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Real-Time Logs

CloudFront Real-Time Logs provide real-time, event-driven monitoring of all HTTP/HTTPS requests intercepted across the global edge network. Instead of batching data into flat files, this feature streams log records directly into an Amazon Kinesis Data Stream with sub-second delivery latency. Developers can tune data volume using a configurable Sampling Rate, select precise log dimensions, and filter streams by specific Cache Behaviors to power near-instantaneous security forensics or performance analytics pipelines.

Key Takeaways​

The Real-Time Streaming Pipeline (How Traffic Flows)​

The most critical architectural rule to memorize for CloudFront Real-Time Logs is the destination target constraint: CloudFront cannot send real-time logs directly to Amazon S3. They must be delivered into an active streaming bus framework.

⚙️ The Two Core Downstream Execution Tracks:​

  1. The Instant Serverless Track (True Real-Time):
    • The Pipeline: CloudFront Edge ⟶\longrightarrow Kinesis Data Stream ⟶\longrightarrow AWS Lambda.
    • The Logic: An active AWS Lambda function continuously polls the Kinesis shards. The moment an anomaly occurs (like an IP address executing 500 requests a second), Lambda triggers an automated script to update your AWS WAF rules and block the attacker instantly.

  2. The Analytical Batch Delivery Track (Near Real-Time)​

    • The Pipeline: CloudFront Edge ⟶\longrightarrow Kinesis Data Stream ⟶\longrightarrow Kinesis Data Firehose ⟶\longrightarrow Amazon S3 / Amazon OpenSearch.
    • The Logic: Kinesis Data Firehose acts as a managed data buffer. It ingests the real-time stream, groups the records into micro-batches (e.g., every 60 seconds or 5 MB), and flushes them cleanly into OpenSearch dashboards for live log parsing or an S3 warehouse for long-term audit storage.

Optimization and Fine-Grained Controls​

Because high-traffic platforms process billions of requests a day, streaming every single transaction line can quickly lead to massive Kinesis shard billing overhead. S3 provides three major dials to keep costs and data footprints optimized:

  • The Sampling Rate Percentage: You don't have to capture everything. You can configure a precise integer value from 1%1\% to 100%100\% specifying the exact percentage of total random traffic queries you want pushed down the stream. For example, setting a 5%5\% sampling rate on a massive e-commerce storefront still yields excellent statistical telemetry data while cutting 95%95\% of your logging execution bills!
  • Cache Behavior Isolation: You can isolate your real-time logging configurations to target explicit path patterns. If you only care about monitoring performance bottlenecks on your active backend API, you attach the real-time log configuration purely to your /api/* cache behavior, leaving your static public images (/images/*) completely out of the pipeline.
  • Custom Field Selectors: You can strip out unnecessary data blocks. CloudFront exposes over 30 distinct field options (such as time-to-first-byte, sc-bytes, c-ip, and cs-user-agent). You select only the specific metrics your dashboard needs to maintain tight, clean log line footprints.

Exam Tips​

Metric VectorStandard S3 Access LogsReal-Time Logs
Immediate Delivery DestinationAmazon S3 BucketsAmazon Kinesis Data Streams
Latency SLA WindowBatch delivery (Up to several hours)Sub-second streaming (Within milliseconds)
Additional Base CostsFree, native feature (Pay only for S3 storage)Additional charges apply per log line generated
Granular Path Filtering❌ Applies globally across the entire bucket layout✅ Supported (Can bind to specific Cache Behaviors)
Primary Architectural RoleHistorical compliance audits, cold-data analysisLive security forensics, real-time alerting

The Compliance Storage Trap: Imagine an exam scenario states, "You are architecting a real-time security dashboard for a global application using CloudFront. Your compliance officer mandates that all edge logs must be analyzed within seconds of generation to catch active fraud vectors. Furthermore, an exact long-term raw copy of every single log record must be safely archived inside an Amazon S3 bucket for a 7-year retention period. How do you implement this cost-effectively?"
The textbook, multi-step answer on test day relies on chaining streaming data services:

  1. You enable CloudFront Real-Time Logs and configure them to stream 100%100\% of data events directly into an Amazon Kinesis Data Stream.
  2. You attach an Amazon Kinesis Data Firehose delivery stream directly to that Kinesis stream instance.
  3. Inside Kinesis Firehose, you split the execution loop: you set the main delivery target path to feed directly into your Amazon OpenSearch Service cluster for your real-time analytical dashboards, and simultaneously configure Firehose's native Backup/S3 Destination parameter to automatically drop an un-mutated copy of the raw compressed records straight down to your long-term S3 archive bucket!