Quick Guide to Realtime Data in PostgreSQL without Webhooks

Aug 2, 2024 • 8 minute read

Hey there, fellow JavaScript devs! Ready to dive into the world of real-time data with PostgreSQL? Let's skip the webhook hassle and explore a straightforward approach using good ol' polling. Buckle up!

Why Polling?

Look, we all love webhooks, but sometimes they're overkill or just not feasible. Polling might seem old school, but it's reliable, easy to implement, and gets the job done. Plus, it puts you in control of when and how you fetch data.

Setting the Stage

I'm assuming you've got PostgreSQL up and running. If not, go grab a coffee, set it up, and come back. We'll wait.

Ready? Great! Let's jump right in.

Polling 101: The Basics

Here's a simple polling function to get us started:

async function pollDatabase(interval) {
  while (true) {
    try {
      const data = await fetchDataFromPostgres();
      processData(data);
    } catch (error) {
      console.error('Polling error:', error);
    }
    await new Promise(resolve => setTimeout(resolve, interval));
  }
}

Easy peasy, right? But wait, there's more!

Level Up: Timestamp-Based Queries

Let's make our polling smarter with timestamps:

let lastPolledAt = new Date(0);

async function pollDatabaseEfficiently(interval) {
  while (true) {
    try {
      const query = `SELECT * FROM your_table WHERE updated_at > $1`;
      const data = await db.query(query, [lastPolledAt]);
      processData(data.rows);
      lastPolledAt = new Date();
    } catch (error) {
      console.error('Polling error:', error);
    }
    await new Promise(resolve => setTimeout(resolve, interval));
  }
}

Now we're cooking with gas! This approach only fetches new or updated data.

When dealing with large datasets, pagination is your best friend:

async function pollWithPagination(pageSize = 100) {
  let lastId = 0;
  while (true) {
    const query = `
      SELECT * FROM your_table
      WHERE id > $1
      ORDER BY id ASC
      LIMIT $2
    `;
    const data = await db.query(query, [lastId, pageSize]);
    if (data.rows.length === 0) break;
    
    processData(data.rows);
    lastId = data.rows[data.rows.length - 1].id;
    
    await new Promise(resolve => setTimeout(resolve, 1000)); // Breathe
  }
}

This cursor-based pagination keeps things smooth and efficient.

Resilience: Error Handling and Retries

Let's add some muscle to our polling with exponential backoff:

async function robustPolling(maxRetries = 5) {
  let retries = 0;
  while (retries < maxRetries) {
    try {
      await pollDatabaseEfficiently(5000);
      retries = 0; // Reset on success
    } catch (error) {
      console.error('Polling failed:', error);
      retries++;
      await new Promise(resolve => setTimeout(resolve, Math.pow(2, retries) * 1000));
    }
  }
  console.error('Max retries reached. Polling stopped.');
}

Now that's what I call resilient!

Performance Boosters

Optimize polling frequency: Find the sweet spot between real-time updates and server load.
Implement caching: Reduce database hits by caching frequently accessed data.

Here's a simple caching example:

const cache = new Map();

async function pollWithCache(interval) {
  while (true) {
    try {
      const data = await fetchDataFromPostgres();
      const newData = data.filter(item => {
        const cached = cache.get(item.id);
        return !cached || cached.updated_at < item.updated_at;
      });
      newData.forEach(item => cache.set(item.id, item));
      processData(newData);
    } catch (error) {
      console.error('Polling error:', error);
    }
    await new Promise(resolve => setTimeout(resolve, interval));
  }
}

Putting It All Together: Live Dashboard

Here's a quick React component showcasing our polling in action:

function LiveDashboard() {
  const [data, setData] = useState([]);

  useEffect(() => {
    const pollInterval = 5000;
    let lastPolledAt = new Date(0);

    const pollData = async () => {
      try {
        const response = await fetch(`/api/data?since=${lastPolledAt.toISOString()}`);
        const newData = await response.json();
        setData(prevData => [...prevData, ...newData]);
        lastPolledAt = new Date();
      } catch (error) {
        console.error('Polling error:', error);
      }
    };

    const intervalId = setInterval(pollData, pollInterval);
    return () => clearInterval(intervalId);
  }, []);

  return (
    <div>
      <h1>Live Dashboard</h1>
      <ul>
        {data.map(item => (
          <li key={item.id}>{item.name}: {item.value}</li>
        ))}
      </ul>
    </div>
  );
}

Polling vs. Webhooks: The Showdown

| Aspect | Polling | Webhooks | |--------|---------|----------| | Setup Complexity | Low | High | | Real-time Accuracy | Depends on interval | Immediate | | Server Load | Consistent | Spiky | | Client Control | High | Low | | Firewall Friendly | Yes | Not Always |

Wrapping Up

There you have it! Polling might not be the new kid on the block, but it's a reliable workhorse for real-time data fetching. With these techniques, you can build robust, efficient systems that keep your users up-to-date without the complexity of webhooks.

Remember, the key is to find the right balance for your specific use case. Don't be afraid to experiment and optimize as you go.

Now go forth and poll with confidence! Your real-time PostgreSQL adventures await!