Merge branch 'liz/doc-banner' of https://github.com/newrelic/docs-web…

…site into liz/doc-banner

gatsby-config.js

@@ -373,8 +373,8 @@ module.exports = {
                 subject
                 releaseDate(fromNow: false)
                 version
-                feature
-                bug
+                features
+                bugs
                 security
                 ingest
               }
@@ -390,8 +390,8 @@ module.exports = {
               agent: getAgentName(frontmatter.subject),
               date: frontmatter.releaseDate,
               version: frontmatter.version,
-              feature: frontmatter.feature,
-              bug: frontmatter.bug,
+              features: frontmatter.features,
+              bugs: frontmatter.bugs,
               security: frontmatter.security,
               ingest: frontmatter.ingest,
               description: excerpt,

gatsby-node.js

@@ -327,8 +327,8 @@ exports.createSchemaCustomization = ({ actions }) => {
     dataSource: String
     isTutorial: Boolean
     signupBanner: SignupBanner
-    feature: [String]
-    bug: [String]
+    features: [String]
+    bugs: [String]
     security: [String]
     ingest: [String]
   }
@@ -379,13 +379,13 @@ exports.createResolvers = ({ createResolvers }) => {
         resolve: (source) =>
           hasOwnProperty(source, 'isTutorial') ? source.isTutorial : null,
       },
-      feature: {
+      features: {
         resolve: (source) =>
-          hasOwnProperty(source, 'feature') ? source.feature : null,
+          hasOwnProperty(source, 'features') ? source.features : null,
       },
-      bug: {
+      bugs: {
         resolve: (source) =>
-          hasOwnProperty(source, 'bug') ? source.bug : null,
+          hasOwnProperty(source, 'bugs') ? source.bugs : null,
       },
       security: {
         resolve: (source) =>

package.json

@@ -9,7 +9,7 @@
     "@github-docs/frontmatter": "^1.3.1",
     "@mdx-js/mdx": "2.0.0-next.8",
     "@mdx-js/react": "2.0.0-next.8",
-    "@newrelic/gatsby-theme-newrelic": "6.16.5",
+    "@newrelic/gatsby-theme-newrelic": "6.16.7",
     "@splitsoftware/splitio-react": "^1.2.4",
     "cockatiel": "^3.0.0-beta.0",
     "common-tags": "^1.8.0",

scripts/actions/__tests__/__snapshots__/serialize-mdx.test.js.snap

@@ -213,6 +213,12 @@ exports[`serialize Tabs Components 1`] = `
 "
 `;
 
+exports[`serialize iframes 1`] = `
+"
+<div data-type=\\"component\\" data-component=\\"iframe\\" data-props=\\"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\\"></div>
+"
+`;
+
 exports[`serializes Button to html 1`] = `
 "
 <div data-type=\\"component\\" data-component=\\"Button\\" data-props=\\"W3sidHlwZSI6Im1keEF0dHJpYnV0ZSIsIm5hbWUiOiJ2YXJpYW50IiwidmFsdWUiOiJub3JtYWwifV0=\\">

scripts/actions/__tests__/deserialize-html.test.js

@@ -272,8 +272,22 @@ test('deserializes Tabs Component', async () => {
 });
 
 test('deserializes LicenseKey component', async () => {
-  const input = '<LicenseKey/>'
+  const input = '<LicenseKey/>';
 
-  const mdx = await deserializeHTML(await serializeMDX(input))
+  const mdx = await deserializeHTML(await serializeMDX(input));
   expect(mdx).toEqual(input);
-})
+});
+
+test('deserialize iframes', async () => {
+  const input = `<iframe
+  width="560"
+  height="315"
+  src="https://www.youtube.com/embed/04JP0ky_hjI"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowfullscreen
+/>`;
+
+  const mdx = await deserializeHTML(await serializeMDX(input));
+  expect(mdx).toEqual(input);
+});

scripts/actions/__tests__/serialize-mdx.test.js

@@ -381,7 +381,14 @@ test('serialize Tabs Components', async () => {
 });
 
 test('serialize LicenseKey component', async () => {
-  const mdx = '<LicenseKey />'
-  const html = await serializeMDX(mdx)
+  const mdx = '<LicenseKey />';
+  const html = await serializeMDX(mdx);
+  expect(html).toMatchSnapshot();
+});
+
+test('serialize iframes', async () => {
+  const mdx =
+    '<iframe width="560" height="315" src="https://www.youtube.com/embed/04JP0ky_hjI" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>';
+  const html = await serializeMDX(mdx);
   expect(html).toMatchSnapshot();
-})
+});

scripts/actions/utils/handlers.js

@@ -354,4 +354,8 @@ module.exports = {
     deserialize: deserializeComponent,
     serialize: serializeComponent,
   },
+  iframe: {
+    deserialize: deserializeComponent,
+    serialize: serializeComponent,
+  },
 };

src/content/docs/alerts-applied-intelligence/applied-intelligence/anomaly-detection/anomaly-detection-applied-intelligence.mdx

@@ -36,7 +36,7 @@ There are two types of anomaly detection at New Relic: [custom](/docs/alerts-app
 
 ## How we use anomalies [#anomalies-in-use]
 
-At New Relic, our own developers know how important it's to monitor the health of our applications. We want our customers to access to the data they need whenever they need it so our team needs to be alerted if there are any outliers in our system's performance. New Relic's anomaly detection uses applied intelligence to monitor three key golden signals: throughput, error rate, and latency. With anomaly detection, our developers monitor the baseline performance for these metrics.
+At New Relic, our own developers know how important it is to monitor the health of our applications. We want our customers to have access to the data they need whenever they need it so our team needs to be alerted if there are any outliers in our system's performance. New Relic's anomaly detection uses applied intelligence to monitor three key golden signals: throughput, error rate, and latency. With anomaly detection, our developers monitor the baseline performance for these metrics.
 
 So, let's say that one afternoon there's a spike in response time and it's taking longer than usual for our customers to access the homepage. Anomaly detection will flag this anomalous behavior because our latency metric data has deviated from its baseline. This doesn't necessarily mean there is a problem, it just indicates that AI has registered something out of the ordinary in our system and we should take a deeper look.