Crate relay_server[][src]

The Sentry relay server application.

This module contains the run function which starts the relay server. It responds on multiple supported endpoints, serves queries to downstream relays and send received events to the upstream.

See the Config documentation for more information on configuration options.

Path of an Event through Relay

Overview

Simplified overview of event ingestion (ignores snuba/postprocessing):

graph LR

loadbalancer(Load Balancer)
relay(Relay)
projectconfigs("Project config endpoint (in Sentry)")
ingestconsumer(Ingest Consumer)
outcomesconsumer(Outcomes Consumer)
preprocess{"<code>preprocess_event</code><br>(just a function call now)"}
process(<code>process_event</code>)
save(<code>save_event</code>)

loadbalancer-->relay
relay---projectconfigs
relay-->ingestconsumer
relay-->outcomesconsumer
ingestconsumer-->preprocess
preprocess-->process
preprocess-->save
process-->save

Processing enabled vs not?

Relay can run as part of a Sentry installation, such as within sentry.io's infrastructure, or next to the application as a forwarding proxy. A lot of steps described here are skipped or run in a limited form when Relay is not running with processing enabled:

Inside the endpoint

When an SDK hits /api/X/store on Relay, the code in server/src/endpoints/store.rs is called before returning a HTTP response.

That code looks into an in-memory cache to answer basic information about a project such as:

Some of the data for this cache is coming from the projectconfigs endpoint. It is refreshed every couple of minutes, depending on configuration (project_expiry).

If the cache is fresh, we may return a 429 for rate limits or a 4xx for invalid auth information.

That cache might be empty or stale. If that is the case, Relay does not actually attempt to populate it at this stage. It just returns a 200 even though the event might be dropped later. This implies:

These examples assume that a project receives one event at a time. In practice one may observe that a highly concurrent burst of store requests for a single project results in 200 OKs only. However, a multi-second flood of incoming events should quickly result in eventually consistent and correct status codes.

The response is completed at this point. All expensive work (such as talking to external services) is deferred into a background task. Except for responding to the HTTP request, there's no I/O done in the endpoint in any form. We didn't even hit Redis to calculate rate limits.

Summary

The HTTP response returned is just a best-effort guess at what the actual outcome of the event is going to be. We only return a 4xx code if we know that the response will fail (based on cached information), if we don't we return a 200 and continue to process the event asynchronously. This asynchronous processing used to happen synchronously in the Python implementation of StoreView.

The effect of this is that the server will respond much faster that before but we might return 200 for events that will ultimately not be accepted.

Generally Relay will return a 200 in many more situations than the old StoreView.

The background task

The HTTP response is out by now. The rest of what used to happen synchronously in the Python StoreView is done asynchronously, but still in the same process.

So, now to the real work:

  1. Project config is fetched. If the project cache is stale or missing, we fetch it. We may wait a couple milliseconds (batch_interval) here to be able to batch multiple project config fetches into the same HTTP request to not overload Sentry too much.

    At this stage Relay may drop the event because it realized that the DSN was invalid or the project didn't even exist. The next incoming event will get a proper 4xx status code.

  2. The event is parsed. In the endpoint we only did decompression, a basic JSON syntax check, and extraction of the event ID to be able to return it as part of the response.

    Now we create an Event struct, which conceptually is the equivalent to parsing it into a Python dictionary: We allocate more memory.

  3. The event is normalized. Event normalization is probably the most CPU-intensive task running in Relay. It discards invalid data, moves data from deprecated fields to newer fields and generally just does schema validation.

  4. Filters ("inbound filters") are applied. Event may be discarded because of IP addresses, patterns on the error message or known web crawlers.

  5. Exact rate limits ("quotas") are applied. is_rate_limited.lua is executed on Redis. The input parameters for is_rate_limited.lua ("quota objects") are part of the project config. See this pull request for an explanation of what quota objects are.

    The event may be discarded here. If so, we write the rate limit info (reason and expiration timestamp) into the in-memory project cache so that the next store request returns a 429 in the endpoint and doesn't hit Redis at all.

    This contraption has the advantage that suspended or permanently rate-limited projects are very cheap to handle, and do not involve external services (ignoring the polling of the project config every couple of minutes).

  6. The event is datascrubbed. We have a PII config (new format) and a datascrubbing config (old format, converted to new format on the fly) as part of the project config fetched from Sentry.

  7. Event is written to Kafka.

Note: If we discard an event at any point, an outcome is written to Kafka if Relay is configured to do so.

Summary

For events that returned a 200 we spawn an in-process background task that does the rest of what the old StoreView did.

This task updates in-memory state for rate limits and disabled projects/keys.

The outcomes consumer

Outcomes are small messages in Kafka that contain an event ID and information about whether that event was rejected, and if so, why.

The outcomes consumer is mostly responsible for updating (user-visible) counters in Sentry (buffers/counters and tsdb, which are two separate systems).

The ingest consumer

The ingest consumer reads accepted events from Kafka, and also updates some stats. Some of those stats are billing-relevant.

Its main purpose is to do what insert_data_to_database in Python store did: Call preprocess_event, after which comes sourcemap processing, native symbolication, grouping, snuba and all that other stuff that is of no concern to Relay.

Sequence diagram of components inside Relay

sequenceDiagram
participant sdk as SDK
participant endpoint as Endpoint
participant projectcache as ProjectCache
participant eventmanager as EventManager
participant cpupool as CPU Pool

sdk->>endpoint:POST /api/42/store
activate endpoint
endpoint->>projectcache: get project (cached only)
activate projectcache
projectcache-->>endpoint: return project
deactivate projectcache
Note over endpoint: Checking rate limits and auth (fast path)
endpoint->>eventmanager: queue event

activate eventmanager
eventmanager-->>endpoint:event ID
endpoint-->>sdk:200 OK
deactivate endpoint

eventmanager->>projectcache:fetch project
activate projectcache
Note over eventmanager,projectcache: web request (batched with other projects)
projectcache-->>eventmanager: return project
deactivate projectcache

eventmanager->>cpupool: .
activate cpupool
Note over eventmanager,cpupool: normalization, datascrubbing, redis rate limits, ...
cpupool-->>eventmanager: .
deactivate cpupool

Note over eventmanager: Send event to kafka

deactivate eventmanager

Macros

project_states_version

The current version of the project states endpoint.

Structs

ServerError

Common error type for the relay server.

Functions

run

Runs a relay web server and spawns all internal worker threads.