Attio Performance Tuning

Overview

Attio's REST API returns JSON over HTTPS. Performance optimization focuses on reducing request count (batching, caching), maximizing throughput (connection reuse, pagination), and minimizing latency (selective field fetching, parallel queries).

Key Performance Facts

Factor	Detail
Rate limit	Sliding 10-second window, shared across all tokens
Pagination default	limit: 500 (max per page)
API base	https://api.attio.com/v2
Auth overhead	Bearer token in header (minimal)
Response format	JSON only (no binary/protobuf)

Instructions

Strategy 1: Response Caching with LRU

Cache read-heavy data (object schemas, attribute definitions) that rarely change:

import { LRUCache } from "lru-cache";

const cache = new LRUCache<string, unknown>({
  max: 500,              // Max entries
  ttl: 5 * 60 * 1000,   // 5 minutes for schema data
});

async function cachedGet<T>(
  client: AttioClient,
  path: string,
  ttlMs?: number
): Promise<T> {
  const cached = cache.get(path) as T | undefined;
  if (cached) return cached;

  const result = await client.get<T>(path);
  cache.set(path, result, { ttl: ttlMs });
  return result;
}

// Schema data: cache for 30 minutes (rarely changes)
const objects = await cachedGet(client, "/objects", 30 * 60 * 1000);
const attrs = await cachedGet(client, "/objects/people/attributes", 30 * 60 * 1000);

// Record data: cache for 1-5 minutes (changes more often)
const person = await cachedGet(client, `/objects/people/records/${id}`, 60 * 1000);

Strategy 2: Batch Queries Instead of N+1

// BAD: N+1 pattern -- one request per email lookup
const people = [];
for (const email of customerEmails) {
  const res = await client.post("/objects/people/records/query", {
    filter: { email_addresses: email },
    limit: 1,
  });
  people.push(res.data[0]);
}
// Cost: N requests

// GOOD: Single query with $in operator
const allPeople = await client.post<{ data: AttioRecord[] }>(
  "/objects/people/records/query",
  {
    filter: {
      email_addresses: {
        email_address: { $in: customerEmails },
      },
    },
    limit: customerEmails.length,
  }
);
// Cost: 1 request

Strategy 3: Parallel Independent Queries

// Fetch multiple object types in parallel
const [people, companies, deals] = await Promise.all([
  client.post<{ data: AttioRecord[] }>(
    "/objects/people/records/query",
    { limit: 100 }
  ),
  client.post<{ data: AttioRecord[] }>(
    "/objects/companies/records/query",
    { limit: 100 }
  ),
  client.post<{ data: AttioRecord[] }>(
    "/objects/deals/records/query",
    { limit: 100 }
  ),
]);

Strategy 4: Efficient Pagination

// Use maximum page size (500) to minimize round trips
async function fetchAllRecords(
  client: AttioClient,
  objectSlug: string,
  filter?: Record<string, unknown>
): Promise<AttioRecord[]> {
  const PAGE_SIZE = 500; // Attio's maximum
  const allRecords: AttioRecord[] = [];
  let offset = 0;

  while (true) {
    const page = await client.post<{ data: AttioRecord[] }>(
      `/objects/${objectSlug}/records/query`,
      {
        ...(filter ? { filter } : {}),
        limit: PAGE_SIZE,
        offset,
      }
    );

    allRecords.push(...page.data);

    // If we got fewer than PAGE_SIZE, we've reached the end
    if (page.data.length < PAGE_SIZE) break;
    offset += PAGE_SIZE;
  }

  return allRecords;
}

Strategy 5: Streaming Pagination with AsyncGenerator

For processing large datasets without loading everything into memory:

async function* streamRecords(
  client: AttioClient,
  objectSlug: string,
  filter?: Record<string, unknown>
): AsyncGenerator<AttioRecord> {
  const PAGE_SIZE = 500;
  let offset = 0;
  let hasMore = true;

  while (hasMore) {
    const page = await client.post<{ data: AttioRecord[] }>(
      `/objects/${objectSlug}/records/query`,
      { ...(filter ? { filter } : {}), limit: PAGE_SIZE, offset }
    );

    for (const record of page.data) {
      yield record;
    }

    hasMore = page.data.length === PAGE_SIZE;
    offset += PAGE_SIZE;
  }
}

// Process without loading all records into memory
let count = 0;
for await (const record of streamRecords(client, "people")) {
  await processRecord(record);
  count++;
}
console.log(`Processed ${count} records`);

Strategy 6: Connection Keep-Alive

import { Agent } from "https";

// Reuse TCP connections across requests
const keepAliveAgent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 30_000,
});

// Use with node-fetch or undici
import { fetch as undiciFetch, Agent as UndiciAgent } from "undici";

const dispatcher = new UndiciAgent({
  keepAliveTimeout: 30_000,
  keepAliveMaxTimeout: 60_000,
  connections: 10,
});

const res = await undiciFetch("https://api.attio.com/v2/objects", {
  headers: { Authorization: `Bearer ${process.env.ATTIO_API_KEY}` },
  dispatcher,
});

Strategy 7: Webhook-Driven Cache Invalidation

Instead of polling for changes, use webhooks to invalidate cached data:

const recordCache = new LRUCache<string, AttioRecord>({ max: 5000, ttl: 300_000 });

// On webhook event
async function handleCacheInvalidation(event: AttioWebhookEvent): Promise<void> {
  switch (event.event_type) {
    case "record.updated":
    case "record.deleted":
    case "record.merged":
      recordCache.delete(event.record?.id?.record_id || "");
      break;
  }
}

Strategy 8: Request Timing and Monitoring

async function timedRequest<T>(
  name: string,
  operation: () => Promise<T>
): Promise<T> {
  const start = performance.now();
  try {
    const result = await operation();
    const duration = performance.now() - start;
    console.log(JSON.stringify({
      metric: "attio_api_duration_ms",
      operation: name,
      duration: Math.round(duration),
      status: "success",
    }));
    return result;
  } catch (err) {
    const duration = performance.now() - start;
    console.error(JSON.stringify({
      metric: "attio_api_duration_ms",
      operation: name,
      duration: Math.round(duration),
      status: "error",
      error: (err as Error).message,
    }));
    throw err;
  }
}

// Usage
const people = await timedRequest("query_people", () =>
  client.post("/objects/people/records/query", { limit: 100 })
);

Error Handling

Performance issue	Symptom	Solution
N+1 queries	Slow bulk operations	Use $in filter in single query
Cache miss storm	Burst of identical requests	Use stale-while-revalidate or mutex
Memory pressure	Large dataset pagination	Use AsyncGenerator streaming
Connection overhead	High latency per request	Enable keep-alive agent
Stale cached data	Showing outdated records	Add webhook-driven invalidation

Resources

• Attio Pagination Guide
• Attio Filtering and Sorting
• Attio Rate Limiting
• LRU Cache Documentation

Next Steps

For cost optimization, see attio-cost-tuning.