Overcoming Product Sync Challenges: Architectural Approaches to Performance and Concurrency
Aug 20, 2024George Chalkiadakis
Overcoming Product Sync Challenges: Architectural Approaches to Performance and Concurrency
In today’s rapidly evolving digital landscape, maintaining consistent product data across various platforms and databases is essential and challenging. At My Buddy AI (https://my-buddy.ai), we encounter daily hurdles with synchronizing data from multiple sources that frequently change, with speed, processing costs, and maintainability being significant concerns. Managing large volumes of product data, ensuring regular updates, and preventing conflicts become crucial as businesses grow. This article explores a practical approach to addressing performance and concurrency challenges in product synchronization, drawing inspiration from an architectural design based on the provided Go code.
source code https://github.com/gchalkman/MBDataSyncProcess
The Problem: Performance and Concurrency Challenges in Product Sync
Product synchronization involves transferring and updating product information from source feeds to target databases or platforms. The challenges arise when:
- High Volume of Data: Processing large XML files with numerous product entries.
- Concurrency Management: Avoid conflicts when multiple processes or workers update the database simultaneously.
- Performance Optimization: Efficiently processing data and reducing the time for updates.
- Fuse data from various sources, such as products and prices from REST APIs, and product characteristics obtained through web crawling.
- Product updates, including the introduction of new products, price changes, and product removals, can be recognized.
The provided code illustrates a robust solution to address these challenges by focusing on three main aspects: parallel processing, concurrency control, and retry mechanisms.
Architectural Approach to Solving the Challenges
1. Parallel Processing with Worker Pools:
```go
sem := make(chan struct{}, maxWorkers)
for _, item := range rss.Channel.Items {
sem <- struct{}{}
wg.Add(1)
go func(item Item) {
defer func() { <-sem }()
worker(db, &wg, item, mutex)
}(item)
}
```
2. Concurrency Control with Mutexes:
To avoid race conditions and ensure data integrity during database operations, a mutex (`dbMutex`) is employed. The mutex locks critical sections of code that involve database transactions, preventing concurrent processes from causing data conflicts.
```go
dbMutex.Lock()
defer dbMutex.Unlock()
```
3. Resiliency with Retry Mechanism:
Database operations often face temporary issues like locks or busy states, especially in high-concurrency environments. The code incorporates a retry mechanism with exponential backoff to handle such transient errors gracefully. This ensures that the system remains resilient even under heavy load.
```go
for i := 0; i < maxRetries; i++ {
_, err = db.Exec(query, args…)
if err == nil {
return nil
}
if sqliteErr, ok := err.(sqlite3.Error); ok && (sqliteErr.Code == sqlite3.ErrBusy || sqliteErr.Code == sqlite3.ErrLocked) {
time.Sleep(time.Duration(i+1) * time.Millisecond * 100)
continue
}
return err
}
```
4. Optimized Database Configuration:
The database is initialized with Write-Ahead Logging (WAL) mode and a busy timeout. WAL mode enhances concurrency by allowing simultaneous reads and writes, which is crucial for high-performance applications.
```go
db, err := sql.Open(“sqlite3”, fmt.Sprintf(“file:%s?_busy_timeout=5000&_journal_mode=WAL”, dbFileName))
```
Architecture Description:
In our architecture, we download the product catalog and use multithreaded workers in Go to determine whether a product is new, unchanged, has price changes, or needs to be removed. For new products, we also need to implement a web crawler to enrich the data and technical specifications by extracting additional information from the manufacturer’s website.
- Worker Pool: Manages concurrent processing of product items.
- Database: Handles product data storage with WAL mode for concurrent reads/writes.
- Mutex: Ensures safe updates by locking critical sections.
- Retry Logic: Handles transient errors with exponential backoff.
Conclusion
This architecture effectively addresses the performance and concurrency challenges in product synchronization. The system can scale efficiently while maintaining data integrity by combining parallel processing, mutex-based concurrency control, and a resilient retry mechanism. Whether you’re syncing product data between systems or updating databases with high-frequency changes, these principles can help you build a reliable and performant solution.
