase cache size
        params.cache_size = params.cache_size * 1.5
        
        -- Record optimization
        record_optimization(db_name, 'cache_size', 
            params.cache_size/1.5, params.cache_size)
    end

    -- Check error rates for quantum DB
    if db_name == 'quantum' and perf.error_rate then
        if perf.error_rate.avg > params.error_threshold then
            params.optimization_level = params.optimization_level + 1
            record_optimization(db_name, 'optimization_level',
                params.optimization_level-1, params.optimization_level)
        end
    end

    -- HDR database optimization
    if db_name == 'hdr' then
        if perf.query_time and perf.query_time.avg > 0.1 then
            -- Increase max depth
            params.max_depth = params.max_depth * 1.5
            
            -- Record optimization
            record_optimization(db_name, 'max_depth', 
                params.max_depth/1.5, params.max_depth)
        end
    end

    return true
end

-- Optimize specific query
function M.optimize_query(db_name, query_id)
    local query_metrics = metrics.get_metrics(db_name, 'query_time', 3600)
    local params = parameters[db_name]
    
    if #query_metrics > 10 then
        local avg_time = calculate_average(query_metrics)
        if avg_time > 0.1 then
            -- Create index for frequently accessed data
            create_optimization_index(db_name, query_id)
        end
    end
end

-- Sync to Arweave
function M.sync_to_arweave(db_name, state_id)
    local params = parameters[db_name]
    if params.sync_to_arweave then
        -- TODO: Implement actual Arweave sync
        -- For now, just record the attempt
        record_optimization(db_name, 'arweave_sync', 0, 1)
    end
end

-- Record optimization attempt
function record_optimization(db_name, parameter, old_value, new_value)
    if not tuning_db then
        M.init()
    end

    local stmt = tuning_db:prepare([[
        INSERT INTO optimizations 
        (db_name, parameter, old_value, new_value, timestamp)
        VALUES (?, ?, ?, ?, ?)
    ]])
    
    stmt:bind(1, db_name)
    stmt:bind(2, parameter)
    stmt:bind(3, old_value)
    stmt:bind(4, new_value)
    stmt:bind(5, os.time())
    
    stmt:step()
    stmt:finalize()
end

-- Helper function to create optimization index
function create_optimization_index(db_name, query_id)
    -- TODO: Implement index creation based on query pattern
    -- This will be called by the optimization process
    return true
end

-- Helper function to calculate average
function calculate_average(metrics)
    local sum = 0
    for _, m in ipairs(metrics) do
        sum = sum + m.value
    end
    return sum / #metrics
end

-- Cleanup
function M.cleanup()
    if tuning_db then
        tuning_db:close()
    end
end

return M
