In Search of Jamstack Search with Dawid Gaweł

Discover the ideal search solution for your JAMstack project, considering performance, scalability, and costs, as we explore pre-generated indexes, build-time indexing, and services like Algolia and Elasticsearch.

Key takeaways

In a JAMstack architecture, generating a search index from scratch is not recommended as it can lead to slow performance and high resource consumption.
Using pre-generated search indexes, such as Google’s, can be a good starting point, but it has limitations and may not scale well.
Building a search index at build time using a tool like Lunar can be a good approach, as it allows for indexing and querying of documents.
Using a service like Algolia can provide a scalable and performant search solution, but it may have costs associated with it.
Pre-rendering search results pages can be beneficial for SEO and user experience, but it can also lead to increased build times and resource consumption.
Using a lambda function to handle search queries can provide a fast and scalable solution, but it may have limitations in terms of query size and resource usage.
The ideal search solution will depend on the specific use case and requirements of the project.
Some important questions to consider when implementing search functionality include:
- What is the scope of the search query?
- How will the search results be presented to the user?
- How will the search results be filtered and ranked?
- How will the search functionality be integrated with the rest of the site?
Some popular search solutions for JAMstack include Algolia, Elasticsearch, and Google’s search engine.
When implementing search functionality, it’s important to consider the trade-offs between performance, scalability, and costs.
Pre-caching search results can be beneficial for performance, but it can also lead to increased build times and resource consumption.
Using a cache layer, such as Redis, can help to improve the performance of search queries.
Implementing search functionality requires careful consideration of the query size, resource usage, and scalability requirements.

In Search of Jamstack Search with Dawid Gaweł

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