Serverless AWS Lambda Development · 课时

使用 Kinesis 进行实时处理

使用 Amazon Kinesis Data Streams 和 Kinesis Firehose，通过 Lambda 函数实时接收、处理和传输大规模数据流。

使用 Kinesis 进行实时处理 是 CoddyKit 上的免费 Serverless AWS Lambda Development 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Serverless AWS Lambda Development 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Serverless AWS Lambda Development 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Why Real-time Data Matters

In today's fast-paced world, many applications need to react to data instantly. This is known as real-time data processing.

Immediate insights: Analyze data as it arrives.
Quick responses: Trigger actions without delay.
Enhanced user experience: Personalize content on the fly.

Think of fraud detection, live dashboards, or IoT device monitoring.

Introducing Amazon Kinesis

Amazon Kinesis is a powerful suite of services designed for collecting, processing, and analyzing streaming data in real-time. It helps you handle massive volumes of data efficiently.

Key Kinesis services include:

Kinesis Data Streams (KDS): For custom applications needing granular control.
Kinesis Firehose: For easy delivery to data stores like S3 or Redshift.

Kinesis Data Streams (KDS)

Kinesis Data Streams (KDS) is like a continuous pipeline for high-throughput data. It captures data from various sources and makes it available for processing by different applications.

KDS organizes data into shards. A shard is a base unit of throughput. Data producers write to shards, and data consumers read from them.

Producing Data to KDS

Applications send data to KDS using operations like PutRecord or PutRecords. Each record includes a partition key, which KDS uses to group data and route it to a specific shard.

A good partition key ensures even distribution of data across shards, preventing hot spots and maximizing throughput.

Lambda as a KDS Consumer

AWS Lambda functions are excellent consumers for Kinesis Data Streams. You can configure a Lambda event source mapping to automatically invoke your function whenever new data records are available in a KDS stream.

Lambda polls the stream, reads records in batches, and passes them to your function for processing. This makes building real-time processors very efficient.

Processing Kinesis Records

Here's a simple Python Lambda function that processes records from a Kinesis Data Stream. It iterates through the records in the event and prints their data.

Try running this example:

import base64
import json

def lambda_handler(event, context):
    for record in event['Records']:
        # Kinesis data is base64 encoded
        payload = base64.b64decode(record['kinesis']['data']).decode('utf-8')
        print(f"Processed record: {payload}")
        
    return {'statusCode': 200}

Kinesis Firehose for Delivery

Kinesis Firehose is a fully managed service for delivering real-time streaming data to destinations like Amazon S3, Amazon Redshift, Amazon OpenSearch Service, or HTTP endpoints.

Unlike KDS, Firehose requires almost no administration. You simply create a delivery stream, specify your source and destination, and Firehose handles all the scaling, buffering, and delivery.

Firehose Destinations

Firehose is designed for simplified data delivery. It automatically batches, compresses, and encrypts data before sending it to your chosen destination.

Common destinations include:

Amazon S3: For long-term storage and data lakes.
Amazon Redshift: For data warehousing and analytics.
Amazon OpenSearch Service: For logging and search.
HTTP endpoints: For custom integrations.

Transform Data with Firehose & Lambda

Kinesis Firehose can integrate with Lambda to transform incoming data before it's delivered to its final destination. This is incredibly useful for cleaning, enriching, or reformatting data on the fly.

You configure a Lambda function within your Firehose delivery stream, and Firehose invokes it for each batch of records, expecting transformed records in return.

Kinesis Service Comparison

Which statements accurately describe the differences or uses of Kinesis Data Streams (KDS) and Kinesis Firehose?

Recap & Next Steps

You've learned about Amazon Kinesis, a key service for real-time data processing.

Kinesis Data Streams (KDS): Offers flexible, shard-based streaming for custom applications, often consumed by Lambda.
Kinesis Firehose: Provides a managed solution for delivering streaming data to various destinations, with optional Lambda transformation.

These services, combined with Lambda, enable powerful event-driven architectures for handling massive data streams in real-time.

常见问题解答

「使用 Kinesis 进行实时处理」课时是免费的吗？

是的 — 「使用 Kinesis 进行实时处理」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Serverless AWS Lambda Development 课程的其余内容，请升级到 CoddyKit PRO。 Serverless AWS Lambda Development 课程共包含 4 节课。

「使用 Kinesis 进行实时处理」这节课中我会学到什么？

使用 Amazon Kinesis Data Streams 和 Kinesis Firehose，通过 Lambda 函数实时接收、处理和传输大规模数据流。你通过在浏览器中直接运行的动手代码来练习 Serverless AWS Lambda Development，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Serverless AWS Lambda Development 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Serverless AWS Lambda Development 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「使用 Kinesis 进行实时处理」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Serverless AWS Lambda Development 课中编写并运行代码吗？

能。每节 Serverless AWS Lambda Development 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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