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The avg_over_time() function calculates the average value of a time series over a specified range vector. It is used to measure how a gauge metric (like available filesystem bytes) behaves over time rather than at a single instant.

For example:

avg_over_time(node_filesystem_avail_bytes[1h])

This query returns the average amount of available filesystem space observed across all samples within the last hour for each time series.

By contrast:

avg() performs aggregation across different series at a single point, not over time.

sum() and sum_over_time() compute totals rather than averages.

Thus, only avg_over_time() provides the correct temporal average.

Extracted and verified from Prometheus documentation -- Range Vector Functions, avg_over_time() Definition, and Working with Gauge Metrics Over Time sections.

Prometheus supports integration with multiple Alertmanager instances for redundancy and high availability. The alerting section of the Prometheus configuration file (prometheus.yml) allows specifying a list of Alertmanager targets, enabling Prometheus to send alerts to several Alertmanager nodes simultaneously.

There is no hard-coded limit on the number of Alertmanagers that can be added. The typical best practice is to run a minimum of three Alertmanagers in a clustered setup to achieve fault tolerance and ensure reliable alert delivery, but Prometheus can be configured with more than three if desired.

Each Alertmanager node in the cluster communicates state information (active, silenced, inhibited alerts) with its peers to maintain consistency.

Verified from Prometheus documentation -- Alertmanager Integration, High Availability Setup, and Prometheus Configuration -- alerting Section.

In Prometheus, when you have multiple time series that represent the same type of measurement across different instances, the sum() aggregation operator is used to compute their total value.

Here, each instance (1 and 2) exposes the metric apiserver_current_inflight_requests, indicating the number of active API requests currently being processed.

To find the total number of in-flight requests across all instances, the correct expression is:

sum(apiserver_current_inflight_requests)

This returns 5 + 7 = 12.

min() would return the lowest value (5).

max() would return the highest value (7).

sum_over_time() calculates the cumulative sum over a range vector, not the current value, so it's incorrect here.

Verified from Prometheus documentation -- Aggregation Operators and Summing Across Dimensions sections.

Prometheus provides a feature called Remote Write to transmit scraped and processed metrics to an external system for long-term storage, aggregation, or advanced analytics. When configured, Prometheus continuously pushes time series data to the remote endpoint defined in the remote_write section of the configuration file.

This mechanism is often used to integrate with long-term data storage backends such as Cortex, Thanos, Mimir, or InfluxDB, enabling durable retention and global query capabilities beyond Prometheus's local time series database limits.

In contrast, ''scraping'' refers to data collection from targets, while ''federation'' allows hierarchical Prometheus setups (pulling metrics from other Prometheus instances) but does not serve as long-term storage. Using ''S3 Buckets'' directly is also unsupported in native Prometheus configurations.

Extracted and verified from Prometheus documentation -- Remote Write/Read APIs and Long-Term Storage Integrations sections.

By default, Prometheus stores its time series data in a local, embedded Time Series Database (TSDB) on disk. The data is organized in block files under the data/ directory inside Prometheus's storage path.

Each block typically covers two hours of data, containing chunks, index, and metadata files. Older blocks are compacted and deleted based on retention settings.