Excluding Files from a List in Terraform

Saturday, Oct 10, 2020 | 2 minute read

Ricardo Ferreira

Since the introduction of the for_each feature in Terraform 0.12 it is now possible to code powerful constructs to express the logic of your infrastructure. But sometimes you have to use some creativity to use this feature. I recently had to write a Terraform code that could upload all the JavaScript files from a local folder into a storage blob in Azure. But one of these files would have some template expressions so I wanted to upload all the files from the local folder except that one — because I would upload it individually.

After some time digging I ended up with a very elegant solution. First I created a variable to hold the original list of files. For this I used the fileset function.

js_files_raw = fileset(path.module, "../../pacman/game/js/*.*")

Then I created a secondary variable to represent all the files from the previous list except the one(s) that I would like to exclude. To exclude the unwanted files I created a for loop block to iterate over all the items of the list and negate the ones that I don’t want.

  js_files_mod = toset([
    for jsFile in local.js_files_raw:
      jsFile if jsFile != "../../pacman/game/js/shared.js"
  ])

Note the usage of the toset function to explicitly create a set of strings out of the array. Finally I could simply reference the mutated variable from the resource that would upload the files to the storage blog.

resource "azurerm_storage_blob" "js_files" {
  for_each = local.js_files_mod
  name = replace(each.key, "../../pacman/", "")
  storage_account_name = azurerm_storage_account.pacman.name
  storage_container_name = "$web"
  content_type = "text/javascript"
  type = "Block"
  source = each.value
}

Here is a summary of all the steps:

I hope that help if you find yourself in the same situation that I did 🙂

© 2018 - 2026 Ricardo Ferreira

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Open Source

I contribute to LangChain4j, an idiomatic open source Java library for building LLM-powered applications on the JVM. Recent work includes adding native vector search embedding stores so developers can build RAG, recommendation engines, and AI memory systems.

I also ported RedisVL to Go, an open source, AI-native client that brings vector search, semantic caching, LLM memory, semantic routing, rerankers, and an MCP server to the Redis ecosystem for Golang developers.

Public Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work with DevRel since 2018. My talks go deep on the systems I build with: distributed systems and event streaming, AI engineering and vector search, and the data infrastructure that has to hold up when the demo ends and production begins. Some of the events I’ve spoken at include AWS re:Invent, Microsoft Ignite, Google Cloud Next, KubeCon, Oracle OpenWorld, QCon, Strange Loop, Kafka Summit, Pulsar Summit, JavaOne, DevNexus, JFokus, JNation, and All Things Open.

Ricardo Ferreira presenting on the main stage at AI DevWorld
On the main stage at AI DevWorld

You can find my upcoming and past talks on my speaking calendar. Recordings also live on my YouTube channel, and the code I write for talks, demos, and workshops is on my GitHub.

Consulting and Professional Services

If you’d like to hire me as a consultant for your projects, speak at your event, or lead a hands-on workshop for your team, contact me at riferrei@riferrei.com. I can understand the scope of your request and provide a free estimate.

Who am I?

I work at the intersection of AI, data infrastructure, and distributed systems, turning complex technology into things developers can understand and products users love.

Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer, and figuring out how to make AI agents secure enough to ship. I contribute to open-source projects like LangChain4j and RedisVL for Golang.

The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years: designing data systems for scale, moving data fast, watching where systems break; now applied to vectors and agents. I have worked on RDBMS and Big Data at Oracle; event streaming with Apache Kafka and Apache Flink at Confluent; observability at Elastic; AI and developer tooling at AWS; and NoSQL and vector stores at Redis. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.

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