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Instead of fetching up to 50k inventory snapshots (~442MB), scope the query to only the time range of the current submission batch with a 15-minute buffer. For a typical 1-hour batch this drops from 50k rows to ~300 rows, well within the 1280Mi pod memory limit. The coalesced_at_idx on intent_inventory_snapshots covers the BETWEEN clause so this remains efficient. Proof: history-writer OOM kills from refreshQuoteOutcomes inventory fetch Assumptions: 15min buffer covers the attribution window for all submissions Still fake: heuristic gap outcomes may attribute trades imprecisely
20 lines
917 B
JavaScript
20 lines
917 B
JavaScript
import { createPostgresPool } from './src/lib/postgres.mjs';
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const pool = createPostgresPool({ connectionString: process.env.POSTGRES_URL });
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async function main() {
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console.log("Memory before:", process.memoryUsage().heapUsed / 1024 / 1024, "MB");
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const result = await pool.query(`
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SELECT event_id, observed_at, ingested_at, quote_id, jsonb_build_object('spendable', payload->'spendable', 'synced_at', payload->'synced_at') AS payload
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FROM (
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SELECT event_id, observed_at, ingested_at, quote_id, payload
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FROM intent_inventory_snapshots
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ORDER BY COALESCE(observed_at, ingested_at) DESC
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LIMIT 50000
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) recent_inventory_snapshots
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ORDER BY COALESCE(observed_at, ingested_at) ASC
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`);
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console.log("Memory after fetch:", process.memoryUsage().heapUsed / 1024 / 1024, "MB");
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console.log("Rows fetched:", result.rows.length);
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process.exit(0);
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}
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main().catch(console.error);
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