How to catch query string in angular js
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This document discusses how to use $Location in AngularJS to catch query strings when passing values between pages. It explains that query strings must use "#?" and provides an example of passing the parameter "accountNo=1001" from index.html to index1.html. Index1.html then uses $location.search() to catch the "accountNo" parameter and display it on the page.
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![Index1.html
<!DOCTYPE html>
<html>
<head>
<title>Howto catch query stringin AngularJs</title>
<linkrel="stylesheet"href="http://getbootstrap.com/2.3.2/assets/css/bootstrap.css">
<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.2.4/angular.js"></script>
<script>
var app = angular.module('queryStringApp',[]);
app.controller('firstCtrl',function($scope,$location) {
alert("AccountNumber is - "+ $location.search()['accountNo']);
$scope.accountNo=$location.search()['accountNo'];
});
</script>
</head>
<body ng-app="queryStringApp">
<div ng-controller="firstCtrl">
Account No - {{accountNo}}
</div>
</body>
</html>](https://image.slidesharecdn.com/howtocatchquerystringinangularjs-150916092917-lva1-app6892/85/How-to-catch-query-string-in-angular-js-3-320.jpg)
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Speaker:
David Zhu (Engineering Manager @ Alluxio)
Storing data as Parquet files on cloud object storage, such as AWS S3, has become prevalent not only for large-scale data lakes but also as lightweight feature stores for training and inference, or as document stores for Retrieval-Augmented Generation (RAG). However, querying petabyte-to-exabyte-scale data lakes directly from S3 remains notoriously slow, with latencies typically ranging from hundreds of milliseconds to several seconds.
In this webinar, David Zhu, Software Engineering Manager at Alluxio, will present the results of a joint collaboration between Alluxio and a leading SaaS and data infrastructure enterprise that explored leveraging Alluxio as a high-performance caching and acceleration layer atop AWS S3 for ultra-fast querying of Parquet files at PB scale.
David will share:
- How Alluxio delivers sub-millisecond Time-to-First-Byte (TTFB) for Parquet queries, comparable to S3 Express One Zone, without requiring specialized hardware, data format changes, or data migration from your existing data lake.
- The architecture that enables Alluxio’s throughput to scale linearly with cluster size, achieving one million queries per second on a modest 50-node deployment, surpassing S3 Express single-account throughput by 50x without latency degradation.
- Specifics on how Alluxio offloads partial Parquet read operations and reduces overhead, enabling direct, ultra-low-latency point queries in hundreds of microseconds and achieving a 1,000x performance gain over traditional S3 querying methods.
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