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    <title>DEV Community: Indhi Farhandika</title>
    <description>The latest articles on DEV Community by Indhi Farhandika (@indhifarhandika).</description>
    <link>https://dev.to/indhifarhandika</link>
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      <title>DEV Community: Indhi Farhandika</title>
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      <title>LunarDump — Lightweight. Fast. Zero-Trust Database Backups.

Github: https://github.com/indhifarhandika/LunarDump
Documentation: https://lunardump.indhifarhandika.dev</title>
      <dc:creator>Indhi Farhandika</dc:creator>
      <pubDate>Wed, 05 Aug 2026 09:52:19 +0000</pubDate>
      <link>https://dev.to/indhifarhandika/lunardump-lightweight-fast-zero-trust-database-backups-github-2fd7</link>
      <guid>https://dev.to/indhifarhandika/lunardump-lightweight-fast-zero-trust-database-backups-github-2fd7</guid>
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          &lt;a href="https://github.com/indhifarhandika/LunarDump" rel="noopener noreferrer" class="c-link"&gt;
            GitHub - indhifarhandika/LunarDump: A lightweight, zero-trust CLI tool built in Python to automate, encrypt, and stream database backups directly to multi-cloud storage. · GitHub
          &lt;/a&gt;
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          &lt;p class="truncate-at-3"&gt;
            A lightweight, zero-trust CLI tool built in Python to automate, encrypt, and stream database backups directly to multi-cloud storage. - indhifarhandika/LunarDump
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          github.com
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          &lt;span class="mr-2"&gt;lunardump.indhifarhandika.dev&lt;/span&gt;
          

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      <category>devops</category>
      <category>python</category>
      <category>database</category>
      <category>productivity</category>
    </item>
    <item>
      <title>Zero-Disk Streaming Database Backup: 8 GB Dump Processed with Only ~119 MB RAM!</title>
      <dc:creator>Indhi Farhandika</dc:creator>
      <pubDate>Mon, 03 Aug 2026 11:56:09 +0000</pubDate>
      <link>https://dev.to/indhifarhandika/zero-disk-streaming-database-backup-8-gb-dump-processed-with-only-119-mb-ram-4c8h</link>
      <guid>https://dev.to/indhifarhandika/zero-disk-streaming-database-backup-8-gb-dump-processed-with-only-119-mb-ram-4c8h</guid>
      <description>&lt;p&gt;When managing database backups, key pain points on entry-level servers are memory spikes (Out of Memory crashes) and running out of disk space due to temporary dump files.&lt;/p&gt;

&lt;p&gt;To validate the efficiency of LunarDump (a zero-disk streaming backup solution), I conducted a benchmark &amp;amp; memory profiling using Memray across a multi-cloud setup based in Jakarta.&lt;/p&gt;

&lt;p&gt;🖥️ Test Environment Setup:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Server A (Database): Tencent Cloud (Jakarta) — Ubuntu 24.04, 2 vCPU, 2 GB RAM, 40 GB SSD&lt;/li&gt;
&lt;li&gt;Server B (LunarDump Engine): Cloudeka (Jakarta) — Ubuntu 24.04, 1 vCPU, 1 GB RAM, 20 GB SSD&lt;/li&gt;
&lt;li&gt;Destina
tion Storage: Google Cloud Storage (GCS) Bucket&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;📊 Benchmark Highlights:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt; Total Data Processed: ~8 GB (MySQL Database Stream)&lt;/li&gt;
&lt;li&gt; Execution Duration: 1 Hour 1 Minute (Continuous Cross-Cloud Streaming)&lt;/li&gt;
&lt;li&gt; Peak Memory Usage (Heap): 119.1 MB (~1.4% of total dataset size)&lt;/li&gt;
&lt;li&gt; Resident Set Size (RSS): Stable at ~180 MB&lt;/li&gt;
&lt;li&gt; Local Disk Footprint: 0 Bytes (Data streamed from Server A, encrypted with AES-256-GCM in Server B's RAM, and uploaded directly to GCS on-the-fly)&lt;/li&gt;
&lt;li&gt; Server B CPU Load: Averaged 4% – 8%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;💡 Key Takeaways:&lt;br&gt;
Through Flamegraph visualizations and Resident Set Size Over Time analysis, LunarDump demonstrated Zero Memory Leak. The flat memory footprint confirms that garbage collection and chunk-based buffer recycling function flawlessly—keeping resource consumption predictable even on a low-spec 1 vCPU/ 1 GB RAM node.&lt;/p&gt;

&lt;p&gt;Github : &lt;a href="https://github.com/indhifarhandika/LunarDump" rel="noopener noreferrer"&gt;lunardump&lt;/a&gt;&lt;/p&gt;

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      <category>programming</category>
      <category>devops</category>
      <category>database</category>
      <category>python</category>
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