Add conceptual and scenario-based questions for Prometheus and Grafana - #4
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Added a comprehensive set of conceptual and scenario-based questions and answers regarding Prometheus and Grafana, covering topics such as observability, metrics, architecture, and performance optimization.
📝 WalkthroughWalkthroughAdds a structured interview-question document covering observability fundamentals, Prometheus concepts and troubleshooting, and Grafana dashboards, alerting, integrations, and production latency investigation. ChangesObservability Interview Questions
Estimated code review effort: 1 (Trivial) | ~3 minutes 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 8
🧹 Nitpick comments (1)
monitoring-logging/conceptual-queries.md (1)
77-82: 🚀 Performance & Scalability | 🔵 Trivial | ⚡ Quick winMake the disk-remediation advice operationally safe.
“Clean unused metrics” is ambiguous and could suggest manual TSDB deletion. Recommend explicit ingestion-time metric relabeling, reducing scrape volume, configured time/size retention, or remote storage instead. (prometheus.io)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@monitoring-logging/conceptual-queries.md` around lines 77 - 82, Update Q11’s disk-remediation choices by removing the ambiguous “Clean unused metrics” advice and replacing it with operationally safe actions: use ingestion-time metric relabeling, reduce scrape volume, configure time/size-based retention, or use remote storage such as Thanos/Cortex. Keep the existing disk expansion and label-cardinality recommendations.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@monitoring-logging/conceptual-queries.md`:
- Around line 14-17: Update the Prometheus scalability bullet in the
introductory list to qualify that its local TSDB is single-node and
non-clustered, and reference the file’s existing sharding, federation, or
remote-storage approaches for scaling. Keep the other bullets unchanged.
- Around line 51-53: Update the Q7 explanation in conceptual-queries.md to
describe rate() as the extrapolated per-second average over the range vector,
generally preferred for alerts and slow-moving counters, and irate() as using
only the last two range-vector points, suited to high-resolution visualization
of volatile, fast-moving counters.
- Around line 146-150: Update Q23’s RBAC option and surrounding explanation to
explicitly qualify granular or custom RBAC as a Grafana Enterprise/Cloud
feature, while keeping Organizations and folder access control edition-agnostic.
Avoid implying that Grafana OSS provides the same fixed/custom role controls.
- Around line 27-32: Update the Q4 Prometheus definition to distinguish a time
series from an individual data point: describe the metric name and label set as
identifying the time series, list metric types or families separately, and
describe each sample as its timestamped value.
- Around line 19-25: Update the Prometheus architecture list in the “Q3. Explain
Prometheus architecture” section by removing the “Grafana – visualization layer”
entry; keep Grafana only in the separate integration or downstream-tools section
if one exists.
- Around line 152-155: Update Q24’s metric-to-log/trace correlation guidance to
remove traceId from the recommended Prometheus metric labels. Recommend
Prometheus exemplars for metric-to-trace correlation, while retaining trace IDs
in logs and traces; keep the service label and Loki/Tempo integrations
unchanged.
- Around line 36-41: Update the Q5 metric-type guide to use valid Markdown table
syntax with pipe-delimited columns and a separator row. Correct the descriptions
to state that counters may reset, summaries calculate fixed quantiles
client-side and cannot be aggregated with histogram_quantile(), and
histogram_quantile() operates on histogram _bucket series, including a
representative query form. Apply the same histogram-versus-summary clarification
to Q14.
- Around line 141-145: Update the Q22 Grafana alerting workflow to use current
terminology: create alert rules, configure evaluation, pending, and keep-firing
behavior, and define no-data and error handling. Replace panel-based alert
conditions and deprecated notification channels with contact points and
notification policies for notification routing.
---
Nitpick comments:
In `@monitoring-logging/conceptual-queries.md`:
- Around line 77-82: Update Q11’s disk-remediation choices by removing the
ambiguous “Clean unused metrics” advice and replacing it with operationally safe
actions: use ingestion-time metric relabeling, reduce scrape volume, configure
time/size-based retention, or use remote storage such as Thanos/Cortex. Keep the
existing disk expansion and label-cardinality recommendations.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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📒 Files selected for processing (1)
monitoring-logging/conceptual-queries.md
| It is popular because it: | ||
| • Works well with Kubernetes | ||
| • Is highly scalable | ||
| • Has strong alerting and visualization support |
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🚀 Performance & Scalability | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
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#!/bin/bash
set -euo pipefail
echo "== file context =="
if [ -f monitoring-logging/conceptual-queries.md ]; then
sed -n '1,80p' monitoring-logging/conceptual-queries.md
else
echo "missing monitoring-logging/conceptual-queries.md"
fi
echo
echo "== references to Prometheus/TSDB/scalability in repo =="
rg -n -i "prometheus|tsdb|federat|remote|scalab" monitoring-logging/conceptual-queries.md monitoring-logging 2>/dev/null || trueRepository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 14345
🌐 Web query:
Prometheus storage documentation single-node TSDB scalability federation remote write
💡 Result:
Prometheus is fundamentally designed as a single-node system, and its local TSDB is not clustered or replicated [1][2][3]. Consequently, it is not arbitrarily scalable, and its durability is limited to the underlying node and drive [1][4]. Scalability Limits and Patterns A single Prometheus instance faces limits primarily dictated by CPU, memory, and disk I/O, with active series cardinality (the number of concurrently tracked time series) being the most critical constraint [2][3]. While the specific ceiling depends on hardware, a practical limit for a single instance is often cited around 10 million active series [2]. When these limits are reached, the standard strategies are: 1. Sharding: Splitting targets across multiple Prometheus instances (e.g., using functional or hashmod sharding) to distribute the load [2][5]. 2. Remote Write: Offloading long-term storage and global data centralization to horizontally scalable external systems (e.g., Mimir, Thanos, or VictoriaMetrics) [1][2][6]. Federation Federation is a mechanism for pulling selected metrics from one Prometheus server into another, typically using the /federate endpoint [6][7]. It is best suited for creating hierarchical views, such as aggregating data from multiple local Prometheus instances into a central global instance [7]. However, federation is not a long-term storage solution, is pull-based, and carries performance overhead if misused to sync large volumes of raw metrics [6][8][9]. The recommended practice is to federate only pre-aggregated metrics (via recording rules) rather than raw data [6][5]. Remote Write Remote write is a push-based mechanism that continuously streams samples from the local write-ahead log (WAL) to one or more remote endpoints [6][10]. It is the primary architectural pattern for achieving long-term storage, high availability, and global data centralization [6][5][11]. - Architecture: Prometheus batches samples in memory and sends them via parallel shards to the configured endpoint [10][12]. To ensure consistency, it guarantees per-series ordering, though samples from different series may arrive out of order [12]. - Tuning: Performance can be adjusted via the queue_config settings (e.g., max_shards, capacity, max_samples_per_send) [10][11]. Users should balance these settings to prevent memory exhaustion or overloading the remote backend [10]. - Trade-offs: While it enables long-term retention and centralized querying, it introduces a dependency on the availability and performance of the remote storage system [8][10]. If the remote endpoint is down for longer than the internal buffer (approximately 2 hours), data loss may occur [10].
Citations:
- 1: https://prometheus.io/docs/prometheus/latest/storage/
- 2: https://systeminternals.dev/prometheus/
- 3: https://www.groundcover.com/learn/observability/prometheus-tsdb
- 4: https://github.com/prometheus/prometheus/blob/main/docs/storage.md
- 5: https://www.wasilzafar.com/pages/series/monitoring-observability/monitoring-observability-prometheus-part07-sharding-federation-ha.html
- 6: https://kloudvin.com/article/prometheus-recording-rules-remote-write-long-term-storage/
- 7: https://prometheus.io/docs/prometheus/latest/federation/
- 8: https://sreschool.com/blog/prometheus-remote-write/
- 9: https://www.groundcover.com/learn/observability/prometheus-federation
- 10: https://prometheus.io/docs/practices/remote_write/
- 11: https://oneuptime.com/blog/post/2026-01-26-prometheus-remote-write/view
- 12: https://prometheus.io/docs/specs/prw/remote_write_spec/
Qualify Prometheus’s scalability claim.
Prometheus’s local TSDB is single-node and not clustered, so “highly scalable” is misleading unless qualified. Add the sharding/federation/remote-storage context already used elsewhere in the file.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 14 - 17, Update the
Prometheus scalability bullet in the introductory list to qualify that its local
TSDB is single-node and non-clustered, and reference the file’s existing
sharding, federation, or remote-storage approaches for scaling. Keep the other
bullets unchanged.
| Q3. Explain Prometheus architecture. | ||
| Prometheus consists of: | ||
| • Prometheus Server – scrapes and stores metrics | ||
| • Exporters – expose metrics (Node Exporter, JVM Exporter) | ||
| • Service Discovery – finds targets dynamically | ||
| • Alertmanager – manages alerts | ||
| • Grafana – visualization layer |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "Repository files matching conceptual-queries.md:"
fd -a 'conceptual-queries\.md$' . || true
echo
echo "Target lines:"
file="$(fd 'conceptual-queries\.md$' . | head -n1)"
if [ -n "${file:-}" ]; then
nl -ba "$file" | sed -n '1,80p'
fi
echo
echo "Search for Prometheus/Grafana references in repo docs:"
rg -n "Prometheus|Grafana|Alertmanager|Exporter" -S . -- '*.md' -- '*.adoc' -- '*.*' 2>/dev/null | head -n 120Repository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 341
🌐 Web query:
Prometheus overview architecture components Prometheus server exporters service discovery Alertmanager Grafana is not part of Prometheus architecture
💡 Result:
Prometheus is a systems and service monitoring system designed for reliability and scalability, featuring a multi-dimensional data model, the PromQL query language, and a pull-based collection model [1][2]. While it is often used in combination with other tools to form a comprehensive monitoring stack, Grafana is not part of the core Prometheus architecture; rather, it is a separate tool used to visualize data stored by Prometheus [1][3]. The Prometheus ecosystem consists of the following core components and optional integrations: 1. Prometheus Server: The central component responsible for scraping and storing time series data [1][4]. It consists of several internal subsystems, including: - Scrape Manager: Discovers targets via service discovery or static configuration and periodically scrapes metrics from them [1][5][4]. - Storage (TSDB): Manages the local on-disk and in-memory storage of time series data [4][6]. - Query Engine: Parses and executes PromQL queries to retrieve and aggregate data [4][6]. - Rules Manager: Evaluates recording rules and alerting rules [4][6]. - Web UI and API: Serves the interface for querying data and managing the server [4]. 2. Exporters: Specialized agents that run alongside services (e.g., databases, web servers, hardware) to collect metrics and expose them in a format Prometheus can scrape [1][7]. 3. Service Discovery: A mechanism that allows Prometheus to automatically identify and monitor targets in dynamic environments, such as Kubernetes or cloud platforms [1][6]. 4. Pushgateway: An intermediary service that allows short-lived or batch jobs to push their metrics to Prometheus, as they may not exist long enough to be scraped [1][2]. 5. Alertmanager: A separate component that handles alerts sent by the Prometheus server, including grouping, deduplication, silencing, and routing notifications to various receivers like email, PagerDuty, or Slack [1][7]. While Grafana is frequently used with Prometheus to provide advanced dashboards and visualization, it is an external consumer of the Prometheus API, not a component of the Prometheus architecture itself [1][3][7].
Citations:
- 1: https://prometheus.io/docs/introduction/overview/
- 2: https://github.com/prometheus/prometheus?tab=readme-ov-file
- 3: https://edana.ch/en/2026/03/12/prometheus-vs-grafana-comparison-metric-collection-or-visualization-understanding-the-real-difference/
- 4: https://deepwiki.com/prometheus/prometheus/1.1-architecture-overview
- 5: https://github.com/prometheus/prometheus/blob/main/documentation/internal_architecture.md
- 6: https://system-design.space/en/chapter/prometheus-architecture
- 7: https://floriancourouge.com/en/blog/prometheus-grafana-monitoring-stack
🏁 Script executed:
#!/bin/bash
set -euo pipefail
file="$(fd 'conceptual-queries\.md$' . | head -n1)"
echo "Target file: ${file:-NONE}"
python3 - <<'PY'
from pathlib import Path
p = next(Path('.').rglob('conceptual-queries.md'), None)
text = p.read_text() if p else ''
for i, line in enumerate(text.splitlines(), 1):
if 1 <= i <= 80:
print(f"{i:3}: {line}")
PY
echo
echo "Prometheus/Grafana/doc references in tracked Markdown:"
git ls-files '*.md' '*.adoc' | xargs rg -n "Prometheus|Grafana|Alertmanager|Exporter" 2>/dev/null | head -n 160 || trueRepository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 20260
Do not list Grafana under Prometheus architecture.
Grafana is an external visualization/API consumer of Prometheus data; keep architecture components separate from downstream monitoring tools. Remove • Grafana – visualization layer, or move it to the Grafana integration section.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 19 - 25, Update the
Prometheus architecture list in the “Q3. Explain Prometheus architecture”
section by removing the “Grafana – visualization layer” entry; keep Grafana only
in the separate integration or downstream-tools section if one exists.
| Q4. What is a metric in Prometheus? | ||
| A metric is a time-series data point identified by: | ||
| • Metric name | ||
| • Labels (key-value pairs) | ||
| • Timestamp | ||
| • Value |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== file context =="
sed -n '1,80p' monitoring-logging/conceptual-queries.md
echo
echo "== repo occurrences of metric/time-series/sample definitions =="
rg -n "metric[s]?|time[s]?[- ]series|time-series|sample|metric family|labels" monitoring-logging/conceptual-queries.md README.md . --glob '*.md' | head -200Repository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 14242
🌐 Web query:
Prometheus documentation metrics time series metric family sample timestamp value
💡 Result:
In Prometheus, data is structured into a dimensional model based on time series, samples, and metric families. Time Series Prometheus fundamentally stores data as time series: streams of timestamped values that share the same metric name and a unique set of labeled dimensions [1][2]. Any change to a label's value, or the addition/removal of a label, results in the creation of a new time series [1]. Samples Samples are the actual data points within a time series [1][2]. Each sample consists of two primary components: 1. Value: Historically a float64, though modern Prometheus also supports native histograms (composite samples containing count, sum, and buckets) [1][3][4]. 2. Timestamp: A millisecond-precision integer representing the time since the epoch (1970-01-01 00:00:00 UTC) [1][5]. Metric Family While Prometheus stores data as time series, metrics are often grouped into metric families in the exposition format [5]. A metric family provides metadata for a set of related time series, such as the metric name, help text, and type (e.g., Counter, Gauge, Histogram, Summary) [3][6]. In many cases, these families are composed of multiple individual time series (e.g., a classic histogram family includes the _bucket, _sum, and _count series) [3]. In the Prometheus exposition format, samples are typically represented as lines containing the metric name (with labels), the numeric value, and an optional timestamp [5]: metric_name{label_name="label_value"} value [timestamp] During queries, Prometheus evaluates these time series. When an instant query is performed, it retrieves the most recent sample at or before the specified timestamp [4]. If multiple time series are involved in an operation, Prometheus aligns them at the requested evaluation timestamp, typically by selecting the newest sample within a defined lookback period (defaulting to 5 minutes) [4].
Citations:
- 1: https://prometheus.io/docs/concepts/data_model/
- 2: https://github.com/prometheus/docs/blob/c4554bded23a544ca8dfc0fd3f6360e13c084378/content/docs/concepts/data_model.md
- 3: https://prometheus.io/docs/concepts/metric_types/
- 4: https://prometheus.io/docs/prometheus/latest/querying/basics/
- 5: https://prometheus.io/docs/instrumenting/exposition_formats/
- 6: https://github.com/prometheus/prometheus/blob/main/prompb/types.proto
Distinguish time series from data points.
In Prometheus, a metric name plus label set identifies a time series; each sample is the timestamped value within that series. List metric name, labels, and metric types/families separately, then describe samples as the data points, so this matches the documented data model.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 27 - 32, Update the Q4
Prometheus definition to distinguish a time series from an individual data
point: describe the metric name and label set as identifying the time series,
list metric types or families separately, and describe each sample as its
timestamped value.
| Q5. What are the different metric types in Prometheus? | ||
| Type Description | ||
| Counter Always increases (e.g., request count) | ||
| Gauge Can go up and down (e.g., memory usage) | ||
| Histogram Measures distributions with buckets | ||
| Summary Similar to histogram but client-side |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== file exists and relevant sections =="
if [ -f monitoring-logging/conceptual-queries.md ]; then
wc -l monitoring-logging/conceptual-queries.md
sed -n '1,130p' monitoring-logging/conceptual-queries.md | cat -n
else
echo "file missing"
fi
echo
echo "== searches for histogram/summary/rate examples and metric type references =="
rg -n "histogram|summary|quantile|bucket|Counter|Gauge|Histogram|Summary|rate\(" monitoring-logging/conceptual-queries.md || true
echo
echo "== all markdown table headers in file by approximate delimiter/separator scan =="
python3 - <<'PY'
from pathlib import Path
p=Path('monitoring-logging/conceptual-queries.md')
lines=p.read_text().splitlines()
print(f"rows={len(lines)}")
# Print lines around metric types and later mention range.
for i,l in enumerate(lines,1):
if 'metric' in l.lower() or 'histogram_quantile' in l or l.strip().startswith('|'):
print(f"{i}: {l[:200]}")
PYRepository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 6556
🌐 Web query:
Prometheus histogram_summary quantile summary histogram_quantile bucket rate documentation counters reset
💡 Result:
In Prometheus, histograms and summaries are both used to track the distribution of observations (such as request durations or response sizes), but they differ significantly in how they process data and handle aggregations [1][2]. Histograms: - Mechanism: Histograms count observations in configurable buckets and provide a sum of all observed values [1][2]. - Aggregation: Because they expose raw bucket counts, they can be aggregated across different instances or dimensions using PromQL (e.g., sum, rate) before calculating quantiles [1][2][3]. - Flexibility: They allow for ad-hoc calculation of arbitrary quantiles over arbitrary time windows using the histogram_quantile function on the server side [1][4]. Summaries: - Mechanism: Summaries calculate configurable φ-quantiles directly within the instrumented application over a sliding time window [1][2]. - Limitation: Because quantiles are pre-calculated on the client side, they cannot be aggregated across multiple instances or combined in the same way as histograms [3]. - Recommendation: It is generally recommended to use histograms over summaries whenever possible [3]. Key Functions and Concepts: - histogram_quantile: This function calculates the φ-quantile (0 ≤ φ ≤ 1) from histogram buckets [5][6]. It is crucial to wrap the histogram buckets with a rate or increase function [7][6]. - The role of rate: When you pass a range vector (e.g., [5m]) to rate or increase inside histogram_quantile, you restrict the quantile calculation to a specific time window [7][6]. Without this, the function calculates quantiles based on the accumulated total since the metric was last reset, which is rarely the desired behavior for time-sensitive performance analysis [7][6]. - Counter Resets: Histograms use counters for their bucket counts [2]. Prometheus's rate and increase functions are designed to automatically handle counter resets (where a counter drops to zero, typically due to a process restart) by assuming the drop is a reset and adjusting the calculation accordingly [8]. For classic histograms, ensure you use the _bucket metric and that your PromQL query accounts for the le (less-than-or-equal) label to correctly identify bucket boundaries [5][4]. For native histograms, the underlying mechanics differ slightly, but the requirement to use rate or increase for windowed quantile calculation remains the same [7].
Citations:
- 1: https://prometheus.io/docs/practices/histograms/
- 2: https://prometheus.io/docs/concepts/metric_types/
- 3: https://prometheus.io/docs/tutorials/understanding_metric_types/
- 4: https://github.com/prometheus/docs/blob/main/docs/practices/histograms.md
- 5: https://prometheus.io/docs/prometheus/latest/querying/functions/
- 6: https://seankhliao.com/blog/12022-07-04-promql-histogram-quantile-rate/
- 7: https://grafana.com/docs/mimir/latest/visualize/native-histograms/
- 8: https://stackoverflow.com/questions/55162093/understanding-histogram-quantile-based-on-rate-in-prometheus
Fix the Prometheus metric-type guide.
The Q5 data is not a Markdown table because it lacks | columns and a separator row, so it won’t render as a table. Also, metric guidance should distinguish: counters can reset; summaries compute fixed quantiles client-side and cannot be aggregated with histogram_quantile(); histogram_quantile() runs on histogram _bucket series, typically with a form like histogram_quantile(0.95, sum(rate(http_duration_seconds_bucket[5m]) by (le))). The same histogram/summary distinction applies at Q14.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 36 - 41, Update the Q5
metric-type guide to use valid Markdown table syntax with pipe-delimited columns
and a separator row. Correct the descriptions to state that counters may reset,
summaries calculate fixed quantiles client-side and cannot be aggregated with
histogram_quantile(), and histogram_quantile() operates on histogram _bucket
series, including a representative query form. Apply the same
histogram-versus-summary clarification to Q14.
| Q7. Difference between rate() and irate()? | ||
| • rate() → average over time (stable,preferred) | ||
| • irate() → instant rate (more volatile) |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== matching file =="
git ls-files | rg '(^|/)conceptual-queries\.md$' || true
echo "== relevant lines =="
if git ls-files | rg -q 'monitoring-logging/conceptual-queries\.md$'; then
cat -n monitoring-logging/conceptual-queries.md | sed -n '1,90p'
fi
echo "== nearby rate/irate mentions =="
rg -n "\b(rate\(|irate\(|irate|rate)\b" monitoring-logging -C 2 || trueRepository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 5340
🌐 Web query:
Prometheus rate vs irate documentation last two samples extrapolated average rate volatile counters
💡 Result:
The functions rate and irate in Prometheus serve different purposes for calculating the rate of increase of counters [1][2]. rate Calculates the per-second average rate of increase over the entire specified time range [1][3]. It includes extrapolation to the boundaries of the time range, which helps account for missed scrapes or misalignment between scrape cycles and the range period [1][4][5]. Because it averages over the entire range, it is recommended for alerting and graphing slow-moving counters, as it provides a smoother, more stable representation of trends [1][6]. irate Calculates the instantaneous per-second rate of increase based solely on the last two data points within the specified range vector [1][4][6]. It does not perform extrapolation [4][5]. Because it reacts only to the most recent samples, it is highly sensitive to rapid fluctuations and is best used for graphing volatile, fast-moving counters when you need to visualize fine-grained detail [1][6][3]. It is generally discouraged for alerting because brief spikes can cause premature resolution of the alert's FOR clause [1][2][6]. Key Comparison 1. Extrapolation: rate extrapolates to the range boundaries; irate does not [1][4]. 2. Scope: rate averages over the full range; irate considers only the last two samples [1][5][7]. 3. Use Case: Use rate for alerts and stable trends; use irate for high-resolution, volatile, or fast-moving counters [1][6][3]. Note: When using either function with an aggregation operator (e.g., sum) or a function that aggregates over time (e.g., _over_time), you must apply the rate or irate function first to ensure Prometheus can correctly detect and adjust for counter resets [1][2][8].
Citations:
- 1: https://prometheus.io/docs/prometheus/latest/querying/functions/
- 2: https://raw.githubusercontent.com/prometheus/prometheus/master/docs/querying/functions.md
- 3: https://last9.io/blog/prometheus-rate-function/
- 4: https://promlabs.com/blog/2021/01/29/how-exactly-does-promql-calculate-rates/
- 5: https://adhdecode.com/articles/prometheus/prometheus-rate-vs-irate-difference/
- 6: https://www.robustperception.io/irate-graphs-are-better-graphs/
- 7: https://blog.devops.dev/prometheus-theory-rate-vs-irate-20e6243a3ab8
- 8: https://github.com/prometheus/prometheus/blob/v2.54.1/docs/querying/functions.md
Describe rate() and irate() precisely.
Replace the ambiguous “instant rate” wording with Prometheus semantics: rate() is the extrapolated per-second average over the range vector and is generally preferable for alerts/slow-moving counters; irate() uses only the last two points in the range vector and is useful for high-resolution visualization of volatile, fast-moving counters.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 51 - 53, Update the Q7
explanation in conceptual-queries.md to describe rate() as the extrapolated
per-second average over the range vector, generally preferred for alerts and
slow-moving counters, and irate() as using only the last two range-vector
points, suited to high-resolution visualization of volatile, fast-moving
counters.
| Q22. How do you create alerts in Grafana? | ||
| • Define alert conditions on panels | ||
| • Configure thresholds | ||
| • Set notification channels(Slack,Email) | ||
|
|
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🌐 Web query:
Grafana Alerting alert rules evaluation behavior contact points notification policies notification channels outdated
💡 Result:
In modern Grafana Alerting, alert rule evaluation is driven by three main configurations: the evaluation group (how often the rule is checked), the pending period (how long a condition must be met before firing), and the keep firing for period (how long an alert stays in the firing state after the condition is no longer met) [1][2][3]. Regarding your concern about "notification channels": they are indeed outdated and officially deprecated [4][5][6]. You should transition to using Contact Points and Notification Policies, which are the core components of the current Grafana Alerting system [7][8][9]. Here is how the current system functions: 1. Alert Rule Evaluation: Every rule belongs to an evaluation group with a defined interval [1][10]. When a condition is met, the alert enters a Pending state for the duration of the pending period [1][3]. If the condition remains true, it transitions to Alerting [2][11]. When the condition ceases, it may enter a Recovering state until the keep firing for period expires [1][2][3]. 2. Contact Points: These define the destination for notifications (e.g., Slack, Email, PagerDuty, Webhooks) [12][8]. They act as the "where" in your notification setup [7]. 3. Notification Policies: These define the "how" and "who" [7]. They use a tree-like structure and label matchers to route alerts to specific contact points [9][13]. They are highly flexible, allowing for alert grouping to minimize noise and the inheritance of properties between parent and child policies [9][13]. Note that even the Provisioning APIs associated with Contact Points and Policies are seeing ongoing refinement, with some older provisioning endpoints being marked as deprecated in favor of newer Grafana App Platform alerting APIs [14]. You should prioritize documentation for the current Grafana Alerting system over any legacy "notification channels" resources [5].
Citations:
- 1: https://grafana.com/docs/grafana/latest/alerting/fundamentals/alert-rule-evaluation/
- 2: https://grafana.com/docs/grafana/v12.1/alerting/fundamentals/alert-rule-evaluation/
- 3: https://grafana.com/docs/grafana-cloud/alerting-and-irm/alerting/fundamentals/alert-rule-evaluation/
- 4: https://grafana.com/docs/grafana/v9.4/developers/http_api/alerting_notification_channels/
- 5: https://grafana.com/blog/legacy-alerting-removal-what-you-need-to-know-about-upgrading-to-grafana-alerting/
- 6: Port NotificationChannel CRD from operator v4 to v5 grafana/grafana-operator#1395
- 7: https://grafana.com/docs/grafana/latest/alerting/configure-notifications/
- 8: https://grafana.com/docs/grafana/latest/alerting/manage-notifications/manage-contact-points/
- 9: https://grafana.com/docs/grafana/latest/alerting/fundamentals/notifications/notification-policies/
- 10: https://grafana.com/docs/grafana/latest/alerting/fundamentals/alert-rule-evaluation/evaluation-within-a-group/
- 11: https://grafana.com/docs/grafana/v12.0/alerting/fundamentals/alert-rule-evaluation/state-and-health/
- 12: https://grafana.com/docs/grafana/latest/alerting/fundamentals/notifications/contact-points/
- 13: https://grafana.com/docs/grafana/latest/alerting/configure-notifications/create-notification-policy/
- 14: Alerting: Mark notification provisioning endpoints deprecated grafana/grafana#121995
🌐 Web query:
Grafana documentation create alerts panels alert rules notification channels contact points
💡 Result:
In Grafana, alerting is structured around alert rules that evaluate data and contact points that determine how notifications are delivered [1][2]. The term "notification channels" is deprecated and has been replaced by "contact points" [3]. Alert Rules An alert rule consists of queries, expressions, and a condition that determines when an alert should fire [1][4]. - Creation: You can create alert rules by navigating to Alerts & IRM > Alert rules and clicking + New alert rule [5][6]. Alternatively, you can create them directly from a panel by editing the panel and navigating to the Alert tab [7]. - Linking to Panels: Alert rules can be linked to specific dashboard panels to simplify investigation. This is done by configuring the alert rule to include dashboard and panel annotations [7]. When creating an alert rule from a panel, these links are often pre-configured [7]. Contact Points Contact points are the destinations where alert notifications are sent (e.g., Slack, Email, PagerDuty) [8][9]. - Function: A contact point contains one or more contact point integrations, which are the specific configurations for sending the notification to a destination [8][9]. - Management: You configure contact points under the Alerting section of Grafana [10]. - Routing: Once created, contact points are used by associating them directly with an alert rule or by routing alerts to them via notification policies [10][9][2]. Notification policies provide a more flexible approach, allowing you to route alerts to specific contact points based on matching labels [2]. Key Differences - Contact Points (formerly Notification Channels): Specify where to send the notification [9]. - Notification Policies: Define how alerts are routed to contact points [10][2]. For further guidance, consult the official Grafana documentation on configuring alert rules and managing contact points [1][9].
Citations:
- 1: https://grafana.com/docs/grafana/latest/alerting/alerting-rules/
- 2: https://grafana.com/docs/grafana/latest/alerting/fundamentals/notifications/
- 3: https://community.grafana.com/t/tutorial-needs-updating-for-notification-channels/65240/3
- 4: https://grafana.com/docs/grafana/latest/alerting/fundamentals/alert-rules/queries-conditions/
- 5: https://grafana.com/docs/grafana/latest/alerting/alerting-rules/create-grafana-managed-rule/
- 6: https://grafana.com/docs/grafana/latest/alerting/alerting-rules/create-grafana-managed-rule/?pg=how-to-create-alert-rules-to-monitor-sensor-data-with-grafana-and-raspberry-pi&plcmt=in-text
- 7: https://grafana.com/docs/grafana/latest/alerting/alerting-rules/link-alert-rules-to-panels/
- 8: https://grafana.com/docs/grafana/latest/alerting/fundamentals/notifications/contact-points/
- 9: https://grafana.com/docs/grafana/latest/alerting/configure-notifications/manage-contact-points/
- 10: https://grafana.com/docs/grafana/latest/alerting/configure-notifications/
🌐 Web query:
Grafana Alerting no data error handling alert rules no data state error state
💡 Result:
In Grafana Alerting, No Data and Error states are specific handling mechanisms for Grafana-managed alert rules when query evaluation does not produce a standard result [1][2]. Definitions: - No Data: Occurs when an alert query executes successfully but returns no data points or all values are null [1][3]. - Error: Occurs when an alert rule fails to evaluate its query due to errors or timeouts [1][3]. Configuration: You can customize how Grafana handles these states in the Configure no data and error handling section of your alert rule [4][5]. For both No Data and Error scenarios, the following options are available [5][6]: - No Data / Error (Default): Triggers a dedicated alert (DatasourceNoData or DatasourceError). This informs you of the issue without necessarily firing the primary alert [1][7][6]. - Alerting: Transitions the alert instance into the Alerting state, treating the missing data or error as if the alert threshold was breached [7][4][6]. - Normal: Ignores the missing data or error and sets the alert instance to the Normal state. This is useful for intermittent data or non-critical errors [7][6]. - Keep Last State: Maintains the alert in its previous state until the next successful evaluation, which helps prevent flapping in unstable environments [7][5][6]. Pending Period: These states honor the defined Pending period [4][8]. If the pending period is set to 0, the alert transitions immediately to the selected state [4][3]. Otherwise, it enters a Pending state first and transitions after the period elapses [1][4]. When a state transition occurs (e.g., to Normal due to data returning), the grafana_state_reason annotation is often included to provide context [1][7].
Citations:
- 1: https://grafana.com/docs/grafana/latest/alerting/fundamentals/alert-rule-evaluation/nodata-and-error-states/
- 2: https://grafana.com/docs/grafana/latest/alerting/fundamentals/alert-rule-evaluation/
- 3: https://grafana.com/docs/grafana/v12.4/alerting/fundamentals/alert-rule-evaluation/nodata-and-error-states/
- 4: https://grafana.com/docs/grafana/latest/alerting/alerting-rules/create-grafana-managed-rule/
- 5: https://grafana.com/docs/grafana/v12.0/alerting/fundamentals/alert-rules/state-and-health/
- 6: https://grafana.com/docs/grafana/latest/alerting/guides/connectivity-errors/
- 7: https://grafana.com/docs/grafana/latest/alerting/guides/missing-data/
- 8: https://grafana.com/docs/grafana/next/alerting/fundamentals/alert-rule-evaluation/state-and-health/
Update the Grafana alerting terminology and workflow.
Use current Grafana Alerting terms: create alert rules, set evaluation/pending/keep-firing behavior, and configure no-data/error handling. Notifications should reference contact points and notification policies for routing instead of panels and deprecated “notification channels.”
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 141 - 145, Update the
Q22 Grafana alerting workflow to use current terminology: create alert rules,
configure evaluation, pending, and keep-firing behavior, and define no-data and
error handling. Replace panel-based alert conditions and deprecated notification
channels with contact points and notification policies for notification routing.
| Q23. Multiple teams use the same Grafana instance. How do you manage access? | ||
| • Use Organizations | ||
| • Use Folders | ||
| • Apply RBAC | ||
| • Integrate with LDAP/OAuth |
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🔒 Security & Privacy | 🟡 Minor | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== file excerpt =="
sed -n '130,160p' monitoring-logging/conceptual-queries.md | cat -n
echo
echo "== search for graphana RBAC/Orgs context =="
rg -n "Grafana|RBAC|LDAP/OAuth|Organizations|Folders|roles-and-permissions" monitoring-logging/conceptual-queries.md || trueRepository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 2257
🌐 Web query:
Grafana roles and permissions edition OSS Enterprise Cloud organization folder permissions
💡 Result:
Grafana manages roles and permissions through a combination of basic organization-level roles and, in more advanced editions, granular Role-Based Access Control (RBAC). Organization Roles Available across all Grafana editions (OSS, Enterprise, and Cloud), organization roles are the primary way to manage permissions for the entire organization. These include: - Admin: Has full access to all resources within the organization, including dashboards, users, and teams [1][2]. - Editor: Can view and edit dashboards, folders, and playlists [1][2]. - Viewer: Can view dashboards, playlists, and query data sources [1][2]. Folder Permissions Folder access control is available in all Grafana editions (OSS, Enterprise, and Cloud) and serves as the primary mechanism for organizing and securing resources [3][4]. Permissions assigned to a folder cascade to all its contents, including dashboards, alert rules, and subfolders [3][4]. There are three permission levels: - View: Allows viewing the folder, navigating contents, and reading resources like dashboards and alert rules [3][4]. - Edit: Includes View capabilities plus creating/editing dashboards, creating/writing alert rules, and managing subfolders [3][4]. - Admin: Includes Edit capabilities plus the ability to manage folder-level permissions and delete the folder [3][4]. Role-Based Access Control (RBAC) Full RBAC features, which allow for fine-grained control beyond basic organization roles, are available in Grafana Enterprise and Grafana Cloud [5][6]. Features include: - Fixed Roles: Predefined roles that provide more granular control than standard basic roles. These cannot be modified [5][7]. - Custom Roles: Allows administrators to create unique combinations of permission actions and scopes to meet specific security requirements [5][7]. - While Grafana OSS is primarily limited to basic roles, recent versions allow for some modification of these basic roles (e.g., adding or removing permissions) [8]. However, the advanced fixed and custom role capabilities are distinct features of the Enterprise and Cloud editions [5][1]. Note: Grafana Admin (Server Administrator) permissions, which manage server-wide settings, exist in OSS and Enterprise but do not exist in Grafana Cloud [1].
Citations:
- 1: https://grafana.com/docs/grafana/latest/administration/roles-and-permissions/
- 2: https://grafana.com/docs/grafana/latest/manage-users/
- 3: https://grafana.com/docs/grafana/latest/administration/roles-and-permissions/folder-access-control/
- 4: https://grafana.com/docs/grafana/v12.4/administration/roles-and-permissions/folder-access-control/
- 5: https://grafana.com/docs/grafana/latest/administration/roles-and-permissions/access-control/
- 6: http://grafana.com/docs/administration/roles-and-permissions/access-control/?plcmt=products-nav
- 7: https://grafana.com/docs/grafana/latest/administration/roles-and-permissions/access-control/?pg=blog&plcmt=body-txt
- 8: https://grafana.com/docs/grafana/latest/administration/roles-and-permissions/access-control/manage-rbac-roles/
Qualify RBAC by Grafana edition.
Organizations and folder access control are available across editions, but granular RBAC is an Enterprise/Cloud feature; clarify that Q23 refers to edition-specific RBAC to avoid implying OSS supports the same fixed/custom role controls.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 146 - 150, Update
Q23’s RBAC option and surrounding explanation to explicitly qualify granular or
custom RBAC as a Grafana Enterprise/Cloud feature, while keeping Organizations
and folder access control edition-agnostic. Avoid implying that Grafana OSS
provides the same fixed/custom role controls.
| Q24. How do you correlate metrics with logs and traces? | ||
| • Use consistent labels(traceId,service) | ||
| • Integrate with Loki(logs) | ||
| • Integrate with Tempo(traces) |
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🚀 Performance & Scalability | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
# Inspect the referenced file around the lines in question.
if [ -f monitoring-logging/conceptual-queries.md ]; then
sed -n '130,170p' monitoring-logging/conceptual-queries.md | cat -n
else
echo "monitoring-logging/conceptual-queries.md not found"
fd -a 'conceptual-queries\.md$'
fi
# Search for metric label guidance in this documentation/repository.
rg -n "traceId|trace_id|exemplar|high-cardinality|metric labels|labels\\(" monitoring-logging .github README.md 2>/dev/null || trueRepository: NotHarshhaa/DevOps-Interview-Questions
Length of output: 1943
🌐 Web query:
Prometheus documentation exemplars metric labels traceId cardinality
💡 Result:
Prometheus exemplars are a mechanism designed to link aggregate metrics to high-cardinality data, such as trace IDs, without incurring the performance costs associated with label cardinality [1][2][3]. Cardinality Impact Using high-cardinality data like trace IDs as metric labels is an anti-pattern that creates a unique time series for every single request [1][4][3]. This leads to cardinality explosions, which consume excessive memory, inflate storage costs, and can cause Prometheus to crash [2][4][5]. Exemplars resolve this by storing the trace ID as sidecar metadata attached to a specific sample, rather than as an indexed metric label [1][3]. Consequently, exemplars have no impact on the time series count or the cardinality of the metric [3]. Technical Requirements and Usage To use exemplars with trace IDs, the following conditions must be met: 1. Enabling: You must enable exemplar storage in Prometheus by passing the flag --enable-feature=exemplar-storage [6][7]. 2. Exposition Format: Metrics must be exposed using the OpenMetrics format, as exemplars are not supported in the original Prometheus text format [8][7]. 3. Implementation: Applications generally attach trace IDs to metrics via instrumentation libraries (e.g., OpenTelemetry) [9][10][11]. When a metric is recorded within the context of a sampled trace, the instrumentation can automatically attach the trace ID and span ID to the exemplar [9][10][11]. 4. Storage and Retention: Exemplars are stored in a fixed-size, in-memory circular buffer [3]. Because this buffer is finite, exemplars are sampled and oldest entries are evicted once the limit is reached, making them a representative link rather than an exhaustive audit trail [3]. Best Practices - Never use trace IDs, request IDs, or user IDs as metric labels [1][2][5]. - Use exemplars specifically for sparse, debug-oriented correlation where you need to jump from a metric point to a specific trace [1][3]. - Exemplars are most effective when attached to histogram buckets or counters to provide examples of specific request flows [10][2][7]. - Rely on logs and dedicated distributed tracing systems for complete, event-level data, while using metrics for aggregated, high-level performance trends [2][4][3].
Citations:
- 1: https://github.com/grafana/skills/blob/main/skills/grafana-cloud/prometheus-label-strategy/SKILL.md
- 2: https://systeminternals.dev/observability/cardinality/
- 3: https://bitstoagent.hashnode.dev/modern-observability-architecture-designing-cross-signal-correlation-from-first-principles-2
- 4: https://www.michal-drozd.com/en/blog/prometheus-cardinality-explosion/
- 5: https://github.com/grafana/skills/blob/main/skills/grafana-cloud/prometheus-cardinality-troubleshooter/SKILL.md
- 6: https://github.com/prometheus/docs/blob/main/docs/instrumenting/exposition_formats.md
- 7: https://grafana.com/docs/grafana-cloud/send-data/traces/configure/exemplars/?pg=blog&plcmt=body-txt
- 8: https://clear-https-mrxwg4zomnwg65lefztw633hnrss4y3pnu.proxy.gigablast.org/stackdriver/docs/managed-prometheus/exemplars
- 9: http://prometheus.github.io/client_java/otel/tracing/
- 10: https://cloud.google.com/stackdriver/docs/instrumentation/advanced-topics/exemplars
- 11: https://google-cloud-opentelemetry.readthedocs.io/en/latest/examples/prometheus%5Fexemplars/README.html
Do not recommend traceId as a Prometheus metric label.
Trace IDs are per-request values and are high-cardinality; using them as metric labels can explode time series count. Use Prometheus exemplars for metric-to-trace correlation and keep trace IDs in logs/traces.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@monitoring-logging/conceptual-queries.md` around lines 152 - 155, Update
Q24’s metric-to-log/trace correlation guidance to remove traceId from the
recommended Prometheus metric labels. Recommend Prometheus exemplars for
metric-to-trace correlation, while retaining trace IDs in logs and traces; keep
the service label and Loki/Tempo integrations unchanged.
1 participant
Added a comprehensive set of conceptual and scenario-based questions and answers regarding Prometheus and Grafana, covering topics such as observability, metrics, architecture, and performance optimization.
Summary by CodeRabbit