General
FAQs
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Do I need to monitor my CDN if the vendor has a status page?
Yes, because vendor status pages typically only report massive, network-wide outages. They rarely reflect regional performance degradation, routing issues specific to your account, or configuration errors that affect only your users.
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How often should I analyze CDN logs?
You should ingest and index CDN logs in real time for alerting on immediate issues like DDoS attacks or error spikes. For deeper analysis, like capacity planning or trend identification, reviewing aggregated log data on a weekly or monthly basis is recommended. For major events, such as yearly sales (for retail businesses) or live streaming (broadcasting), CDN log analytics should be a critical component of your observability.
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How does CDN monitoring help optimize application performance?
CDN monitoring helps optimize performance by providing actionable insights from log data. For instance, a low cache hit ratio indicates that content is not being effectively cached at the edge, leading to slower load times that need to be investigated. Similarly, tracking response codes allows teams to quickly identify and troubleshoot errors that degrade the end-user experience.
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How can analyzing CDN logs improve security?
Analyzing CDN logs is essential for security because it helps detect threats by revealing unusual activity. Sudden spikes in traffic from specific IP addresses or geographic regions can indicate a DDoS attack or stream piracy. By monitoring these patterns in real time, security teams can quickly identify and mitigate potential threats before they impact users.
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How does CDN monitoring improve SEO?
Search engines favor fast-loading sites, specifically measuring Core Web Vitals like Largest Contentful Paint (LCP). By monitoring and optimizing CDN latency and cache hit ratios, you ensure assets load quickly, which directly improves these scores and supports better search rankings.
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What is the difference between synthetic monitoring and RUM?
Synthetic monitoring uses automated scripts/bots to test the performance of webpages and endpoints from known locations at set intervals, providing a stable baseline for performance. Real User Monitoring (RUM) collects data from actual user browsers, capturing the true experience of users across different devices and networks.
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Why is my cache hit ratio low?
A low cache hit ratio is often caused by misconfigured Cache-Control headers, setting Time-to-Live (TTL) values too low, or using unique query strings that force the CDN to treat every request as a new object. It can also happen if your content is rarely requested (the “long tail”) and gets evicted from the cache to make room for popular items.
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What does a cache hit ratio below 60 percent indicate?
A ratio below 60 percent typically indicates a configuration error, such as a too-short Time to Live (TTL) or an origin that is incorrectly sending no-cache headers. According to Last9, while 95 percent is the gold standard for static content, a drop below 60 percent suggests the CDN is failing to offload traffic, which will likely spike your origin costs.
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Which is better for CDN monitoring: real-time streaming or batch export?
Real-time streaming is necessary for operational alerting because it delivers logs in seconds, allowing you to catch cache misconfigurations or origin failures before users report them. Batch export is easier to set up but introduces a lag of several minutes to an hour, making it better suited for long-term trend analysis rather than incident response.
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When should I use synthetic monitoring versus log analysis?
Synthetic monitoring is best for outside-in verification of regional availability and DNS routing, while log analysis provides the inside-out view of what actually happened to every user request. Practitioners often use synthetic probes to detect "ghost" regional failures that aggregate log metrics might smooth over, but rely on logs for deep forensic troubleshooting of specific 5xx error spikes.
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How long should I retain CDN logs for DORA and PCI DSS v4.0 compliance?
DORA requires financial entities to maintain incident logs for the duration of the service relationship, while PCI DSS v4.0 mandates a 12-month audit trail with three months immediately available. In Hydrolix, you can retain full-fidelity logs for 15 months by default using high-density compression to keep storage costs predictable.
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At what traffic volume do native CDN analytics become insufficient?
Native analytics typically become a bottleneck around 100 million requests per day, where sampled data and hourly summaries hide critical P99 latency spikes. Teams at this scale, such as FOX Sports, move to dedicated log platforms to maintain sub-second query speeds on petabytes of full-fidelity data without the limitations of pre-aggregated vendor reports.
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How do I normalize field names across Akamai, Cloudflare, and Fastly?
Normalization should happen at the ingest layer rather than during queries to avoid performance lag. Use a unified schema that maps provider-specific terms, such as Cloudflare’s CacheCacheStatus and Akamai’s X-Cache-Remote, into a single cache_status field. Hydrolix CDN Insights provides pre-built collectors that handle this mapping for 80 percent of global CDN traffic.
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Does passive measurement replace synthetic probing for tracking edge latency?
Passive measurement supplements synthetic probing by analyzing existing TCP and DNS traffic to predict network latencies without adding overhead. While synthetics provide a clean baseline from specific locations, passive measurement captures the actual experience of every user. This is critical for continuous, on-the-fly monitoring across thousands of edge nodes simultaneously.
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How can I reduce false positives in 4xx burst-rate alerts?
To reduce noise, transition from raw count thresholds to a composite alert that requires both a 4xx surge and a high IP concentration ratio. Legitimate traffic spikes typically show a broad distribution of unique IPs, whereas DDoS attacks often concentrate millions of requests into a narrow range of addresses.
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What happens to CDN monitoring when migrating from HTTP/2 to HTTP/3?
The migration to HTTP/3 and QUIC reduces the visibility of certain network-layer fields once accessible via deep packet inspection. You must verify that your security queries rely on application-layer headers like Alt-Svc and Server-Timing rather than transport-layer data. Plan schema versioning before the migration to ensure your 4xx and latency signals remain queryable.
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How Long Should I Retain CDN Logs for DORA and PCI DSS V4.0 Compliance?
DORA requires financial entities to maintain incident logs for the duration of the service relationship, while PCI DSS v4.0 mandates a 12-month audit trail with three months immediately available. In Hydrolix, you can retain full-fidelity logs for 15 months by default using high-density compression to keep storage costs predictable.
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How long should I retain bot logs for reliable trend analysis?
Effective purpose-based classification requires at least 12 months of full-fidelity data to distinguish between seasonal referral patterns and long-term resource extraction. While many legacy tools default to 30-day windows, this history is too short to calculate stable crawl-to-refer ratios or detect when a previously beneficial AI crawler shifts toward aggressive scraping.
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What Happens to CDN Monitoring When Migrating from HTTP/2 to HTTP/3?
The migration to HTTP/3 and QUIC reduces the visibility of certain network-layer fields once accessible via deep packet inspection. You must verify that your security queries rely on application-layer headers like Alt-Svc and Server-Timing rather than transport-layer data. Plan schema versioning before the migration to ensure your 4xx and latency signals remain queryable.
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What is a good crawl-to-refer ratio for AI search agents?
A healthy ratio for a value-driving AI search agent typically falls below 100:1, meaning it sends one referral for every 100 pages indexed. In contrast, extractors like ClaudeBot have been measured crawling over 11,000 pages per referral, signaling a high resource drain with minimal business return.
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Does blocking AI training crawlers hurt my GEO rankings?
Blocking crawlers that exclusively harvest data for LLM training generally does not impact Generative Engine Optimization (GEO) because these bots do not drive direct referral traffic. However, misclassifying AI search agents as training bots can suppress your visibility in generative answers, making it essential to use long-term behavioral data to separate the two.
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What happens when an AI agent bypasses behavioral fingerprinting?
When an agent mimics human signals like scroll velocity and typing cadence successfully, the escalation path shifts from detection to cryptographic verification. Implementing the Bot Service Index (BSI) standard allows you to challenge unverified traffic to prove its identity via a secure discovery infrastructure, moving beyond easily spoofed user-agent strings.
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How do I deploy purpose-based bot control across multiple CDNs?
To implement purpose-based control, you must aggregate full-fidelity logs from all providers into a single repository to calculate global bot behavior. In Hydrolix Bot Insights, you can use a unified dashboard to apply consistent intent-based policies across Akamai, Cloudflare, Fastly, and AWS CloudFront without manually reconciling different vendor log formats.
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Is it better to build one large summary table or multiple custom tables?
A single monolithic summary table is convenient for centralizing data. However, smaller, custom summary tables tailored to specific dashboard use cases generally yield superior performance. Large tables with excessive fields and grouping clauses often suffer from slower query speeds and increased resource consumption even when processing simple requests. Adopting a bespoke approach allows you to optimize each table for its intended purpose. This ensures that dashboards only query the specific data they need.
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How does high cardinality affect summary table performance?
High cardinality can cause data “explosions” that severely degrade summary table performance and increase storage overhead. Best practices suggest limiting aggregations to columns with lower cardinality or using techniques like data normalization and string transformation to keep unique attributes within a manageable range. If high-cardinality data is required for specific metrics, it is often better to isolate those fields in separate, bespoke tables to avoid slowing down broader analytical queries.
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What is the ideal data reduction rate for a summary table?
To maximize query performance and minimize storage costs, a well-designed summary table should aim for a data reduction rate of at least 97% to 98% compared to the raw source data. Achieving this level of compression transforms unwieldy terabyte-scale datasets into manageable gigabyte-sized tables that support sub-second response times. If the reduction rate is significantly lower, you should re-evaluate your aggregation strategy to ensure you are not carrying unnecessary granularity.
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Should I delete raw data after creating summary tables?
You should retain all underlying raw data even after creating summary tables. It’s essential for root cause analysis, fraud detection, and granular investigations. While aggregate tables provide efficient high-level metrics, they lack the specific details required to pinpoint the exact source of an anomaly or performance issue. Keeping the raw data ensures you can generate new, accurate aggregations in the future if your business requirements or dashboard strategies change.
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What is a summary table in data analytics?
A summary table (also known as an aggregate table) stores pre-computed data states to enable significantly faster query responses for dashboards and real-time reporting. By grouping raw data into metrics like sums, averages, or counts during the ingestion process, these tables reduce the computational load required to visualize trends. This approach allows organizations to analyze massive volumes of information efficiently without the latency associated with scanning raw logs.
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What data security and privacy features does Hydrolix offer?
Cloud and on-premises offerings all adhere to the following security configurations:
- Role-based access control (RBAC) to limit access to project data.
- Strict data separation between projects – Customers can have multiple projects so that you can limit access to teams and individuals as needed.
- SOC 2 compliance to ensure that all data is stored and processed securely.
- GDPR compliance
- TLS for data in transit – To retrieve data from cloud storage, Hydrolix clusters use token-based authentication over TLS
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I want to avoid vendor lock-in. Can I migrate data out of Hydrolix?
Vendor lock-in is not a concern with Hydrolix, as your data remains within your architecture.
For cloud deployments, your data resides inside your chosen object storage environment, such as Amazon S3 or Azure Blob Storage.
This flexibility ensures you can easily migrate your data, offering peace of mind and data sovereignty.
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Is Hydrolix a replacement for the ELK stack?
Yes. Hydrolix offers several advantages: high-performance querying, cost-efficient data retention, and seamless scalability.
Hydrolix’s architecture and features make it a compelling choice for organizations seeking improved data management and analytics capabilities beyond what the ELK stack provides.
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My company uses multiple CDNs. Can I use Hydrolix to monitor them in the same dashboard?
Yes, Hydrolix is designed to accommodate multi-CDN monitoring seamlessly. You can learn how our client, Paramount, uses Hydrolix for that purpose.
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Is there a SaaS version of Hydrolix?
Yes, there is a SaaS version of Hydrolix. We work with multiple cloud vendors to provide the SaaS version.
Please get in touch with the Hydrolix sales team, and they will guide you.
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Is there a free version for developers?
Hydrolix offers a free trial, allowing developers to experience the platform’s capabilities firsthand.
Explore Hydrolix’s features, assess its suitability for your projects, and gain insights into its performance and benefits.
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How much does it cost?
Here are the estimated costs for running Hydrolix on Amazon EKS, ingesting two terabytes of raw data per day and assuming 12 months of data retention and 15x compression.
Estimated Monthly Cloud Provider Expenses
Core Platform: $2,237
Ingest Capacity: $1,470
Query Capacity: $1,960
Cloud Storage: $1,146Cloud Provider Total: $6,814 / mo
Hydrolix License & Support: $5,840 / moTotal Cost of Ownership: $12,654 / mo
Effective Cost: $0.20 / GBYou can use this online pricing calculator to get a cost estimate.
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What dashboard software does Hydrolix support?
For maximum flexibility, Hydrolix is designed to work out of the box with popular data visualization tools like Grafana, Redash, Superset, and Looker.
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How does Hydrolix compare with other database and data lake solutions?
Hydrolix is a real-time data platform designed for the challenges of real-time processing of high volumes of event data. Hydrolix combines stream processing, indexed search, advanced compression techniques, and decoupled storage into a single, stateless architecture, a design that delivers a combination of high-performance, longer hot data retention, and low cost.
The ingest system can handle both event streams (via Kafka, Kinesis, and HTTP streaming) and batch loading.
The query system scales independently and specific workloads can be partitioned into query pools to avoid resource contention and to allow teams to assign compute resources in accordance to their own specific budget and performance goals.
Hydrolix uses object storage (rather than block storage) to keep log storage costs low and extend hot data retention windows. Patented compression techniques further reduce your data footprint and log storage costs.
Hydrolix’s approach to partitioning data plus it’s default of indexing every column, together with customizable query pools, deliver SSD performance at block storage prices.
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What cloud vendors does Hydrolix support?
Hydrolix can be deployed on Amazon Web Services, Google Cloud Platform, Microsoft Azure, and Akamai Connected Cloud.
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Is Hydrolix suitable for businesses of all sizes?
Hydrolix is purpose-built to make big data cost-effective to collect, retain, and query. If you are currently working with or planning to work with datasets at volumes of 1 TB/day or greater, then Hydrolix is an excellent choice.
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What is Hydrolix?
Hydrolix is a real-time data platform built for massive data. It combines streaming ingest, real-time queries on decoupled storage, and up to 50x compression. Hydrolix includes full-fidelity data (no sampling) with long-term retention, all at a fraction of the cost of traditional solutions.
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