When your observability stack isn’t built for launch day scale, some of the most serious CDN delivery failures won’t show up at all. Your dashboards might show metrics that look normal, with no alerts firing. Meanwhile, the logs that would tell you how an incident is impacting game delivery in a specific region is aggregated or sampled away. By the time you find out, through a spike in support tickets and social media, the launch is over and you’re explaining to leadership why nobody caught it sooner.
This post covers three distinct technical problems that combine to make CDN observability genuinely hard at gaming launch scale for game publishers, and what’s required from an architectural perspective to solve them.
Learn about the business value of complete CDN data for game publishers.
Sampling Leads to Gaps in Visibility
During a major title drop, launch day traffic can spike 25x or more compared to regular traffic patterns. Typically, game publishers use multi-CDN infrastructure to handle the load, resulting in millions (or even tens of millions) of log lines per second. Traditional observability platforms can’t handle this scale, and the most common tradeoff is aggressive sampling. Instead of processing every log event, the platform processes a subset and uses statistical inference to represent the full picture.
However, many of the error and performance issues that occur during launch days are geographically concentrated and comparatively low-frequency. A delivery failure affecting 2% of download attempts in a region generates a small number of error events relative to total traffic volume. Those events are already underrepresented in raw log data, and aggressive sampling can make these issues invisible to your teams. Either you’ll learn about the issue after it’s reached a critical threshold, or you won’t learn about it until after the event is over and revenue has already been impacted.
Sampling degrades data quality most severely during the high-load conditions that are most likely to produce the delivery failures you need to find. At the launch scale of AAA game releases, the issues that sampling obscures will only seem small relative to total traffic volume. A 2% regional error rate still means thousands of players are affected.
Sampling is a response to the constraints of legacy architecture. Most observability platforms treat ingestion, storage, and query as a tightly coupled pipeline. As a result, none of the three layers can scale independently. A spike in ingestion volume creates back-pressure on storage and query simultaneously. Scaling all three layers 25x to keep up with launch day volume isn’t economically viable on infrastructure that would sit mostly idle the rest of the year. Sampling is cheaper and easier than scaling, so data fidelity is compromised during your most important events of the year.
The architectural fix is to decouple ingestion, storage, and query so each scales independently. The ingest process must be able to scale horizontally without creating back-pressure on the query layer or limiting ingest speed. Meanwhile, there shouldn’t be resource contention that impacts either query performance or ingestion, since slowdowns in either will impact time to glass (the delay between an event occurring and it appearing in your dashboard). Finally, decoupled object storage with high-density compression makes full-fidelity data economically viable, even at petabyte scale.
Data Across CDN Providers Is Fragmented and Difficult to Correlate
Most major game publishers run three or more CDN providers simultaneously. This improves resilience and performance, allowing you to route traffic based on metrics like performance and errors and the regional strengths of each CDN. But it’s much more challenging to correlate data across multi-CDN architectures. You must manage three (or more) high-volume log streams, all with disparate schema and naming conventions.
At a lower scale, you may be able to normalize data at query time. Preserving raw logs from each provider and translating on demand works at lower scale, but it adds latency when you can least afford it. A query that has to reconcile three schemas, align timestamps across providers, and resolve field name conflicts before returning results isn’t fast enough to be useful during an active incident.
To solve this problem, normalization has to happen at ingestion, in real time, at launch-scale volume. Every CDN provider’s log format needs to be mapped to a common schema before being written to storage, which means maintaining parser configurations for each provider, handling schema changes when providers update their log formats, and resolving field conflicts at ingestion rather than at query time.
The data should also be streamed to a single table. Join statements across provider-specific tables are expensive and time-consuming for launch day scale. They are too slow for incident response, making cross-correlation at launch day scale impractical.
By ingesting multiple, normalized streams into a single table, queries are straightforward and don’t require joins. An engineer asking whether a latency anomaly visible in one provider’s logs is also present in the other two runs a single query against a single table rather than three separate queries followed by manual comparison. In a unified stack with normalization at ingestion, it takes seconds. Just as importantly, it makes dashboard queries efficient, giving you the ability to cross-correlate performance at a glance.
Short Retention Windows Limit Operational Intelligence
Traditional observability platforms use SSD for fast, “hot” storage. This makes queries more performant, but it also results in higher storage costs. At launch day volume, these costs quickly become prohibitive. To manage these costs, most solutions offer some combination of short retention windows and tiered storage.
Standard data retention windows are typically in the range of 30-90 days, but when you’re ingesting terabytes of data on a daily basis, practical retention windows become much shorter. You may find yourself with retention windows of 30 days or less, not because 30 days is operationally sufficient, but because of high costs.
The operational consequences of short retention windows compound over time. Short retention windows limit your options for post-event forensics, capacity planning, security investigations, and business intelligence. When a title underperforms in a specific market or region, you can’t go back and determine whether a delivery issue was a contributing factor, because the log data that would have answered that question is gone.
Alternatively, you can move older data into frozen storage where it can be rehydrated if needed. However, for practical purposes, because rehydration is so operationally inefficient, this data will only be queried during security investigations or for compliance purposes.
To solve the problem of retention, modern data platforms are using high-density compression and maximizing the performance of cost-effective object storage.
- High-density compression: Modern platforms use columnar storage, which can be heavily compressed. Normalized CDN log data is highly repetitive: the same IP ranges, ASNs, status codes, and URL patterns appear millions of times across a log stream. Columnar formats compress that repetition extremely efficiently. As an example, Hydrolix can achieve up to 50x compression in production, with one terabyte of raw logs reduced to approximately 55 gigabytes on object storage. A publisher retaining 30 days of full-resolution data at the limit of their storage budget can retain the same data for more than a year while reducing costs.
- Partition pruning: To optimize the performance of object storage, modern solutions partition by fields such as time stamp, allowing for partition pruning during the query process. In this case, queries that include a time range will only scan partitions that include that range, pruning the rest. This makes queries over both short-term and long-term data more performant.
- Efficient data transfer: The combination of columnar storage and high-density compression dramatically reduces the volume of data that queries need to read and transfer, making them both more performant and cost-effective.
To see how these three work together, consider a query like this:
SELECT timestamp, client_ip, status_code FROM myproject.cdn_logs WHERE timestamp >= toDateTime('2024-03-15 14:00:00') AND timestamp < toDateTime('2024-03-15 14:05:00') LIMIT 100
With columnar storage, this query only needs to retrieve data from three columns. Considering that enriched, enterprise CDN log data can easily consist of 50-100 fields, this is a massive reduction in data transferred compared to row-based storage, where full rows need to be retrieved.
With partition pruning based on timestamps, only partitions in the five-minute time range need to be scanned, removing all other partitions from consideration. For a modern, highly optimized platform, this is a sub-second query even when you’re storing years of data.
Solving Launch Day Scale With Modern Data Platforms
Launch day scale creates extreme pressure on traditional observability platforms, leading to tradeoffs when it comes to data fidelity, correlation, and retention. Combined, they create a visibility gap that is most severe during the events that matter most to your business.
When evaluating any existing or prospective vendor, ask:
- What happens to data resolution when ingestion volume spikes by 10x? If you need to heavily overprovision or sample most of the data away, the solution isn’t designed for launch day scale.
- How long does correlation across CDN providers take during an active incident, and does it require compute-intensive join statements and normalization at query time?
- How much does it cost to retain data long-term and at full-fidelity, including for peak traffic? If you are only able to retain data for a short period of time because of cost, or you’re forced to archive data, then the platform isn’t optimized for critical use cases like capacity planning and forensic investigation.
If you’re using general-purpose observability tools to cover major launch events, you likely recognize the problems described in this post. They’re not edge cases. They’re architectural limitations of platforms that weren’t designed for extreme scale.
Next Steps
To reach the final stage of the maturity journey for CDN visibility, consider a solution like Hydrolix, which is designed to handle launch day scale. Get a quick value assessment from an expert.
