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      <title>DoorDash Uses Envoy and Valkey for a 1.5M RPS Proxy Cache with 99.99999% Availability</title>
      <link>https://www.infoq.com/news/2026/07/doordash-entity-cache-proxy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Microservices</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;DoorDash has developed Entity Cache, a transparent proxy caching platform built on Envoy and Valkey to reduce redundant service-to-service requests across its microservices architecture. Operating within DoorDash’s service mesh, the platform serves over 1.5M requests per second with 99.99999% availability through caching, event-driven invalidation, failure handling, and performance optimizations.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Distributed Cache</category>
      <category>Cloud Architecture</category>
      <category>Service Mesh</category>
      <category>Envoy</category>
      <category>Microservices</category>
      <category>Algorithms</category>
      <category>Performance</category>
      <category>Optimization</category>
      <category>Backend For Frontend</category>
      <category>Availability</category>
      <category>Service Reliability</category>
      <category>gRPC</category>
      <category>Valkey</category>
      <category>Caching</category>
      <category>HTTP</category>
      <category>Distributed Systems</category>
      <category>Reliability</category>
      <category>Event Driven Architecture</category>
      <category>Apache Kafka</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 20 Jul 2026 13:53:00 GMT</pubDate>
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      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-07-20T13:53:00Z</dc:date>
      <dc:identifier>/news/2026/07/doordash-entity-cache-proxy/en</dc:identifier>
    </item>
    <item>
      <title>How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone</title>
      <link>https://www.infoq.com/news/2026/07/doordash-ai-ask-assistant/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Microservices</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/07/doordash-ai-ask-assistant/en/headerimage/generatedHeaderImage-1782515732223.jpg"/&gt;&lt;p&gt;DoorDash details the architecture behind Ask DoorDash, its AI-powered conversational shopping assistant, combining LLMs, specialized AI agents, MCP-based tooling, and an intelligence layer with persistent consumer memory and live backend data. Early results show up to 24% higher checkout conversion, 17% larger baskets, and improved intent accuracy using memory-backed sessions.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Artificial Intelligence</category>
      <category>ChatBots</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Memory</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Large language models</category>
      <category>Natural Language Processing</category>
      <category>Distributed Systems</category>
      <category>Microservices</category>
      <category>Prompt Engineering</category>
      <category>Platform Engineering</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 13 Jul 2026 14:08:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/doordash-ai-ask-assistant/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Microservices</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-07-13T14:08:00Z</dc:date>
      <dc:identifier>/news/2026/07/doordash-ai-ask-assistant/en</dc:identifier>
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