Logit.io
Solutions

Root Cause Analysis

Logit.io's root cause analysis tool aggregates data from a vast variety of sources enabling a more thorough analysis when investigating incidents or problems.

observability stack
Ingest
Correlate
Visualize
Act
Logs
Logstash · OpenSearch
12.4k/s
Metrics
Prometheus · Grafana
10k series
APM
OTLP · traces
p95 84ms
--:--:-- INFO request.completed duration_ms=42 route="/api/v1/orders"
--:--:-- WARN latency.spike service=checkout p95=820ms threshold=500ms
--:--:-- INFO trace.exported spans=128 backend=jaeger status="ok"
--:--:-- INFO metric.scrape target=prometheus job=k8s-pods samples=8421
--:--:-- INFO log.shipped bytes=184032 index=logs-prod
--:--:-- WARN auth.failure ip=203.0.113.42 attempts=3 action=rate_limit
--:--:-- INFO alert.routed severity=high channel="#incidents" dedupe=on
--:--:-- INFO dashboard.refresh uid=ops-overview panels=14 cache=hit
--:--:-- ERROR disk.pressure node=worker-3 usage=92% reclaim=started
--:--:-- INFO pipeline.batch size=2048 lag_ms=18 status=healthy
--:--:-- WARN queue.backpressure topic=ingest depth=1200
--:--:-- INFO otel.export endpoint=collector.svc spans_ok=512
--:--:-- INFO search.query hits=1284 took_ms=37 index=logs-*
--:--:-- INFO retention.policy applied hot=14d warm=30d
--:--:-- WARN tls.cert.expiring host=ingest.logit.io days=12
--:--:-- INFO ha.failover check region=eu-west status=ready
--:--:-- INFO request.completed duration_ms=42 route="/api/v1/orders"
--:--:-- WARN latency.spike service=checkout p95=820ms threshold=500ms
--:--:-- INFO trace.exported spans=128 backend=jaeger status="ok"
--:--:-- INFO metric.scrape target=prometheus job=k8s-pods samples=8421
--:--:-- INFO log.shipped bytes=184032 index=logs-prod
--:--:-- WARN auth.failure ip=203.0.113.42 attempts=3 action=rate_limit
--:--:-- INFO alert.routed severity=high channel="#incidents" dedupe=on
--:--:-- INFO dashboard.refresh uid=ops-overview panels=14 cache=hit
--:--:-- ERROR disk.pressure node=worker-3 usage=92% reclaim=started
--:--:-- INFO pipeline.batch size=2048 lag_ms=18 status=healthy
--:--:-- WARN queue.backpressure topic=ingest depth=1200
--:--:-- INFO otel.export endpoint=collector.svc spans_ok=512
--:--:-- INFO search.query hits=1284 took_ms=37 index=logs-*
--:--:-- INFO retention.policy applied hot=14d warm=30d
--:--:-- WARN tls.cert.expiring host=ingest.logit.io days=12
--:--:-- INFO ha.failover check region=eu-west status=ready
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When an issue arises, finding the root cause of the problem is almost always the first step required to rectify the issue. This is particularly vital when you are not aware of why an issue occurred, executing root cause analysis (RCA) here can help to clarify these problems. Our root cause analysis tool can assist you in streamlining your root cause analysis processes, allowing you to quickly comprehend the underlying issues and significantly reduce downtime.

Guaranteeing 100% uptime of your services is critical for all organizations. Therefore, rectifying issues promptly and fully comprehending the cause of the problem so these issues don't reoccur is paramount to your organization.

By utilizing Logit.io's observability platform your team can effectively carry out root cause analysis. Our platform analyses logs, metrics, and APM while offering comprehensive data collection. This comprehensive data collection enables complete analysis for investigating incidents or problems.

What is Root Cause Analysis?

Root cause analysis is defined as a systematic process used to highlight the underlying causes of problems or incidents within a system, process, or organization. The main objective of RCA is to determine what happened, why it happened, and how to prevent it from happening again in the future.

Root cause analysis is widely used across various industries, including manufacturing, healthcare, IT, engineering, and quality management. It helps organizations learn from past mistakes, optimize their operations, and create a culture of continuous improvement and problem-solving. By identifying and addressing the root causes of problems, organizations can enhance safety, reliability, efficiency, and customer satisfaction.

pipeline
Ingest
Parse
Index
Query
+streams normalized · schema applied
output ready for search, alerts, and dashboards

How to Perform Root Cause Analysis

Root cause analysis can be executed slightly differently depending on the issue that you're attempting to analyze, but a typical root cause analysis process follows these steps.

1. Outline the Issue: Clearly articulate the problem or issue that needs to be inspected. This can entail collecting information from multiple sources such as incident reports, user complaints, or system logs.

2. Decide Root Cause(s): Harness data analysis to determine the root cause or causes of the problem. The root cause is the underlying factor or factors that, if addressed, could stop the problem from recurring.

3. Assemble Corrective Actions: Now that the root cause(s) have been highlighted, develop and prioritize corrective actions to rectify them. These actions should be practical, feasible, and targeted at preventing similar incidents in the future.

stream
--:--:-- INFO request.completed duration_ms=42 route="/api/v1/orders"
--:--:-- WARN latency.spike service=checkout p95=820ms threshold=500ms
--:--:-- INFO trace.exported spans=128 backend=jaeger status="ok"
--:--:-- INFO metric.scrape target=prometheus job=k8s-pods samples=8421
--:--:-- INFO log.shipped bytes=184032 index=logs-prod
--:--:-- WARN auth.failure ip=203.0.113.42 attempts=3 action=rate_limit
--:--:-- INFO alert.routed severity=high channel="#incidents" dedupe=on
--:--:-- INFO dashboard.refresh uid=ops-overview panels=14 cache=hit
--:--:-- ERROR disk.pressure node=worker-3 usage=92% reclaim=started
--:--:-- INFO pipeline.batch size=2048 lag_ms=18 status=healthy
--:--:-- WARN queue.backpressure topic=ingest depth=1200
--:--:-- INFO otel.export endpoint=collector.svc spans_ok=512
--:--:-- INFO search.query hits=1284 took_ms=37 index=logs-*
--:--:-- INFO retention.policy applied hot=14d warm=30d
--:--:-- WARN tls.cert.expiring host=ingest.logit.io days=12
--:--:-- INFO ha.failover check region=eu-west status=ready
--:--:-- INFO request.completed duration_ms=42 route="/api/v1/orders"
--:--:-- WARN latency.spike service=checkout p95=820ms threshold=500ms
--:--:-- INFO trace.exported spans=128 backend=jaeger status="ok"
--:--:-- INFO metric.scrape target=prometheus job=k8s-pods samples=8421
--:--:-- INFO log.shipped bytes=184032 index=logs-prod
--:--:-- WARN auth.failure ip=203.0.113.42 attempts=3 action=rate_limit
--:--:-- INFO alert.routed severity=high channel="#incidents" dedupe=on
--:--:-- INFO dashboard.refresh uid=ops-overview panels=14 cache=hit
--:--:-- ERROR disk.pressure node=worker-3 usage=92% reclaim=started
--:--:-- INFO pipeline.batch size=2048 lag_ms=18 status=healthy
--:--:-- WARN queue.backpressure topic=ingest depth=1200
--:--:-- INFO otel.export endpoint=collector.svc spans_ok=512
--:--:-- INFO search.query hits=1284 took_ms=37 index=logs-*
--:--:-- INFO retention.policy applied hot=14d warm=30d
--:--:-- WARN tls.cert.expiring host=ingest.logit.io days=12
--:--:-- INFO ha.failover check region=eu-west status=ready

Why is Root Cause Analysis Important?

Root cause analysis is vital for numerous reasons, a primary example being the process prevents recurrence. By highlighting the underlying causes of issues, organizations can execute corrective actions that address these root causes, instead of just treating the symptoms. This proactive approach assists in creating more resilient systems and processes.

Minimizing costs is important to all organizations and with effective root cause analysis, you can achieve this. Instead of continually dealing with the same problems, organizations can invest resources in implementing permanent solutions. This can reduce expenses associated with downtime, repairs, rework, and customer compensation. Also, stopping incidents that may result in legal liabilities or regulatory fines can save substantial costs in the long run.

Understanding the root causes of defects or quality issues allows your organization to make targeted enhancements to its products or services. By rectifying these root causes, organizations can optimize product reliability, consistency, and performance. This could lead to higher customer satisfaction, increased customer loyalty, and a stronger reputation in the market.

shell
$
logit trace export --protocol=otlp --backend=jaeger
→ distributed spans indexed · p99=84ms

Extensive Data Collection for In-Depth Analysis

At Logit.io, our service contains built-in support for sending data from countless different sources. Whether you're looking to be in full control or employ a degree of automation by using our lightweight data shippers, Logit.io is fully able to meet your data ingestion requirements. Using Logit.io for log analysis grants you more flexibility when it comes to making the most of AWS, GCP and Azure services and also provides a hosted platform in which you can easily launch OpenSearch Stacks.

metrics
latency
throughput
errors
p95 latency 84ms · ingest 12.4k/s · error rate 0.08%

Real-Time Monitoring and Analysis

With our root cause analysis tool you can track system performance and health continuously, enabling rapid detection of anomalies. Also, you can attain real-time alerts for unusual behavior or performance degradation, allowing for a prompt response. Benefit from live analysis of data streams, facilitating immediate troubleshooting and problem resolution.

alerts
1
Detect
2
Enrich
3
Route
4
Notify
! anomaly detected · checkout p95 > 500ms
→ context attached · service map · recent deploy
→ routed to #incidents · ack in 12s

Effective Analysis with Historical Data and Trending

  • Use historical data for retrospective analysis and trend identification.
  • Compare current incidents with past occurrences, aiding in root cause identification.
  • Employ trend analysis over time, highlighting recurring issues and underlying patterns.
  • stack
    OpenSearchPrometheusGrafanaJaegerLogstash
    $ logit stack status --managed
    all services healthy · HA enabled · backups current

    Scalability and Flexibility for All Organizations

  • The Logit.io platform scales to manage large volumes of data and diverse workloads.
  • As your organization's needs evolve, Logit.io will adapt, supporting new data sources and analysis techniques.
  • Flexibility in data storage, processing, and visualization, accommodating changing requirements.
  • pipeline
    Ingest
    Parse
    Index
    Query
    +streams normalized · schema applied
    output ready for search, alerts, and dashboards

    Companies Feel The Difference When They Use Logit.io

    Internally, Logit.io has made it easier for us to provide better support for our customers, since finding individual messages based on various data in the payload has become easier.

    At Youredi, pretty much everyone from our technical support teams through to our professional services teams uses Logit.io.

    Youredi

    Mats von Weissenberg

    CTO @ Youredi

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