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Erik Johnston
2023-12-13 15:41:11 +00:00
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docs/usage/administration/understanding_synapse_through_grafana_graphs.md
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## Understanding Synapse through Grafana graphs
It is possible to monitor much of the internal state of Synapse using [Prometheus](https://prometheus.io)
metrics and [Grafana](https://grafana.com/).
A guide for configuring Synapse to provide metrics is available [here](../../metrics-howto.md)
and information on setting up Grafana is [here](https://github.com/matrix-org/synapse/tree/master/contrib/grafana).
It is possible to monitor much of the internal state of Synapse using [Prometheus](https://prometheus.io)
metrics and [Grafana](https://grafana.com/).
A guide for configuring Synapse to provide metrics is available [here](../../metrics-howto.md)
and information on setting up Grafana is [here](https://github.com/element.-hq/synapse/tree/master/contrib/grafana).
In this setup, Prometheus will periodically scrape the information Synapse provides and
store a record of it over time. Grafana is then used as an interface to query and
present this information through a series of pretty graphs.
Once you have grafana set up, and assuming you're using [our grafana dashboard template](https://github.com/matrix-org/synapse/blob/master/contrib/grafana/synapse.json), look for the following graphs when debugging a slow/overloaded Synapse:
Once you have grafana set up, and assuming you're using [our grafana dashboard template](https://github.com/element.-hq/synapse/blob/master/contrib/grafana/synapse.json), look for the following graphs when debugging a slow/overloaded Synapse:
## Message Event Send Time
@@ -57,7 +57,7 @@ Cross-referencing this with the Eviction Rate graph, which shows that entries ar
![image](https://user-images.githubusercontent.com/1342360/82240766-de95df80-9932-11ea-8c15-5acfc57c48da.png)
we should probably consider raising the size of that cache by raising its cache factor (a multiplier value for the size of an individual cache). Information on doing so is available [here](https://github.com/matrix-org/synapse/blob/ee421e524478c1ad8d43741c27379499c2f6135c/docs/sample_config.yaml#L608-L642) (note that the configuration of individual cache factors through the configuration file is available in Synapse v1.14.0+, whereas doing so through environment variables has been supported for a very long time). Note that this will increase Synapse's overall memory usage.
we should probably consider raising the size of that cache by raising its cache factor (a multiplier value for the size of an individual cache). Information on doing so is available [here](https://github.com/element.-hq/synapse/blob/ee421e524478c1ad8d43741c27379499c2f6135c/docs/sample_config.yaml#L608-L642) (note that the configuration of individual cache factors through the configuration file is available in Synapse v1.14.0+, whereas doing so through environment variables has been supported for a very long time). Note that this will increase Synapse's overall memory usage.
## Forward Extremities
@@ -71,14 +71,13 @@ If a room has >10 forward extremities, it's worth checking which room is the cul
![image](https://user-images.githubusercontent.com/1342360/82241911-da6ac180-9934-11ea-9a0d-a311fe22acd0.png)
Large spikes in garbage collection times (bigger than shown here, I'm talking in the
multiple seconds range), can cause lots of problems in Synapse performance. It's more an
Large spikes in garbage collection times (bigger than shown here, I'm talking in the
multiple seconds range), can cause lots of problems in Synapse performance. It's more an
indicator of problems, and a symptom of other problems though, so check other graphs for what might be causing it.
## Final Thoughts
If you're still having performance problems with your Synapse instance and you've
If you're still having performance problems with your Synapse instance and you've
tried everything you can, it may just be a lack of system resources. Consider adding
more CPU and RAM, and make use of [worker mode](../../workers.md)
more CPU and RAM, and make use of [worker mode](../../workers.md)
to make use of multiple CPU cores / multiple machines for your homeserver.