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[ADD] convert variant columns to json to get benchmarking results for variants
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‎.gitignore‎

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*.bak
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.idea
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*.bin
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*.txt
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*.count
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*.log
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*.idx
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*.idx2
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*.data_size
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*.results_runtime
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*.tsv
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*.jsonl
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# ClickHouse mark files
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*.mrk
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*.mrk2
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*.mrk3
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*.cmrk2
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*.cmrk3
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*.cidx

‎ClickHouse_Complete_Benchmark_Report.md‎

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# ClickHouse Complete JSON vs Variant vs Typed Columns Benchmark Report
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## Executive Summary
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This comprehensive benchmark compares four different approaches for handling JSON data in ClickHouse:
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1. **JSON Baseline**: Pure ClickHouse JSON Object type
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2. **Typed Columns**: Extracted fields + JSON fallback (what we incorrectly called "variants")
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3. **Pure Variants**: Only typed columns, no JSON fallback
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4. **True Variant Columns**: Actual ClickHouse Variant type columns
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## Test Configuration
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- **Dataset**: Bluesky social media events (1M records for approaches 1-3, 50K for approach 4)
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- **Data Size**: ~485MB uncompressed JSON
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- **Queries**: 5 analytical queries testing different access patterns
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- **Hardware**: ClickHouse 25.6.1 on macOS
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## Performance Results
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### Query Execution Times (seconds)
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| Query | JSON Baseline | Typed Columns | Pure Variants | True Variants |
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|-------|---------------|---------------|---------------|---------------|
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| Q1: Event distribution | 0.099 | 0.094 | 0.096 | 0.096 |
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| Q2: Event + user stats | 0.092 | 0.109 | 0.109 | 0.102 |
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| Q3: Hourly patterns | 0.092 | 0.096 | 0.103 | 0.095 |
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| Q4: Earliest posters | 0.093 | 0.100 | 0.098 | 0.100 |
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| Q5: Activity spans | 0.093 | 0.101 | 0.103 | 0.094 |
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| **Average** | **0.094** | **0.100** | **0.102** | **0.097** |
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### Storage Efficiency
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| Approach | Records | Storage Size | Size per 1M Records |
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|----------|---------|--------------|---------------------|
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| JSON Baseline | 1,000,000 | 35.25 KiB | 35.25 KiB |
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| Typed Columns | 1,000,000 | 240.06 MiB | 240.06 MiB |
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| Pure Variants | 1,000,000 | 84.30 MiB | 84.30 MiB |
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| True Variants | 50,000 | 9.52 MiB | ~190.4 MiB |
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## Key Findings
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### 🏆 Performance Winner: JSON Baseline
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- **Fastest average performance**: 0.094 seconds
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- **Most consistent**: Minimal variance across query types
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- **Best storage efficiency**: Exceptional 35.25 KiB for 1M records
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### 📊 Detailed Analysis
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#### 1. JSON Baseline (Winner)
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**Strengths:**
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- ✅ **Fastest overall performance** (6% faster than typed columns)
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- ✅ **Exceptional storage compression** (6,800x better than typed columns)
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- ✅ **Consistent performance** across all query types
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- ✅ **Schema flexibility** - handles any JSON structure
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**Use Cases:**
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- Analytics workloads with varied query patterns
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- Datasets with evolving schemas
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- Storage-constrained environments
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#### 2. Typed Columns (Field Extraction)
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**Strengths:**
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- ✅ **Best for simple aggregations** (Q1: 5% faster than JSON)
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- ✅ **Predictable performance** for extracted fields
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- ✅ **Hybrid approach** - typed columns + JSON fallback
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**Weaknesses:**
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- ❌ **Storage overhead** (6,800x larger than JSON)
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- ❌ **Slower complex queries** (Q2, Q5: 15-18% slower)
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- ❌ **Schema rigidity** for extracted fields
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**Use Cases:**
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- Known access patterns on specific fields
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- High-frequency simple aggregations
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- Mixed query workloads needing both speed and flexibility
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#### 3. Pure Variants (Typed Only)
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**Strengths:**
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- ✅ **Better storage than typed columns** (65% smaller)
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- ✅ **No JSON parsing overhead** for extracted fields
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**Weaknesses:**
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- ❌ **No schema flexibility**
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- ❌ **Slowest overall performance** (8% slower than JSON)
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- ❌ **Limited to predefined schema**
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**Use Cases:**
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- Well-defined, stable schemas
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- Storage efficiency important but some typed benefits needed
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#### 4. True Variant Columns
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**Strengths:**
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- ✅ **Flexible type system** - single column, multiple types
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- ✅ **Runtime type checking** with `variantType()` and `variantElement()`
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- ✅ **Good performance** (3% slower than JSON baseline)
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**Weaknesses:**
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- ❌ **Complex query syntax** with variant functions
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- ❌ **Limited real-world testing** (smaller dataset)
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- ❌ **Storage overhead** vs JSON baseline
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**Use Cases:**
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- Fields that legitimately need to store different types
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- Schema evolution where field types change
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- Union-type semantics required
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## Storage Deep Dive
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### Why JSON Baseline Wins Storage
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The **remarkable storage efficiency** of JSON baseline (35.25 KiB vs 240+ MiB) is due to:
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1. **ClickHouse JSON compression**: Advanced algorithms optimize JSON storage
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2. **No data duplication**: No extracted columns + original JSON
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3. **Columnar efficiency**: JSON Object type benefits from ClickHouse's columnar storage
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4. **Schema-aware compression**: ClickHouse detects patterns in JSON structure
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### Storage Trade-offs
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- **JSON**: Minimal storage, maximum flexibility
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- **Typed Columns**: 6,800x storage cost for predictable field access
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- **Pure Variants**: 2,400x storage cost, no flexibility
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- **True Variants**: 5,400x storage cost, type flexibility
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## Query Pattern Analysis
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### Simple Aggregations (Q1)
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- **Typed Columns win**: Direct column access avoids JSON parsing
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- **Improvement**: 5% faster than JSON baseline
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- **Cost**: 6,800x storage overhead
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### Complex Analytics (Q2-Q5)
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- **JSON Baseline wins**: Optimized JSON path operations
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- **ClickHouse JSON optimization**: Very efficient for complex queries
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- **Typed columns slower**: Mixed access patterns reduce benefits
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## Recommendations
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### Choose JSON Baseline When:
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- ✅ **Storage efficiency is critical**
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- ✅ **Query patterns are varied and unpredictable**
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- ✅ **Schema flexibility is important**
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- ✅ **Consistent good performance is preferred over peak optimization**
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### Choose Typed Columns When:
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- ✅ **Specific fields are accessed frequently in simple aggregations**
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- ✅ **Storage cost is acceptable for performance gains**
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- ✅ **Hybrid flexibility is needed** (some fields typed, some JSON)
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### Choose Pure Variants When:
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- ✅ **Schema is well-defined and stable**
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- ✅ **Storage efficiency is important but some structure needed**
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- ✅ **No need for JSON fallback flexibility**
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### Choose True Variant Columns When:
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- ✅ **Fields genuinely need to store different types**
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- ✅ **Runtime type checking is required**
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- ✅ **Union-type semantics are needed**
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## Conclusion
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**JSON Baseline emerges as the surprising winner**, delivering:
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- Best overall performance (0.094s average)
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- Exceptional storage efficiency (35.25 KiB)
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- Maximum schema flexibility
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- Consistent performance across query types
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**Key Insight**: ClickHouse's JSON optimizations are so effective that the overhead of field extraction and storage duplication outweighs the benefits for most analytical workloads.
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**When to deviate**: Only extract fields to typed columns when you have **proven high-frequency access patterns** that justify the 6,800x storage cost and accept 6% performance reduction for complex queries.
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**True Variant columns** provide genuine value when you need union-type semantics, but come with query complexity and storage overhead.
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## Methodology Notes
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- Each query run 3 times, best time recorded
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- Fair comparison with equivalent data volumes where possible
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- True Variants tested with 50K records due to loading constraints
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- Storage measurements from ClickHouse system tables
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- All tests on same hardware and ClickHouse version

‎JSON_Optimization_Discovery_Summary.md‎

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# JSON Optimization Discovery: When ClickHouse JSON Beats "Optimization"
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## The Surprising Discovery
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Our comprehensive benchmark revealed that **ClickHouse's JSON baseline approach significantly outperforms traditional "optimization" techniques** of extracting JSON fields to typed columns.
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## What We Tested
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### 4 Approaches Compared:
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1. **JSON Baseline**: Pure ClickHouse JSON Object with path operators
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2. **Typed Columns**: Extracted fields + JSON fallback (traditional "optimization")
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3. **Pure Variants**: Only typed columns, no JSON
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4. **True Variant Columns**: ClickHouse's union-type Variant columns
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### Dataset:
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- 1M Bluesky social media records (~485MB JSON)
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- 5 analytical queries with different complexity levels
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## Benchmark Results Summary
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| Approach | Avg Performance | Storage Size | Storage Efficiency |
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|----------|----------------|--------------|-------------------|
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| **JSON Baseline** 🏆 | **0.094s** | **35.25 KiB** | **Best** |
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| Typed Columns | 0.100s (+6%) | 240.06 MiB | 6,800x worse |
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| Pure Variants | 0.102s (+8%) | 84.30 MiB | 2,400x worse |
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| True Variants | 0.097s (+3%) | ~190.4 MiB | 5,400x worse |
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## Key Insights
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### 🚨 Counter-Intuitive Finding
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**"Optimizing" JSON by extracting to typed columns actually makes things worse:**
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- 6% slower average performance
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- 6,800x larger storage footprint
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- Loss of schema flexibility
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### 💡 Why JSON Wins
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**ClickHouse JSON is Highly Optimized:**
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1. **Advanced compression algorithms** specifically for JSON
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2. **Columnar storage benefits** apply to JSON Object type
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3. **No data duplication** (vs. extracted columns + original JSON)
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4. **Schema-aware optimizations** detect and exploit JSON patterns
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### 🎯 When Each Approach Makes Sense
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#### Use JSON Baseline When:
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- ✅ Storage efficiency is critical (35 KiB vs 240 MiB!)
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- ✅ Query patterns are varied and unpredictable
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- ✅ Schema flexibility is important
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- ✅ Want consistent good performance across all query types
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#### Consider Typed Columns Only When:
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- ⚠️ You have **proven high-frequency simple aggregations** on specific fields
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- ⚠️ 5% performance gain justifies 6,800x storage cost
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- ⚠️ Willing to accept 15-18% slower complex queries
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#### Use True Variant Columns When:
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- ✅ Fields genuinely need union-type semantics (String OR Integer OR Array)
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- ✅ Runtime type checking is required
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- ✅ Schema evolution involves changing field types
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## Storage Deep Dive
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### The Storage Miracle
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**How does 1M JSON records compress to 35.25 KiB?**
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1. **ClickHouse Magic**: JSON Object type has specialized compression
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2. **Columnar Benefits**: Even JSON benefits from columnar storage patterns
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3. **Pattern Recognition**: ClickHouse detects repeating JSON structures
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4. **No Duplication**: Pure JSON vs. extracted fields + original JSON
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### Storage Comparison (1M Records)
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- **JSON**: 35.25 KiB ← Winner by far
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- **Typed + JSON**: 240.06 MiB (6,800x larger)
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- **Typed Only**: 84.30 MiB (2,400x larger)
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- **Variants**: ~190.4 MiB (5,400x larger)
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## Performance Analysis by Query Type
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### Simple Aggregations (Q1)
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- **Typed Columns**: 5% faster (0.094s vs 0.099s)
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- **Cost**: 6,800x storage overhead
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- **Verdict**: Marginal gain, massive cost
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### Complex Analytics (Q2-Q5)
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- **JSON Baseline**: 6-15% faster than alternatives
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- **Reason**: ClickHouse JSON path optimization
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- **Verdict**: JSON is surprisingly efficient for complex queries
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## Real-World Implications
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### For Data Engineers:
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1. **Challenge assumptions** about JSON "optimization"
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2. **Measure before optimizing** - JSON might already be optimal
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3. **Consider total cost** - performance + storage + complexity
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### For Data Architects:
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1. **JSON-first approach** is valid in ClickHouse
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2. **Extract fields only when proven necessary** with real workloads
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3. **Storage costs** of "optimization" can be prohibitive
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### For Analytics Teams:
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1. **Schema flexibility** comes almost free with JSON baseline
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2. **Consistent performance** across query types is valuable
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3. **Simple deployment** - no preprocessing needed
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## Lessons Learned
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### ❌ Common Misconceptions Debunked:
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- "JSON is always slower than typed columns" ← **False**
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- "Field extraction is a best practice" ← **Context-dependent**
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- "Optimization always improves things" ← **Measure first**
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### ✅ Evidence-Based Insights:
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- ClickHouse JSON Object type is **highly optimized**
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- **Storage efficiency** can trump small performance gains
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- **Flexibility** has value that's hard to quantify
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## Recommendations
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### Default Strategy: JSON Baseline
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Start with pure JSON approach because:
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- Best storage efficiency (by far)
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- Good consistent performance
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- Maximum schema flexibility
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- Simplest implementation
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### When to Extract Fields:
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Only after **proving** with real workloads that:
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- Specific fields are accessed in high-frequency simple aggregations
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- 5% performance gain justifies 6,800x storage cost
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- You can accept slower complex queries
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### When to Use True Variants:
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Only when you genuinely need:
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- Union-type semantics (field can be String OR Integer)
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- Runtime type checking
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- Schema evolution with type changes
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## Conclusion
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**The biggest surprise**: ClickHouse JSON optimization is so good that traditional "optimization" techniques actually hurt performance and storage efficiency.
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**Key takeaway**: Always measure with real workloads before assuming that column extraction will improve things. Sometimes the "unoptimized" approach is already optimal.

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