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We can add filters to the queries in both Prometheus console or in grafana, e. Some of the paths contains capturing groups that are not named, and Prometheus will give them a name in the following format: unnamedLabelX.
For example, for interface counters on a device I have the following path:. From the above path you can see for example that the direction of the packets is captured in a named capturing group that also contains a non-capturing group:?
Named capturing groups can be created with? Now for Octets Errors Discards there is no name defined, so the result will be.
The open-source community is really helpful and thanks to Be Smart Be Open and Vista Technology we have a pre-defined grafana dashboard for Arista devices which you can find here:.
You can download the dashboard as a JSON file and then import it in Grafana. Rule records allow you to precompute frequently needed expressions and save the result as a new set of time series.
Querying this result will be much faster then executing the original expression and very useful for dashboards, which need to query the same expression repeatedly.
For more details on rule records, please visit the best practices and the configuration section on the official Prometheus website.
Checking on the GUI, it will give you the following results. After you create the records and update the yml files on the server, make sure you can see the rule created in the Status — Rules submenu.
The PrometheusExampleJob has three operators: Random numbers up to 10, are generated, then a map counts the events and creates a histogram of the values passed through.
Finally, the events are discarded without further output. The very simple code below is from the second operator. It illustrates how easy it is to add custom metrics relevant to your business logic into your Flink job.
Make the PrometheusReporter jar available to the classpath of the Flink cluster it comes with the Flink distribution :.
All job managers and task managers will expose the metrics on the configured port. Prometheus needs to know where to scrape metrics.
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As you can see the frame does not include raw samples anymore. That's the second improvement we did: We send in the message samples batched in chunks see this video to learn more about chunks , which are exactly the same chunks we store in the TSDB.
You can find full design here. Let's compare remote read characteristics between Prometheus 2. As for the initial results presented at the beginning of this article, I was using Prometheus as a server, and a Thanos sidecar as a client of remote read.
I was invoking testing remote read request by running gRPC call against Thanos sidecar using grpcurl. Test was performed from my laptop Lenovo X1 16GB, i7 8th with Kubernetes in docker using kind.
The data was artificially generated, and represents highly dynamic 10, series worst case scenario. The full test bench is available in thanosbench repo.
Reducing memory was the key item we aimed for with our solution. Instead of allocating GBs of memory, Prometheus buffers roughly 50MB during the whole request, whereas for Thanos there is only a marginal memory use.
Thanks to the streamed Thanos gRPC StoreAPI, sidecar is now a very simple proxy. Additionally, I tried different time ranges and number of series, but as expected I kept seeing a maximum of 50MB in allocations for Prometheus and nothing really visible for Thanos.
This proves that our remote read uses constant memory per request no matter how many samples you ask for.
Allocated memory per request is also drastically less influenced by the cardinality of the data, so number of series fetched like it used to be.
We achieved to reduce remote read request latency as well, thanks to streaming and less encoding. Remote read was extended in a backward and forward compatible way.
To use the new, streamed remote read in Prometheus v2.