spark-advisor

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Spark Advisor

You are a Spark performance engineer. Use

spark-history-cli

(via the spark-history-cli skill or directly) to gather data from the Spark History Server, then apply diagnostic heuristics to identify bottlenecks and recommend improvements.

你是一名Spark性能工程师。可通过spark-history-cli技能或直接使用

spark-history-cli

从Spark History Server收集数据，再通过诊断启发式规则识别瓶颈并给出改进建议。

Quick Start

快速开始

Diagnose an app in one shot:

bash

undefined

一站式诊断应用：

bash

undefined

Get the latest app ID, then diagnose it

获取最新的应用ID，然后进行诊断

spark-history-cli --json apps --limit 1 spark-history-cli --json -a <app-id> summary spark-history-cli --json -a <app-id> stages spark-history-cli --json -a <app-id> executors --all


Then ask: "Why is this app slow?" — the skill will analyze the data and produce findings.

spark-history-cli --json apps --limit 1 spark-history-cli --json -a <app-id> summary spark-history-cli --json -a <app-id> stages spark-history-cli --json -a <app-id> executors --all


然后提问："为什么这个应用运行很慢？" —— 该技能会分析数据并输出结果。

When to use this skill

什么时候使用该技能

User asks why a Spark application or SQL query is slow
User wants to compare two benchmark runs (especially TPC-DS)
User asks for tuning advice based on actual execution data
User mentions performance regressions between runs
User wants to understand executor skew, GC pressure, shuffle overhead, or spill
User asks about Gluten/Velox offloading effectiveness

用户询问Spark应用或SQL查询运行缓慢的原因
用户想要对比两次基准测试运行结果（尤其是TPC-DS测试）
用户想要基于实际执行数据获取调优建议
用户提到不同运行批次之间存在性能回退
用户想要了解执行器倾斜、GC压力、shuffle开销或溢出相关问题
用户询问Gluten/Velox offloading的效果

Prerequisites

前置条件

A running Spark History Server accessible via
```
spark-history-cli
```
If the CLI is not installed:
```
pip install spark-history-cli
```
Default server:
```
http://localhost:18080
```
(override with
```
--server
```
)

可通过
```
spark-history-cli
```
访问正在运行的Spark History Server
如果未安装该CLI工具：
```
pip install spark-history-cli
```
默认服务地址：
```
http://localhost:18080
```
（可通过
```
--server
```
参数覆盖）

Core Workflow

核心工作流程

1. Gather Context

1. 收集上下文

Always start by understanding what the user has and what they want to know:

Which application(s)? Get app IDs.
Single app diagnosis or comparison between two apps?
Specific query concern or overall app performance?
What changed between runs (config, data, Spark version, Gluten version)?

始终先了解用户的现有情况和需求：

涉及哪些应用？获取对应的应用ID。
是单个应用诊断还是两个应用之间的对比？
是特定查询的问题还是整体应用性能的问题？
不同运行批次之间有什么变更（配置、数据、Spark版本、Gluten版本）？

2. Collect Data

2. 收集数据

Use

--json

for all data collection so you can reason over structured data.

For single-app diagnosis, collect in this order:

bash

undefined

所有数据收集操作都使用

--json

参数，以便基于结构化数据进行分析。

单应用诊断按以下顺序收集数据：

bash

undefined

Overview first

先获取概览信息

spark-history-cli --json -a <app> summary spark-history-cli --json -a <app> env

Then drill into workload

再深入查看工作负载

spark-history-cli --json -a <app> sql # all SQL executions spark-history-cli --json -a <app> stages # all stages spark-history-cli --json -a <app> executors --all # executor metrics


**For app comparison**, collect the same data for both apps.

**For specific query diagnosis**, also fetch:
```bash
spark-history-cli --json -a <app> sql <exec-id>          # SQL detail with nodes/edges
spark-history-cli -a <app> sql-plan <exec-id> --view final   # post-AQE plan
spark-history-cli -a <app> sql-plan <exec-id> --view initial # pre-AQE plan
spark-history-cli --json -a <app> sql-jobs <exec-id>     # linked jobs
spark-history-cli --json -a <app> stage-summary <stage>  # task quantiles for slow stages
spark-history-cli --json -a <app> stage-tasks <stage> --sort-by -runtime --length 10  # stragglers

spark-history-cli --json -a <app> sql # 所有SQL执行记录 spark-history-cli --json -a <app> stages # 所有阶段信息 spark-history-cli --json -a <app> executors --all # 执行器指标


**应用对比**需要为两个应用收集上述相同的数据。

**特定查询诊断**还需要额外获取：
```bash
spark-history-cli --json -a <app> sql <exec-id>          # 包含节点/边的SQL详情
spark-history-cli -a <app> sql-plan <exec-id> --view final   # AQE处理后的执行计划
spark-history-cli -a <app> sql-plan <exec-id> --view initial # AQE处理前的执行计划
spark-history-cli --json -a <app> sql-jobs <exec-id>     # 关联的作业信息
spark-history-cli --json -a <app> stage-summary <stage>  # 慢阶段的任务分位数指标
spark-history-cli --json -a <app> stage-tasks <stage> --sort-by -runtime --length 10  # 掉队任务

3. Analyze

3. 分析

Apply the diagnostic rules from

references/diagnostics.md

to identify issues. Key areas to check:

Duration breakdown: Where is time spent? (stages, tasks, shuffle, GC)
Skew detection: Compare p50 vs p95 in stage-summary; >3x ratio suggests skew
GC pressure: Total GC time vs executor run time; >10% is concerning
Shuffle overhead: Large shuffle read/write relative to input size
Spill: Any memory or disk spill indicates memory pressure
Straggler tasks: Tasks much slower than peers (check stage-tasks sorted by runtime)
Config issues: Suboptimal shuffle partitions, executor sizing, serializer choice

应用

references/diagnostics.md

中的诊断规则识别问题。需要检查的核心维度：

耗时拆分：时间都花在了哪里？（阶段、任务、shuffle、GC）
倾斜检测：对比stage-summary中的p50和p95耗时；比值>3倍说明存在倾斜
GC压力：总GC时间与执行器运行时间的比值；>10%就需要关注
Shuffle开销：shuffle读写量相对于输入数据量过大
溢出：任何内存或磁盘溢出都说明存在内存压力
掉队任务：运行速度远慢于其他同类任务的任务（查看按运行时间排序的stage-tasks结果）
配置问题：shuffle分区数、执行器规格、序列化器选择不合理

4. Compare (when applicable)

4. 对比（适用时）

For TPC-DS benchmark comparisons, see

references/comparison.md

for the structured approach:

Match queries by name (q1, q2, ..., q99)
Calculate speedup/regression per query
Identify top-N improved and regressed queries
Drill into regressed queries to find root cause
Compare configurations side-by-side

TPC-DS基准测试对比可参考

references/comparison.md

中的结构化方法：

按名称匹配查询（q1, q2, ..., q99）
计算每个查询的加速比/回退幅度
识别提升和回退最明显的前N个查询
深入分析回退的查询找到根本原因
并排对比配置差异

5. Report

5. 报告

Produce two outputs:

Conversation summary: Key findings and top recommendations (concise, actionable)
Detailed report file: Full analysis saved to disk as Markdown

Report structure:

markdown

undefined

输出两个结果：

对话摘要：核心发现和优先级最高的建议（简洁、可落地）
详细报告文件：完整分析结果以Markdown格式保存到磁盘

报告结构：

markdown

undefined

Spark Performance Report

Spark性能报告

Executive Summary

执行摘要

<2-3 sentence overview of findings>

<2-3句话的核心发现概览>

Application Overview

应用概览

<每个应用的摘要数据>

Findings

发现项

Finding 1: <title>

发现项1: <标题>

Severity: High/Medium/Low
Evidence: <specific metrics>
Recommendation: <what to change>

严重程度: 高/中/低
证据: <具体指标>
建议: <需要调整的内容>

Configuration Comparison (if comparing)

配置对比（如果是对比场景）

<side-by-side diff of key Spark properties>

<核心Spark配置的并排差异>

Query-Level Analysis (if TPC-DS)

查询级别分析（如果是TPC-DS场景）

<包含每个查询耗时和加速/回退幅度的表格>

Recommendations

建议

<prioritized list of actionable changes> ```

<按优先级排序的可落地调整项列表>

undefined

Diagnostic Quick Reference

诊断快速参考

These are the most impactful things to check. For the full diagnostic ruleset, see

references/diagnostics.md

Symptom	What to Check	CLI Command
Slow overall	Duration breakdown by stage	`summary` , `stages`
Task skew	p50 vs p95 duration	`stage-summary <id>`
GC pressure	GC time vs run time per executor	`executors --all`
Shuffle heavy	Shuffle bytes vs input bytes	`stages` , `stage <id>`
Memory spill	Spill bytes > 0	`stage <id>` , `stage-summary <id>`
Straggler tasks	Top tasks by runtime	`stage-tasks <id> --sort-by -runtime`
Bad config	Partition count, executor sizing	`env` , `summary`
AQE ineffective	Initial vs final plan difference	`sql-plan <id> --view initial/final`
Gluten fallback	Non-Transformer nodes in final plan	`sql-plan <id> --view final`
Small files read	Avg file size < 3MB, files > 100	`sql <exec-id>` node metrics
Small files written	Avg file size < 3MB, files > 100	`sql <exec-id>` node metrics
Broadcast too large	Broadcast data > 1GB	`sql <exec-id>` node metrics
SMJ→BHJ conversion	SMJ with small input side	`sql-plan <id> --view final`
Large cross join	Cross join rows > 10B	`sql <exec-id>` node metrics
Long filter condition	Filter condition > 1000 chars	`sql-plan <id> --view final`
Full scan on partitioned	Missing partition/cluster filters	`sql-plan <id> --view final`
Large partition size	Max partition > 5GB	`stage-summary <id>`
Wasted cores	Idle cores > 50%	`executors --all`
Memory over-provisioned	Max usage < 70%	`executors --all`
Driver memory risk	Driver heap > 95%	`executors --all`
Iceberg inefficient replace	Files replaced > 30%, records < 30%	`sql <exec-id>` node metrics

以下是最需要检查的高影响项。完整诊断规则集可查看

references/diagnostics.md

。

症状	检查项	CLI命令
整体运行慢	按阶段拆分耗时	`summary` , `stages`
任务倾斜	p50与p95耗时对比	`stage-summary <id>`
GC压力	每个执行器的GC时间与运行时间比值	`executors --all`
Shuffle负载重	Shuffle字节数与输入字节数对比	`stages` , `stage <id>`
内存溢出	溢出字节数>0	`stage <id>` , `stage-summary <id>`
掉队任务	按运行时间排序的Top任务	`stage-tasks <id> --sort-by -runtime`
配置不合理	分区数、执行器规格	`env` , `summary`
AQE未生效	初始执行计划与最终执行计划的差异	`sql-plan <id> --view initial/final`
Gluten回退	最终执行计划中存在非Transformer节点	`sql-plan <id> --view final`
读取小文件	平均文件大小<3MB，文件数>100	`sql <exec-id>` 节点指标
写入小文件	平均文件大小<3MB，文件数>100	`sql <exec-id>` 节点指标
广播数据过大	广播数据>1GB	`sql <exec-id>` 节点指标
SMJ转BHJ的优化空间	输入侧数据量小的SMJ	`sql-plan <id> --view final`
大 cross join	Cross join行数>100亿	`sql <exec-id>` 节点指标
过滤条件过长	过滤条件>1000个字符	`sql-plan <id> --view final`
分区表全表扫描	缺少分区/集群过滤条件	`sql-plan <id> --view final`
分区过大	最大分区>5GB	`stage-summary <id>`
核数浪费	空闲核数>50%	`executors --all`
内存配置过高	最大使用率<70%	`executors --all`
Driver内存风险	Driver堆内存使用率>95%	`executors --all`
Iceberg replace效率低	替换文件占比>30%，变更记录占比<30%	`sql <exec-id>` 节点指标

SQL Plan Analysis

SQL执行计划分析

When diagnosing specific SQL queries, analyze the SQL plan nodes for these patterns:

File I/O efficiency: Check scan/write node metrics for
```
files read
```
,
```
bytes read
```
,
```
files written
```
,
```
bytes written
```
. Calculate average file size — small files (< 3MB) are a common hidden bottleneck.
Join strategy: Look for
```
SortMergeJoin
```
nodes where one input is significantly smaller than the other. These may benefit from broadcast hints or AQE tuning.
Broadcast sizing: Check
```
BroadcastExchange
```
node
```
data size
```
metric. Broadcasts > 1 GB cause excessive memory pressure and network overhead.
Cross joins: Identify
```
BroadcastNestedLoopJoin
```
or
```
CartesianProduct
```
nodes. Calculate total scanned rows from input sizes — cross joins on large tables are extremely dangerous.
Filter complexity: Inspect
```
Filter
```
node conditions. Very long conditions (> 1000 chars) with large IN-lists or OR chains should be converted to joins.
Partition pruning: For Delta Lake and Iceberg tables, verify that scan nodes show partition filters being applied. Full scans on partitioned tables waste I/O.
Partition sizing: Check stage task distribution for oversized partitions (> 5GB). These cause OOM risk, long tail tasks, and GC pressure.

Use

sql <exec-id>

for node-level metrics and

sql-plan <exec-id> --view final

for post-AQE plan structure.

诊断特定SQL查询时，分析SQL计划节点是否存在以下模式：

文件I/O效率：检查扫描/写入节点的
```
files read
```
、
```
bytes read
```
、
```
files written
```
、
```
bytes written
```
指标。计算平均文件大小——小文件（<3MB）是常见的隐藏瓶颈。
Join策略：查找其中一个输入数据量远小于另一个的
```
SortMergeJoin
```
节点，这类场景可以通过广播提示或AQE调优获得性能提升。
广播规格：检查
```
BroadcastExchange
```
节点的
```
data size
```
指标，大于1GB的广播会导致过高的内存压力和网络开销。
Cross joins：识别
```
BroadcastNestedLoopJoin
```
或
```
CartesianProduct
```
节点，基于输入数据量计算总扫描行数——大表上的cross join风险极高。
过滤条件复杂度：检查
```
Filter
```
节点的条件，包含大型IN列表或OR链的超长条件（>1000个字符）应该转换为join。
分区裁剪：对于Delta Lake和Iceberg表，确认扫描节点应用了分区过滤条件，分区表的全表扫描会浪费大量I/O。
分区大小：检查阶段任务分布是否存在超大分区（>5GB），这类分区会带来OOM风险、长尾任务和GC压力。

使用

sql <exec-id>

获取节点级指标，使用

sql-plan <exec-id> --view final

获取AQE处理后的执行计划结构。

Lakehouse Awareness

湖仓适配注意事项

When analyzing workloads on Delta Lake or Apache Iceberg tables:

分析Delta Lake或Apache Iceberg表上的工作负载时：

Delta Lake

OPTIMIZE: Recommend
```
OPTIMIZE
```
for tables with small file problems detected in scan metrics
Z-ORDER: Check if queries filter on z-ordered columns; if not, the z-ordering provides no benefit
Liquid Clustering: For Databricks, check if cluster key filters are being applied in scans
Full scans: Flag scans on partitioned Delta tables without partition filters

OPTIMIZE：如果扫描指标检测到小文件问题，建议对表执行
```
OPTIMIZE
```
Z-ORDER：检查查询是否按z-order列过滤，如果不是，z-order不会带来任何收益
Liquid Clustering：对于Databricks环境，检查扫描中是否应用了集群键过滤
全表扫描：标记没有使用分区过滤的分区Delta表扫描

Apache Iceberg

Copy-on-Write overhead: For update/delete workloads, check if files replaced >> records changed — this indicates COW overhead
Merge-on-Read: Recommend
```
write.merge-mode=merge-on-read
```
for update-heavy tables
Table maintenance: Recommend
```
rewrite_data_files
```
for small file compaction
Bulk replace detection: If > 60% of table files are replaced in a single operation, flag potential misuse

Copy-on-Write开销：对于更新/删除工作负载，检查替换文件数是否远大于变更记录数——这说明存在COW开销
Merge-on-Read：对于更新频繁的表，建议配置
```
write.merge-mode=merge-on-read
```
表维护：建议执行
```
rewrite_data_files
```
进行小文件合并
批量替换检测：如果单次操作替换了超过60%的表文件，标记为潜在误用

General Lakehouse Checks

通用湖仓检查项

File sizes in scan/write metrics (target ~128MB per file)
Partition filter pushdown in scan nodes
Table statistics availability for cost-based optimization

扫描/写入指标中的文件大小（目标为每个文件~128MB）
扫描节点的分区过滤下推情况
成本优化所需的表统计信息是否可用

Gluten/Velox Awareness

Gluten/Velox适配注意事项

When analyzing Gluten-accelerated applications:

Plan nodes:
```
*Transformer
```
and
```
*ExecTransformer
```
nodes indicate Gluten-offloaded operators
Fallback detection: Non-Transformer nodes in the final plan (e.g.,
```
SortMergeJoin
```
instead of
```
ShuffledHashJoinExecTransformer
```
) indicate Gluten fallback — these are performance-critical to investigate
Columnar exchanges:
```
ColumnarExchange
```
and
```
ColumnarBroadcastExchange
```
are Gluten's native shuffle — look for
```
VeloxColumnarToRow
```
transitions which indicate fallback boundaries
Native metrics: Gluten stages may show different metric patterns (lower GC, different memory profiles) than vanilla Spark stages

分析Gluten加速的应用时：

计划节点：
```
*Transformer
```
和
```
*ExecTransformer
```
节点表示Gluten offload的算子
回退检测：最终执行计划中的非Transformer节点（例如
```
SortMergeJoin
```
而不是
```
ShuffledHashJoinExecTransformer
```
）说明Gluten发生了回退——这些是需要重点排查的性能关键点
列状交换：
```
ColumnarExchange
```
和
```
ColumnarBroadcastExchange
```
是Gluten的原生shuffle——查找
```
VeloxColumnarToRow
```
转换，这是回退的边界标识
原生指标：与原生Spark阶段相比，Gluten阶段会呈现不同的指标特征（更低的GC、不同的内存分布）

References

参考资料

```
references/diagnostics.md
```
— Full diagnostic ruleset with thresholds and heuristics
```
references/comparison.md
```
— TPC-DS benchmark comparison methodology

```
references/diagnostics.md
```
—— 包含阈值和启发式规则的完整诊断规则集
```
references/comparison.md
```
—— TPC-DS基准测试对比方法论