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Diagnose slow, expensive, or regressed Apache Spark and PySpark applications by comparing runtime evidence against a healthy run. Use for long stages, stragglers, skew, shuffle, spill, garbage collection, poor parallelism, small files, slow scans, scheduler delay, executor imbala
Diagnose slow, expensive, or regressed Apache Spark and PySpark applications by comparing runtime evidence against a healthy run. Use for long stages, stragglers, skew, shuffle, spill, garbage collection, poor parallelism, small files, slow scans, scheduler delay, executor imbalance, and unexplained compute-cost growth.
Source documentation, not instructions for this website. Review permissions before running any commands.
Find the root cause of the slowdown or cost growth. Compare against a healthy run whenever one exists, and normalize for input size before calling anything a regression.
export SPARK_HISTORY_URL="https://history.example.com"
python3 scripts/spark_history_api.py slow --app-id <slow-application-id> > /tmp/spark-slow.json
python3 scripts/spark_history_api.py slow --app-id <healthy-application-id> > /tmp/spark-healthy.json
Run from this skill directory. If SPARK_HISTORY_URL is unset, find the server before asking the user: try http://localhost:18080, a running application's UI on http://localhost:4040, and the history-server or eventLog settings in the local Spark config; ask only when nothing responds. Authentication comes from SPARK_HISTORY_AUTHORIZATION, SPARK_HISTORY_COOKIE, or SPARK_HISTORY_HEADERS_JSON; never ask for credentials in chat and never disable TLS verification (--ca-file for a private CA).
Other subcommands: applications [--status completed|running] to find app IDs, sql-list --app-id <id> and sql --app-id <id> --execution-id <n> for the executed plan with per-node metrics, failure --app-id <id> for failed-stage detail; all accept --stage-limit N --task-limit N. For anything the profiles omit, call $SPARK_HISTORY_URL/api/v1 directly with the same auth headers: /applications/{app}/jobs, /stages/{stage}/{attempt}/taskSummary?quantiles=0.05,0.5,0.95, /stages/{stage}/{attempt}/taskList?sortBy=-runtime, /allexecutors, /environment, /sql/{execution}?details=true&planDescription=true.
In taskSummary, metric arrays align with quantiles [0, 0.5, 0.95, 0.99, 1.0]: the middle entry is the median, the last is the max. Read spark.master and deploy mode from environment.sparkProperties to know where driver and executor logs live. Compare stage timelines between the two runs and start where they first diverge; wall time alone mixes queue time, driver work, execution, and commit.
Skew. Max far above median for run time, input, or shuffle read in one stage:
jq '.longestStages[] | {stage: .stage.stageId, quantiles: .taskSummary.quantiles, run: .taskSummary.executorRunTime, input: .taskSummary.inputMetrics.bytesRead, shuffleRead: .taskSummary.shuffleReadMetrics.readBytes}' /tmp/spark-slow.json
.longestStages[].tasks names the hot partitions and hosts. Check the executed plan (sql subcommand) for AQE skew handling (skewed=true) before proposing salting.
Wrong partition count. Tasks uniformly large and spilling mean too few; tens of thousands of sub-second tasks mean scheduler overhead:
jq '{stageTasks: [.longestStages[].stage | {id: .stageId, tasks: .numTasks}], totalCores: ([.allExecutors[] | select(.isActive) | .totalCores] | add), conf: [.environment.sparkProperties[] | select(.[0] | test("shuffle.partitions|executor.cores"))]}' /tmp/spark-slow.json
Try spark.sql.shuffle.partitions or AQE targets before inserting explicit repartitions.
Excess spill. Spill without skew means operator state outgrew execution memory:
jq '.longestStages[] | {stage: .stage.stageId, memSpill: .taskSummary.memoryBytesSpilled, diskSpill: .taskSummary.diskBytesSpilled}' /tmp/spark-slow.json
Reduce state (narrower rows, partial aggregation) or raise partitions before raising memory.
Large shuffles. Shuffle bytes dominating stage runtime:
jq '.longestStages[].stage | {id: .stageId, shuffleRead: .shuffleReadBytes, shuffleWrite: .shuffleWriteBytes, run: .executorRunTime}' /tmp/spark-slow.json
Map the stage to its Exchange in the executed plan and ask whether the shuffle is avoidable: broadcast, pre-aggregation, or already-partitioned data.
Slow shuffle fetch. Low CPU with high fetch wait or heavy remote reads:
jq '.longestStages[] | {stage: .stage.stageId, fetchWait: .taskSummary.shuffleReadMetrics.fetchWaitTime, remote: .taskSummary.shuffleReadMetrics.remoteBytesRead}' /tmp/spark-slow.json
Dead entries in .allExecutors[] | select(.isActive | not) mean data was refetched or recomputed. If environment shows Celeborn or an external shuffle service, confirm from the driver log it actually served the shuffle before tuning it.
The commands above are starting points, not limits: compose your own jq, call the REST API directly, or pull logs, code, and platform state when a question needs it. Rule each candidate in or out with evidence, and when a signal is suggestive but not conclusive, go a level deeper (more task samples, the exact log lines, the executed plan) until you are confident it is or is not the cause. Do not settle for the first plausible explanation.
For a regression, end by naming what changed: code, data volume or distribution, configuration (diff the two snapshots' environment), or infrastructure. Adding memory for a skewed partition, adding executors when task count caps parallelism, and caching once-used data are common wrong answers.
State the root cause and confidence, the first divergent stage, the evidence for and against, alternatives you rejected, and the most likely fix. Do not change production settings or launch expensive reruns without approval.
name: debug-slow-spark-job description: Diagnose slow, expensive, or regressed Apache Spark and PySpark applications by comparing runtime evidence against a healthy run. Use for long stages, stragglers, skew, shuffle, spill, garbage collection, poor parallelism, small files, slow scans, scheduler delay, executor imbalance, and unexplained compute-cost growth.
---
name: debug-slow-spark-job
description: Diagnose slow, expensive, or regressed Apache Spark and PySpark applications by comparing runtime evidence against a healthy run. Use for long stages, stragglers, skew, shuffle, spill, garbage collection, poor parallelism, small files, slow scans, scheduler delay, executor imbalance, and unexplained compute-cost growth.
---
# Debug a slow Spark job
Find the root cause of the slowdown or cost growth. Compare against a healthy run whenever one exists, and normalize for input size before calling anything a regression.
## Get the evidence
```bash
export SPARK_HISTORY_URL="https://history.example.com"
python3 scripts/spark_history_api.py slow --app-id <slow-application-id> > /tmp/spark-slow.json
python3 scripts/spark_history_api.py slow --app-id <healthy-application-id> > /tmp/spark-healthy.json
```
Run from this skill directory. If `SPARK_HISTORY_URL` is unset, find the server before asking the user: try `http://localhost:18080`, a running application's UI on `http://localhost:4040`, and the history-server or eventLog settings in the local Spark config; ask only when nothing responds. Authentication comes from `SPARK_HISTORY_AUTHORIZATION`, `SPARK_HISTORY_COOKIE`, or `SPARK_HISTORY_HEADERS_JSON`; never ask for credentials in chat and never disable TLS verification (`--ca-file` for a private CA).
Other subcommands: `applications [--status completed|running]` to find app IDs, `sql-list --app-id <id>` and `sql --app-id <id> --execution-id <n>` for the executed plan with per-node metrics, `failure --app-id <id>` for failed-stage detail; all accept `--stage-limit N --task-limit N`. For anything the profiles omit, call `$SPARK_HISTORY_URL/api/v1` directly with the same auth headers: `/applications/{app}/jobs`, `/stages/{stage}/{attempt}/taskSummary?quantiles=0.05,0.5,0.95`, `/stages/{stage}/{attempt}/taskList?sortBy=-runtime`, `/allexecutors`, `/environment`, `/sql/{execution}?details=true&planDescription=true`.
In `taskSummary`, metric arrays align with `quantiles` `[0, 0.5, 0.95, 0.99, 1.0]`: the middle entry is the median, the last is the max. Read `spark.master` and deploy mode from `environment.sparkProperties` to know where driver and executor logs live. Compare stage timelines between the two runs and start where they first diverge; wall time alone mixes queue time, driver work, execution, and commit.
## Likeliest causes, in order, and how to check each
1. **Skew.** Max far above median for run time, input, or shuffle read in one stage:
```bash
jq '.longestStages[] | {stage: .stage.stageId, quantiles: .taskSummary.quantiles, run: .taskSummary.executorRunTime, input: .taskSummary.inputMetrics.bytesRead, shuffleRead: .taskSummary.shuffleReadMetrics.readBytes}' /tmp/spark-slow.json
```
`.longestStages[].tasks` names the hot partitions and hosts. Check the executed plan (`sql` subcommand) for AQE skew handling (`skewed=true`) before proposing salting.
2. **Wrong partition count.** Tasks uniformly large and spilling mean too few; tens of thousands of sub-second tasks mean scheduler overhead:
```bash
jq '{stageTasks: [.longestStages[].stage | {id: .stageId, tasks: .numTasks}], totalCores: ([.allExecutors[] | select(.isActive) | .totalCores] | add), conf: [.environment.sparkProperties[] | select(.[0] | test("shuffle.partitions|executor.cores"))]}' /tmp/spark-slow.json
```
Try `spark.sql.shuffle.partitions` or AQE targets before inserting explicit repartitions.
3. **Excess spill.** Spill without skew means operator state outgrew execution memory:
```bash
jq '.longestStages[] | {stage: .stage.stageId, memSpill: .taskSummary.memoryBytesSpilled, diskSpill: .taskSummary.diskBytesSpilled}' /tmp/spark-slow.json
```
Reduce state (narrower rows, partial aggregation) or raise partitions before raising memory.
4. **Large shuffles.** Shuffle bytes dominating stage runtime:
```bash
jq '.longestStages[].stage | {id: .stageId, shuffleRead: .shuffleReadBytes, shuffleWrite: .shuffleWriteBytes, run: .executorRunTime}' /tmp/spark-slow.json
```
Map the stage to its `Exchange` in the executed plan and ask whether the shuffle is avoidable: broadcast, pre-aggregation, or already-partitioned data.
5. **Slow shuffle fetch.** Low CPU with high fetch wait or heavy remote reads:
```bash
jq '.longestStages[] | {stage: .stage.stageId, fetchWait: .taskSummary.shuffleReadMetrics.fetchWaitTime, remote: .taskSummary.shuffleReadMetrics.remoteBytesRead}' /tmp/spark-slow.json
```
Dead entries in `.allExecutors[] | select(.isActive | not)` mean data was refetched or recomputed. If `environment` shows Celeborn or an external shuffle service, confirm from the driver log it actually served the shuffle before tuning it.
6. **GC pressure.** GC a large fraction of run time:
```bash
jq '{stages: [.longestStages[] | {id: .stage.stageId, gc: .taskSummary.jvmGcTime, run: .taskSummary.executorRunTime}], executors: [.allExecutors[] | {id, gc: .totalGCTime, dur: .totalDuration}]}' /tmp/spark-slow.json
```
Check peak memory, cache use, and object-heavy code before resizing heaps.
7. **Python UDF transport.** Time concentrated at Python boundaries in the plan:
```bash
python3 scripts/spark_history_api.py sql --app-id <id> --execution-id <n> | jq '.sqlExecution.nodes[] | select(.nodeName | test("Python")) | {nodeName, metrics}'
```
Prefer native expressions or vectorized UDFs over resource changes.
8. **Retry churn.** Failures that succeeded on retry inflate runtime without failing the job:
```bash
jq '{stages: [.longestStages[].stage | select(.numFailedTasks > 0) | {id: .stageId, failed: .numFailedTasks}], jobs: [.jobs[] | select(.numFailedTasks > 0) | {jobId, failed: .numFailedTasks}]}' /tmp/spark-slow.json
```
Find the flaky cause (one bad host, preemption, timeouts) in the executor log or node events.
9. **Scan overhead.** Long scans with little output:
```bash
python3 scripts/spark_history_api.py sql --app-id <id> --execution-id <n> | jq '.sqlExecution.nodes[] | select(.nodeName | startswith("Scan")) | {nodeName, metrics}'
```
Too many small files, missing partition or pushed filters, or slow storage.
10. **Driver and queue time.** Gaps before the first task or between jobs:
```bash
jq '[.jobs[] | {jobId, submissionTime, completionTime}] | sort_by(.submissionTime)' /tmp/spark-slow.json
```
That time is planning, file listing, queueing, or provisioning: check the driver log for what it was doing, not stage tuning.
The commands above are starting points, not limits: compose your own jq, call the REST API directly, or pull logs, code, and platform state when a question needs it. Rule each candidate in or out with evidence, and when a signal is suggestive but not conclusive, go a level deeper (more task samples, the exact log lines, the executed plan) until you are confident it is or is not the cause. Do not settle for the first plausible explanation.
For a regression, end by naming what changed: code, data volume or distribution, configuration (diff the two snapshots' `environment`), or infrastructure. Adding memory for a skewed partition, adding executors when task count caps parallelism, and caching once-used data are common wrong answers.
## Report
State the root cause and confidence, the first divergent stage, the evidence for and against, alternatives you rejected, and the most likely fix. Do not change production settings or launch expensive reruns without approval.
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
58/100
Promising
Trust
53
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
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"task": "Use debug-slow-spark-job in an agent workflow",
"agent": "codex",
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"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/embrasureai-debug-slow-spark-job",
"api": "https://www.openagentskill.com/api/agent/skills/embrasureai-debug-slow-spark-job",
"audit": "https://www.openagentskill.com/skills/embrasureai-debug-slow-spark-job/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=embrasureai-debug-slow-spark-job&task=Use%20debug-slow-spark-job%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20debug-slow-spark-job%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20debug-slow-spark-job%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/embrasureai-debug-slow-spark-job/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/embrasureai-debug-slow-spark-job"
}
}Listing source
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GC pressure. GC a large fraction of run time:
jq '{stages: [.longestStages[] | {id: .stage.stageId, gc: .taskSummary.jvmGcTime, run: .taskSummary.executorRunTime}], executors: [.allExecutors[] | {id, gc: .totalGCTime, dur: .totalDuration}]}' /tmp/spark-slow.json
Check peak memory, cache use, and object-heavy code before resizing heaps.
Python UDF transport. Time concentrated at Python boundaries in the plan:
python3 scripts/spark_history_api.py sql --app-id <id> --execution-id <n> | jq '.sqlExecution.nodes[] | select(.nodeName | test("Python")) | {nodeName, metrics}'
Prefer native expressions or vectorized UDFs over resource changes.
Retry churn. Failures that succeeded on retry inflate runtime without failing the job:
jq '{stages: [.longestStages[].stage | select(.numFailedTasks > 0) | {id: .stageId, failed: .numFailedTasks}], jobs: [.jobs[] | select(.numFailedTasks > 0) | {jobId, failed: .numFailedTasks}]}' /tmp/spark-slow.json
Find the flaky cause (one bad host, preemption, timeouts) in the executor log or node events.
Scan overhead. Long scans with little output:
python3 scripts/spark_history_api.py sql --app-id <id> --execution-id <n> | jq '.sqlExecution.nodes[] | select(.nodeName | startswith("Scan")) | {nodeName, metrics}'
Too many small files, missing partition or pushed filters, or slow storage.
Driver and queue time. Gaps before the first task or between jobs:
jq '[.jobs[] | {jobId, submissionTime, completionTime}] | sort_by(.submissionTime)' /tmp/spark-slow.json
That time is planning, file listing, queueing, or provisioning: check the driver log for what it was doing, not stage tuning.
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Do not auto-install
Audit
69/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.