Adobe Performance Tuning

Overview

Optimize Adobe API performance across Firefly Services, PDF Services, and Photoshop APIs. Key bottlenecks include IMS token generation, async job polling overhead, and cold-start latency on serverless platforms.

Prerequisites

• Adobe SDK installed and functional
• Understanding of which APIs your app uses most
• Redis or in-memory cache available (optional)
• Performance monitoring in place

Latency Benchmarks (Real-World)

Operation	P50	P95	P99
IMS Token Generation	200ms	500ms	1s
Firefly Text-to-Image (sync)	5s	12s	20s
Firefly Text-to-Image (async poll)	8s	15s	25s
PDF Extract (10-page doc)	3s	8s	15s
PDF Create from HTML	2s	5s	10s
Photoshop Remove Background	4s	10s	18s
Lightroom Auto Tone	3s	8s	15s

Instructions

Optimization 1: Cache IMS Access Tokens (Biggest Win)

The IMS token endpoint returns tokens valid for 24 hours. Never re-generate per request:

// WRONG: generates new token every call (adds 200-500ms each time)
async function makeRequest() {
  const token = await getAccessToken(); // hits IMS every time
}

// RIGHT: cache token and only refresh when expiring
let tokenCache: { token: string; expiresAt: number } | null = null;

async function getCachedToken(): Promise<string> {
  if (tokenCache && tokenCache.expiresAt > Date.now() + 300_000) {
    return tokenCache.token; // Cache hit — 0ms
  }
  const res = await fetch('https://ims-na1.adobelogin.com/ims/token/v3', {
    method: 'POST',
    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
    body: new URLSearchParams({
      client_id: process.env.ADOBE_CLIENT_ID!,
      client_secret: process.env.ADOBE_CLIENT_SECRET!,
      grant_type: 'client_credentials',
      scope: process.env.ADOBE_SCOPES!,
    }),
  });
  const data = await res.json();
  tokenCache = { token: data.access_token, expiresAt: Date.now() + data.expires_in * 1000 };
  return tokenCache.token;
}

Optimization 2: Parallel Async Job Submission

Firefly and Photoshop APIs are async — submit all jobs first, then poll all:

// SLOW: sequential (total = sum of all job times)
for (const prompt of prompts) {
  const result = await generateImageSync(prompt); // 5-20s each
}

// FAST: parallel submit + parallel poll (total = max job time)
async function batchFireflyGenerate(prompts: string[]) {
  const token = await getCachedToken();

  // 1. Submit all jobs simultaneously
  const jobSubmissions = await Promise.all(
    prompts.map(prompt =>
      fetch('https://firefly-api.adobe.io/v3/images/generate-async', {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${token}`,
          'x-api-key': process.env.ADOBE_CLIENT_ID!,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({ prompt, n: 1, size: { width: 1024, height: 1024 } }),
      }).then(r => r.json())
    )
  );

  // 2. Poll all jobs in parallel
  const results = await Promise.all(
    jobSubmissions.map(job => pollUntilDone(job.statusUrl, token))
  );

  return results;
}

Optimization 3: Response Caching for Repeated Operations

import { LRUCache } from 'lru-cache';

// Cache PDF extraction results (same PDF = same output)
const extractionCache = new LRUCache<string, any>({
  max: 100,
  ttl: 3600_000, // 1 hour
});

async function cachedPdfExtract(pdfHash: string, pdfPath: string) {
  const cached = extractionCache.get(pdfHash);
  if (cached) {
    console.log('PDF extraction cache hit');
    return cached;
  }

  const result = await extractPdfContent(pdfPath);
  extractionCache.set(pdfHash, result);
  return result;
}

Optimization 4: Connection Keep-Alive

import { Agent } from 'https';

// Reuse TCP connections to Adobe endpoints
const adobeAgent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 60_000,
});

// Use with node-fetch or undici
const response = await fetch(url, {
  // @ts-ignore — agent option supported by node-fetch
  agent: adobeAgent,
  headers: { ... },
});

Optimization 5: Smart Polling Intervals

// Adaptive polling: start fast, slow down over time
async function adaptivePoll(statusUrl: string, token: string) {
  const intervals = [1000, 2000, 3000, 5000, 5000, 10000]; // ms
  let attempt = 0;

  while (true) {
    const res = await fetch(statusUrl, {
      headers: {
        'Authorization': `Bearer ${token}`,
        'x-api-key': process.env.ADOBE_CLIENT_ID!,
      },
    });
    const status = await res.json();

    if (status.status === 'succeeded') return status;
    if (status.status === 'failed') throw new Error(status.error?.message);

    const delay = intervals[Math.min(attempt, intervals.length - 1)];
    await new Promise(r => setTimeout(r, delay));
    attempt++;
  }
}

Output

• IMS token cached for 24h (eliminates 200-500ms per request)
• Parallel job submission for batch operations
• LRU response caching for repeated extractions
• Connection keep-alive reducing TLS handshake overhead
• Adaptive polling reducing unnecessary API calls

Error Handling

Issue	Cause	Solution
Stale cached token	Token revoked mid-lifecycle	Catch 401, clear cache, retry once
Parallel rate limiting	Too many concurrent jobs	Add p-queue concurrency limit
Cache memory pressure	Too many cached results	Set LRU max size
Connection pool exhaustion	Too many parallel requests	Limit maxSockets to 10-20

Examples

Start with the smallest applicable command or code example already provided in this guide, using a non-production Adobe environment and credentials. Confirm the documented response or validation result before applying the pattern to production.

Resources

• Firefly Async API Guide
• PDF Services Quickstart
• LRU Cache npm

Next Steps

For cost optimization, see adobe-cost-tuning.