**Running a Search Engine in AWS Lambda: Pay Only When You Search**

Modern serverless search solutions often rely on a hidden pool of nodes, with costs evenly distributed among all users. This approach can be inefficient, especially when scaling search engines is challenging. However, what if you could run a search engine in a truly serverless environment, paying only when you search?

We took our JVM-based search engine, Nixiesearch, and optimized it to run in AWS Lambda. Here’s how we did it:

**Why AWS Lambda?**

Using AWS Lambda allows us to achieve minimal startup times, enabling scale-to-zero capabilities when there’s no traffic. This approach contrasts with traditional methods that rely on warm standby nodes, which can be costly and inefficient.

**Challenges and Solutions**

1. **Container Size**: The original container size was around 700MB, which slowed down startup times. We used GraalVM native-image to compile our JVM application into a native x86_64 binary, reducing the Docker image size and eliminating JVM warmup.

2. **Container Startup Time**: The startup time for Elasticsearch 9.x is about 40 seconds. By using GraalVM, we reduced the startup time significantly, achieving sub-second latency.

3. **Index and State Management**: Search engines like Elasticsearch and Qdrant require each node to host a fraction of the total cluster state. We addressed this by storing the index outside the search engine, using AWS S3 and AWS EFS for remote index storage.

**Implementation Details**

– **GraalVM Native-Image**: This Ahead-Of-Time (AOT) compiler converts a JVM application JAR file into a native binary with minimal dependencies. However, not all applications can be statically compiled easily, especially those using reflection.

– **Reflection Configuration**: If your application uses reflection, you need to create a reflection-config.json file listing all reflective operations. This can be challenging, but modern GraalVM versions offer a tracing agent that records all reflective operations during execution.

By following these steps, we successfully ran Nixiesearch in AWS Lambda, achieving minimal startup times and reasonable search latency. This approach not only reduces costs but also enhances scalability and efficiency.

**Frequently Asked Questions (FAQ)**

1. **What is GraalVM native-image?**
GraalVM native-image is an Ahead-Of-Time (AOT) compiler that converts a JVM application into a native binary, reducing startup times and eliminating JVM warmup.

2. **How does storing the index in AWS S3 or EFS improve performance?**
Storing the index remotely allows for faster startup times and easier scaling, as the search engine no longer needs to manage the index locally.

3. **Can this approach be applied to other search engines?**
Yes, the principles can be applied to other JVM-based search engines, but the implementation details may vary.

4. **What are the limitations of this approach?**
While this approach reduces costs and enhances scalability, it may not be suitable for all use cases, especially those requiring real-time updates to the index.

5. **How does this approach compare to traditional serverless search solutions?**
This approach offers lower costs and better scalability, but it may require more effort in terms of implementation and maintenance.