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      <title>Netflix Serves 84% of Query Results from Cache with Interval-Aware Caching in Apache Druid</title>
      <link>https://www.infoq.com/news/2026/05/netflix-druid-interval-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/2026/05/netflix-druid-interval-cache/en/headerimage/generatedHeaderImage-1777092326529.jpg"/&gt;&lt;p&gt;Netflix improves Apache Druid performance with interval aware caching, serving 84% of analytics results from cache and reducing query load by 33%. The system decomposes rolling window queries into reusable time segments, enabling partial cache reuse and recomputation only for recent data. At scale, it reduces scan volume, improves P90 latency, and optimizes real time analytics workloads.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Optimization</category>
      <category>Data Analytics</category>
      <category>Apache</category>
      <category>Observability</category>
      <category>Distributed Systems</category>
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      <pubDate>Mon, 11 May 2026 14:36:00 GMT</pubDate>
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      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-05-11T14:36:00Z</dc:date>
      <dc:identifier>/news/2026/05/netflix-druid-interval-cache/en</dc:identifier>
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      <title>OpenAI Introduces Websocket-Based Execution Mode to Reduce Latency in Agentic Workflows</title>
      <link>https://www.infoq.com/news/2026/05/openai-websocket-responses-api/?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/2026/05/openai-websocket-responses-api/en/headerimage/generatedHeaderImage-1777845282531.jpg"/&gt;&lt;p&gt;OpenAI introduces a WebSocket-based execution mode for its Responses API to improve agentic workflow performance in coding agents and real-time AI systems. The update reduces latency by up to 40 percent by replacing HTTP request-response cycles with persistent connections, improving streaming, tool execution, and multi-step orchestration in production-scale AI systems.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
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      <category>Artificial Intelligence</category>
      <category>AI Architecture</category>
      <category>Orchestration</category>
      <category>Realtime API</category>
      <category>Large language models</category>
      <category>AI Assisted Coding</category>
      <category>Optimization</category>
      <category>SDK</category>
      <category>Low Latency</category>
      <category>OpenAI</category>
      <category>WebSocket</category>
      <category>API</category>
      <category>Workflow Foundation</category>
      <category>Distributed Systems</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
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      <pubDate>Thu, 07 May 2026 14:48:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/openai-websocket-responses-api/?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>2026-05-07T14:48:00Z</dc:date>
      <dc:identifier>/news/2026/05/openai-websocket-responses-api/en</dc:identifier>
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