Databricks Cluster Forensics

Databricks Cluster Forensics

The operational SRE spine of the pack — what a Databricks engineer reaches for at 2 AM when the compute layer is broken or unexplained. It correlates a cluster's live event stream across API surfaces to name the failure with its actual error code and its version-specific mitigation, not "network problem, try again".

Overview

Six real compute-layer failures live in this skill; each has a deterministic detector and an on-demand reference:

1. Cold-start long-tail (D01) — on VNet/VPC-injected workspaces a 5-minute start randomly takes 20-35, and Databricks reports only the aggregate. scripts/cluster-coldstart-forensics.py splits the PENDING window into stages (provisioning / init-scripts / spark-startup) so you see which stage spiked.
2. Photon premium without the speedup (D02) — Photon silently falls back to Spark on UDFs while the cluster still bills the ~2× Photon DBU premium for its whole uptime. See references/photon-eligibility-and-fallback.md.
3. DBR upgrade landmines (D03/D04/D05) — 14.x moved the working dir to the workspace filesystem (a ~500 MB cap that silently breaks large intermediate writes); 15.1 removed DBFS-root library storage and JDK 11; 15.4 flipped a JDBC calendar default. scripts/find-cwd-writes.py (AST) and scripts/scan-jar-jdk.sh (bytecode target) are the pre-upgrade detectors; references/dbr-upgrade-paths.md is the per-hop encyclopedia.
4. The launch-failure umbrella (D06) — CLOUD_PROVIDER_LAUNCH_FAILURE / NPIP_TUNNEL_SETUP_FAILURE each hide five distinct causes (subnet IP exhaustion, DNS, NSG/security-group block, deleted VNet, cloud throttling). references/termination-codes.md disambiguates them.
5. Spot shuffle aborts (D10) — a reclaimed spot node forces a shuffle recompute; lose another mid-recompute and the stage exceeds spark.stage.maxConsecutiveAttempts and the job aborts. references/spot-vs-ondemand-decision.md is the config decision tree.

It is architecturally distinct from the v1 databricks-common-errors and databricks-incident-runbook skills: those narrate. This one reads live cluster events, buckets them deterministically (the arithmetic is in scripts/, never eyeballed), fans out parallel root-cause threads via the cluster-event-investigator subagent, and loads deep knowledge from references/ only when a symptom needs it.

Two data planes. Cluster control-plane evidence (spec, state, event stream) comes from the custom databricks-workspace-mcp (clusters_get / clusters_events / clusters_list). The Photon audit's system.query.history read runs through the CLI Statement Execution API (databricks api post /api/2.0/sql/statements) — the same path databricks-cost-leak-hunter uses. Either surface absent, the skill degrades to advisory mode and accepts pasted event JSON / query plans so it still produces value.

Prerequisites

• databricks-workspace-mcp registered — the source of clusters_get, clusters_events, clusters_list. Absent, the skill accepts a pasted clusters.events response and says so (advisory mode).
• Databricks CLI authenticated (databricks auth login, or the DATABRICKS_HOST + DATABRICKS_TOKEN env pair) and jq — for the Photon system.query.history read.
• DATABRICKS_WAREHOUSE_ID set to a running SQL warehouse — required only for the Photon audit (Step 2); the cold-start / launch-failure flows need only the workspace MCP.
• unzip (and ideally a JDK's javap) on PATH for the DBR-15.1 JAR scan (scan-jar-jdk.sh falls back to reading class-file bytes if javap is absent).

The skill checks which surfaces are present in Step 0 and reports what is missing before starting a flow it cannot finish.

Instructions

Pick the flow by symptom. Each is independent; run only what the question needs.

Step 0: Detect Available Surfaces

Confirm the workspace MCP answers (clusters_list returns) and, for a Photon audit, that the CLI is authenticated and DATABRICKS_WAREHOUSE_ID is set. Name any missing surface and switch that flow to advisory mode (pasted input) rather than failing mid-diagnosis.

Step 1: Cold-Start / Launch-Failure Forensics (D01, D06)

Pull the cluster's event stream and bucket its PENDING time:

# events from the workspace MCP (clusters_events) or the CLI, saved to a file:
databricks clusters events --cluster-id "$CLUSTER_ID" --output json > "$OUT/events.json"
python3 "${CLAUDE_SKILL_DIR}/scripts/cluster-coldstart-forensics.py" \
  --input "$OUT/events.json"

• If the start succeeded but was slow, the dominant stage names the layer: provisioning → cloud VM allocation or network/DNS/NPIP; init-scripts → a slow init script or library install; spark-startup → driver spin-up.
• If the start failed, read the terminal termination_reason.code and disambiguate with ${CLAUDE_SKILL_DIR}/references/termination-codes.md — especially the CLOUD_PROVIDER_LAUNCH_FAILURE / NPIP_TUNNEL_SETUP_FAILURE umbrella and its five sub-causes.

For a messy failure, hand the cluster_id to the cluster-event-investigator subagent (/investigate-cluster <id>): it fans out one thread per cause class and returns the single most-likely cause with its evidence.

Step 2: Photon Fallback Audit (D02)

Check whether Photon is earning its premium. Query recent query history for plans that fell back to Spark, then corroborate the cluster is Photon (runtime_engine via clusters_get):

databricks api post /api/2.0/sql/statements --json "$(jq -n --arg wh "$DATABRICKS_WAREHOUSE_ID" \
  '{warehouse_id:$wh, wait_timeout:"30s",
    statement:"SELECT statement_id, executed_by, total_duration_ms FROM system.query.history WHERE end_time > now() - INTERVAL 1 DAY ORDER BY total_duration_ms DESC LIMIT 50"}')"

Then read the physical plan of the slow statements for the "Photon does not support" seam and the ColumnarToRow / RowToColumnar boundaries — the detection recipe and the UDF-rewrite fixes are in ${CLAUDE_SKILL_DIR}/references/photon-eligibility-and-fallback.md.

Step 3: DBR Upgrade Readiness (D03, D04, D05)

Before bumping the runtime, run the two pre-upgrade detectors against the job's code and libraries:

# D03 — writes to the CWD that the DBR-14 500 MB workspace-FS cap will break:
python3 "${CLAUDE_SKILL_DIR}/scripts/find-cwd-writes.py" --risk-only path/to/job/

# D04 — JARs built for a pre-17 JDK that DBR 15.1's JDK 17 may reject at runtime:
bash "${CLAUDE_SKILL_DIR}/scripts/scan-jar-jdk.sh" path/to/libs/

Cross-reference each hop's landmines (the 14.x CWD cap, the 15.1 DBFS-root-library and JDK-11 removals, the 15.4 JDBC calendar flip) in ${CLAUDE_SKILL_DIR}/references/dbr-upgrade-paths.md.

Step 4: Spot Configuration Review (D10)

If a job keeps aborting after NODES_LOST / SPOT_INSTANCE_TERMINATION around a shuffle, read the cluster's aws_attributes (clusters_get) and check the driver-on-demand rule and the spot ratio against ${CLAUDE_SKILL_DIR}/references/spot-vs-ondemand-decision.md. The #1 fix is pinning the driver (and a floor of workers) to on-demand so a spot reclaim can never take the driver.

Output

• A cold-start stage breakdown — total PENDING time split into provisioning / init-scripts / spark-startup, the dominant stage named, and the layer to investigate (or, for a failed start, the terminal code + its cause).
• A root-cause verdict (from cluster-event-investigator) — the single most-likely cause with the specific events/codes that point to it, and the cause classes ruled out.
• A Photon audit — the queries paying the premium while falling back to Spark, with the plan seam and the UDF-rewrite fix.
• A DBR-upgrade risk list — the CWD writes at risk under the 500 MB cap and the JARs built for a pre-17 JDK, each with its line/file, plus the per-hop breaking-change notes.
• A spot recommendation — the corrected aws_attributes (driver on-demand, spot ratio) for the job class.

Error Handling

Examples

Example 1: "My cluster randomly takes 25 minutes to start."

Step 1 buckets the events: provisioning 21m (84%), init-scripts 1m, spark-startup 3m. Dominant is provisioning → the skill points at cloud VM allocation / subnet-IP / DNS, not init scripts, and loads termination-codes.md for the provisioning sub-causes to check.

Example 2: "Cluster failed with NPIP_TUNNEL_SETUP_FAILURE."

The investigator subagent runs its threads; the network/NPIP thread owns it and disambiguates to "custom DNS could not resolve the control-plane hostname" (vs the other four causes), citing the exact check from termination-codes.md.

Example 3: "We're upgrading DBR 13.3 → 15.4. What breaks?"

Step 3 runs find-cwd-writes.py (flags 3 to_parquet("staging/…") writes at risk under the 14.x cap) and scan-jar-jdk.sh (flags 2 JARs built for JDK 11), and dbr-upgrade-paths.md surfaces the 15.4 JDBC calendar flip for the pipeline's pre-Gregorian date handling.

Example 4: "Job keeps dying after losing spot nodes."

Step 4 reads aws_attributes, finds the driver is on spot, and recommends first_on_demand covering the driver + a worker floor with SPOT_WITH_FALLBACK, citing the shuffle-recompute cascade in spot-vs-ondemand-decision.md.

Resources

• ${CLAUDE_SKILL_DIR}/references/termination-codes.md — codebook for every termination_reason.code, with the five-cause launch-failure umbrella.
• ${CLAUDE_SKILL_DIR}/references/dbr-upgrade-paths.md — per-hop DBR breaking changes (14.x CWD cap, 15.1 lib/JDK removals, 15.4 calendar flip).
• ${CLAUDE_SKILL_DIR}/references/photon-eligibility-and-fallback.md — what drops Photon to Spark and how to detect the premium-without-speedup.
• ${CLAUDE_SKILL_DIR}/references/spot-vs-ondemand-decision.md — the spot config decision tree + driver-on-demand rule.
• ${CLAUDE_SKILL_DIR}/scripts/cluster-coldstart-forensics.py — buckets cold-start PENDING time by stage.
• ${CLAUDE_SKILL_DIR}/scripts/find-cwd-writes.py — AST scanner for DBR-14 CWD writes.
• ${CLAUDE_SKILL_DIR}/scripts/scan-jar-jdk.sh — JAR bytecode-target (JDK) scanner.
• ${CLAUDE_SKILL_DIR}/agents/cluster-event-investigator.md — parallel root-cause fanout subagent.
• Databricks cluster events API · Databricks Runtime release notes · Photon