Quick Start

Prerequisites

• Rust toolchain — version 1.85 or later (spark-tui uses edition 2024)
• Databricks workspace — with a running cluster and an active Spark application
• Personal access token — generated from Databricks Settings > Developer > Access Tokens

Installation

git clone https://github.com/tadeasf/spark-tui.git
cd spark-tui
cargo install --path .

Or run directly without installing:

cargo run -- --host adb-123.azuredatabricks.net --token dapi... --cluster-id 0123-...

Configuration

You need three pieces of information:

Provide them via any of these methods (highest priority first):

Option 1: CLI flags

spark-tui \
  --host adb-123.azuredatabricks.net \
  --token dapi0123456789abcdef \
  --cluster-id 0123-456789-abcdef

Option 2: Environment variables

export DATABRICKS_HOST=adb-123.azuredatabricks.net
export DATABRICKS_TOKEN=dapi0123456789abcdef
export DATABRICKS_CLUSTER_ID=0123-456789-abcdef
spark-tui

Option 3: ~/.databrickscfg file

Create or edit ~/.databrickscfg:

[my-workspace]
host = adb-123.azuredatabricks.net
token = dapi0123456789abcdef
cluster_id = 0123-456789-abcdef

Then run with a specific profile:

spark-tui --profile my-workspace

Or let spark-tui auto-detect the first complete profile:

spark-tui

First Run

When spark-tui starts, it will:

1. Resolve configuration (CLI > env > databrickscfg)
2. Connect to the Spark REST API via the driver proxy
3. Discover the active Spark application
4. Fetch jobs, stages, and SQL executions
5. Display the Jobs tab with results ranked by duration

You should see a table of Spark jobs. Use j/k or arrow keys to navigate, Enter to drill into a job, and Tab to switch to the Suspects tab.

If something goes wrong, check the Troubleshooting guide.

Next Steps

• Configuration — full reference for all options
• Navigation — keybindings and view modes
• Understanding Analysis — interpreting suspects