Hi there! I have a problem that’s been bugging me for a while now. I’m working with a setup where I need to grab data from a PLC device every 150 milliseconds using Node.js with the nodes7 package. The tricky part is that I also need to save all this information into a MySQL database and then do some calculations on it before sending the results to my web interface through websockets. What worries me is that doing all these operations so often might put too much stress on my system or crash the database. Has anyone dealt with something similar? What’s the best way to architect this kind of real-time data pipeline without running into performance issues?
I’ve been running a similar setup for about two years now, though with slightly different timing intervals. One approach that worked well for me was implementing a dual-buffer system where you collect the PLC data into memory buffers and then batch-insert into MySQL every few seconds rather than on every single read. This dramatically reduces database load while still maintaining your real-time calculations. For the websocket part, I found it helpful to separate the data collection thread from the web interface updates - you can push calculated results to clients at a different frequency than your raw data collection. Also consider using connection pooling for your MySQL connections and maybe look into implementing some basic circuit breaker patterns to handle database timeouts gracefully. The key is decoupling your data acquisition from your storage operations so one doesn’t block the other.
been there! skip the fancy architectures for now - just use async/await properly and batch your inserts every 1-2 seconds instead of individual writes. also consider using a lightweight db like sqlite for buffering before mysql if you’re really worried about performance hits.
From my experience dealing with industrial data streams, the most critical aspect is handling connection failures gracefully since PLC communications can be unreliable. I would recommend implementing exponential backoff for your nodes7 connections and keeping a local timestamp for each reading to detect gaps in your data stream. Another thing that saved me headaches was pre-allocating your MySQL table structure with proper indexing on timestamp columns - the difference in write performance is substantial when you’re dealing with high frequency inserts. Consider using MySQL’s LOAD DATA INFILE for bulk operations if you go the batching route, it’s significantly faster than individual INSERT statements. Also monitor your Node.js event loop lag closely because blocking operations at 150ms intervals will quickly cause memory issues. I found that keeping raw calculations in memory and only persisting aggregated results every few minutes worked better for my use case than trying to store everything.
We ran into this exact scenario at my previous job and ended up solving it with a message queue architecture. Rather than hitting MySQL directly from your data collection process, consider pushing the PLC readings into something like Redis or RabbitMQ first. Then have a separate worker process that consumes from the queue and handles the database writes at a more manageable pace. This approach gave us much better resilience because if the database goes down temporarily, you don’t lose any data - it just queues up until the connection is restored. For the calculations part, we found it worked better to do the math on the raw data in memory before it hits the database, then store both the raw values and computed results. This way your websocket updates can pull from either source depending on what the frontend needs. Just make sure to monitor your queue depth so you know if you’re falling behind on processing.
One thing I learned the hard way is that MySQL’s default configuration often isn’t optimized for high-frequency writes. You might want to adjust the innodb_flush_log_at_trx_commit setting to 2 instead of the default 1 - this reduced our write latency significantly without major data integrity risks for our use case. Also check your innodb_buffer_pool_size since frequent inserts benefit from having more memory allocated. What really helped us was implementing a circular buffer pattern where we keep the last N readings in memory for immediate websocket responses while async operations handle the database persistence. This way your web interface stays responsive even if the database gets temporarily sluggish. Make sure you’re monitoring your Node.js memory usage too because at 150ms intervals, small memory leaks become big problems fast. We ended up using process.memoryUsage() calls every few minutes to catch issues early.