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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
38 lines
1.4 KiB
JavaScript
38 lines
1.4 KiB
JavaScript
import { createPostgresPool } from './src/lib/postgres.mjs';
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async function main() {
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const pool = createPostgresPool({ connectionString: process.env.POSTGRES_URL });
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const inventoryRows = await pool.query(`
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SELECT event_id as inventory_id, observed_at, 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 2000
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`);
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const snapshots = inventoryRows.rows.map(r => ({
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inventory_id: r.inventory_id,
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observed_at: r.observed_at,
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spendable: r.payload?.spendable || {},
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})).sort((a,b) => new Date(a.observed_at) - new Date(b.observed_at));
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const activeAssetIds = Object.keys(snapshots[0].spendable);
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const deltas = [];
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for (let i = 1; i < snapshots.length; i++) {
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const prev = snapshots[i-1].spendable;
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const curr = snapshots[i].spendable;
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const delta = {};
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for (const a of activeAssetIds) {
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const p = BigInt(prev[a] || 0);
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const c = BigInt(curr[a] || 0);
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if (c - p !== 0n) delta[a] = (c - p).toString();
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}
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if (Object.keys(delta).length > 0) {
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deltas.push({ observed_at: snapshots[i].observed_at, delta });
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}
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}
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console.log("Total deltas:", deltas.length);
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for (let i = 0; i < Math.min(5, deltas.length); i++) {
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console.log(deltas[i].observed_at, Object.entries(deltas[i].delta).filter(x => x[1] !== '0'));
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}
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process.exit(0);
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}
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main().catch(console.error);
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