SuperEx Educational Series: Understanding Rollup Congestion

Recently, our updates have consistently focused on the Rollup series. Some users have left comments asking: How many Rollup-related educational articles are there going to be? Even those who were mentally prepared did not expect the topic to require such extensive coverage.

Here, we would like to share the learning plan: the Rollup educational series is expected to conclude within seven working days. Now, back to the main topic. Today’s subject is “Rollup Congestion”—a discussion about congestion and scalability within Rollups.

For users who have read all of our Rollup articles, a question may naturally arise: Why do Rollups get congested at all? After all, in many narratives, Rollups are often presented as the ultimate answer to Ethereum scalability.

But reality tells a different story: Rollups themselves are becoming increasingly congested—gas fees spike, batch submissions are delayed, transactions queue up, and user experience deteriorates. Problems once associated only with L1 are now reappearing in the Rollup world.

This is not a failure of Rollups. Rather, it marks the moment when the scalability narrative has entered its next, more realistic phase.

A Common Misconception: Rollups = No Congestion

Many people subconsciously accept an equation that originated from the simplified messaging of early scalability narratives. When Rollups were first introduced to the Ethereum community, the common explanation was:Execution moves off-chain, the main chain only handles settlement, so throughput naturally increases.

This direction is correct—but in widespread communication, a critical premise was often omitted:

Higher throughput ≠ infinite throughput.

Rollups do not eliminate all constraints; they restructure constraints across layers.

On L1, constraints mainly include:

Limited block space
Rigid gas pricing
Extremely high global consensus costs

Rollups do bypass global execution, but in exchange they introduce another set of constraints:

Transaction ordering must have an exit
Data must have a final landing point
Final states must be accepted by L1

In other words, Rollups do not detach from L1—they embed themselves within L1’s security boundary. As long as a Rollup aims to inherit L1 security, it can never fully escape L1’s cadence.

Congestion Is Essentially the External Expression of Resource Competition

When a Rollup lacks sufficient real demand, it naturally appears “never congested.” But once a Rollup genuinely supports DeFi, NFTs, airdrop interactions, and bot-driven competition, its bottlenecks inevitably surface.

A more accurate understanding is this:

Rollups remove some L1 bottlenecks, but they do not eliminate bottlenecks as a concept.

The Three Core Causes of Rollup Congestion

1. Ordering Bottlenecks: Sequencers Are Not Infinite

In current Rollup architectures, the Sequencer plays a far heavier role than many users realize. It is not merely a transaction relay—it simultaneously handles:

Transaction intake
Ordering decisions
Execution batching
Batch timing control

Under low load, this design is extremely efficient: fast confirmations, smooth UX, and low gas. However, most Rollups rely on a single Sequencer or a very small whitelist, which structurally imposes a hard upper limit on ordering capacity.

When short-term demand spikes—such as:

Meme token on-chain speculation
Large-scale airdrop qualification battles
Arbitrage bots entering en masse

The Sequencer does not face linear request growth, but exponential competitive behavior.

At that point, the system can only resolve conflicts in the most primitive way:

Higher bids go first
Lower latency wins

What users observe then is:

Priority fees spiraling upward
Ordinary users pushed to the back of the queue
Transactions broadcast but not confirmed

This is not a Rollup bug—it is the inevitable outcome of centralized ordering under intense competition.

That is why the industry is now seriously discussing:

Decentralized Sequencers
Shared Sequencers
Based Rollups

All of which ultimately ask the same question: Can transaction ordering avoid becoming a single point of control?

2. Data Layer Pressure: Rollups Still Have to “Write the Ledger”

Many users overlook a basic fact when using Rollups: Rollup transactions must ultimately leave a trace.

Whether Optimistic or ZK Rollups, state roots must be accepted by L1, and transaction data must remain externally verifiable. This means Rollups are not fully off-chain systems—they are highly sensitive to data availability.

When L1 is under low load, this issue is barely noticeable. But once L1 experiences high-traffic events such as:

Large-scale NFT mints
Cascading DeFi liquidations
Native L1 gas spikes

The cost of publishing Rollup data rises passively—and more importantly, this cost is not something Rollups can independently control.

As a result, Sequencers face only two choices:

Delay submission
Pass costs on to users

This leads to the familiar experience: L2 gas increases, UX worsens, yet the Rollup itself has done nothing “wrong.”

This is why many users feel confused for the first time:

I left L1—why am I still paying for L1 congestion?

The answer is straightforward:You escaped execution, not settlement.

3. Batch Processing: The Trade-Off Between Efficiency and Latency

Batching is the most critical—and most misunderstood—component of Rollup cost structures.

From an economic perspective:

Submitting one transaction individually → extremely expensive
Submitting 1,000 transactions together → very cheap per transaction

Thus, Rollups are naturally inclined to “wait a bit.”

But from a UX perspective, waiting itself is a cost. State delays mean uncertainty. As a result, Rollup design is always caught in a tension between:

Maximizing efficiency
Minimizing latency

During peak periods, Sequencers often make a rational but unpopular choice: wait a little longer and include more transactions in one batch.

What users see then is:

Funds still present
Transactions not failed
Interfaces frozen

This is not a system failure—it is the execution of an economically optimal strategy.

From the system’s view: rational
From the user’s view: congested

Understanding this matters, because it shows that Rollup congestion is often not a technical flaw, but a trade-off between cost structure and user experience.

How the Industry Is Responding to Rollup Congestion

1. Modular Decomposition

Execution / DA / Settlement decoupling
DA layers such as Celestia and EigenDA offloading pressure

The goal is simple: prevent all traffic from converging on L1.

2. Ordering Mechanism Evolution

Including but not limited to:

Decentralized Sequencers
Shared Sequencers
Based Rollups (ordering reverts to L1)

All of these answer one question: Who decides transaction order?

3. Multiple Rollups in Parallel, Not a Single “Super L2”

Industry consensus is shifting: instead of one Rollup handling everything, multiple Rollups will specialize in different scenarios.

The future looks more like a Rollup cluster, not a single monolithic scaling solution.

A More Realistic Conclusion

Rollups solve scalability for execution and security—but they never promised:

Infinite throughput
Zero congestion
Permanently low fees

True scalability is not about eliminating congestion, but about ensuring that when congestion occurs, the system remains secure, verifiable, and recoverable.

From this perspective, Rollup congestion is not a failure—it is proof that Rollups are beginning to shoulder L1-level responsibilities.

Closing Thoughts

The fact that we are now asking why Rollups get congested rather than whether Rollups are useful reveals a deeper truth:

The scalability war has entered its second phase.

The question is no longer whether Rollups exist, but how Rollups evolve.