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< h2 id = "understanding-synapse-through-grafana-graphs" > < a class = "header" href = "#understanding-synapse-through-grafana-graphs" > Understanding Synapse through Grafana graphs< / a > < / h2 >
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< p > It is possible to monitor much of the internal state of Synapse using < a href = "https://prometheus.io" > Prometheus< / a >
metrics and < a href = "https://grafana.com/" > Grafana< / a > .
A guide for configuring Synapse to provide metrics is available < a href = "../../metrics-howto.html" > here< / a >
and information on setting up Grafana is < a href = "https://github.com/element-hq/synapse/tree/master/contrib/grafana" > here< / a > .
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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.< / p >
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< p > Once you have grafana set up, and assuming you're using < a href = "https://github.com/element-hq/synapse/blob/master/contrib/grafana/synapse.json" > our grafana dashboard template< / a > , look for the following graphs when debugging a slow/overloaded Synapse:< / p >
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< h2 id = "message-event-send-time" > < a class = "header" href = "#message-event-send-time" > Message Event Send Time< / a > < / h2 >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82239409-a1c8e900-9930-11ea-8081-e4614e0c63f4.png" alt = "image" / > < / p >
< p > This, along with the CPU and Memory graphs, is a good way to check the general health of your Synapse instance. It represents how long it takes for a user on your homeserver to send a message.< / p >
< h2 id = "transaction-count-and-transaction-duration" > < a class = "header" href = "#transaction-count-and-transaction-duration" > Transaction Count and Transaction Duration< / a > < / h2 >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82239985-8d392080-9931-11ea-80d0-843ab2f22e1e.png" alt = "image" / > < / p >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82240050-ab068580-9931-11ea-98f1-f94671cbac9a.png" alt = "image" / > < / p >
< p > These graphs show the database transactions that are occurring the most frequently, as well as those are that are taking the most amount of time to execute.< / p >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82240192-e86b1300-9931-11ea-9aac-3e2c9bfa6fdc.png" alt = "image" / > < / p >
< p > In the first graph, we can see obvious spikes corresponding to lots of < code > get_user_by_id< / code > transactions. This would be useful information to figure out which part of the Synapse codebase is potentially creating a heavy load on the system. However, be sure to cross-reference this with Transaction Duration, which states that < code > get_users_by_id< / code > is actually a very quick database transaction and isn't causing as much load as others, like < code > persist_events< / code > :< / p >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82240467-62030100-9932-11ea-8db9-917f2d977fe1.png" alt = "image" / > < / p >
< p > Still, it's probably worth investigating why we're getting users from the database that often, and whether it's possible to reduce the amount of queries we make by adjusting our cache factor(s).< / p >
< p > The < code > persist_events< / code > transaction is responsible for saving new room events to the Synapse database, so can often show a high transaction duration.< / p >
< h2 id = "federation" > < a class = "header" href = "#federation" > Federation< / a > < / h2 >
< p > The charts in the " Federation" section show information about incoming and outgoing federation requests. Federation data can be divided into two basic types:< / p >
< ul >
< li > PDU (Persistent Data Unit) - room events: messages, state events (join/leave), etc. These are permanently stored in the database.< / li >
< li > EDU (Ephemeral Data Unit) - other data, which need not be stored permanently, such as read receipts, typing notifications.< / li >
< / ul >
< p > The " Outgoing EDUs by type" chart shows the EDUs within outgoing federation requests by type: < code > m.device_list_update< / code > , < code > m.direct_to_device< / code > , < code > m.presence< / code > , < code > m.receipt< / code > , < code > m.typing< / code > .< / p >
< p > If you see a large number of < code > m.presence< / code > EDUs and are having trouble with too much CPU load, you can disable < code > presence< / code > in the Synapse config. See also < a href = "https://github.com/matrix-org/synapse/issues/3971" > #3971< / a > .< / p >
< h2 id = "caches" > < a class = "header" href = "#caches" > Caches< / a > < / h2 >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82240572-8b239180-9932-11ea-96ff-6b5f0e57ebe5.png" alt = "image" / > < / p >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82240666-b8703f80-9932-11ea-86af-9f663988d8da.png" alt = "image" / > < / p >
< p > This is quite a useful graph. It shows how many times Synapse attempts to retrieve a piece of data from a cache which the cache did not contain, thus resulting in a call to the database. We can see here that the < code > _get_joined_profile_from_event_id< / code > cache is being requested a lot, and often the data we're after is not cached.< / p >
< p > Cross-referencing this with the Eviction Rate graph, which shows that entries are being evicted from < code > _get_joined_profile_from_event_id< / code > quite often:< / p >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82240766-de95df80-9932-11ea-8c15-5acfc57c48da.png" alt = "image" / > < / p >
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< p > 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 < a href = "https://github.com/element-hq/synapse/blob/ee421e524478c1ad8d43741c27379499c2f6135c/docs/sample_config.yaml#L608-L642" > here< / a > (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.< / p >
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< h2 id = "forward-extremities" > < a class = "header" href = "#forward-extremities" > Forward Extremities< / a > < / h2 >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82241440-13566680-9934-11ea-8b88-ba468db937ed.png" alt = "image" / > < / p >
< p > Forward extremities are the leaf events at the end of a DAG in a room, aka events that have no children. The more that exist in a room, the more < a href = "https://spec.matrix.org/v1.1/server-server-api/#room-state-resolution" > state resolution< / a > that Synapse needs to perform (hint: it's an expensive operation). While Synapse has code to prevent too many of these existing at one time in a room, bugs can sometimes make them crop up again.< / p >
< p > If a room has > 10 forward extremities, it's worth checking which room is the culprit and potentially removing them using the SQL queries mentioned in < a href = "https://github.com/matrix-org/synapse/issues/1760" > #1760< / a > .< / p >
< h2 id = "garbage-collection" > < a class = "header" href = "#garbage-collection" > Garbage Collection< / a > < / h2 >
< p > < img src = "https://user-images.githubusercontent.com/1342360/82241911-da6ac180-9934-11ea-9a0d-a311fe22acd0.png" alt = "image" / > < / p >
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< p > 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
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indicator of problems, and a symptom of other problems though, so check other graphs for what might be causing it.< / p >
< h2 id = "final-thoughts" > < a class = "header" href = "#final-thoughts" > Final Thoughts< / a > < / h2 >
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< p > If you're still having performance problems with your Synapse instance and you've
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tried everything you can, it may just be a lack of system resources. Consider adding
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more CPU and RAM, and make use of < a href = "../../workers.html" > worker mode< / a >
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to make use of multiple CPU cores / multiple machines for your homeserver.< / p >
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