// import BigQuery module
const {BigQuery} = require('@google-cloud/bigquery');

// you can deploy this directly from your machine using gcloud functions deploy ceo --runtime nodejs8 --trigger-http

exports.ceo = functions.https.onRequest((request,response) =>{

        async function datarequest(agent) { // async -> so we can use await later

        const bigquery = new BigQuery();

        // declare variables
        let metric
        let dimension
        let date = agent.parameters.date // from Dialogflow
        
        let dateString = date.substr(0, 10)

        // this could be done better
        if (agent.parameters.querymetric === 'revenue') { // user asked for Revenue
            metric    = 'revenue'
            dimension = 'pounds'
        } else if (agent.parameters.querymetric === 'bookings') { // user asked for Revenue
            metric    = 'bookings'
            dimension = 'bookings'
        }

        console.log(`Reading ${metric} ${dimension}`) // testing
        
        // SQL Query
        const query = `SELECT ${metric} FROM testceo.ceo WHERE date = '${dateString}'`;

        const options = {
            query: query,
            // Location must match that of the dataset(s) referenced in the query.
            location: 'US',
        };

        // Run the query as a job
        const [job] = await bigquery.createQueryJob(options);
        console.log(`Job ${job.id} started.`);

        // Wait for the query to finish
        const [rows] = await job.getQueryResults();

        // Print the results
        console.log('Rows:');

        const revenue = rows[0][metric]
        agent.add(`${revenue.toString()} ${dimension}`);
        // add vlY
        // add error handler
        //rows.forEach(row => console.log(row));


    }
    
 
}