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      <h1>CSV import benchmarks</h1>
      <p class="lede">One million CSV rows imported in 8.747 seconds on PostgreSQL and 7.950 seconds on MariaDB with Scriptella 1.7.0.</p>
      <p>Handwritten JDBC was faster, at 7.642 and 5.755 seconds. Pandas took 31.551 and 20.377 seconds. PostgreSQL COPY took 1.750 seconds, and MariaDB LOAD DATA LOCAL took 3.287 seconds.</p>

      <h2 id="ci-results">Benchmark results</h2>
      <p>Benchmark v5: median of two runs on 2026-11-03, Intel Mac, Colima x86_64 with 4 CPUs and 6 GiB. Lower is faster.</p>
      <figure>
        <img src="images/benchmarks/v5-local.svg" width="806" height="490" alt="Median import times. PostgreSQL: Scriptella 8.747 seconds, JDBC 7.642 seconds, Pandas 31.551 seconds, COPY 1.750 seconds. MariaDB: Scriptella 7.950 seconds, JDBC 5.755 seconds, Pandas 20.377 seconds, LOAD DATA LOCAL 3.287 seconds.">
        <figcaption>Linear duration axis starting at zero. Bars are medians; dots are the two runs.</figcaption>
      </figure>
      <div class="table-scroll" tabindex="0" role="region" aria-label="Benchmark v5 median durations">
        <table>
          <thead>
            <tr>
              <th scope="col">Database</th>
              <th scope="col">Scriptella 1.7.0</th>
              <th scope="col">JDBC</th>
              <th scope="col">Pandas</th>
              <th scope="col">Native reference</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th scope="row">PostgreSQL</th>
              <td>8.747 s</td>
              <td>7.642 s</td>
              <td>31.551 s</td>
              <td>COPY: 1.750 s</td>
            </tr>
            <tr>
              <th scope="row">MariaDB</th>
              <td>7.950 s</td>
              <td>5.755 s</td>
              <td>20.377 s</td>
              <td>LOAD DATA LOCAL: 3.287 s</td>
            </tr>
          </tbody>
        </table>
      </div>
      <p><small>Same million-row CSV, pre-created tables, one transaction per import, no indexes or transformations. Timings include client startup, CSV parsing, insertion, and commit; setup and verification are excluded. Scriptella and JDBC use batch size 1000. PostgreSQL batch rewriting is enabled for both; MariaDB uses driver defaults. Pandas reads 10,000-row chunks and writes 1,000-row chunks. Values are medians of two runs. Results depend on workload and environment. COPY and LOAD DATA LOCAL are database-specific references with different parsing and error semantics.</small></p>
      <p>See the <a href="https://github.com/scriptella/scriptella-benchmarks">benchmarks repository</a> for <a href="https://github.com/scriptella/scriptella-benchmarks/tree/main/docs/results/2026-11-03-local-v5">full results and environment details</a>, methodology, and instructions to reproduce the comparison.</p>
      <p>For tuning advice, see the <a href="https://github.com/scriptella/scriptella-etl/blob/master/docs/batching-performance.md">batching and insert performance guide</a>.</p>
      <p>Further reading: <a href="https://pvrlabs.xyz/articles/importing-a-million-csv-rows.html">Importing a million CSV rows</a> and <a href="https://pvrlabs.xyz/java-performance/experiments/postgresql-batch-rewrite.html">how PostgreSQL batch rewriting improved import performance</a> on PVR Labs.</p>

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