How do I install the Performance Optimization skill?

Run npx skillmds@latest add 2dmurali/performance-optimization in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Performance Optimization free to use?

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Who published Performance Optimization?

2dmurali (@2dmurali) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Performance Optimization by 2dmurali

Name: Performance Optimization
Author: 2dmurali

File contents

Skill: Enterprise Lakehouse Performance Optimization

Description

Equips the agent with the ability to detect performance bottlenecks in data lakehouses, such as the small-file problem, poor partitioning, missed query optimization opportunities, and provide actionable recommendations like Liquid Clustering and Bloom Filters.

Context

Lakehouse performance degrades significantly without routine maintenance. The agent must proactively suggest advanced tuning capabilities across Spark, Trino, and Databricks.

Instructions

1. Small-File Detection & Compaction

Action: If the average file size is < 50 MB, trigger a "Small File Warning".
Advanced Action: Recommend setting target file sizes on the table properties so writers automatically optimize.

-- Delta Lake (Databricks)
ALTER TABLE table_name SET TBLPROPERTIES ('delta.targetFileSize' = '134217728');

2. Advanced Layout & Clustering

Stop recommending simple ZORDER blindly. Evaluate the table size and write pattern:

Databricks Liquid Clustering: For tables experiencing frequent updates or varying query patterns, strongly recommend Liquid Clustering over Z-Ordering.
```
ALTER TABLE table_name CLUSTER BY (col1, col2);
```
Iceberg Hidden Partitioning: Check if users are extracting date strings manually (e.g., date_format(ts, 'yyyy-MM-dd')). Recommend Iceberg's native partition transforms (partition by days(ts)).

3. Data Skipping & Bloom Filters

If queries filtering on high-cardinality strings (like user_id or uuid) are slow:

Suggest enabling Bloom Filter indexes.

-- Databricks Delta
CREATE BLOOMFILTER INDEX ON TABLE my_table FOR COLUMNS(user_id OPTIONS (fpp=0.1, numItems=50000000));

4. Query Optimization & CBO (Cost-Based Optimizer)

If the user provides a slow Spark SQL query:

Check for SortMergeJoin vs BroadcastHashJoin. Suggest /*+ BROADCAST(table) */ for small dimension tables.
Suggest running ANALYZE TABLE my_table COMPUTE STATISTICS so the engine can utilize the Cost-Based Optimizer (CBO).

Output Format: Performance Report

### 🚀 Enterprise Performance Report: `[Table Name]`

**Health Score:** [e.g., 65/100]

#### ⚠️ Issues Detected
1. **Small File Problem:** Average file size is 12 MB (Target: 128 MB).
2. **Missing Statistics:** CBO cannot optimize joins because table stats are missing.

#### 🛠️ Recommended Actions
- [ ] **Liquid Clustering:** Migrate from Z-Ordering to Liquid Clustering for better write concurrency. `ALTER TABLE [table] CLUSTER BY (...)`
- [ ] **Compute Stats:** Execute `ANALYZE TABLE [table] COMPUTE STATISTICS`.

---name: performance-optimizationdescription: Detect bottlenecks (small-files, poor partitioning) and suggest optimizations (Liquid Clustering, Bloom Filters) across Spark, Trino, and Databricks.---# Skill: Enterprise Lakehouse Performance Optimization## DescriptionEquips the agent with the ability to detect performance bottlenecks in data lakehouses, such as the small-file problem, poor partitioning, missed query optimization opportunities, and provide actionable recommendations like Liquid Clustering and Bloom Filters.## ContextLakehouse performance degrades significantly without routine maintenance. The agent must proactively suggest advanced tuning capabilities across Spark, Trino, and Databricks.## Instructions### 1. Small-File Detection & Compaction- **Action:** If the average file size is < 50 MB, trigger a "Small File Warning".- **Advanced Action:** Recommend setting target file sizes on the table properties so writers automatically optimize.```sql-- Delta Lake (Databricks)ALTER TABLE table_name SET TBLPROPERTIES ('delta.targetFileSize' = '134217728');```### 2. Advanced Layout & ClusteringStop recommending simple `ZORDER` blindly. Evaluate the table size and write pattern:- **Databricks Liquid Clustering:** For tables experiencing frequent updates or varying query patterns, strongly recommend Liquid Clustering over Z-Ordering. ```sql ALTER TABLE table_name CLUSTER BY (col1, col2); ```- **Iceberg Hidden Partitioning:** Check if users are extracting date strings manually (e.g., `date_format(ts, 'yyyy-MM-dd')`). Recommend Iceberg's native partition transforms (`partition by days(ts)`).### 3. Data Skipping & Bloom FiltersIf queries filtering on high-cardinality strings (like `user_id` or `uuid`) are slow:- Suggest enabling Bloom Filter indexes.```sql-- Databricks DeltaCREATE BLOOMFILTER INDEX ON TABLE my_table FOR COLUMNS(user_id OPTIONS (fpp=0.1, numItems=50000000));```### 4. Query Optimization & CBO (Cost-Based Optimizer)If the user provides a slow Spark SQL query:- Check for `SortMergeJoin` vs `BroadcastHashJoin`. Suggest `/*+ BROADCAST(table) */` for small dimension tables.- Suggest running `ANALYZE TABLE my_table COMPUTE STATISTICS` so the engine can utilize the Cost-Based Optimizer (CBO).## Output Format: Performance Report```markdown### 🚀 Enterprise Performance Report: `[Table Name]`**Health Score:** [e.g., 65/100]#### ⚠️ Issues Detected1. **Small File Problem:** Average file size is 12 MB (Target: 128 MB).2. **Missing Statistics:** CBO cannot optimize joins because table stats are missing.#### 🛠️ Recommended Actions- [ ] **Liquid Clustering:** Migrate from Z-Ordering to Liquid Clustering for better write concurrency. `ALTER TABLE [table] CLUSTER BY (...)`- [ ] **Compute Stats:** Execute `ANALYZE TABLE [table] COMPUTE STATISTICS`.```

Frequently asked questions

Performance Optimization