Staff Engineering Skills Streams Vs Batch

Streams vs Batch Trap

Batch-to-stream is a rewrite, not a refactor. Before writing a processing pipeline, ask: what's the latency requirement, and will it change?

Decision Framework

Ask these questions before writing the first line of processing code:

If the answer to ANY row points to stream, build for streaming from the start. You cannot cheaply add streaming later.

The Rule

Reducing a batch interval is not a scaling strategy. A 10-second batch interval is a bad stream processor -- it has all the complexity of streaming with none of the benefits (no ordering, no backpressure, no offset tracking, overlap risk).

Detection: Batch Patterns That Will Need Streaming

Stop and reassess if you see:

1. A cron job processing "new" or "unprocessed" items -- SELECT * FROM events WHERE processed = false. What's the latency requirement? If "as fast as possible," this is the wrong model.

2. setInterval or setTimeout for processing -- what happens when processing takes longer than the interval? Overlapping batches cause duplicate processing and resource contention.

3. Shrinking batch intervals over time -- started at 5 minutes, now at 10 seconds. This is the symptom. The disease is: you need streaming.

4. Items collected into an array before processing -- what bounds the array? If it's time-based ("all items in the last 5 minutes"), memory grows with throughput.

5. "We'll add real-time later" -- flag this immediately. This is not an incremental change. The data flow, error handling, and ordering assumptions are fundamentally different.

When Batch Is Correct

Batch is the right choice when:

• Latency requirements are hours or days (daily reports, nightly ETL, weekly digests)
• The processing needs a complete view of a time window (aggregations, reconciliation)
• Throughput is stable and predictable
• The workload is compute-heavy and benefits from bulk operations (ML training, data export)

// Batch is correct here: daily revenue report. Nobody needs this in real-time.
async function generateDailyReport() {
  const revenue = await db.$queryRaw`
    SELECT DATE_TRUNC('hour', created_at) as hour, SUM(amount) as total
    FROM orders
    WHERE created_at >= ${startOfDay} AND created_at < ${endOfDay}
    GROUP BY DATE_TRUNC('hour', created_at)
  `;
  await saveReport({ date: today, hourlyRevenue: revenue });
}

When You Need Event-Driven Processing

For most applications, you don't need Kafka. You need event-driven task processing with proper failure handling.

// Process each item as it arrives. Failure isolated per item.
// Latency is seconds, not minutes. Scales by adding workers.
import { task } from "@trigger.dev/sdk";

export const processSignup = task({
  id: "process-signup",
  retry: { maxAttempts: 3 },
  run: async (payload: { userId: string }) => {
    const user = await db.user.findUnique({ where: { id: payload.userId } });
    await sendWelcomeEmail(user);
    await createDefaultWorkspace(user);
    await trackSignupAnalytics(user);
  },
});

// In the signup handler -- trigger immediately, don't batch
async function handleSignup(data: SignupInput) {
  const user = await db.user.create({ data });
  await processSignup.trigger({ userId: user.id });
  return user;
}

Micro-Batching: The Middle Ground

When per-item overhead is too high but you need low latency, use small frequent batches with per-item failure handling.

import { task } from "@trigger.dev/sdk";

export const processEventBatch = task({
  id: "process-event-batch",
  queue: { concurrencyLimit: 5 },
  run: async (payload: { eventIds: string[] }) => {
    const events = await db.event.findMany({
      where: { id: { in: payload.eventIds } },
    });

    // Process individually within the batch -- failure isolation
    const results = await Promise.allSettled(
      events.map(event => processEvent(event))
    );

    // Retry only failures, not the whole batch
    const failures = results
      .map((r, i) => r.status === "rejected" ? events[i] : null)
      .filter(Boolean);
    if (failures.length > 0) await enqueueRetry(failures);
  },
});

Anti-Patterns

// Dangerous: polling for unprocessed rows on a timer
// Race conditions with multiple instances, no failure isolation,
// duplicate emails if process crashes between send and flag update
const job = cron("*/5 * * * *", async () => {
  const users = await db.user.findMany({ where: { welcomeEmailSent: false } });
  for (const user of users) {
    await sendWelcomeEmail(user);
    await db.user.update({ where: { id: user.id }, data: { welcomeEmailSent: true } });
  }
});

// Dangerous: shrinking interval as scaling strategy
// Started at 5min, now 10sec. What if processing takes 15sec? Overlap.
setInterval(async () => {
  const events = await db.event.findMany({
    where: { processedAt: null }, take: 1000,
  });
  await processEvents(events); // takes longer than interval under load
}, 10_000);

// Dangerous: one bad item kills the whole batch
const orders = await db.order.findMany({ where: { date: today } });
const report = orders.map(order => ({
  revenue: calculateRevenue(order),  // throws on malformed data
  tax: calculateTax(order),          // throws on missing region
}));
// Order #5,000 throws. All 50,000 orders lost. Retry all or skip?

Related Traps

• Cardinality -- high-cardinality data growing over time is the forcing function that breaks batch. When batch size grows because data volume grows, you need streaming, not a shorter interval.
• Backpressure -- stream processors handle backpressure naturally (consumer pulls at its own pace). Batch processors don't -- if the batch is bigger than the system can handle, it fails.
• Idempotency -- stream/event processing requires idempotent handlers because messages can be delivered more than once. Batch systems often skip this and break when they retry.
• Race Conditions -- polling-based batch processing is inherently racy. Two instances polling for WHERE processed = false at the same time pick up the same rows.