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Description
The is_single_node
represents a simpler way of configuring single node clusters, but it automatically adds necessary spark_conf
and custom_tags
values, leading to the configuration drift, like this:
# databricks_cluster.single_node_new_1228_101700_uxdk2a63 will be updated in-place
~ resource "databricks_cluster" "single_node_new_1228_101700_uxdk2a63" {
~ custom_tags = {
- "ResourceClass" = "SingleNode" -> null
}
id = "1228-102112-aco3jrk3"
~ spark_conf = {
- "spark.databricks.cluster.profile" = "singleNode" -> null
- "spark.master" = "local[*, 4]" -> null
}
# (19 unchanged attributes hidden)
# (1 unchanged block hidden)
}
for the following configuration:
resource "databricks_cluster" "single_node_new_1228_101700_uxdk2a63" {
spark_version = "15.4.x-scala2.12"
single_user_name = "[email protected]"
runtime_engine = "STANDARD"
node_type_id = "Standard_D4ds_v5"
kind = "CLASSIC_PREVIEW"
is_single_node = true
data_security_mode = "SINGLE_USER"
cluster_name = "Single Node new (TF)"
autotermination_minutes = 10
}
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