From Storage to Stream: Delivering MongoDB Data Directly to Users

Step 1: MongoDB Cursor

Here’s how we set up the cursor (reusing your snippet):

const cursor =
    userObject?.data?.serviceProviderName === 'ZYRO'
        ? zyroTransactionModel.find(query).cursor()
        : finoTransactionModel.find(query).cursor();

console.log("Cursor created successfully");

Step 2: Setting Up the ZIP File
Use the yazl library to stream CSV data into a ZIP file:

const yazl = require('yazl');
const zipfile = new yazl.ZipFile();

reply.raw.writeHead(200, {
    "Content-Type": "application/zip",
    "Content-Disposition": "attachment; filename=transactions.zip",
});

zipfile.outputStream.pipe(reply.raw);

const cleanup = async () => {
    console.log("Cleaning up resources...");
    zipfile.end(); // Finalize ZIP
    await cursor.close();
};
reply.raw.on("close", cleanup);
reply.raw.on("error", cleanup);

Step 3: Creating Dynamic CSV Streams
Generate CSV data dynamically and stream it into the ZIP file:

const createNewCSVStream = (headers) => {
    const csvStream = new Readable({ read() {} });
    csvStream.push(headers.join(",") + "\n"); // Add headers
    return csvStream;
};

const filteredHeaders = getHeaders(transactionDownloadFields, userObject?.state?.auth?.role);
const currentCSVStream = createNewCSVStream(filteredHeaders);

zipfile.addReadStream(currentCSVStream, "transactions_part_1.csv");

Step 4: Streaming MongoDB Data to CSV
Stream the data from MongoDB directly into the CSV:

cursor.on('data', (doc) => {
    const csvRow = filteredHeaders.map(header => doc[header.key] || '').join(',');
    currentCSVStream.push(csvRow + '\n'); // Write row
});

cursor.on('end', () => {
    currentCSVStream.push(null); // End the stream
    zipfile.end(); // Finalize the ZIP
});

Step 5: Processing Data from MongoDB Cursor
Stream documents from the MongoDB cursor, transform them as needed, and dynamically write rows to the CSV stream:

try {
    for await (const doc of cursor) {
        if (clientDisconnected) {
            console.log("Client disconnected. Stopping processing...");
            break;
        }

        streamedCount++;
        rowCount++;

        let row = "";
        const filteredHeaders = getHeaders(
            transactionDownloadFields,
            userObject?.state?.auth?.role
        );

        for (let i = 0; i  f.value === field.value);

            // Get the value from the transaction document
            let value = getValueFromTransaction(doc, field.value);

            // Apply transformation if the field has a transform function
            if (originalField?.transform) {
                value = originalField.transform(value);
            }

            // Enclose the value in double quotes
            value = value !== undefined ? `"${value}"` : '"N/A"';
            row += (i > 0 ? "," : "") + value;
        }
        row += "\n";
        currentCSVStream.push(row);

        // Check if the row count has reached the threshold for the current CSV file
        if (rowCount >= MAX_ROWS_PER_FILE) {
            console.log(`Threshold reached for file ${fileIndex - 1}. Starting new file...`);
            currentCSVStream.push(null); // End the current CSV stream
            currentCSVStream = createNewCSVStream(); // Start a new stream
            rowCount = 0; // Reset the row count
        }
    }

    // Finalize the current CSV stream if it has data
    if (currentCSVStream) {
        currentCSVStream.push(null);
    }

    // Finalize the ZIP file
    zipfile.end();
    console.log(`Successfully streamed ${streamedCount} rows across ${fileIndex - 1} files.`);
} catch (error) {
    console.error("Error during processing:", error);
    if (!headersSent) reply.status(500).send({ error: "Failed to generate ZIP file" });
} finally {
    // Cleanup: Close the MongoDB cursor
    await cursor.close().catch((err) => console.error("Error closing cursor:", err));
}

Summary

Document Iteration Using for await...of:

Streams documents one by one from the MongoDB cursor efficiently.
Enables real-time processing without loading all data into memory.

• Dynamic CSV Row Generation:

Constructs each row dynamically by iterating over filteredHeaders.
Applies transformations using a transform function, if defined in transactionDownloadFields.
Row Threshold and File Splitting:

Monitors the row count against the threshold (MAX_ROWS_PER_FILE).
Ends the current CSV stream and starts a new one when the threshold is reached.

• Error Handling:

Logs and sends an error response if an issue occurs during processing.
Ensures proper cleanup by closing the MongoDB cursor in the finally block.

• Finalizing Streams:

Pushes null to terminate the current CSV stream.
Completes the ZIP file once all rows are processed.

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