topic Re: How to attach multiple libraries to a cluster terraform in Databricks in Data Engineering

How to attach multiple libraries to a cluster terraform in Databricks

User16826994223 — Tue, 08 Jun 2021 06:59:44 GMT

I'm currently trying to attach more than one maven artifact to my terraform configuration of a cluster.

How can we add more than one artifact in my terraform configuration ?

Re: How to attach multiple libraries to a cluster terraform in Databricks

User16826994223 — Tue, 08 Jun 2021 07:00:10 GMT

library {

maven {

coordinates = "..."

}

library {

maven {

coordinates = "..."

}

Re: How to attach multiple libraries to a cluster terraform in Databricks

Simranarora — Mon, 10 Oct 2022 06:57:26 GMT

Hi @KunalGaurav,

This can be done by using a dynamic configuration block inside your databricks_cluster resource definition.

In variable.tf make a library block as:-

variable "listOfMavenPackages" {
  type = list(string)
  
  default = [ "com.google.guava:guava:23.0" , "com.google.protobuf:protobuf-java-util:3.17.3" ]
  
}

In main.tf, within databricks_cluster you may write as:-

resource "databricks_cluster" "shared_autoscaling" {
  cluster_name            = "Shared Autoscaling"
  spark_version           = data.databricks_spark_version.latest_lts.id
  node_type_id            = data.databricks_node_type.smallest.id
  autotermination_minutes = 20
  autoscale {
    min_workers = 1
    max_workers = 2
  }
  dynamic "library" {
    for_each = toset(var.listOfMavenPackages)
    content {
      maven {
        coordinates = library.value
      }
    }
  }
}

I hope this helps you.

Re: How to attach multiple libraries to a cluster terraform in Databricks

pac03 — Fri, 13 Oct 2023 08:13:06 GMT

Thank you, this was exactly what I've been looking for! A way to install packages dynamically. I can confirm it works great for other methods like pypi. If any moderators see this please add this method to the documentation as it is perfect for CICD. My example below.

locals {

requirements = split("\n", trimspace(file("requirements_frozen.txt")))

}

task {

task_key = var.task_name

job_cluster_key = "temp"

spark_python_task {

python_file = var.python_file

source = var.task_source

parameters = var.parameters

}

dynamic "library" {

for_each = toset(local.requirements)

content {

pypi {

package = library.value

}