investigating-error-issue

name

investigating-error-issue

description

Investigates a single PostHog error tracking issue end-to-end. Use when the user provides an issue ID or pastes an issue URL (`/error_tracking/<id>`) and wants to understand the error — who it affects, what triggers it, when it started, whether it correlates with a release, browser, OS, or feature flag, and what the next step should be. Pulls aggregated metrics, sample exception events, segment breakdowns, linked replays, and synthesizes a hypothesis-grade summary in one pass.

Investigating an error tracking issue

When a user asks "what's going on with this error?" or pastes an issue URL, gather the context they would otherwise have to assemble manually: who is hitting it, what changed, where it happens, and whether a replay shows the cause.

Available tools

Tool	Purpose
posthog:query-error-tracking-issue	Compact issue details (status, assignee, top frame, release, aggregates)
posthog:query-error-tracking-issue-events	Sampled $exception events with stack, URL, browser, $session_id
posthog:execute-sql	Breakdowns, release / flag correlations, surrounding events + console logs around the error
posthog:query-logs	OTEL log entries around the error timestamp for server-side issues
posthog:query-session-recordings-list	Linked replays (delegate ranking to finding-replay-for-issue)
posthog:read-data-schema	Confirm property keys before filtering on them

Workflow

Step 1 — Establish the issue baseline

Fetch the issue record with its compact aggregates and a sparkline:

posthog:query-error-tracking-issue
{
  "issueId": "<issue_id>",
  "dateRange": { "date_from": "-30d" },
  "includeSparkline": true,
  "volumeResolution": 12
}

Capture: name, description, status, first_seen, last_seen, assignee, total occurrences / users / sessions, top in-app frame, latest release metadata, and the volume buckets.

The sparkline tells you the shape — flat, spike, ramp, or recurring — and that shape drives the rest of the investigation. If the user only asked a status question, skip includeSparkline to save tokens.

Step 2 — Pull a sample exception event

A captured event has the stack frames, URL, browser, and properties needed to reason about cause. Pull a recent sample first, then an early one to compare.

posthog:query-error-tracking-issue-events
{
  "issueId": "<issue_id>",
  "limit": 1,
  "verbosity": "stack"
}

Use verbosity: "raw" only if the truncated stack hides the answer. The tool defaults to onlyAppFrames: true, which strips vendor frames; flip to false when the bug appears to live in a third-party library — or when the response comes back with stacktrace.type: "resolved" but no frames at all (common for minified bundles where every frame looks vendor-y to the resolver, e.g. React production builds).

For the earliest sample, narrow dateRange to a tight window around the issue's first_seen (e.g. set date_from slightly before and date_to slightly after) and pass orderDirection: "ASC" so you get the earliest event in the window rather than the latest — the tool defaults to DESC, which would return a recent event and silently duplicate the first call. If recent and earliest events look materially different — different stack root, different URL pattern — the issue may be a grouping mistake. Flag for grouping-noisy-errors instead of continuing as if it were one bug.

Step 3 — Run breakdowns to isolate the cause

Breakdowns aren't a typed tool — drop into execute-sql. Run only the breakdowns the issue's shape suggests; each one costs a query and clutters the synthesis.

Sparkline shape	First breakdown to try
Spike from zero	By app version / release — almost always a deploy regression (see below)
Steady-state high	By browser / OS — rendering or platform-specific bug
Ramp	By geography or feature flag — gradual rollout exposure
Bursts then quiet	By time of day or $current_url — scheduled job or specific page

Picking the right version property

PostHog emits three version-shaped fields. They mean different things and only one of them answers "what version of the user's app introduced this?":

Property	What it is	Auto-captured by	Use for
$exception_releases	Cymbal-managed release map, keyed by release ID	Only when SDK publishes release metadata (e.g. sourcemap upload tied to a release)	Most precise release attribution when present
$app_version	The user's deployed app version	iOS (CFBundleShortVersionString), React Native (Expo / react-native-device-info)	"What deploy of my app introduced this?" — the question users care about
$lib_version	The PostHog SDK library version (e.g. posthog-js 1.298.0)	Every SDK on every event	The narrow "did upgrading the PostHog SDK introduce this?" question

$lib_version is on virtually every event, which makes it tempting — but it's the PostHog library version, not the user's app version. A constant $lib_version paired with a spike means the user shipped a regression in their own code with the SDK unchanged, which is the common case. Reach for $lib_version only when nothing else is populated and you're explicitly asking "did upgrading PostHog cause this?".

Web / server / Node / Java / Python projects do not auto-capture $app_version — the customer has to set it (via register, a context provider, or before_send). If the breakdown comes back with one $app_version row of all-NULL, say so explicitly in the synthesis and suggest the customer wire it up; falling back to $exception_releases or to a per-day timeline by first_seen keeps the investigation moving.

Example ($app_version — populated automatically on mobile, manually on web / server):

posthog:execute-sql
SELECT
    properties.$app_version AS app_version,
    count() AS occurrences,
    uniq(person_id) AS users,
    min(timestamp) AS first_seen,
    max(timestamp) AS last_seen
FROM events
WHERE event = '$exception'
    AND (issue_id = '<issue_id>' OR properties.$exception_issue_id = '<issue_id>')
    AND timestamp > now() - INTERVAL 30 DAY
GROUP BY app_version
ORDER BY occurrences DESC
LIMIT 20

The (issue_id = ... OR properties.$exception_issue_id = ...) pattern mirrors the canonical build_issue_where clause from products/error_tracking/backend/api/query_utils.py. issue_id is the resolved virtual field on events (it follows fingerprint overrides so merged/split issues route correctly); properties.$exception_issue_id is the raw event property captured at ingestion. Filtering on only the property silently undercounts events for issues that have been merged or split.

If first_seen for one app_version is much later than the issue's overall first_seen, that release introduced or worsened the bug — strong root-cause signal. If every row is NULL, the SDK isn't reporting an app version on this project (common on web / server) — switch to $exception_releases if the customer ships releases, or fall back to a toDate(timestamp) timeline.

When $exception_releases is populated, it's a JSON dict keyed by release ID. There is no top-level $release property; query $exception_releases directly when you need release attribution and the customer has it wired up.

Repeat with properties.$browser, properties.$os, properties.$current_url, or any feature flag the project tags errors with.

Step 4 — Check feature flag exposure

If the user suspects an experiment or rollout, check whether affected users had a flag enabled when the error fired.

To enumerate which flags were evaluated on affected users, parse the $active_feature_flags property — it is materialized as a Nullable(String) JSON-encoded column in ClickHouse, so arrayJoin(properties.$active_feature_flags) directly will fail. JSONExtract is the working pattern, but you must coerce the argument to a non-nullable String first: ClickHouse refuses to return a nested Array(String) from JSONExtract when the input is Nullable, and this type error is raised at query planning, so the notEmpty(...) guard in the WHERE clause does not prevent it. Wrap the argument in ifNull(..., '[]') (or assumeNotNull(...)):

posthog:execute-sql
SELECT
    arrayJoin(JSONExtract(ifNull(toString(properties.$active_feature_flags), '[]'), 'Array(String)')) AS flag,
    count() AS occurrences,
    uniq(person_id) AS users
FROM events
WHERE event = '$exception'
    AND (issue_id = '<issue_id>' OR properties.$exception_issue_id = '<issue_id>')
    AND timestamp > now() - INTERVAL 14 DAY
    AND notEmpty(toString(properties.$active_feature_flags))
GROUP BY flag
ORDER BY occurrences DESC
LIMIT 20

Caveat: every event captures every evaluated flag key, so this enumeration often returns identical counts across flags and doesn't tell you which flag correlates with the error — only which were on the user. To actually test a hypothesis, query the per-flag value column properties.$feature/<flag-key>, which carries the evaluated value (true/false/variant name):

posthog:execute-sql
SELECT
    properties.`$feature/my-flag-key` AS variant,
    count() AS occurrences,
    uniq(person_id) AS users
FROM events
WHERE event = '$exception'
    AND (issue_id = '<issue_id>' OR properties.$exception_issue_id = '<issue_id>')
    AND timestamp > now() - INTERVAL 14 DAY
GROUP BY variant
ORDER BY occurrences DESC

Compare the variant split here to the project's overall exposure on the same flag in the same window. Disproportionate representation of one variant suggests the flag is involved in the cause — not a guarantee, but a strong hypothesis.

Step 5 — Reconstruct what happened around the error

Use the $session_id from the sample event in step 2 to pull the activity surrounding the exception. Three sources stack on each other; run the ones that make sense for the SDK that captured the error.

5a. Surrounding events (client SDKs by $session_id)

Mirrors the ET frontend session timeline. Pulls custom events, page views, and other exceptions captured under the same session within a ±1h window:

posthog:execute-sql
SELECT
    uuid,
    event,
    timestamp,
    properties.$lib AS lib,
    properties.$current_url AS url
FROM events
WHERE $session_id = '<session_id_from_step_2>'
    AND (event = '$exception' OR event = '$pageview' OR left(event, 1) != '$')
    AND timestamp >= toDateTime('<error_timestamp>', 'UTC') - INTERVAL 1 HOUR
    AND timestamp <= toDateTime('<error_timestamp>', 'UTC') + INTERVAL 1 HOUR
ORDER BY timestamp ASC
LIMIT 100

The left(event, 1) != '$' clause drops PostHog autocapture / system events while keeping every custom event. The OR event = '$pageview'/'$exception' exceptions re-add the two system events worth seeing on the timeline. This is the same filter the ET UI uses.

Mixed $lib values in the output are a feature, not noise. When a server SDK propagates $session_id from the client request (PostHog's own backend does this), the timeline shows server-side activity inline with the browser side — "both SDKs when available" for free. Skim the lib column to see how each row was produced.

The skill defaults to a ±1h window because that's what the UI uses; widen it when an issue's actions are slow (long batch jobs, background workers) or tighten it when only the seconds right before the throw matter.

5b. Console logs (web / React Native session replay)

When session replay is enabled, the replay pipeline emits console.* calls into the log_entries table tagged with the same session id. Pull them with the matching window:

posthog:execute-sql
SELECT timestamp, level, message
FROM log_entries
WHERE log_source = 'session_replay'
    AND log_source_id = '<session_id_from_step_2>'
    AND timestamp >= toDateTime('<error_timestamp>', 'UTC') - INTERVAL 1 HOUR
    AND timestamp <= toDateTime('<error_timestamp>', 'UTC') + INTERVAL 1 HOUR
ORDER BY timestamp ASC
LIMIT 200

log_source = 'session_replay' is the discriminator — log_entries is shared with other sources. Empty results are common: either replay isn't enabled, or this specific session wasn't recorded. Mention that in the synthesis rather than treating it as a failure.

5c. Server logs around the error (OTEL via query-logs)

For server-side exceptions, correlate the exception timestamp with OTEL log entries the customer ingests. Many projects don't ingest logs at all — if query-logs returns nothing or errors, say so and move on. Discover available services first with logs-attribute-values-list when you don't know which service produced the error.

posthog:query-logs
{
  "query": {
    "dateRange": {
      "date_from": "<error_timestamp minus 5 minutes>",
      "date_to":   "<error_timestamp plus 5 minutes>"
    },
    "severityLevels": ["error", "warn"],
    "serviceNames": ["<service.name if known>"],
    "limit": 50,
    "orderBy": "earliest"
  }
}

Caveats worth knowing before relying on this output:

• Logs are ingested separately from events and typically have shorter retention. Old exceptions may return empty even though the issue is still active.
• trace_id / span_id come back zero-padded ("00000000...") when not set. Trace-based correlation only works for explicitly instrumented requests, not for every event.
• service.name is a resource attribute. Narrow with serviceNames rather than a free-text searchTerm when you know the producer.

5d. Find a representative replay

Hand off to finding-replay-for-issue when picking the best session matters — popular issues link hundreds of recordings, mostly short crash fragments or idle-tab sessions, and that skill applies the duration / active-time / recency ranking that finds the one most likely to show the cause. Hand off too when the user asks for "a replay" without specifying which.

Skip the hand-off and pull a recording inline via query-session-recordings-list with session_ids from the sample exception events you already fetched in step 2 when only a handful of sessions are linked, the user already named a specific session, or any working example will do (e.g. proving the error reproduces).

If neither path returns a recording, mention that session replay may not be enabled for the affected users — useful context, not a failure.

Step 6 — Synthesize

Present in this order:

1. What it is — type, message, where in the stack
2. Who it affects — total users, sessions, and any segment breakdown that stood out
3. When it started — first_seen, plus the release / version that introduced it if a breakdown found one
4. Likely cause — one or two hypotheses backed by the breakdowns above
5. Next step — a concrete action: investigate the suspected release, watch the linked replay, ping the assignee, or escalate

Keep the synthesis tight. The user wants the answer, not a tour of the data.

Tips

• The canonical join key from events to an issue is the resolved issue_id virtual field, with properties.$exception_issue_id as fallback — see Step 3 for the reason and the build_issue_where pattern.
• For a "what version introduced this?" breakdown, prefer $app_version (the user's deployed app version, auto-captured on iOS / React Native and manually set on web / server) or $exception_releases when populated. Avoid $lib_version for this question — it's the PostHog SDK library version, not the user's app. See the "Picking the right version property" subsection in Step 3.
• If the issue spans more than 30 days, widen the date range explicitly. Defaults often truncate the original first_seen event off the breakdown.
• Don't propose a fix in the synthesis unless the cause is obvious from the sample stack. Hypotheses backed by data are more useful than confident guesses.
• If query-error-tracking-issue returns an external_issues array, the issue is already linked to a Linear / Jira / GitHub ticket. Mention the link in the synthesis so the user doesn't open a duplicate.