Reduce usage service readiness threshold #6759
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jdolle
commented
Apr 28, 2025
| @@ -322,6 +322,7 @@ export function createKVBuffer<T>(config: { | |||
| logger.info('Stopping buffer'); | |||
| if (timeoutId) { | |||
| clearTimeout(timeoutId); | |||
| timeoutId = null; | |||
There was a problem hiding this comment.
this isn't necessary based on the current logic, but it's best practice being a good steward of our variables to avoid confusing logic potentially later.
jdolle
commented
Apr 28, 2025
| producer.on(producer.events.REQUEST_TIMEOUT, () => { | ||
| logger.info('Kafka producer: request timeout'); | ||
| }); | ||
|
|
||
| producer.on(producer.events.DISCONNECT, () => { | ||
| logger.info(`Kafka producer: disconnected`); |
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I want to know whenever these things happen to better understand what our kafka producer is doing when we see the spikes in memory and in general.
📚 Storybook DeploymentThe latest changes are available as preview in: https://pr-6759.hive-storybook.pages.dev |
🚀 Snapshot Release (
|
| Package | Version | Info |
|---|---|---|
@graphql-hive/apollo |
0.39.0-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
@graphql-hive/cli |
0.50.0-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
@graphql-hive/core |
0.11.0-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
@graphql-hive/envelop |
0.36.0-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
@graphql-hive/yoga |
0.42.0-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
hive |
6.1.0-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
hive-apollo-router-plugin |
2.1.1-alpha-20250428220850-b4d7256e98b87630973813de1ab7f90dc8d56238 |
npm ↗︎ unpkg ↗︎ |
💻 Website PreviewThe latest changes are available as preview in: https://pr-6759.hive-landing-page.pages.dev |
|
🐋 This PR was built and pushed to the following Docker images: Targets: Platforms: Image Tag: Docker Bake metadata{
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jdolle
commented
Apr 28, 2025
| @@ -54,7 +54,7 @@ export function deployUsage({ | |||
| readinessProbe: { | |||
| initialDelaySeconds: 10, | |||
| periodSeconds: 5, | |||
| failureThreshold: 2, | |||
| failureThreshold: 1, | |||
There was a problem hiding this comment.
Fail fast. Avoid 503s
jdolle
commented
Apr 28, 2025
| @@ -32,7 +41,7 @@ const levelMap = { | |||
|
|
|||
| const retryOptions = { | |||
| maxRetryTime: 30_000, | |||
| initialRetryTime: 500, | |||
| initialRetryTime: 300, | |||
There was a problem hiding this comment.
A continuation of more aggressive times in general. 300ms is the default for kafkajs and I don't see a reason to wait longer.
jdolle
commented
Apr 28, 2025
| : net.connect({ host, port }, onConnect); | ||
|
|
||
| // This is equivalent to kafka's "connections.max.idle.ms" | ||
| socket.setKeepAlive(true, 180_000); |
There was a problem hiding this comment.
default is 60s in kafkajs, but event hub recommends 180s https://github.com/Azure/azure-event-hubs-for-kafka/blob/master/CONFIGURATION.md#producer-configurations-only
jdolle
commented
Apr 28, 2025
| @@ -140,10 +168,11 @@ export function createUsage(config: { | |||
| }; | |||
| }, | |||
| // settings recommended by Azure EventHub https://docs.microsoft.com/en-us/azure/event-hubs/apache-kafka-configurations | |||
| requestTimeout: 60_000, // | |||
| requestTimeout: 30_000, | |||
There was a problem hiding this comment.
recommendation is 30-60s.
Our average duration is 190ms, so 30s is more than enough.
jdolle
commented
Apr 28, 2025
| logger.info(`Kafka producer: connected`); | ||
|
|
||
| if (status === Status.Unhealthy) { | ||
| changeStatus(Status.Ready); |
There was a problem hiding this comment.
My thought here is that we should be more proactive with setting the usage service to unhealthy and ready, instead of waiting for messages to fail.
n1ru4l
approved these changes
Apr 30, 2025
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Background
We've been investigating an issue with the usage producer where the memory spikes and the service may or may not crash or remain with high memory.
The fallback/retry logic was adjusted here #6755
Description
This PR reduces the failure threshold for readiness with usage service.
We know that there's a window where requests can continue to be made to the usage service after it's flagged as unhealthy. I had incorrectly thought previously, that these messages were being send to kafka via our producer in the normal (non-fallback) flow, even though the service was unhealthy. However, inside the endpoint's handler, we check readiness of the usage client and if it's unhealthy, return a 503.
To reduce the number of 503s, we should be more aggressive with the readiness check for usage. Even if it triggers more false positives (which I doubt), moving a pod from ready to not ready isn't a big deal and having fewer unnecessary 503s is a positive thing for our consumers.