To correctly handle JDBC transactions, you must first turn off the automatic commit mode, then perform multiple operations, and finally commit or rollback according to the results; 1. Call conn.setAutoCommit(false) to start the transaction; 2. Execute multiple SQL operations, such as INSERT and UPDATE; 3. Call conn.commit() if all operations are successful, and call conn.rollback() if an exception occurs to ensure data consistency; at the same time, try-with-resources should be used to manage resources, properly handle exceptions and close connections to avoid connection leakage; in addition, it is recommended to use connection pools and set save points to achieve partial rollback, and keep transactions as short as possible to improve performance.

Python is an efficient tool to implement ETL processes. 1. Data extraction: Data can be extracted from databases, APIs, files and other sources through pandas, sqlalchemy, requests and other libraries; 2. Data conversion: Use pandas for cleaning, type conversion, association, aggregation and other operations to ensure data quality and optimize performance; 3. Data loading: Use pandas' to_sql method or cloud platform SDK to write data to the target system, pay attention to writing methods and batch processing; 4. Tool recommendations: Airflow, Dagster, Prefect are used for process scheduling and management, combining log alarms and virtual environments to improve stability and maintainability.

Use classes in the java.time package to replace the old Date and Calendar classes; 2. Get the current date and time through LocalDate, LocalDateTime and LocalTime; 3. Create a specific date and time using the of() method; 4. Use the plus/minus method to immutably increase and decrease the time; 5. Use ZonedDateTime and ZoneId to process the time zone; 6. Format and parse date strings through DateTimeFormatter; 7. Use Instant to be compatible with the old date types when necessary; date processing in modern Java should give priority to using java.timeAPI, which provides clear, immutable and linear

Pre-formanceTartuptimeMoryusage, Quarkusandmicronautleadduetocompile-Timeprocessingandgraalvsupport, Withquarkusoftenperforminglightbetterine ServerLess scenarios.2.Thyvelopecosyste,

Java's garbage collection (GC) is a mechanism that automatically manages memory, which reduces the risk of memory leakage by reclaiming unreachable objects. 1.GC judges the accessibility of the object from the root object (such as stack variables, active threads, static fields, etc.), and unreachable objects are marked as garbage. 2. Based on the mark-clearing algorithm, mark all reachable objects and clear unmarked objects. 3. Adopt a generational collection strategy: the new generation (Eden, S0, S1) frequently executes MinorGC; the elderly performs less but takes longer to perform MajorGC; Metaspace stores class metadata. 4. JVM provides a variety of GC devices: SerialGC is suitable for small applications; ParallelGC improves throughput; CMS reduces

defer is used to perform specified operations before the function returns, such as cleaning resources; parameters are evaluated immediately when defer, and the functions are executed in the order of last-in-first-out (LIFO); 1. Multiple defers are executed in reverse order of declarations; 2. Commonly used for secure cleaning such as file closing; 3. The named return value can be modified; 4. It will be executed even if panic occurs, suitable for recovery; 5. Avoid abuse of defer in loops to prevent resource leakage; correct use can improve code security and readability.

Gradleisthebetterchoiceformostnewprojectsduetoitssuperiorflexibility,performance,andmoderntoolingsupport.1.Gradle’sGroovy/KotlinDSLismoreconciseandexpressivethanMaven’sverboseXML.2.GradleoutperformsMaveninbuildspeedwithincrementalcompilation,buildcac

ExecutorService is suitable for asynchronous execution of independent tasks, such as I/O operations or timing tasks, using thread pool to manage concurrency, submit Runnable or Callable tasks through submit, and obtain results with Future. Pay attention to the risk of unbounded queues and explicitly close the thread pool; 2. The Fork/Join framework is designed for split-and-governance CPU-intensive tasks, based on partitioning and controversy methods and work-stealing algorithms, and realizes recursive splitting of tasks through RecursiveTask or RecursiveAction, which is scheduled and executed by ForkJoinPool. It is suitable for large array summation and sorting scenarios. The split threshold should be set reasonably to avoid overhead; 3. Selection basis: Independent