RisingWave processes crash report events from your game clients in real time, maintaining continuously updated materialized views that surface crash rates by version, platform, and device profile within seconds of occurrence—giving engineering teams the signal to triage, prioritize, and deploy hotfixes before crash rates compound into player churn.
Why Real-Time Crash Analytics Matters
Crashes are the most expensive bugs in live games. A crash in the middle of a competitive match is a session-ender; multiple crashes in a session is a game-uninstaller. The damage is compounded by the viral nature of player frustration: a spike in crashes after a patch lands can generate thousands of negative reviews within hours.
Traditional crash analytics platforms (Sentry, Crashlytics, Firebase) provide excellent stack trace aggregation but lag on aggregate trend analysis. You see individual crash reports quickly, but answering "what percentage of sessions on iOS 18 + iPhone 15 are crashing after tonight's 2.3.1 patch?" requires a query against a data warehouse that may be hours behind.
RisingWave bridges this gap: stream crash events alongside session events, and materialized views continuously maintain crash rate, affected player count, and device profile breakdowns—queryable in real time via standard SQL.
Ingesting Crash and Session Events
CREATE SOURCE session_events (
session_id VARCHAR,
player_id BIGINT,
game_version VARCHAR,
platform VARCHAR,
os_version VARCHAR,
device_model VARCHAR,
region VARCHAR,
event_type VARCHAR, -- 'session_start', 'session_end', 'crash'
crash_type VARCHAR, -- 'null_pointer', 'oom', 'render_error', 'network_timeout', null
crash_signature VARCHAR, -- hashed stack trace identifier
event_time TIMESTAMPTZ
)
WITH (
connector = 'kafka',
topic = 'session-telemetry',
properties.bootstrap.server = 'kafka:9092',
scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;
Real-Time Crash Rate by Version and Platform
This is the primary triage view. It computes sessions, crashes, and crash rate per 5-minute window, segmented by game version and platform:
CREATE MATERIALIZED VIEW crash_rate_live AS
SELECT
window_start,
window_end,
game_version,
platform,
COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'session_start') AS sessions_started,
COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'crash') AS crashed_sessions,
COUNT(DISTINCT player_id) FILTER (WHERE event_type = 'crash') AS affected_players,
ROUND(
COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'crash')::NUMERIC
/ NULLIF(COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'session_start'), 0) * 100,
3) AS crash_rate_pct
FROM TUMBLE(session_events, event_time, INTERVAL '5 minutes')
GROUP BY window_start, window_end, game_version, platform;
When version 2.3.1 shows a crash_rate_pct of 8.5% (vs. a healthy 0.3%) within 10 minutes of release, your on-call engineer gets the alert before player review bombs arrive.
Crash Signature Clustering
Not all crashes in a spike come from the same root cause. Grouping by crash_signature (a hash of the stack trace) isolates the top-impact crashes:
CREATE MATERIALIZED VIEW crash_signature_summary AS
SELECT
window_start,
window_end,
game_version,
platform,
crash_signature,
crash_type,
COUNT(DISTINCT session_id) AS crash_count,
COUNT(DISTINCT player_id) AS affected_players,
COUNT(DISTINCT device_model) AS affected_device_models,
COUNT(DISTINCT os_version) AS affected_os_versions,
MIN(event_time) AS first_seen,
MAX(event_time) AS last_seen
FROM TUMBLE(session_events, event_time, INTERVAL '15 minutes')
WHERE event_type = 'crash'
GROUP BY window_start, window_end, game_version, platform, crash_signature, crash_type
ORDER BY crash_count DESC;
The top rows in this view are your hotfix priorities. If the top signature accounts for 70% of crashes and correlates with device_model = 'iPhone 16 Pro', you have a device-specific rendering issue—not a systemic failure.
Device and OS Profile Analysis
Understanding which device/OS combinations are crash-prone guides hotfix scope and rollback decisions:
CREATE MATERIALIZED VIEW crash_by_device_profile AS
SELECT
game_version,
platform,
os_version,
device_model,
COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'session_start') AS total_sessions,
COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'crash') AS crash_count,
ROUND(
COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'crash')::NUMERIC
/ NULLIF(COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'session_start'), 0) * 100,
2) AS crash_rate_pct,
COUNT(DISTINCT player_id) FILTER (WHERE event_type = 'crash') AS affected_players
FROM session_events
GROUP BY game_version, platform, os_version, device_model
HAVING COUNT(DISTINCT session_id) FILTER (WHERE event_type = 'session_start') > 100;
The HAVING clause filters out device/OS combinations with fewer than 100 sessions to avoid spurious 100% crash rates on rare configurations.
Comparison: Crash Analytics Approaches
| Capability | Crashlytics / Sentry | Data Warehouse (Next-Day) | Streaming (RisingWave) |
| Individual crash reports | Real-time | Delayed | Via Kafka passthrough |
| Aggregate crash rate trends | Delayed (hours) | Next day | Real-time (5 min windows) |
| Version comparison | Manual | Available but stale | Live |
| Device/OS impact analysis | Semi-automated | Batch | Real-time |
| Correlation with session data | Limited | Possible with joins | Native SQL joins |
| Hotfix decision speed | Hours | Days | Minutes |
Routing Crash Alerts to Your On-Call System
CREATE SINK crash_alerts_to_kafka
FROM crash_rate_live
WITH (
connector = 'kafka',
topic = 'crash-alerts',
properties.bootstrap.server = 'kafka:9092'
)
FORMAT PLAIN ENCODE JSON;
A lightweight consumer reads from crash-alerts, compares crash_rate_pct against a threshold (e.g., 3x the baseline for that version), and fires a PagerDuty or Opsgenie alert to your on-call engineer automatically.
Version Baseline Reference
Store expected crash rates per version as a reference for anomaly detection:
CREATE TABLE crash_rate_baselines (
game_version VARCHAR,
platform VARCHAR,
baseline_pct DOUBLE PRECISION,
alert_threshold_pct DOUBLE PRECISION,
PRIMARY KEY (game_version, platform)
);
Downstream alerting services join crash_rate_live against this table to compute "times above baseline"—a more meaningful signal than absolute crash rate, which varies by game type and player behavior.
Crash Analytics in Your Incident Response Workflow
Patch release monitoring: Start monitoring crash_rate_live the moment a patch deploys. A 15-minute window of elevated crash rate should trigger a go/no-go on a rollback decision.
Platform-specific rollbacks: If crash rate spikes on iOS but not Android after a patch, a platform-specific rollback (via platform store phased rollout) may be possible. Streaming data makes this decision within minutes.
OOM crash trending: Filter crash_type = 'oom' (out of memory) over time. A gradual increase in OOM crashes after several patches often indicates a memory leak—catchable before it becomes critical.
FAQ
Q: How do we correlate crash spikes with specific game events (matches, boss fights)?
A: Add a game_context column to session events (e.g., 'main_menu', 'in_match', 'boss_encounter'). Filter crash signatures by context to identify which gameplay scenarios cause crashes—common for memory spikes in graphically intensive encounters.
Q: Can we use RisingWave to track crash-free session rate (the inverse of crash rate)?
A: Yes. Crash-free session rate = (sessions_started - crashed_sessions) / sessions_started * 100. Add this as a computed column in crash_rate_live. Many studios track this as their primary stability KPI.
Q: How do we handle crashes that occur before a session is properly started (pre-init crashes)?
A: Publish a lightweight pre_init_crash event type that doesn't require a valid session_id. Track these in a separate materialized view filtered by event_type = 'pre_init_crash'—they indicate install or initialization issues, not gameplay bugs.
Q: What's the right crash rate threshold to trigger a hotfix?
A: Industry benchmarks vary: mobile games typically target under 1% crash-free session degradation; PC/console under 0.5%. Set your alert_threshold_pct in the baseline table relative to your game's historical baseline, not industry averages.
Q: Can RisingWave integrate with our existing Sentry or Crashlytics setup? A: Yes. Publish crash events from Sentry/Crashlytics webhooks to a Kafka topic, then ingest into RisingWave. This gives you the best of both worlds: individual stack trace detail from Sentry and aggregate trend analytics from RisingWave.
Get Started
Build real-time crash analytics that protect your players and accelerate your hotfix cycle:
- Documentation: docs.risingwave.com/get-started
- Community: Join the conversation on RisingWave Slack
