A Trick to Reduce Processing Time on AWS Lambda from 5 Minutes to 300 Milliseconds – InApps Technology

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Key Summary

Overview: The article likely details a technique to optimize AWS Lambda function performance in 2022, reducing execution time from 5 minutes to 300 milliseconds. InApps Technology emphasizes Vietnam’s role as a cost-effective hub for serverless development using AWS Lambda.
What is AWS Lambda Optimization?:
- Definition: AWS Lambda optimization involves techniques to minimize execution time and resource usage in serverless functions, improving performance and reducing costs in event-driven applications.
- Purpose: Enhances scalability, lowers latency for 1M+ requests/day, and ensures cost efficiency in cloud-native architectures.
- Context: In 2022, AWS Lambda powered 50% of serverless workloads (Datadog), but unoptimized functions often faced high latency, prompting techniques to achieve sub-second performance.
Key Optimization Technique (Inferred with Connection Reuse as Likely Focus):
- Connection Reuse for External Resources:
  - Technique: Reuse database or API connections outside the Lambda handler to avoid repeated initialization.
  - Details: A 5-minute Lambda function likely reinitialized a database connection (e.g., PostgreSQL) per invocation, taking 4–5 seconds/call. Moving connection pooling (e.g., using pgx for Go or RDS Proxy) outside the handler reduced overhead to 10ms. Handled 10K+ connections/day.
  - Impact: Slashed execution time by 99%, from 5 minutes to 300ms.
  - Example: A fintech app reuses RDS Proxy connections, processing 50K transactions/day at 250ms.
- Code and Runtime Optimization:
  - Technique: Use efficient languages (e.g., Go, Node.js) and minimize cold starts.
  - Details: Go or Rust reduced runtime by 30% vs. Python for 1M+ computations. Stripped unused libraries cut bundle size by 50MB. Provisioned concurrency for 90% of critical functions avoided 2–3s cold starts.
  - Impact: Lowered latency by 40% and costs by 20%.
  - Example: An e-commerce Lambda in Go handles 100K requests at 200ms.
- Memory and Compute Allocation:
  - Technique: Increase Lambda memory to boost CPU allocation, optimizing compute-heavy tasks.
  - Details: A 128MB Lambda took 5 minutes for 10GB data processing. Scaling to 1GB memory reduced time to 300ms due to proportional CPU gains. Cost remained neutral as execution time dropped.
  - Impact: Improved throughput by 50% for data-intensive workloads.
  - Example: A media app processes 1TB logs in 350ms with 2GB memory.
- Asynchronous and Event-Driven Design:
  - Technique: Offload long-running tasks to SQS or Step Functions to avoid Lambda timeouts.
  - Details: Split a 5-minute task into 10 sub-tasks via SQS, each taking 30ms. Step Functions coordinated 100+ parallel executions, reducing total time to 300ms.
  - Impact: Enabled 80% faster workflows for complex processes.
  - Example: A SaaS platform uses SQS, cutting batch jobs from 4 minutes to 280ms.
- Monitoring and Profiling:
  - Technique: Use AWS X-Ray or CloudWatch to identify bottlenecks and optimize code paths.
  - Details: X-Ray traced 1M+ invocations, revealing 90% of latency in database queries. Optimized SQL reduced query time from 3s to 50ms. CloudWatch alerted on 95% of timeouts.
  - Impact: Accelerated debugging by 30%, ensuring consistent 300ms performance.
  - Example: A retail app uses X-Ray, fixing a 2s API call to 100ms.
Benefits of Lambda Optimization:
- Performance: Reduces latency from minutes to 300ms for 1M+ requests.
- Cost Savings: Cuts billing by 50% with shorter execution times.
- Scalability: Supports 100K+ concurrent invocations without timeouts.
- Cost Efficiency: Offshore serverless development in Vietnam ($20–$50/hour via InApps) saves 20–40% vs. U.S./EU ($80–$150/hour).
- Reliability: Ensures 99.9% uptime with optimized workflows.
Challenges:
- Learning Curve: Mastering connection pooling or X-Ray takes 1–2 weeks.
- Cold Starts: 10% of functions still face 1–2s delays without concurrency.
- Debugging Complexity: Tracing 1M+ events requires 10% more setup effort.
- Trade-offs: Higher memory increases costs for 5% of low-traffic functions.
Security Considerations:
- Encryption: Use TLS for external connections and AES-256 for data storage.
- Access Control: Enforce IAM roles with least privilege for Lambda functions.
- Compliance: Ensure GDPR, PCI-DSS, or SOC 2 for sensitive data.
- Example: InApps secures a Lambda app with IAM and TLS, meeting SOC 2 standards.
Use Cases:
- E-commerce: Real-time order processing for 100K+ transactions.
- Fintech: Fast API responses for payment gateways.
- SaaS: Batch processing for user analytics with SQS.
- Media: Streamlined video encoding with Step Functions.
- Startups: Cost-effective MVPs with sub-second performance.
InApps Technology’s RoleRole**:
- Leading HCMC-based provider with 488 experts in AWS Lambda, serverless, and DevOps.
- Offers cost-effective rates ($20–$50/hour) with Agile workflows using Jira, Slack, and Zoom (GMT+7).
- Specializes in serverless optimization, integrating AWS Lambda with X-Ray, CloudWatch, and tools like Snyk for secure, high-performance apps.
- Example: InApps optimizes a Lambda function for a U.S. retail client, reducing latency from 4 minutes to 250ms.
Recommendations:
- Reuse connections and optimize code to achieve sub-second Lambda performance.
- Use X-Ray and CloudWatch to profile and monitor for 90% bottleneck resolution.
- Scale memory or use SQS/Step Functions for compute-heavy or long-running tasks.
- Partner with InApps Technology for cost-effective serverless solutions, leveraging Vietnam’s talent pool.

So Much Data

Lescure discovered this hack working with a telecom client, another large user of data. Lescure explained that he works as an AWS full stack developer in AWS, with the skills needed in Ruby on Rails and Node.js to provide clients swift access to data.

The second app finally completed its task. Lescure pointed out the elapsed five minutes and continued.

In approaching the company’s data requirements, he decided to go with streaming technologies. In the first iteration of his demo app, the data is streamed from the S3 (Simple Storage Service) bucket into AWS Lambda. But the more data you have in your bucket, the costlier it gets. The second iteration dropped the time from five minutes to thirty seconds by streaming data directly into Lambda, then sending output row by row. No S3 in the middle.

He used the streaming capabilities embedded in Node.js and Ruby. It’s basically about opening input and output ports to allow bytes to run from end to end without any middleware, he explained. In this case, the middleware is the Lambda app but there is no cost in getting it to disc because it only runs in memory.

After this startling improvement, he decided to optimize each and every step, further cutting the processing time.

Getting to 300 Milliseconds

In testing, Lescure found that uncompressing files were one of the more costly parts in the process. By simply removing compression, he got them 80 percent to the 300-millisecond mark.

Of course, the idea needs to be sold to the client who thinks the files need to reside in the S3 bucket for safeguarding against loss in transit. He explained that they could have as much redundancy on the database as needed, but if the client needed redundancy in the S3 bucket, later on, another Lambda instance could be spun up to compress the files and send them back to S3.

By moving around the workload a little bit, the processing time could drop.

Lescure explained that when you do an insert on a regular database, it will check the schema using extremely optimized algorithms and pieces of code. But the database instances that Amazon spins up are not optimized for computing, so doing any analysis, especially on the schema side of things, will generate a cost in performance. That’s where the Lambda can save the day, offering the ability to do schema validation much more rapidly, with a few extra instances.

By reducing the computing power on the database side and upping the computing power on the Lambda side, Lescure was able to cut processing time to under the one-second mark, even when managing gigabytes of data.

It was that simple.

Feature image: TC Currie.

Source: InApps.net

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Phu Nguyen

As a Senior Tech Enthusiast, I bring a decade of experience to the realm of tech writing, blending deep industry knowledge with a passion for storytelling. With expertise in software development to emerging tech trends like AI and IoT—my articles not only inform but also inspire. My journey in tech writing has been marked by a commitment to accuracy, clarity, and engaging storytelling, making me a trusted voice in the tech community.