fabric-spark-compute-remediate

Name: fabric-spark-compute-remediate
Author: PatrickGallucci

16stars1forks

How to install fabric-spark-compute-remediate?

patrickgallucci/fabric-skills/fabric-spark-compute-remediate

$npx -y skills add PatrickGallucci/fabric-skills --skill fabric-spark-compute-remediate

Installs into the current project.

Summary

Diagnose and resolve Microsoft Fabric Spark compute issues including HTTP 430 throttling errors, job queueing failures, slow session startup, autoscale problems, capacity SKU limits, environment publishing failures, library conflicts, node sizing, burst factor configuration, and

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SKILL.md

1	# Microsoft Fabric Spark Compute remediate
2
3	Structured diagnostic workflows for resolving Apache Spark compute issues in Microsoft Fabric Data Engineering and Data Science workloads.
4
5	## When to Use This Skill
6
7	- Spark jobs fail with HTTP 430 throttling or TooManyRequestsForCapacity errors
8	- Notebook or Spark job sessions are slow to start (>30 seconds)
9	- Environment publishing fails or hangs
10	- Autoscale is not scaling up or down as expected
11	- Jobs are queued indefinitely or expiring after 24 hours
12	- Custom pool creation fails or pools are undersized
13	- Library installation causes session startup delays
14	- Capacity appears exhausted despite low job counts
15	- VNet/Private Link provisioning adds unexpected delays
16	- Burst factor or job-level bursting behavior is unclear
17
18	## Prerequisites
19
20	- Workspace Admin or Capacity Admin role in Microsoft Fabric
21	- Access to the Monitoring Hub for active Spark sessions
22	- Access to Workspace Settings > Data Engineering/Science
23	- Knowledge of current Fabric capacity SKU (F2 through F2048)
24
25	## Quick Diagnostic: Identify Your Issue
26
27	Start here. Match your symptom to a diagnostic path:
28
29	\| Symptom \| Diagnostic Path \|
30	\|---------\|----------------\|
31	\| HTTP 430 error \| See [Throttling and Concurrency](./references/throttling-and-concurrency.md) \|
32	\| Jobs stuck in queue \| See [Throttling and Concurrency](./references/throttling-and-concurrency.md#job-queueing) \|
33	\| Slow session startup \| See [Session and Environment](./references/session-and-environment.md#slow-startup) \|
34	\| Environment publish fails \| See [Session and Environment](./references/session-and-environment.md#publishing-failures) \|
35	\| Autoscale not working \| See [Pool Configuration](./references/pool-configuration.md#autoscale) \|
36	\| Pool sizing questions \| See [Pool Configuration](./references/pool-configuration.md#node-sizing) \|
37	\| Library conflicts \| See [Session and Environment](./references/session-and-environment.md#library-issues) \|
38	\| VNet delay on first job \| See [Session and Environment](./references/session-and-environment.md#vnet-delay) \|
39
40	## Core Concepts
41
42	### Capacity Unit to VCore Mapping
43
44	Every Fabric capacity SKU provides Spark VCores at a fixed ratio with a 3x burst factor:
45
46	1 Capacity Unit = 2 Spark VCores
47
48	For an F64 SKU: 64 CU x 2 = 128 base VCores, with 3x burst = 384 max Spark VCores.
49
50	### Job Admission Model
51
52	Fabric Spark uses optimistic job admission: jobs are admitted based on their minimum core requirement (determined by the pool's minimum node setting). Jobs start with minimum nodes and scale up toward maximum nodes as cores become available. If no cores are available for the minimum requirement, the job is rejected or queued.
53
54	### Two Pool Types
55
56	- Starter Pools: Pre-warmed, medium nodes only, 5-10 second startup, always available
57	- Custom Pools: User-configured node sizes (Small through XX-Large), 2-5 minute cold start, full flexibility
58
59	## remediate Workflows
60
61	### Workflow 1: HTTP 430 Throttling
62
63	1. Confirm the error: `HTTP Response code 430: This Spark job can't be run because you have hit a Spark compute or API rate limit`
64	2. Open the Monitoring Hub and count active Spark sessions
65	3. Calculate your capacity's max VCores: `SKU CU × 2 × 3 (burst) = max VCores`
66	4. Compare active usage against max VCores
67	5. Resolve by canceling idle sessions, upgrading SKU, or enabling job queueing for pipeline/scheduler jobs
68
69	See [throttling-and-concurrency.md](./references/throttling-and-concurrency.md) for the full SKU limits table and queue configuration.
70
71	### Workflow 2: Slow Session Startup
72
73	1. Determine pool type (Starter vs Custom)
74	2. If Starter Pool with no custom libraries: expect 5-10 seconds; if slower, check capacity utilization
75	3. If custom libraries or Spark properties are attached via environment: expect 30 seconds to 5 minutes
76	4. If using non-Medium node size: Starter Pool fast-start is unavailable; expect 2-5 minutes (on-demand)
77	5. If Private Link is enabled and this is the first job: expect 10-15 minute VNet provisioning delay
78
79	See [session-and-environment.md](./references/session-and-environment.md) for detailed diagnosis.
80
81	### Workflow 3: Environment Publishing Failure
82
83	1. Check if another Publish action is already in progress (only one at a time)
84	2. Verify library compatibility with the selected Spark runtime version
85	3. If runtime was recently changed, remove incompatible libraries and republish
86	4. If Private Link is enabled, the fir

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fabric-spark-compute-remediate

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View on GitHub

How to install fabric-spark-compute-remediate?

patrickgallucci/fabric-skills/fabric-spark-compute-remediate

$npx -y skills add PatrickGallucci/fabric-skills --skill fabric-spark-compute-remediate

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Microsoft Fabric Spark Compute remediate
2
3	Structured diagnostic workflows for resolving Apache Spark compute issues in Microsoft Fabric Data Engineering and Data Science workloads.
4
5	## When to Use This Skill
6
7	- Spark jobs fail with HTTP 430 throttling or TooManyRequestsForCapacity errors
8	- Notebook or Spark job sessions are slow to start (>30 seconds)
9	- Environment publishing fails or hangs
10	- Autoscale is not scaling up or down as expected
11	- Jobs are queued indefinitely or expiring after 24 hours
12	- Custom pool creation fails or pools are undersized
13	- Library installation causes session startup delays
14	- Capacity appears exhausted despite low job counts
15	- VNet/Private Link provisioning adds unexpected delays
16	- Burst factor or job-level bursting behavior is unclear
17
18	## Prerequisites
19
20	- Workspace Admin or Capacity Admin role in Microsoft Fabric
21	- Access to the Monitoring Hub for active Spark sessions
22	- Access to Workspace Settings > Data Engineering/Science
23	- Knowledge of current Fabric capacity SKU (F2 through F2048)
24
25	## Quick Diagnostic: Identify Your Issue
26
27	Start here. Match your symptom to a diagnostic path:
28
29	\| Symptom \| Diagnostic Path \|
30	\|---------\|----------------\|
31	\| HTTP 430 error \| See [Throttling and Concurrency](./references/throttling-and-concurrency.md) \|
32	\| Jobs stuck in queue \| See [Throttling and Concurrency](./references/throttling-and-concurrency.md#job-queueing) \|
33	\| Slow session startup \| See [Session and Environment](./references/session-and-environment.md#slow-startup) \|
34	\| Environment publish fails \| See [Session and Environment](./references/session-and-environment.md#publishing-failures) \|
35	\| Autoscale not working \| See [Pool Configuration](./references/pool-configuration.md#autoscale) \|
36	\| Pool sizing questions \| See [Pool Configuration](./references/pool-configuration.md#node-sizing) \|
37	\| Library conflicts \| See [Session and Environment](./references/session-and-environment.md#library-issues) \|
38	\| VNet delay on first job \| See [Session and Environment](./references/session-and-environment.md#vnet-delay) \|
39
40	## Core Concepts
41
42	### Capacity Unit to VCore Mapping
43
44	Every Fabric capacity SKU provides Spark VCores at a fixed ratio with a 3x burst factor:
45
46	1 Capacity Unit = 2 Spark VCores
47
48	For an F64 SKU: 64 CU x 2 = 128 base VCores, with 3x burst = 384 max Spark VCores.
49
50	### Job Admission Model
51
52	Fabric Spark uses optimistic job admission: jobs are admitted based on their minimum core requirement (determined by the pool's minimum node setting). Jobs start with minimum nodes and scale up toward maximum nodes as cores become available. If no cores are available for the minimum requirement, the job is rejected or queued.
53
54	### Two Pool Types
55
56	- Starter Pools: Pre-warmed, medium nodes only, 5-10 second startup, always available
57	- Custom Pools: User-configured node sizes (Small through XX-Large), 2-5 minute cold start, full flexibility
58
59	## remediate Workflows
60
61	### Workflow 1: HTTP 430 Throttling
62
63	1. Confirm the error: `HTTP Response code 430: This Spark job can't be run because you have hit a Spark compute or API rate limit`
64	2. Open the Monitoring Hub and count active Spark sessions
65	3. Calculate your capacity's max VCores: `SKU CU × 2 × 3 (burst) = max VCores`
66	4. Compare active usage against max VCores
67	5. Resolve by canceling idle sessions, upgrading SKU, or enabling job queueing for pipeline/scheduler jobs
68
69	See [throttling-and-concurrency.md](./references/throttling-and-concurrency.md) for the full SKU limits table and queue configuration.
70
71	### Workflow 2: Slow Session Startup
72
73	1. Determine pool type (Starter vs Custom)
74	2. If Starter Pool with no custom libraries: expect 5-10 seconds; if slower, check capacity utilization
75	3. If custom libraries or Spark properties are attached via environment: expect 30 seconds to 5 minutes
76	4. If using non-Medium node size: Starter Pool fast-start is unavailable; expect 2-5 minutes (on-demand)
77	5. If Private Link is enabled and this is the first job: expect 10-15 minute VNet provisioning delay
78
79	See [session-and-environment.md](./references/session-and-environment.md) for detailed diagnosis.
80
81	### Workflow 3: Environment Publishing Failure
82
83	1. Check if another Publish action is already in progress (only one at a time)
84	2. Verify library compatibility with the selected Spark runtime version
85	3. If runtime was recently changed, remove incompatible libraries and republish
86	4. If Private Link is enabled, the fir

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6 picks

Type