Hi everyone,
I’m currently developing a custom subgraph to index real-time interaction data for a mobile-based dApp, but I’ve run into a frustrating bottleneck with the sync speed. I’m attempting to map several custom events that are triggered by a high volume of smart contract interactions, but the Graph Node seems to be lagging behind the head of the chain by several blocks whenever the activity spikes.
To help simulate this high-velocity traffic for my stress tests, I’ve been using a delta apk as a mobile execution environment to trigger automated contract calls on the testnet. The issue I’m raising is that while the transactions are succeeding on-chain, the subgraph’s mapping functions are taking significantly longer to process the logs when they originate from this automated environment. I’m seeing a weird discrepancy where manual transactions index almost instantly, but the batched calls from the executor seem to cause the Indexer’s WASM runtime to hang momentarily.
Has anyone else noticed performance issues with the Graph Node when dealing with rapid-fire event logs from a mobile script runner? I’m trying to figure out if I should be optimizing my subgraph.yaml to handle these bursts better, or if there’s a specific pruning strategy I should use to prevent the database from getting bogged down by the executor’s high-frequency data. I’m worried that if I can’t get the indexing latency down, the frontend will show stale data to my users. I’d love to hear if any other devs are using automated tools for their integration testing and how you keep your subgraphs from falling behind the real-time execution!
Hey, I’ve seen this kind of behavior before when subgraphs get hit with bursty, automated traffic.
What usually ends up happening isn’t that “mobile-triggered txs” are inherently slower to index, but that the pattern of submission creates short-lived backpressure in the Graph Node. When you have rapid-fire events (especially batched contract calls), the indexing loop can momentarily fall behind the chain head because the handler queue + WASM execution + Postgres writes all compete at once.
Batching at the contract level if possible (fewer, richer events instead of many small ones helps a lot).
Check database contention — Postgres often becomes the real bottleneck under spikes, not the Graph Node itself.
Increase resources / parallelism if you’re running your own node (CPU throttling shows up exactly like “random lag behind head”).
Avoid unnecessary event subscriptions in subgraph.yaml—even unused logs still get filtered through.
If you’re stress testing with automated scripts, make sure the traffic isn’t unintentionally burst-synchronous (that pattern is especially rough on the indexer).
Also worth noting: when manual txs look “instant” but scripted ones lag, it’s often just timing coincidence + batching effects rather than the executor itself causing issues.
If you’re experimenting with tooling around this, I’ve seen some dev workflows documented here that might be useful: click here.
If you want, you can share your handler logic or subgraph config — there are usually a couple of very specific hotspots that cause the kind of lag you’re describing.