Be careful not to make heap dumps to often and it can also take quite some time to get the dump data from the back-end to the front-end. BTW: I can recommend Athom to use a tool like AppDynamics to get a good view on what is going on internally (it has a 30-day free trial ).Īnother ting to try is using a module like 'idle-gc' (or just running node with '-expose-gc' and forcing GC with 'global.gc()').įor developers that want to debug memory usage, have a look at how I've added it one of my apps. I had to rewrite my app code to make sure it was easier to GC.Īs in the example mentioned above, memory leaks often occur with callback and closures, which Homey uses a lot. Some memory is only freed when it really runs out of memory and runs a full garbage collect. `savingCallbacks` in my case related to `saveHomeySettings`) it is also related to the incremental garbage collector that will not directly free up all memory. This blog discusses why you should monitor your Node.js app, explores which app monitoring metrics matter most. For example, by implementing monitoring, you can detect problems, such as a memory leak or long-running processes that block the Node.js event loop.

With the help of I was able to install and run the heapdump module to look into possible leaks.Īlthough there are some leaks (e.g. Monitoring is key to gain a better understanding of the health of your application. Memory monitoring tools track the memory usage of your application and give you insights into how your application is using memory through reports/graphs. Monitoring memory usage in Node.js with Prometheus. I’ll carry out some further investigation before the next update.Since one of my apps seemed to be using memory like crazy I've looking more into this in the last week. Next, you will look at the tools you can use to detect memory leaks. When you enable object instance tracking for a node, the Node.js Agent. The Reset method is particularly useful as it allows the same JS container to be reused – and, as such, avoids V8 allocating further memory to hold the (new) Cache object. The Node.js Agent provides object instance tracking (OIT) for monitoring memory usage. With this ‘issue’ in mind all object classes managed by mg-dbx (global, class and cursor) all have Close and Reset methods.

Most of the time it simply appears that Node.js/V8 is a bit greedy. However, the garbage collector should (and usually does) spring into action before memory usage becomes critical. This tab displays performance metrics such as CPU and memory usage. A consequence of this is that JS scripts that quickly generate lots of JS objects can consume a lot of memory in a short period of time. You can monitor the performance metrics of your Node.js apps on the Monitoring. The V8 engine tends to be a little slow in cleaning up objects that it identifies as being out of scope. One notorious area where the V8 engine can consume an excess amount of memory is related to the garbage collector. Memory Usage Active Handlers and Requests Event Loop Latency GC Duration. I’m not aware of any memory leaks in mg-dbx but I’ll check this – specifically the part that invokes Cache functions – before the next release. This quickstart helps you monitor your Node.js server by setting up prom-client. It does sound like there’s some kind of fault here. In Node.js in particular, I see the big increase mostly in the RSS (resident set size) part when you log a moryUsage() call on the console in the QEWD.js handler.Ĭonfig.addMiddleware = function (bodyParser, app, q, qx, qewdConfig)`) As I'm using QEWD.js's workers with an mg-dbx in-process connection to our Caché, this means the memory increase must come from mg-dbx or Caché's memory usage itself. If you already have some dump there, delete it first, otherwise button 'Load' will not appear. The result is that the first handler with the static data returned does not increase memory consumption, but the second handler returning the data from Caché increases memory usage by 45 MB. Open DevTools in Chrome, open tab 'Memory', click button 'Load' and select your. The second handler does a mg-dbx function call to Caché to return the product's json dynamically from the Caché database using an extrinsic function.

I created a testrest.js module in QEWD.js with two handlers: the first handler returns a json response with the static data result from the same 24 products request (I saved the product's json response in a test.json file and the handler simply require()'s it to return it to the browser). Today I did more testing to find what's causing this.

Monitor node js memory usage