Common Interface Performance Optimization Strategies

Background: In an existing project, many cost‑saving and efficiency‑improving measures were taken last year, and the most frequent issue discovered was excessive interface response time, prompting a focused effort on interface performance optimization.

1. Batch Processing

Batch operations reduce database I/O by aggregating multiple inserts or updates into a single batch execution.

batchInsert();

2. Asynchronous Processing

Long‑running, non‑critical logic can be moved to asynchronous execution (e.g., thread pools, message queues, or scheduled tasks) to lower perceived latency.

Example: a financial purchase interface where accounting and file writing are not required in real‑time can be handled asynchronously.

3. Space‑for‑Time (Caching)

Cache frequently accessed, rarely changed data to avoid repeated database queries or calculations. Choose the appropriate cache (in‑memory, Redis, Memcached, etc.) and be aware of consistency trade‑offs.

4. Pre‑Processing

Pre‑compute and store results (e.g., annualized return rates for financial products) so that the interface can retrieve ready‑made values instead of performing calculations on each request.

5. Pooling

Reuse expensive resources such as database connections or threads via connection pools and thread pools, reducing the overhead of creation and destruction.

6. Serial to Parallel

Convert sequential dependent calls into parallel independent calls when there is no data dependency, dramatically cutting total response time.

7. Indexing

Proper indexes greatly accelerate data retrieval; the article notes common scenarios where indexes may not be effective.

8. Avoid Large Transactions

Long‑running transactions hold database connections and degrade performance. Example annotation:

@Transactional(value = "taskTransactionManager", propagation = Propagation.REQUIRED, isolation = Isolation.READ_COMMITTED, rollbackFor = {RuntimeException.class, Exception.class})
public BasicResult purchaseRequest(PurchaseRecord record) {
    BasicResult result = new BasicResult();
    // ...
    pushRpc.doPush(record);
    result.setInfo(ResultInfoEnum.SUCCESS);
    return result;
}

Mitigation: keep RPC calls and heavy queries outside the transaction, and limit the amount of data processed within a transaction.

9. Refactor Program Structure

Repeated iterations can lead to tangled code with redundant queries and object creations; a systematic refactor of the interface logic can improve performance.

10. Deep Pagination

Using LIMIT on large offsets can be slow; leveraging a continuously increasing primary key (e.g., WHERE id > 100000 LIMIT 200 ) can maintain performance.

11. SQL Optimization

Combine indexing, pagination, and other SQL tuning techniques to boost query speed.

12. Lock Granularity

Apply locks only to the minimal critical section (e.g., synchronize only the shared resource) to avoid unnecessary contention.

Conclusion: Interface performance issues often accumulate over multiple development cycles; adopting a higher‑level design mindset and these optimization patterns can significantly reduce latency and improve system efficiency.