Frequently Asked Questions
General topics
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
FAQs about collection
-
Does Hydrolix support batch processing?
Yes, Hydrolix fully supports batch processing. This collection feature allows you to efficiently load data from a storage bucket into a target table, ensuring you can work with your data at scale.
Hydrolix offers two mechanisms for batch ingestion: the Batch Job API, which handles one-off tasks for loading one or more files based on job configurations, and Batch Auto-Ingest, which continuously ingests new files arriving in a storage bucket.
Supported data formats include CSV and JSON. Please note that Hydrolix requires read permissions to access external storage buckets for batch ingestion.
-
Does Hydrolix support data ingest from AWS Kinesis?
Yes, Hydrolix supports AWS Kinesis. You can ingest data into Hydrolix with AWS Kinesis, making it easy to work with your real-time streaming data.
-
Does Hydrolix support data ingest from Apache Kafka?
Yes, Hydrolix integrates with Apache Kafka. Hydrolix Projects and Tables can continuously ingest data from one or more Kafka-based streaming sources.
-
What data collection processes does Hydrolix support?
Hydrolix supports streaming data ingestion through the Stream API and batch ingestion through the Batch API. There are also special connectors for Apache Kafka and AWS Kinesis.
Check out data collection in the Hydrolix documentation.
FAQs about query
-
Does Hydrolix use SQL, or does it have a vendor-specific query language?
Hydrolix uses an ANSI-compliant SQL interface. This interface uses the syntax and some of the SQL engine of Clickhouse. All standard features, including how the interface API works for querying data, are supported.
-
What are Hydrolix query pools, and how do they help businesses scale?
Because Hydrolix query infrastructure is decoupled from storage and collection, you can quickly scale query pools up or down. Small query pools give you consistent low-cost queries, while large pools give you consistent low-latency queries.
You can create separate query pools for different groups of users. For example, you might configure separate sandboxes for administrator, interactive analyst, and monitoring queries.
Pool groups support independent scaling, so the capacity for each pool can adjust automatically to satisfy demand. You can even scale an entire pool to zero when demand is negligible–for example, over the weekend when staff do not need to access data. When demand returns, you can scale the pool back up within minutes.
-
How does Hydrolix make queries more efficient than other cloud data platforms?
Hydrolix improves query efficiency compared to other cloud data platforms through its unique architecture. Our decoupled and stateless design separates ingest and query resources from storage, allowing us to focus on efficiently handling high-cardinality and high-dimensionality data.
Here’s how our architecture achieves query efficiency:
- Scalable Query Pools: Hydrolix enables you to scale query resources independently, ensuring consistently low-latency queries as your data workload grows.
- Partition Metadata: We utilize partition metadata to speed up time-based queries, which is particularly beneficial for time-series data analysis.
- Full Column Indexing: Hydrolix leverages full-column indexing, which optimizes query performance by swiftly locating the necessary data.
- Predicate Pushdown: Our platform efficiently filters datasets using predicate pushdown, further enhancing query efficiency.
FAQs about retention
-
Does Hydrolix use tiered storage to manage data?
No, Hydrolix doesn’t distinguish between hot, warm, or cold data. Queries against all data, whether minutes or years old, deliver sub-second performance, ensuring all of your data remains accessible.
Because Hydrolix combines high-density compression technology with decoupled storage, the cost to deliver low-latency queries on your data, regardless of age, is 4x lower than other databases.
-
What does zero-egress mean?
In the context of Hydrolix, zero-egress means that when you deploy our solution on-premises, you have complete control over your data, and no additional egress costs are incurred.
This allows you to manage your data efficiently and cost-effectively within your own infrastructure.
-
What benefits does Hydrolix offer in terms of data retention and storage costs?
Hydrolix provides a cost-effective data retention solution with patented high-density compression technology. This technology lets you keep all your data online without offloading or sampling. And because of reduced storage costs, you can retain data for analysis, compliance, and security, eliminating the trade-off between data retention and cost.
You also get the additional benefit of reducing your environmental footprint by reducing the storage infrastructure required to store massive datasets.
Intelligence Report
