<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Anshita Verma</title>
    <description>The latest articles on DEV Community by Anshita Verma (@anshita_verma_1cbd4718b8e).</description>
    <link>https://dev.to/anshita_verma_1cbd4718b8e</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3843805%2F8652f82e-ba37-4d81-89a4-a6cf4d3aab7e.jpg</url>
      <title>DEV Community: Anshita Verma</title>
      <link>https://dev.to/anshita_verma_1cbd4718b8e</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/anshita_verma_1cbd4718b8e"/>
    <language>en</language>
    <item>
      <title>Lately, I’ve been digging into what actually slows down APIs. Here are a few bottlenecks that changed how I think about performance</title>
      <dc:creator>Anshita Verma</dc:creator>
      <pubDate>Thu, 26 Mar 2026 18:22:14 +0000</pubDate>
      <link>https://dev.to/anshita_verma_1cbd4718b8e/lately-ive-been-digging-into-what-actually-slows-down-apis-here-are-a-few-bottlenecks-that-11fo</link>
      <guid>https://dev.to/anshita_verma_1cbd4718b8e/lately-ive-been-digging-into-what-actually-slows-down-apis-here-are-a-few-bottlenecks-that-11fo</guid>
      <description>&lt;p&gt;• It’s not always the database, sometimes it’s how we call it.&lt;br&gt;
Sending multiple small requests instead of batching them adds network overhead.&lt;br&gt;
• Connections are expensive.&lt;br&gt;
Rebuilding them on every request means repeated handshakes that could’ve been avoided with reuse.&lt;br&gt;
• Even logging can slow you down.&lt;br&gt;
Synchronous logs make the system wait after every write.&lt;br&gt;
• Repeated data fetching is often self-inflicted.&lt;br&gt;
If the same response is requested again and again, caching at the right layer can remove unnecessary load.&lt;br&gt;
• Payload size matters more than expected.&lt;br&gt;
Uncompressed JSON responses increase latency, especially at scale.&lt;br&gt;
• Database connections are costly to create.&lt;br&gt;
Without pooling, each request pays the setup cost&lt;br&gt;
• And sometimes it’s just the format.&lt;br&gt;
JSON is convenient, but not always efficient when compared to something like Protobuf&lt;/p&gt;

</description>
      <category>webdev</category>
      <category>api</category>
      <category>programming</category>
      <category>perl</category>
    </item>
    <item>
      <title>Lately, I’ve been digging into what actually slows down APIs. Here are a few bottlenecks that changed how I think about performance</title>
      <dc:creator>Anshita Verma</dc:creator>
      <pubDate>Thu, 26 Mar 2026 18:22:14 +0000</pubDate>
      <link>https://dev.to/anshita_verma_1cbd4718b8e/lately-ive-been-digging-into-what-actually-slows-down-apis-here-are-a-few-bottlenecks-that-3f0n</link>
      <guid>https://dev.to/anshita_verma_1cbd4718b8e/lately-ive-been-digging-into-what-actually-slows-down-apis-here-are-a-few-bottlenecks-that-3f0n</guid>
      <description>&lt;p&gt;• It’s not always the database, sometimes it’s how we call it.&lt;br&gt;
Sending multiple small requests instead of batching them adds network overhead.&lt;br&gt;
• Connections are expensive.&lt;br&gt;
Rebuilding them on every request means repeated handshakes that could’ve been avoided with reuse.&lt;br&gt;
• Even logging can slow you down.&lt;br&gt;
Synchronous logs make the system wait after every write.&lt;br&gt;
• Repeated data fetching is often self-inflicted.&lt;br&gt;
If the same response is requested again and again, caching at the right layer can remove unnecessary load.&lt;br&gt;
• Payload size matters more than expected.&lt;br&gt;
Uncompressed JSON responses increase latency, especially at scale.&lt;br&gt;
• Database connections are costly to create.&lt;br&gt;
Without pooling, each request pays the setup cost&lt;br&gt;
• And sometimes it’s just the format.&lt;br&gt;
JSON is convenient, but not always efficient when compared to something like Protobuf&lt;/p&gt;

</description>
      <category>webdev</category>
      <category>api</category>
      <category>programming</category>
      <category>perl</category>
    </item>
    <item>
      <title>Lately, I’ve been digging into what actually slows down APIs.
Here are a few bottlenecks that changed how I think about performance:
• It’s not always the database, sometimes it’s how we call it.
Sending multiple small requests instead of batching them add</title>
      <dc:creator>Anshita Verma</dc:creator>
      <pubDate>Thu, 26 Mar 2026 18:20:54 +0000</pubDate>
      <link>https://dev.to/anshita_verma_1cbd4718b8e/lately-ive-been-digging-into-what-actually-slows-down-apis-here-are-a-few-bottlenecks-that-44ml</link>
      <guid>https://dev.to/anshita_verma_1cbd4718b8e/lately-ive-been-digging-into-what-actually-slows-down-apis-here-are-a-few-bottlenecks-that-44ml</guid>
      <description></description>
    </item>
    <item>
      <title>When handling large lists, what actually slows things down: the number of elements we render, or when we choose to load more?</title>
      <dc:creator>Anshita Verma</dc:creator>
      <pubDate>Wed, 25 Mar 2026 22:31:02 +0000</pubDate>
      <link>https://dev.to/anshita_verma_1cbd4718b8e/when-handling-large-lists-what-actually-slows-things-down-the-number-of-elements-we-render-or-5eic</link>
      <guid>https://dev.to/anshita_verma_1cbd4718b8e/when-handling-large-lists-what-actually-slows-things-down-the-number-of-elements-we-render-or-5eic</guid>
      <description>&lt;p&gt;Turns out, &lt;em&gt;both&lt;/em&gt;. But they're separate problems that need separate tools.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;
&lt;strong&gt;Virtual Scroll&lt;/strong&gt; → manages how much is rendered at once. &lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Intersection Observer&lt;/strong&gt; → manages when work gets triggered&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Virtual Scroll keeps the DOM lean no matter how large the dataset grows. Intersection Observer watches the viewport and fires logic only when something actually comes into view.&lt;/p&gt;

&lt;p&gt;Which one has made a bigger difference in your projects?&lt;/p&gt;

</description>
      <category>webdev</category>
      <category>performance</category>
      <category>architecture</category>
      <category>learning</category>
    </item>
  </channel>
</rss>
