Optimizing XML‑to‑MySQL Data Import: Reducing Execution Time from 300 s to 4 s

The author, a senior architect, encountered a common data‑import scenario: loading more than 60,000 rows from an XML file into MySQL. The initial implementation, a simple procedural Java routine, took about 300 seconds and consumed 656 MB of memory.

Environment : Java 21 on an 8‑core i9 laptop (2.3 GHz, 16 GB RAM) communicating with MySQL 8.0 running inside a VirtualBox VM (4 CPU, 4 GB RAM). Each XML record read took 0.08 s; each MySQL insert took 0.5 s.

Baseline code (simplified):

void importData() {
    Document doc = parseXmlFile();
    List
products = extractProducts(doc);
    for (Product p : products) {
        // insert into MySQL one by one
    }
}

To achieve a dramatic speedup, the author explored several optimization directions.

1. MySQL Batch Processing

Enabling JDBC batch mode and the rewriteBatchedStatements=true connection parameter reduces the number of round‑trips to the server. The core batch code is:

Connection connection = ...;
PreparedStatement stmt = connection.prepareStatement(sql);
connection.setAutoCommit(false);
for (int i = 0; i < batchSize; i++) {
    stmt.setString(...);
    stmt.addBatch();
}
stmt.executeBatch();
connection.commit();

Key JDBC methods used are addBatch() , executeBatch() and clearBatch() . After enabling batch processing, the import time dropped to about 9 seconds with similar memory usage.

2. Asynchronous Writing with Disruptor

To further reduce latency, the author introduced a producer‑consumer model using LMAX Disruptor. XML parsing produces ProductEvent objects that are placed into a ring buffer; multiple consumer handlers write batches to MySQL.

var disruptor = new Disruptor<>(
        ProductEvent::new,
        16384,
        DaemonThreadFactory.INSTANCE,
        ProducerType.SINGLE,
        new BusySpinWaitStrategy());

var consumers = new SaveDbHandler[4];
for (int i = 0; i < consumers.length; i++) {
    consumers[i] = new SaveDbHandler(i, consumers.length, shutdownLatch);
}

disruptor.handleEventsWith(new SimpleBatchRewindStrategy(), consumers)
        .then(new ClearingEventHandler());
RingBuffer
ringBuffer = disruptor.start();

for (Iterator
it = document.getRootElement().elementIterator(); it.hasNext(); ) {
    Element element = it.next();
    if (!StringUtils.hasText(element.elementTextTrim("id"))) continue;
    Product product = ObjectMapper.buildProduct(element);
    ringBuffer.publishEvent((event, seq, buf) -> event.setProduct(product));
}

When combined with batch mode, the total runtime fell to roughly 4.5 seconds, at the cost of higher peak memory (≈1 GB) due to multiple threads.

3. Further Optimizations

Using SAX (event‑driven) XML parsing to lower object allocation when handling very large files.

Increasing MySQL buffers ( innodb_log_buffer_size , innodb_buffer_pool_size ) and adjusting innodb_flush_log_at_trx_commit for better write throughput.

Adjusting max_allowed_packet to accommodate larger batch sizes.

Exploring larger Disruptor batchSize (e.g., 16384) to push runtime down to ~3.5 s, while monitoring ring‑buffer memory impact.

All source code is available on GitHub: https://github.com/xioshe/xml-to-mysql .

In summary, by focusing on MySQL write‑path optimizations—batch statements, rewrite flag, and asynchronous multithreaded consumption—the import duration was reduced from 300 seconds to under 4 seconds, demonstrating a practical performance‑engineering workflow for backend data‑loading tasks.