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* feat(server): Enqueue jobs in bulk The Job Repository now has a `queueAll` method, that enqueues messages in bulk (using BullMQ's [`addBulk`](https://docs.bullmq.io/guide/queues/adding-bulks)), improving performance when many jobs must be enqueued within the same operation. Primary change is in `src/domain/job/job.service.ts`, and other services have been refactored to use `queueAll` when useful. As a simple local benchmark, triggering a full thumbnail generation process over a library of ~1,200 assets and ~350 faces went from **~600ms** to **~250ms**. * fix: Review feedback
100 lines
3.4 KiB
TypeScript
100 lines
3.4 KiB
TypeScript
import { ImmichLogger } from '@app/infra/logger';
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import { Inject, Injectable } from '@nestjs/common';
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import { setTimeout } from 'timers/promises';
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import { usePagination } from '../domain.util';
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import { IBaseJob, IEntityJob, JOBS_ASSET_PAGINATION_SIZE, JobName, QueueName } from '../job';
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import {
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DatabaseLock,
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IAssetRepository,
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IDatabaseRepository,
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IJobRepository,
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IMachineLearningRepository,
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ISmartInfoRepository,
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ISystemConfigRepository,
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WithoutProperty,
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} from '../repositories';
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import { SystemConfigCore } from '../system-config';
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@Injectable()
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export class SmartInfoService {
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private configCore: SystemConfigCore;
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private logger = new ImmichLogger(SmartInfoService.name);
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constructor(
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@Inject(IAssetRepository) private assetRepository: IAssetRepository,
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@Inject(IDatabaseRepository) private databaseRepository: IDatabaseRepository,
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@Inject(IJobRepository) private jobRepository: IJobRepository,
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@Inject(IMachineLearningRepository) private machineLearning: IMachineLearningRepository,
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@Inject(ISmartInfoRepository) private repository: ISmartInfoRepository,
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@Inject(ISystemConfigRepository) configRepository: ISystemConfigRepository,
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) {
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this.configCore = SystemConfigCore.create(configRepository);
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}
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async init() {
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await this.jobRepository.pause(QueueName.SMART_SEARCH);
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let { isActive } = await this.jobRepository.getQueueStatus(QueueName.SMART_SEARCH);
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while (isActive) {
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this.logger.verbose('Waiting for CLIP encoding queue to stop...');
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await setTimeout(1000).then(async () => {
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({ isActive } = await this.jobRepository.getQueueStatus(QueueName.SMART_SEARCH));
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});
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}
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const { machineLearning } = await this.configCore.getConfig();
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await this.databaseRepository.withLock(DatabaseLock.CLIPDimSize, () =>
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this.repository.init(machineLearning.clip.modelName),
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);
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await this.jobRepository.resume(QueueName.SMART_SEARCH);
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}
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async handleQueueEncodeClip({ force }: IBaseJob) {
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const { machineLearning } = await this.configCore.getConfig();
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if (!machineLearning.enabled || !machineLearning.clip.enabled) {
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return true;
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}
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const assetPagination = usePagination(JOBS_ASSET_PAGINATION_SIZE, (pagination) => {
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return force
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? this.assetRepository.getAll(pagination)
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: this.assetRepository.getWithout(pagination, WithoutProperty.CLIP_ENCODING);
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});
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for await (const assets of assetPagination) {
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await this.jobRepository.queueAll(assets.map((asset) => ({ name: JobName.ENCODE_CLIP, data: { id: asset.id } })));
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}
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return true;
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}
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async handleEncodeClip({ id }: IEntityJob) {
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const { machineLearning } = await this.configCore.getConfig();
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if (!machineLearning.enabled || !machineLearning.clip.enabled) {
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return true;
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}
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const [asset] = await this.assetRepository.getByIds([id]);
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if (!asset.resizePath) {
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return false;
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}
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const clipEmbedding = await this.machineLearning.encodeImage(
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machineLearning.url,
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{ imagePath: asset.resizePath },
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machineLearning.clip,
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);
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if (this.databaseRepository.isBusy(DatabaseLock.CLIPDimSize)) {
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this.logger.verbose(`Waiting for CLIP dimension size to be updated`);
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await this.databaseRepository.wait(DatabaseLock.CLIPDimSize);
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}
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await this.repository.upsert({ assetId: asset.id }, clipEmbedding);
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return true;
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}
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}
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