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[ML] Fixing ML saved object cache (#151122)

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PR https://github.com/elastic/kibana/pull/146155 introduced a cache of
the ML saved objects to the `mlSavedObjectService` to improve
performance.

This can cause a problem for the module `setup` function when called
more than once from an external plugin in a single request.
A single instance of the `mlSavedObjectService` is used per request and
so for each `setup` call the same cache is used. Any saved objects
created, updated or removed in the first `setup` call are missing from
the cache in subsequent calls.

This means any jobs being created in the second call to `setup` cannot
be opened as do not exist in the cache.

This PR clears the cache after every write action to the saved object
client causing it to be repopulated the next time it is read.
This commit is contained in:
James Gowdy 2023-02-20 09:41:14 +00:00 • committed by GitHub
parent eba0dc6eb5
commit f6bb0f4fcc
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1 changed files with 26 additions and 2 deletions
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28
x-pack/plugins/ml/server/saved_objects/service.ts
View file

@ -63,6 +63,12 @@ export function mlSavedObjectServiceFactory(
(options: SavedObjectsFindOptions) => JSON.stringify(options)
);
function _clearSavedObjectsClientCache() {
if (_savedObjectsClientFindMemo.cache.clear) {
_savedObjectsClientFindMemo.cache.clear();
}
}
async function _getJobObjects(
jobType?: JobType,
jobId?: string,
@ -118,6 +124,7 @@ export function mlSavedObjectServiceFactory(
} else {
// the saved object has no spaces, this is unexpected, attempt a normal delete
await savedObjectsClient.delete(ML_JOB_SAVED_OBJECT_TYPE, id, { force: true });
_clearSavedObjectsClientCache();
}
}
} catch (error) {
@ -128,11 +135,12 @@ export function mlSavedObjectServiceFactory(
await savedObjectsClient.create<JobObject>(ML_JOB_SAVED_OBJECT_TYPE, job, {
id,
});
_clearSavedObjectsClientCache();
}
async function _bulkCreateJobs(jobs: Array<{ job: JobObject; namespaces: string[] }>) {
await isMlReady();
return await savedObjectsClient.bulkCreate<JobObject>(
const results = await savedObjectsClient.bulkCreate<JobObject>(
jobs.map((j) => ({
type: ML_JOB_SAVED_OBJECT_TYPE,
id: _jobSavedObjectId(j.job),
@ -140,6 +148,8 @@ export function mlSavedObjectServiceFactory(
initialNamespaces: j.namespaces,
}))
);
_clearSavedObjectsClientCache();
return results;
}
function _jobSavedObjectId(job: JobObject) {
@ -154,6 +164,7 @@ export function mlSavedObjectServiceFactory(
}
await savedObjectsClient.delete(ML_JOB_SAVED_OBJECT_TYPE, job.id, { force: true });
_clearSavedObjectsClientCache();
}
async function _forceDeleteJob(jobType: JobType, jobId: string, namespace: string) {
@ -169,6 +180,7 @@ export function mlSavedObjectServiceFactory(
namespace: namespace === '*' ? undefined : namespace,
force: true,
});
_clearSavedObjectsClientCache();
}
async function createAnomalyDetectionJob(jobId: string, datafeedId?: string) {
@ -250,6 +262,7 @@ export function mlSavedObjectServiceFactory(
const jobObject = job.attributes;
jobObject.datafeed_id = datafeedId;
await savedObjectsClient.update<JobObject>(ML_JOB_SAVED_OBJECT_TYPE, job.id, jobObject);
_clearSavedObjectsClientCache();
}
async function deleteDatafeed(datafeedId: string) {
@ -262,6 +275,7 @@ export function mlSavedObjectServiceFactory(
const jobObject = job.attributes;
jobObject.datafeed_id = null;
await savedObjectsClient.update<JobObject>(ML_JOB_SAVED_OBJECT_TYPE, job.id, jobObject);
_clearSavedObjectsClientCache();
}
async function _getIds(jobType: JobType, idType: keyof JobObject) {
@ -372,6 +386,8 @@ export function mlSavedObjectServiceFactory(
spacesToAdd,
spacesToRemove
);
_clearSavedObjectsClientCache();
updateResult.objects.forEach(({ id: objectId, error }) => {
const jobId = jobObjectIdMap.get(objectId)!;
if (error) {
@ -492,6 +508,7 @@ export function mlSavedObjectServiceFactory(
await savedObjectsClient.delete(ML_TRAINED_MODEL_SAVED_OBJECT_TYPE, modelId, {
force: true,
});
_clearSavedObjectsClientCache();
}
}
} catch (error) {
@ -518,6 +535,7 @@ export function mlSavedObjectServiceFactory(
...(initialNamespaces ? { initialNamespaces } : {}),
}
);
_clearSavedObjectsClientCache();
}
async function _bulkCreateTrainedModel(models: TrainedModelObject[], namespaceFallback?: string) {
@ -528,7 +546,7 @@ export function mlSavedObjectServiceFactory(
return acc;
}, {} as Record<string, string[] | undefined>);
return await savedObjectsClient.bulkCreate<TrainedModelObject>(
const results = await savedObjectsClient.bulkCreate<TrainedModelObject>(
models.map((m) => {
let initialNamespaces = m.job && namespacesPerJob[m.job.job_id];
if (!initialNamespaces?.length && namespaceFallback) {
@ -546,6 +564,8 @@ export function mlSavedObjectServiceFactory(
};
})
);
_clearSavedObjectsClientCache();
return results;
}
async function getAllTrainedModelObjectsForAllSpaces(modelIds?: string[]) {
@ -577,6 +597,7 @@ export function mlSavedObjectServiceFactory(
}
await savedObjectsClient.delete(ML_TRAINED_MODEL_SAVED_OBJECT_TYPE, model.id, { force: true });
_clearSavedObjectsClientCache();
}
async function _forceDeleteTrainedModel(modelId: string, namespace: string) {
@ -586,6 +607,7 @@ export function mlSavedObjectServiceFactory(
namespace: namespace === '*' ? undefined : namespace,
force: true,
});
_clearSavedObjectsClientCache();
}
async function filterTrainedModelsForSpace<T>(list: T[], field: keyof T): Promise<T[]> {
@ -729,6 +751,8 @@ export function mlSavedObjectServiceFactory(
spacesToAdd,
spacesToRemove
);
_clearSavedObjectsClientCache();
updateResult.objects.forEach(({ id: objectId, error }) => {
const model = trainedModelObjectIdMap.get(objectId)!;
if (error) {