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Use case / Incident response

Live Tail Incident Response for Active Log Streams

Live tail helps when an incident is still active and recent logs need to stay readable. Fluxtail receives logs into streams, then helps narrow the active window with filters, facets, histograms, alerts, MCP diagnostics, and AI chat while the raw rows remain visible.

Live tail incident response Live log viewer Real time logs Filtered rows
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Watch current logs

Follow new rows as traffic, deploys, retries, and errors happen.

Narrow before reading deeply

Filter by stream, service, host, namespace, container, severity, label, request ID, or message text.

Keep the sequence visible

A good live window shows warnings, retries, errors, and recovery messages in order.

Summarize selected rows after filtering

Use MCP diagnostics and AI chat after the stream and time range are focused.

Source

When this source should be centralized

Use this path when the source already emits logs and central reading, filtering, or alerting is the next need.

01

The issue is active now

Use live tail during a deploy, restart, traffic spike, dependency failure, or degraded path that is still producing fresh logs.

02

Raw terminal tailing is too noisy

Several services, hosts, pods, or containers can produce similar rows. Filters and stream boundaries keep the active window readable.

03

An alert needs row-level evidence

The next step after an alert is reading the exact rows behind the condition, not exporting a large after-the-fact file.

Incident window

Example live tail rows from one active window

Start with a stream, service, severity band, and short time window. Then read the sequence before asking for summaries.

Filter target
output
stream=checkout-live service=checkout-api level>=warn window=14:20-14:30
Live rows
output
2026-04-25T14:21:09Z ERROR checkout-api timeout after 3 retries order_id=ord_4921
2026-04-25T14:21:12Z WARN  checkout-api retry scheduled order_id=ord_4921 attempts=2
2026-04-25T14:21:15Z ERROR checkout-api payment retry budget exhausted order_id=ord_4921
2026-04-25T14:22:01Z INFO  checkout-api gateway latency returned to baseline
Diagnostic prompt after filtering
text
1summarize errors in checkout-live for service checkout-api from 14:20 to 14:30
Filters

Filters That Keep Live Logs Readable

Live tail works best when the stream is narrowed before the row count becomes unreadable.

01

Start broad, then narrow

Begin with stream and severity, then add service, host, namespace, container, request_id, trace_id, or message text.

Useful live filters
text
1service = checkout-api
2level >= warn
3namespace = production
4container = checkout-api
5host = app-prod-03
6request_id = req_9d81f3
7message contains "timeout"
02

Use facets before changing the query

Facets can show whether errors concentrate on one service, host, pod, container, severity, or error code.

03

Use histograms for time shape

A histogram shows whether the incident is a sudden spike, slow climb, repeated burst, or recovery pattern.

Setup

How logs move into Fluxtail

Keep the sender configuration explicit, then confirm the resulting stream keeps the fields needed for reading and filtering.

01

Start with the stream and time window

Start with the stream closest to the failing service, namespace, host class, or receiver.

02

Filter before reading deeply

Apply service, severity, host, label, Kubernetes, request, or message filters before the stream becomes unreadable.

03

Read the sequence

Keep warnings, retries, failures, and recovery rows in order so the event chain is visible.

04

Use diagnostics after narrowing

Use alerts, MCP diagnostics, or AI chat only after the selected rows are narrow enough to inspect.

Incident window

Example live tail rows from one active window

Start with a stream, service, severity band, and short time window. Then read the sequence before asking for summaries.

Filter target
output
stream=checkout-live service=checkout-api level>=warn window=14:20-14:30
Live rows
output
2026-04-25T14:21:09Z ERROR checkout-api timeout after 3 retries order_id=ord_4921
2026-04-25T14:21:12Z WARN  checkout-api retry scheduled order_id=ord_4921 attempts=2
2026-04-25T14:21:15Z ERROR checkout-api payment retry budget exhausted order_id=ord_4921
2026-04-25T14:22:01Z INFO  checkout-api gateway latency returned to baseline
Diagnostic prompt after filtering
text
1summarize errors in checkout-live for service checkout-api from 14:20 to 14:30
Verification

What to check before relying on it

Collection is useful only when the resulting rows still carry enough context to search, filter, and alert on.

01

Live tail shows new logs

Confirm new rows appear without manual refresh while the source is producing events.

02

Filters reveal the first useful error

Confirm the first useful error is visible after applying stream, service, severity, host, namespace, container, request, or message filters.

03

Facets and histograms match the rows

Facets should identify noisy services, hosts, pods, containers, severities, or error codes. Histograms should show when the spike started and whether it is falling.

04

Summaries stay tied to selected rows

MCP diagnostics or AI chat should summarize the selected rows, not an unbounded stream.

Incident window

Example live tail rows from one active window

Start with a stream, service, severity band, and short time window. Then read the sequence before asking for summaries.

Filter target
output
stream=checkout-live service=checkout-api level>=warn window=14:20-14:30
Live rows
output
2026-04-25T14:21:09Z ERROR checkout-api timeout after 3 retries order_id=ord_4921
2026-04-25T14:21:12Z WARN  checkout-api retry scheduled order_id=ord_4921 attempts=2
2026-04-25T14:21:15Z ERROR checkout-api payment retry budget exhausted order_id=ord_4921
2026-04-25T14:22:01Z INFO  checkout-api gateway latency returned to baseline
Diagnostic prompt after filtering
text
1summarize errors in checkout-live for service checkout-api from 14:20 to 14:30
Incident window

Example live tail rows from one active window

Start with a stream, service, severity band, and short time window. Then read the sequence before asking for summaries.

Filter target
output
stream=checkout-live service=checkout-api level>=warn window=14:20-14:30
Live rows
output
2026-04-25T14:21:09Z ERROR checkout-api timeout after 3 retries order_id=ord_4921
2026-04-25T14:21:12Z WARN  checkout-api retry scheduled order_id=ord_4921 attempts=2
2026-04-25T14:21:15Z ERROR checkout-api payment retry budget exhausted order_id=ord_4921
2026-04-25T14:22:01Z INFO  checkout-api gateway latency returned to baseline
Diagnostic prompt after filtering
text
1summarize errors in checkout-live for service checkout-api from 14:20 to 14:30
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Next step

Send one real source and read the logs

The fastest check is to point one real source at Fluxtail and see whether the resulting stream is easier to read.

Point one real source at Fluxtail.

Create a receiver, send one source, and inspect the first stream.

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