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    <title>InfoQ - Distributed Systems - News</title>
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    <description>InfoQ Distributed Systems News feed</description>
    <item>
      <title>Cloudflare Cuts 100 TB of Memory from 1.1.1.1 DNS Cache</title>
      <link>https://www.infoq.com/news/2026/09/cloudflare-dns-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/cloudflare-dns-cache/en/headerimage/generatedHeaderImage-1788755192272.jpg"/&gt;&lt;p&gt;Cloudflare redesigned the in-memory representation of its Big Pineapple DNS cache, reducing the per-entry footprint by 56% and freeing roughly 100 TB of working-set memory across its fleet. The Rust-based changes also increased cache insertion throughput by 43% and reduced lookup latency by 19%, while enabling Cloudflare to increase cache capacity without additional memory.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>DNS</category>
      <category>Scalability</category>
      <category>Caching</category>
      <category>Cloudflare</category>
      <category>Performance</category>
      <category>Infrastructure</category>
      <category>Memory</category>
      <category>Rust</category>
      <category>Distributed Systems</category>
      <category>Optimization</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 23 Sep 2026 13:22:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/cloudflare-dns-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-23T13:22:00Z</dc:date>
      <dc:identifier>/news/2026/09/cloudflare-dns-cache/en</dc:identifier>
    </item>
    <item>
      <title>Uber Redesigns M3DB Sharding with Subclusters to Limit Failure Impact</title>
      <link>https://www.infoq.com/news/2026/09/uber-m3db-subcluster-sharding/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/uber-m3db-subcluster-sharding/en/headerimage/generatedHeaderImage-1788717337142.jpg"/&gt;&lt;p&gt;Uber has redesigned shard placement in M3DB with fixed size subclusters to limit the impact of node failures, maintenance, and cluster scaling. The approach bounds shard dependencies, preserves replica isolation, and uses a greedy algorithm to select shard migrations while avoiding a separate rebalancing pass and unnecessary data movement.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Database</category>
      <category>Load Balancing</category>
      <category>Database Replication</category>
      <category>Algorithms</category>
      <category>Microservices</category>
      <category>Fault Tolerance</category>
      <category>Open Source</category>
      <category>Scalability</category>
      <category>Time Series Data</category>
      <category>Sharding</category>
      <category>Uber</category>
      <category>Availability</category>
      <category>Distributed Systems</category>
      <category>Clusters</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 21 Sep 2026 14:37:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/uber-m3db-subcluster-sharding/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-21T14:37:00Z</dc:date>
      <dc:identifier>/news/2026/09/uber-m3db-subcluster-sharding/en</dc:identifier>
    </item>
    <item>
      <title>Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads</title>
      <link>https://www.infoq.com/news/2026/09/dropbox-riviera-ai-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Asynchronous Architecture</category>
      <category>Tika</category>
      <category>plugins</category>
      <category>Apache</category>
      <category>Large language models</category>
      <category>Platform Engineering</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Distributed Systems</category>
      <category>Enterprise Content Management</category>
      <category>Architecture</category>
      <category>Data Pipelines</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 16 Sep 2026 14:42:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/dropbox-riviera-ai-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-16T14:42:00Z</dc:date>
      <dc:identifier>/news/2026/09/dropbox-riviera-ai-platform/en</dc:identifier>
    </item>
    <item>
      <title>Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB</title>
      <link>https://www.infoq.com/news/2026/09/agoda-price-cache-dragonflydb/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/agoda-price-cache-dragonflydb/en/headerimage/generatedHeaderImage-1787939534250.jpg"/&gt;&lt;p&gt;Agoda migrated its 1.5 TB hotel Price Cache from 72 SQL Server shards to DragonflyDB to handle growing read and write volumes. The migration used staged dual reads, parity validation, gradual traffic shifting, and decentralized failover detection. Agoda reports an approximately eightfold reduction in P99 read latency, with two DragonflyDB clusters providing high availability.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Distributed Data</category>
      <category>A/B Testing</category>
      <category>Caching</category>
      <category>Redis</category>
      <category>Microservices</category>
      <category>SQL Server</category>
      <category>Distributed Systems</category>
      <category>Distributed Cache</category>
      <category>Prometheus</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 14 Sep 2026 13:48:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/agoda-price-cache-dragonflydb/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Distributed+Systems-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-14T13:48:00Z</dc:date>
      <dc:identifier>/news/2026/09/agoda-price-cache-dragonflydb/en</dc:identifier>
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