How to extract GA4’s event sequencing in BigQuery using the new batch fields

Google Analytics 4 (GA4) exports event data to BigQuery, enabling detailed user behavior analysis. However, GA4 batches events before sending them, making GA4 event sequencing in BigQuery more complex. Fortunately, three fields—batch_event_index, batch_ordering_id, and batch_page_id—help provide precise sequencing information.

For a full schema of the GA4 export, head over to google documentation.

This article breaks down these fields in a clear, practical way and shows how to use them together to reconstruct a user’s journey in GA4 BigQuery exports.

batch_event_index: The Order of Events Within a Batch

Ever wondered if GA4 sends events in real time? Spoiler alert: it doesn’t! GA4 often groups multiple events together in a batch before sending them to improve efficiency. This means that events can arrive at the same time even if they actually happened in a specific order.

What is batch_event_index?

• Shows the order of events within a batch, based on when they actually happened on the user’s device.
• Makes sure that even if events are sent together, we can still analyse them in their true sequence.

Example: Events in a batch

Picture this: A user interacts with a webpage and does the following:

1. Clicks a button
2. Scrolls down
3. Views a product

If GA4 sends these events as a batch, they might appear in BigQuery like this:

Even though they were transmitted together, batch_event_index keeps the correct event order intact.

Why this matters

batch_ordering_id: How GA4 sends batches

While batch_event_index helps us analyse event order within a batch, batch_ordering_id tells us the order of different batches sent from a page.

What is batch_ordering_id?

• A steadily increasing number that increments every time GA4 sends a new batch of events.
• Helps separate events that were sent in different network requests.

Example: Two separate batches

A user browses a website and interacts with multiple elements. Due to network optimisation, their browser sends two batches of events instead of one.

What this tells us:

Why this matters

batch_page_id: Tracking Events Across Pages

We’ve talked about tracking event order on a single page, but what about users navigating across multiple pages in one session? That’s where batch_page_id comes in.

What is batch_page_id?

• A sequential number assigned to each page a user visits.
• Increases each time the user navigates to a new page.
• Ensures that all events from the same page share the same batch_page_id.

Example: Multi-Page Journey

Let’s say a user moves through a website:

1. Homepage (batch_page_id = 1)
2. Product Page (batch_page_id = 2)
3. Checkout Page (batch_page_id = 3)

Why this matters

How to use these fields together for customer journey analysis

To accurately reconstruct a user’s journey, we need to use all three fields together.

Example Query: Rebuilding a customer journey in BigQuery

<code>SELECT 
  user_pseudo_id, 
  event_name, 
  event_timestamp, 
  batch_page_id, 
  batch_ordering_id, 
  batch_event_index
FROM `project.dataset.events_*`
WHERE _TABLE_SUFFIX = FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
ORDER BY user_pseudo_id, batch_page_id, batch_ordering_id, batch_event_index;</code>Code language: HTML, XML (xml)

How this works

• Groups user interactions by page (batch_page_id)
• Orders batches of events sent together (batch_ordering_id)
• Preserves the order of events within each batch (batch_event_index)

This lets us accurately rebuild a user’s journey step-by-step, even when GA4 sends events in batches.

Final thoughts

GA4’s batching system improves efficiency, but it also adds complexity. By using batch_event_index, batch_ordering_id, and batch_page_id, we can reconstruct user interactions with confidence. These fields are essential for:

Using these fields in BigQuery unlocks deeper insights into customer journeys, helping you make better, data-driven decisions.

Have you started using these fields in your GA4 analysis? What challenges have you faced in reconstructing user journeys? Let’s discuss!