glean-performance-tuning

# Glean Performance Tuning ## Overview Glean's enterprise search API handles search queries across multiple connectors, bulk document indexing, and connector sync throughput. Search latency compounds when querying across dozens of datasources simultaneously. Large indexing jobs (10K+ documents) require careful batching to avoid rate limits and maintain connector sync schedules. Optimizing batch sizes, caching frequent search results, and tuning connector configurations reduces search P95 latency and keeps indexing pipelines within SLA windows. ## Caching Strategy ```typescript const cache = new Map<string, { data: any; expiry: number }>(); const TTL = { search: 60_000, suggestions: 30_000, datasources: 600_000 }; async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) { const entry = cache.get(key); if (entry && entry.expiry > Date.now()) return entry.data; const data = await fn(); cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] }); return data; } // Search results expire fast (1 min). Datasource metadata is stable (10 min). ``` ## Batch Operations ```typescript import PQueue from 'p-queue'; const BATCH_SIZE = 100; async function indexDocsBatched(glean: any, dsName: string, docs: any[]) { const batches = []; for (let i = 0; i < docs.length; i += BATCH_SIZE) batches.push(docs.slice(i, i + BATCH_SIZE)); const queue = new PQueue({ concurrency: 3, interval: 500 }); await Promise.all(batches.map(batch => queue.add(()...