步骤 4：验证应用程序输出 - SQL适用于应用程序的 Amazon Kinesis Data Analytics 开发者指南

经过仔细考虑，我们决定分两个步骤停止使用亚马逊 Kinesis Data Analytics SQL 的应用程序：

1. 从 2025 年 10 月 15 日起，您将无法为应用程序创建新的 Kinesis Data Analytic SQL s。

2. 我们将从 2026 年 1 月 27 日起删除您的申请。您将无法启动或操作适用于应用程序的 Amazon Kinesis Data Analytic SQL s。从那时起，亚马逊 Kinesis Data Analytics SQL 将不再提供支持。有关更多信息，请参阅 适用于应用程序的 Amazon Kinesis Data Analytic SQL s 停产。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

步骤 4：验证应用程序输出

在热点示例的此部分中，您将设置一个 Web 应用程序，该应用程序在可扩展矢量图形 (SVG) 控件中显示热点信息。

  1. 使用以下内容创建名为 index.html 的文件：

    <!doctype html>
<html lang=en>
<head>
    <meta charset=utf-8>
    <title>hotspots viewer</title>

    <style>
    #visualization {
      display: block;
      margin: auto;
    }

    .point {
      opacity: 0.2;
    }

    .hot {
      fill: red;
    }

    .cold {
      fill: blue;
    }

    .hotspot {
      stroke: black;
      stroke-opacity: 0.8;
      stroke-width: 1;
      fill: none;
    }
    </style>
    <script src="https://sdk.amazonaws.com/js/aws-sdk-2.202.0.min.js"></script>
    <script src="https://d3js.org/d3.v4.min.js"></script>
</head>
<body>
<svg id="visualization" width="600" height="600"></svg>
<script src="hotspots_viewer.js"></script>
</body>
</html>

  2. 在同一目录中创建一个名为 hotspots_viewer.js 的文件，该文件包含以下内容。在提供的变量中包含您的 、凭证和输出流名称。

    // Visualize example output from the Kinesis Analytics hotspot detection algorithm.
// This script assumes that the output stream has a single shard.

// Modify this section to reflect your AWS configuration
var awsRegion = "",        // The  where your Kinesis Analytics application is configured.
    accessKeyId = "",      // Your Access Key ID
    secretAccessKey = "",  // Your Secret Access Key
    outputStream = "";     // The name of the Kinesis Stream where the output from the HOTSPOTS function is being written

// The variables in this section should reflect way input data was generated and the parameters that the HOTSPOTS
// function was called with.
var windowSize = 1000, // The window size used for hotspot detection
    minimumDensity = 40,  // A filter applied to returned hotspots before visualization
    xRange = [0, 10],  // The range of values to display on the x-axis
    yRange = [0, 10];  // The range of values to display on the y-axis

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// D3 setup
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

var svg = d3.select("svg"),
    margin = {"top": 20, "right": 20, "bottom": 20, "left": 20},
    graphWidth = +svg.attr("width") - margin.left - margin.right,
    graphHeight = +svg.attr("height") - margin.top - margin.bottom;

// Return the linear function that maps the segment [a, b] to the segment [c, d].
function linearScale(a, b, c, d) {
    var m = (d - c) / (b - a);
    return function(x) {
        return c + m * (x - a);
    };
}

// helper functions to extract the x-value from a stream record and scale it for output
var xValue = function(r) { return r.x; },
    xScale = linearScale(xRange[0], xRange[1], 0, graphWidth),
    xMap = function(r) { return xScale(xValue(r)); };

// helper functions to extract the y-value from a stream record and scale it for output
var yValue = function(r) { return r.y; },
    yScale = linearScale(yRange[0], yRange[1], 0, graphHeight),
    yMap = function(r) { return yScale(yValue(r)); };

// a helper function that assigns a CSS class to a point based on whether it was generated as part of a hotspot
var classMap = function(r) { return r.is_hot == "Y" ? "point hot" : "point cold"; };

var g = svg.append("g")
    .attr("transform", "translate(" + margin.left + "," + margin.top + ")");

function update(records, hotspots) {

    var points = g.selectAll("circle")
        .data(records, function(r) { return r.dataIndex; });

    points.enter().append("circle")
        .attr("class", classMap)
        .attr("r", 3)
        .attr("cx", xMap)
        .attr("cy", yMap);

    points.exit().remove();

    if (hotspots) {
        var boxes = g.selectAll("rect").data(hotspots);

        boxes.enter().append("rect")
            .merge(boxes)
            .attr("class", "hotspot")
            .attr("x", function(h) { return xScale(h.minValues[0]); })
            .attr("y", function(h) { return yScale(h.minValues[1]); })
            .attr("width", function(h) { return xScale(h.maxValues[0]) - xScale(h.minValues[0]); })
            .attr("height", function(h) { return yScale(h.maxValues[1]) - yScale(h.minValues[1]); });

        boxes.exit().remove();
    }
}

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Use the AWS SDK to pull output records from Kinesis and update the visualization
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

var kinesis = new AWS.Kinesis({
    "region": awsRegion,
    "accessKeyId": accessKeyId,
    "secretAccessKey": secretAccessKey
});

var textDecoder = new TextDecoder("utf-8");

// Decode an output record into an object and assign it an index value
function decodeRecord(record, recordIndex) {
    var record = JSON.parse(textDecoder.decode(record.Data));
    var hotspots_result = JSON.parse(record.HOTSPOTS_RESULT);
    record.hotspots = hotspots_result.hotspots
        .filter(function(hotspot) { return hotspot.density >= minimumDensity});
    record.index = recordIndex
    return record;
}

// Fetch a new records from the shard iterator, append them to records, and update the visualization
function getRecordsAndUpdateVisualization(shardIterator, records, lastRecordIndex) {
    kinesis.getRecords({
        "ShardIterator": shardIterator
    }, function(err, data) {
        if (err) {
            console.log(err, err.stack);
            return;
        }

        var newRecords = data.Records.map(function(raw) { return decodeRecord(raw, ++lastRecordIndex); });
        newRecords.forEach(function(record) { records.push(record); });

        var hotspots = null;
        if (newRecords.length > 0) {
            hotspots = newRecords[newRecords.length - 1].hotspots;
        }

        while (records.length > windowSize) {
            records.shift();
        }

        update(records, hotspots);

        getRecordsAndUpdateVisualization(data.NextShardIterator, records, lastRecordIndex);
    });
}

// Get a shard iterator for the output stream and begin updating the visualization. Note that this script will only
// read records from the first shard in the stream.
function init() {
    kinesis.describeStream({
        "StreamName": outputStream
    }, function(err, data) {
        if (err) {
            console.log(err, err.stack);
            return;
        }

        var shardId = data.StreamDescription.Shards[0].ShardId;

        kinesis.getShardIterator({
            "StreamName": outputStream,
            "ShardId": shardId,
            "ShardIteratorType": "LATEST"
        }, function(err, data) {
            if (err) {
                console.log(err, err.stack);
                return;
            }
            getRecordsAndUpdateVisualization(data.ShardIterator, [], 0);
        })
    });
}

// Start the visualization
init();

  3. 在第一个部分中 Python 代码运行时，在 Web 浏览器中打开 index.html。热点信息显示在页面上，如下所示。

    显示热点信息的可扩展矢量图形图表。