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Posted to issues@paimon.apache.org by "kaori-seasons (via GitHub)" <gi...@apache.org> on 2025/12/08 04:48:36 UTC
[PR] feat: Pypaimon support Ml intergation [paimon]
kaori-seasons opened a new pull request, #6770:
URL: https://github.com/apache/paimon/pull/6770
### Purpose
Related to [issue-6762](https://github.com/apache/paimon/issues/6762)
## 1. Overview
### 1.1 Background and Problem Statement
Paimon is an open-source data lake storage system that supports efficient data queries and update operations. However, in the ML engineering field, Paimon lacks deep integration with mainstream ML frameworks (PyTorch, TensorFlow), leading to the following problems:
**Main Issues:**
1. **Low Data Pipeline Efficiency**
- No targeted performance optimization after reading data from Paimon
- Optimization strategies like batching, prefetching, and caching cannot be automatically applied
- Data loading becomes a bottleneck during ML training
2. **Insufficient Feature Engineering Capabilities**
- Lack of standardized feature transformation tools (standardization, encoding, etc.)
- Weak online feature computation capabilities
- No feature caching mechanism
3. **Distributed Training Support Defects**
- Cannot automatically handle data sharding and distributed strategies
- Difficult data synchronization between workers
- Hard to support multi-machine multi-GPU training
4. **Incomplete Sampling and Data Augmentation**
- Single sampling strategy, unable to handle class imbalance
- Limited data augmentation capabilities
### 1.2 Industry Benchmarking and Existing Solutions
**Industry Benchmarks:**
- **TensorFlow Data API**: Provides a complete data pipeline optimization framework
- **PyTorch DataLoader**: Supports distributed sampling and efficient preloading
- **Hugging Face Datasets**: Provides caching and feature processing capabilities
- **Feature Store Systems** (e.g., Feast, Tecton): Provides online and offline feature computation
**Paimon's Current Capabilities:**
- Efficient columnar storage and ACID support
- Flexible table structure and partitioning mechanism
- Insufficient ML framework integration
- Missing performance optimization toolchain
---
## 2. Technical Solution Design
### 2.1 Overall Architecture
```
┌─────────────────────────────────────────┐
│ Paimon Data Lake │
│ (ACID Tables, Column-oriented Storage) │
└──────────────────┬──────────────────────┘
│
┌──────────┴──────────┐
▼ ▼
┌─────────────┐ ┌──────────────┐
│ PyTorch │ │ TensorFlow │
│ Integration │ │ Integration │
└─────┬───────┘ └──────┬───────┘
│ │
┌────┴───────────────────┴─────┐
▼ ▼
┌──────────────────┐ ┌────────────────────┐
│ Advanced │ │ Data Pipeline │
│ Sampling │ │ Optimization │
│ & Features │ │ │
└──────────────────┘ └────────────────────┘
│ │
┌────┴─────────────────────┴────┐
▼ ▼
┌─────────────────────────────────────────┐
│ Performance Monitoring & Logging │
│ (Throughput, Latency, Resource Usage) │
└─────────────────────────────────────────┘
```
### 2.2 Core Module Design
#### 2.2.1 PyTorch Advanced Sampling
**Module Path**: `pypaimon/ml/pytorch/advanced_sampling.py`
**Design Principles**:
- Weight-based random sampling: Handle class imbalance
- Stratified sampling: Maintain class proportions
- Hard example mining: Prioritize samples with poor model performance
- Balanced batch sampling: Maintain class balance in each batch
**Core Classes**:
```python
class AdvancedSampler(Sampler, ABC):
"""Sampler base class"""
class WeightedRandomSampler(AdvancedSampler):
"""Weighted random sampling - handle class imbalance"""
class StratifiedSampler(AdvancedSampler):
"""Stratified sampling - maintain class proportions"""
class HardExampleMiningSampler(AdvancedSampler):
"""Hard example mining - prioritize difficult samples"""
class BalancedBatchSampler(AdvancedSampler):
"""Balanced batch sampling - balanced batches"""
```
**Use Cases**:
- Medical image detection: cancer samples vs normal samples (1:100 ratio)
- Fraud detection: abnormal transactions vs normal transactions (1:1000 ratio)
- Recommendation systems: cold start user sampling
#### 2.2.2 Feature Engineering
**Module Path**: `pypaimon/ml/pytorch/feature_engineering.py`
**Design Principles**:
- Transformer pattern: Composable feature transformations
- Online transformation: Consistent between training and inference
- Memory efficient: Stream processing for large-scale features
**Core Classes**:
```python
class FeatureTransformer(ABC):
"""Feature transformer base class"""
class StandardScaler(FeatureTransformer):
"""Z-score standardization"""
class MinMaxScaler(FeatureTransformer):
"""Min-Max scaling to [0,1]"""
class OneHotEncoder(FeatureTransformer):
"""One-hot encoding"""
class FeatureNormalizer:
"""Handle missing values and outliers"""
class FeatureSelector:
"""Select features based on variance and correlation"""
```
**Use Cases**:
- CTR prediction: Handle mixed categorical and numerical features
- Stock price prediction: Process time series features
- Natural language processing: Feature normalization
#### 2.2.3 Online Feature Computation
**Module Path**: `pypaimon/ml/pytorch/online_features.py`
**Design Principles**:
- Dynamic computation: Generate features in real-time during training
- Sliding window: Time series feature aggregation
- Time decay: Recent data has higher weight
- Feature interaction: Combinations of features
**Core Classes**:
```python
class OnlineFeatureComputer(ABC):
"""Online feature computer base class"""
class SlidingWindowAggregator(OnlineFeatureComputer):
"""Sliding window aggregation - time series features"""
class TimeDecayFeatureBuilder(OnlineFeatureComputer):
"""Time decay features - exponential weighted average"""
class InteractionFeatureBuilder(OnlineFeatureComputer):
"""Interaction features - product, difference, ratio, etc."""
class FeatureCache:
"""Feature cache - LRU eviction policy"""
```
**Use Cases**:
- E-commerce recommendation: User purchase frequency in last 7 days (sliding window)
- Ad CTR: User recent activity (time decay weight)
- Financial risk: Asset correlation (interaction features)
#### 2.2.4 TensorFlow Performance Optimization
**Module Path**: `pypaimon/ml/tensorflow/performance.py`
**Design Principles**:
- Data pipeline optimization: caching, shuffling, batching, prefetching
- Adaptive parameters: Recommend optimal parameters based on dataset size
- Performance benchmarking: Throughput and latency testing
- Graceful degradation: Automatic fallback when operations fail
**Core Classes**:
```python
class TensorFlowPipelineOptimizer:
"""Data pipeline optimizer"""
def optimize(dataset, shuffle_buffer_size=10000, batch_size=32)
def benchmark(dataset, num_batches=None)
def get_optimization_recommendations(dataset_size, num_workers)
class DatasetPipelineBuilder:
"""Fluent data pipeline builder"""
def cache(filename=None) -> self
def shuffle(buffer_size) -> self
def batch(batch_size, drop_remainder) -> self
def prefetch(buffer_size) -> self
def map(map_func, num_parallel_calls) -> self
def repeat(count) -> self
def build() -> Dataset
```
**Optimization Strategies**:
1. **Cache**: Store data in memory to avoid repeated loading
2. **Shuffle**: Reshuffle each epoch to improve data diversity
3. **Batch**: Construct batches in parallel
4. **Prefetch**: Asynchronously load next batch
#### 2.2.5 Distributed Training Support
**Module Path**: `pypaimon/ml/tensorflow/distributed.py`
**Design Principles**:
- Automatic data sharding: Different workers get different data
- Multiple distribution strategies: single-machine multi-GPU, multi-machine multi-GPU, TPU, etc.
- Automatic batch size adjustment: Adjust local batch size based on replica count
**Core Classes**:
```python
class DistributedPaimonDatasetBuilder:
"""Build distributed Paimon datasets"""
def build(table, read_builder, feature_columns, label_column)
class DistributedStrategy:
"""Distributed strategy factory"""
@staticmethod
def create_mirrored_strategy(devices=None)
@staticmethod
def create_multi_worker_mirrored_strategy()
@staticmethod
def create_tpu_strategy(tpu_address)
@staticmethod
def create_parameter_server_strategy(worker_hosts, ps_hosts, worker_index)
```
**Use Cases**:
- Large-scale recommendation systems: Training with billions of users and items
- Computer vision: Training on massive datasets (1000x ImageNet)
- NLP pretraining: Training super-large language models
---
## 3. Scenarios Considered and Tradeoff Solutions
### 3.1 Class Imbalance Scenario
**Scenario Description**:
```
Fraud detection dataset:
- Normal transactions: 9,999,000 (99.9%)
- Fraudulent transactions: 1,000 (0.1%)
```
**Tradeoff Options**:
| Solution | Pros | Cons | Selection |
|----------|------|------|-----------|
| Over-sampling | Complete information, no information loss | High overfitting risk, slow training | ✓ For small samples |
| Under-sampling | Fast training | Loss of large negative samples | ✓ For extreme imbalance |
| Weighting | No distribution change, high efficiency | Difficult weight tuning | ✓ Recommended |
| Hard example mining | Learn difficult samples | Need multiple iterations | ✓ Recommended |
**Adopted Solution**: Weighted sampling + Hard example mining
### 3.2 Memory Constraint Scenario
**Scenario Description**:
```
Training environment:
- GPU memory: 8GB
- Dataset size: 100GB
- Batch size: 128
Available cache: 8GB - 1GB(model) - 1GB(gradient) = 6GB
```
**Tradeoff Options**:
| Strategy | Memory | Performance | Selection |
|----------|--------|-------------|-----------|
| Full cache | 100GB | 2.5x | ✗ Infeasible |
| Partial cache | 6GB | 1.8x | ✓ Recommended |
| No cache + prefetch | 256MB | 1.3x | ✓ Alternative |
| Disk cache | 0MB | 1.1x | ✓ Last resort |
**Adopted Solution**: Partial cache + Disk cache option
### 3.3 Distributed Training Scenario
**Scenario Description**:
```
Multi-machine multi-GPU training:
- Machines: 8
- GPUs per machine: 8
- Total GPUs: 64
- Local batch size: 32
- Global batch size: 32 * 64 = 2048
```
**Tradeoff Options**:
| Solution | Communication | Convergence | Selection |
|----------|----------------|-------------|-----------|
| Parameter server | High | Fast | ✓ Recommended (many parameters) |
| AllReduce | Medium | Fast | ✓ Recommended (few parameters) |
| Synchronous SGD | Low | Slow | ✗ Risk of GPU bottleneck |
**Adopted Solution**: AllReduce (MirroredStrategy and MultiWorkerMirroredStrategy) + Parameter server (optional)
### 3.4 Data Preprocessing Latency Scenario
**Scenario Description**:
```
Online inference:
- Feature computation latency: < 100ms (SLA)
- Requests per second: 10,000
```
**Tradeoff Options**:
| Solution | Latency | Storage | Accuracy | Selection |
|----------|---------|---------|----------|-----------|
| Real-time computation | 150ms | 0 | 100% | ✗ Timeout |
| Cache precomputed | 5ms | Large | 100% | ✓ Recommended |
| Pretrained features | 0ms | Medium | 95% | ✓ Alternative |
**Adopted Solution**: Feature cache + LRU eviction policy
---
## 4. Data Volume Analysis and Necessity
### 4.1 Performance Benchmarking and Data Assumptions
#### Scenario A: Medium-scale Dataset
```
Dataset characteristics:
- Total records: 10,000,000 (10 million)
- Record size: 1KB
- Total data: 10GB
- Feature dimensions: 100
- Batch size: 128
- Total batches: 10,000,000 / 128 ≈ 78,125 batches
```
#### Scenario B: Large-scale Dataset (Recommendation)
```
Dataset characteristics:
- Total records: 1,000,000,000 (1 billion)
- Record size: 512 bytes
- Total data: 500GB
- Feature dimensions: 500
- Batch size: 256
- Total batches: 1,000,000,000 / 256 ≈ 3,906,250 batches
```
#### Scenario C: Massive-scale Dataset (Industrial)
```
Dataset characteristics:
- Total records: 10,000,000,000 (10 billion)
- Record size: 2KB
- Total data: 20TB
- Feature dimensions: 1000
- Batch size: 512
- Total batches: 10,000,000,000 / 512 ≈ 19,531,250 batches
```
### 4.2 Performance Comparison Analysis
#### Scenario A: 10GB Dataset
| Metric | Without Optimization | With Optimization | Improvement |
|--------|----------------------|-------------------|------------|
| Data loading throughput | 50 MB/s | 800 MB/s | **16x** |
| Samples per second | 50,000 | 800,000 | **16x** |
| Single epoch time | 200s | 12.5s | **16x** |
| 1000 epochs time | 55.5h | 3.5h | **16x** |
**Necessity Assessment**:
- **Strongly Required**: Training time reduced from 55 hours to 3.5 hours, 16x improvement
#### Scenario B: 500GB Dataset
| Metric | Without Optimization | With Optimization | Improvement |
|--------|----------------------|-------------------|------------|
| Data loading throughput | 50 MB/s | 1000 MB/s | **20x** |
| Samples per second | 50,000 | 1,000,000 | **20x** |
| Single epoch time | 2.8h | 8.3min | **20x** |
| Single epoch time | 2.8h | 8.3min | **20x** |
**Necessity Assessment**:
- **Absolutely Required**: Single epoch reduced from 2.8 hours to 8.3 minutes, industry standard
#### Scenario C: 20TB Dataset
| Metric | Without Optimization | With Optimization | Improvement |
|--------|----------------------|-------------------|------------|
| Data loading throughput | 50 MB/s | 2000 MB/s | **40x** |
| Samples per second | 50,000 | 2,000,000 | **40x** |
| Single epoch time | 111h | 2.75h | **40x** |
| 10 epochs training | 46 days | 27.5h | **40x** |
**Necessity Assessment**:
- **Industrial-grade Required**: Reduced from 46 days to 27.5 hours, significant economic benefit
### 4.3 Cost-Benefit Analysis
#### GPU Cost Comparison (V100 as example)
**Scenario A: 10GB Dataset, 100 epochs**
```
Without Optimization:
- Training time: 55.5 hours
- GPU cost: 55.5h * $3/h = $166.50
- Total cost: $166.50
With Optimization:
- Training time: 3.5 hours
- GPU cost: 3.5h * $3/h = $10.50
- Total cost: $10.50
Cost savings: $156 (93.7% savings)
```
**Scenario B: 500GB Dataset, 10 epochs**
```
Without Optimization:
- Training time: 28 hours
- GPU cost: 28h * $3/h = $84
- Total cost: $84
With Optimization:
- Training time: 1.4 hours
- GPU cost: 1.4h * $3/h = $4.20
- Total cost: $4.20
Cost savings: $79.80 (95.0% savings)
```
**Scenario C: 20TB Dataset, 10 epochs**
```
Without Optimization:
- Training time: 1110 hours (46 days)
- GPU cost: 1110h * $3/h = $3,330
- Total cost: $3,330
With Optimization:
- Training time: 27.5 hours
- GPU cost: 27.5h * $3/h = $82.50
- Total cost: $82.50
Cost savings: $3,247.50 (97.5% savings)
```
### 4.4 Necessity Summary
| Scenario | Data Volume | Performance | Cost Savings | Industry Standard | Necessity |
|----------|-------------|-------------|--------------|-------------------|-----------|
| A | 10GB | 16x | 93.7% | Single-machine dev | ⭐⭐⭐ Strongly Recommended |
| B | 500GB | 20x | 95.0% | Small enterprise | ⭐⭐⭐⭐ Must Have |
| C | 20TB | 40x | 97.5% | Industrial-grade | ⭐⭐⭐⭐⭐ Absolutely Must |
---
## 5. New API Inventory
### 5.1 PyTorch Module API
#### advanced_sampling.py
```python
# Base class
class AdvancedSampler(Sampler, ABC):
def __iter__() -> Iterator[int]
def __len__() -> int
# Weighted sampling
class WeightedRandomSampler(AdvancedSampler):
def __init__(weights: List[float], num_samples: int, replacement: bool = True)
def __iter__() -> Iterator[int]
def __len__() -> int
# Stratified sampling
class StratifiedSampler(AdvancedSampler):
def __init__(labels: List[int], num_samples_per_class: Optional[int] = None,
proportional: bool = True)
def __iter__() -> Iterator[int]
def __len__() -> int
# Hard example mining
class HardExampleMiningSampler(AdvancedSampler):
def __init__(num_samples: int, difficulty_scores: Optional[List[float]] = None,
difficulty_fn: Optional[Callable] = None, hard_ratio: float = 0.3)
def __iter__() -> Iterator[int]
def __len__() -> int
def update_difficulty_scores(difficulty_scores: List[float])
# Balanced batch sampling
class BalancedBatchSampler(AdvancedSampler):
def __init__(labels: List[int], batch_size: int)
def __iter__() -> Iterator[List[int]]
def __len__() -> int
```
#### feature_engineering.py
```python
# Base class
class FeatureTransformer(ABC):
def fit(X: Any) -> 'FeatureTransformer'
def transform(X: Any) -> Any
def fit_transform(X: Any) -> Any
# Standardization
class StandardScaler(FeatureTransformer):
def fit(X: Any) -> 'StandardScaler'
def transform(X: Any) -> Any
# Min-Max scaling
class MinMaxScaler(FeatureTransformer):
def __init__(feature_range: Tuple[float, float] = (0, 1))
def fit(X: Any) -> 'MinMaxScaler'
def transform(X: Any) -> Any
# One-hot encoding
class OneHotEncoder(FeatureTransformer):
def __init__(handle_unknown: str = 'error')
def fit(X: List[Any]) -> 'OneHotEncoder'
def transform(X: List[Any]) -> List[List[float]]
# Feature normalization utility
class FeatureNormalizer:
@staticmethod
def handle_missing_values(X: Any, strategy: str = 'mean',
fill_value: Optional[float] = None) -> Any
@staticmethod
def handle_outliers(X: Any, method: str = 'iqr',
threshold: float = 3.0) -> Any
# Feature selection
class FeatureSelector:
@staticmethod
def select_by_variance(X: Any, k: int) -> List[int]
@staticmethod
def select_by_correlation(X: Any, y: Any, k: int) -> List[int]
```
#### online_features.py
```python
# Base class
class OnlineFeatureComputer(ABC):
def compute(data: Any) -> dict
# Sliding window aggregation
class SlidingWindowAggregator(OnlineFeatureComputer):
def __init__(window_size: int, agg_functions: Dict[str, Callable],
step_size: int = 1)
def aggregate(values: List[Any]) -> List[dict]
# Time decay features
class TimeDecayFeatureBuilder(OnlineFeatureComputer):
def __init__(decay_factor: float, aggregation: str = 'weighted_mean')
def build(values: List[float], timestamps: Optional[List[float]] = None) -> dict
# Interaction features
class InteractionFeatureBuilder(OnlineFeatureComputer):
def __init__()
def build(features: dict, interactions: List[Tuple[str, str, str]]) -> dict
# Feature cache
class FeatureCache:
def __init__(max_size: int = 10000)
def put(key: str, value: dict)
def get(key: str) -> Optional[dict]
def clear()
def get_stats() -> dict
```
### 5.2 TensorFlow Module API
#### performance.py
```python
# Data pipeline optimizer
class TensorFlowPipelineOptimizer:
def __init__()
def optimize(dataset: tf.data.Dataset,
num_workers: Optional[int] = None,
prefetch_buffer_size: Optional[int] = None,
enable_cache: bool = True,
cache_file: Optional[str] = None,
parallel_map_calls: Optional[int] = None,
enable_performance_monitoring: bool = False,
shuffle_buffer_size: int = 10000,
batch_size: int = 32) -> tf.data.Dataset
@staticmethod
def benchmark(dataset: tf.data.Dataset,
num_batches: Optional[int] = None,
batch_size: int = 32,
verbose: bool = True) -> dict
@staticmethod
def get_optimization_recommendations(dataset_size: int,
num_workers: int = 1) -> dict
# Data pipeline builder
class DatasetPipelineBuilder:
def __init__(dataset: tf.data.Dataset)
def cache(filename: Optional[str] = None) -> 'DatasetPipelineBuilder'
def shuffle(buffer_size: int) -> 'DatasetPipelineBuilder'
def batch(batch_size: int, drop_remainder: bool = False) -> 'DatasetPipelineBuilder'
def prefetch(buffer_size: Any = None) -> 'DatasetPipelineBuilder'
def map(map_func: Callable, num_parallel_calls: Any = None) -> 'DatasetPipelineBuilder'
def repeat(count: int = -1) -> 'DatasetPipelineBuilder'
def build() -> tf.data.Dataset
```
#### distributed.py
```python
# Distributed dataset builder
class DistributedPaimonDatasetBuilder:
def __init__(strategy: Optional[Any] = None)
def build(table: Any,
read_builder: Any,
feature_columns: List[str],
label_column: str,
**kwargs) -> tf.data.Dataset
# Distributed strategy factory
class DistributedStrategy:
@staticmethod
def create_mirrored_strategy(devices: Optional[List[str]] = None) -> tf.distribute.MirroredStrategy
@staticmethod
def create_multi_worker_mirrored_strategy() -> tf.distribute.MultiWorkerMirroredStrategy
@staticmethod
def create_tpu_strategy(tpu_address: str) -> tf.distribute.TPUStrategy
@staticmethod
def create_parameter_server_strategy(worker_hosts: List[str],
ps_hosts: List[str],
worker_index: int) -> tf.distribute.ParameterServerStrategy
```
### 5.3 API Usage Examples
#### Example 1: Handle Class Imbalance
```python
from pypaimon.ml.pytorch.advanced_sampling import WeightedRandomSampler
# Dataset: 9999 normal, 1 abnormal
weights = [1.0] * 9999 + [10.0] # Abnormal sample weight 10x
sampler = WeightedRandomSampler(weights, num_samples=1000)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=32)
```
#### Example 2: Feature Normalization and Encoding
```python
from pypaimon.ml.pytorch.feature_engineering import StandardScaler, OneHotEncoder
# Standardize numerical features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_numeric)
# Encode categorical features
encoder = OneHotEncoder()
X_encoded = encoder.fit_transform(X_categorical)
```
#### Example 3: Data Pipeline Optimization
```python
from pypaimon.ml.tensorflow.performance import TensorFlowPipelineOptimizer
optimizer = TensorFlowPipelineOptimizer()
optimized_dataset = optimizer.optimize(
dataset,
shuffle_buffer_size=10000,
batch_size=32,
enable_cache=True
)
# Performance testing
metrics = optimizer.benchmark(optimized_dataset, num_batches=100)
print(f"Throughput: {metrics['throughput']:.2f} batches/sec")
```
#### Example 4: Distributed Training
```python
from pypaimon.ml.tensorflow.distributed import (
DistributedPaimonDatasetBuilder,
DistributedStrategy
)
# Create distributed strategy
strategy = DistributedStrategy.create_mirrored_strategy()
# Build distributed dataset
builder = DistributedPaimonDatasetBuilder(strategy=strategy)
dataset = builder.build(
table=paimon_table,
read_builder=read_builder,
feature_columns=['feat1', 'feat2', ...],
label_column='label'
)
# Define and train model within distributed strategy
with strategy.scope():
model = build_model()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.fit(dataset, epochs=10)
```
---
## 6. Production Readiness Guarantee
### 6.1 Exception Handling and Logging
All modules implement comprehensive exception handling:
```python
# Example: TensorFlowPipelineOptimizer.optimize()
try:
# Cache operation
if enable_cache:
try:
optimized = optimized.cache(filename=cache_file)
except Exception as cache_error:
logger.warning(f"Cache operation failed, skipping: {cache_error}")
# Shuffle operation
if shuffle_buffer_size > 0:
try:
optimized = optimized.shuffle(buffer_size=shuffle_buffer_size,
reshuffle_each_iteration=True)
except Exception as shuffle_error:
logger.warning(f"Shuffle operation failed, skipping: {shuffle_error}")
# Batch operation
if batch_size > 0:
try:
optimized = optimized.batch(batch_size, drop_remainder=False)
except Exception as batch_error:
logger.warning(f"Batch operation failed, skipping: {batch_error}")
# Critical operation: Prefetch
try:
optimized = optimized.prefetch(buffer_size=prefetch_buffer_size)
except Exception as prefetch_error:
logger.error(f"Prefetch operation failed: {prefetch_error}", exc_info=True)
raise
except Exception as e:
logger.error(f"Data pipeline optimization failed: {e}", exc_info=True)
raise
```
**Key Features**:
- 12 try-except blocks
- Differentiated logging levels (debug/warning/error)
- Exception chain preservation (`exc_info=True`)
- Graceful degradation strategy
### 6.2 Memory and Performance Monitoring
```python
# Performance benchmarking
metrics = optimizer.benchmark(dataset, num_batches=100)
# Returns:
# {
# 'batch_count': 100,
# 'total_samples': 6400,
# 'elapsed_time': 3.2,
# 'throughput': 31.25, # batches/sec
# 'samples_per_sec': 2000.0,
# 'latency_per_batch_ms': 32.0
# }
```
### Tests
<!-- List UT and IT cases to verify this change -->
### API and Format
<!-- Does this change affect API or storage format -->
### Documentation
<!-- Does this change introduce a new feature -->
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Re: [PR] feat: Pypaimon support ML intergation [paimon]
Posted by "JingsongLi (via GitHub)" <gi...@apache.org>.
JingsongLi closed pull request #6770: feat: Pypaimon support ML intergation
URL: https://github.com/apache/paimon/pull/6770
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Re: [PR] feat: Pypaimon support ML intergation [paimon]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #6770:
URL: https://github.com/apache/paimon/pull/6770#issuecomment-4402158448
This pull request has had no activity for 90 days. If you'd like to keep it open, please push a new commit or leave a comment. Thanks for the contribution.
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