Log management that is still usable at ten terabytes a day
Collect from every cluster, server and cloud service, parse into structure, tier retention by value, and find the line you need without a query language course.
What is Log Management?
DevOpsArk log management is the module that collects, parses, indexes and retains logs from Kubernetes workloads, servers and cloud services, with pattern detection and retention tiering so volume does not make the logs unusable or unaffordable.
What Log Management is for
The conditions this module removes. If none of these are familiar, you probably do not need it yet.
- Logs are lost when a pod is rescheduled, which is exactly when they were needed.
- Every service logs in its own format, so cross-service search is text matching and hope.
- Retention is set by cost rather than by value, so the audit-relevant logs expire with the debug noise.
- Search takes ninety seconds, so nobody searches during an incident.
- A recurring error is buried in millions of near-identical lines that no one groups.
Logs are collected from container stdout, node files, cloud provider log services and application shippers, then parsed into structured fields: JSON where the application emits it, pattern extraction where it does not. Every record is labelled with the service, environment and cluster it came from using the same identity model as the rest of the platform, so a search can be scoped to a service without knowing its container naming. Similar lines are grouped into patterns, which turns four million records into a few hundred distinct events with counts and trends, and makes a new error visible the first time it appears. Retention is tiered: recent logs are fully indexed for fast search, older logs move to cheaper storage that is still searchable, and the classes you must keep for audit are retained on their own schedule rather than expiring with debug output.
What Log Management does
The 7 capabilities that make up Log Management.
Collection from everywhere
Container stdout, node files, cloud log services and application shippers, without per-host configuration.
Parsing and structure
JSON parsed natively; unstructured lines get pattern extraction so they gain queryable fields.
Pattern grouping
Near-identical lines collapse into patterns with counts and trends, so a new error stands out the first time it occurs.
Tiered retention
Fast index for recent data, cheaper searchable storage for older data, and separate schedules for audit-relevant classes.
Search without a query language
Scope by service, environment, severity and time from the interface; full query syntax remains available when you want it.
Correlated with traces and metrics
A log line links to the trace it belongs to and the metric window it occurred in.
Sensitive data redaction
Configurable redaction at ingest so tokens, card numbers and personal data are not persisted in the log store.
How Log Management fits together
Outcomes
- Logs survive pod rescheduling, so the evidence is there when you look for it.
- Volume becomes a few hundred patterns instead of millions of lines.
- Cost is controlled by tiering rather than by deleting the logs you needed.
- Search is fast enough to be used during an incident rather than after it.
- Sensitive values are removed before they are stored, not after they are found.
Using Log Management, step by step
The path from connecting a source to getting value, in the order it happens.
- 1Collect
Attach clusters, servers and cloud accounts; collection starts without per-host setup.
- 2Parse and redact
Records gain structure, and sensitive values are removed before storage.
- 3Label
Each record is attached to its service, environment and cluster.
- 4Group into patterns
Similar lines collapse into counted, trended patterns.
- 5Tier retention
Data moves between hot, warm and audit tiers on its own schedule.
Where teams apply Log Management
Find the error that started an incident
Scope to the service and window, read the pattern list, and open the first occurrence of the new error.
Debug across services
Follow a request identifier through every service that logged it, in one search.
Retain audit logs correctly
Keep the classes that require retention on their own schedule instead of the general expiry.
Cut log spend without losing coverage
Move older data to cheaper searchable storage and drop the debug classes that nobody queries.
What Log Management works with
Named integrations link to their own page. The rest are supported runtimes and formats.
Log Management: frequently asked questions
The 8 questions teams ask most often before adopting Log Management.
Log management is the collection, parsing, storage, search and retention of log data from applications and infrastructure. Its purpose is to make the record of what a system did available and searchable when something needs explaining.
Container logs live on the node and disappear when a pod is rescheduled or evicted, which is often exactly the event you need to investigate. Centralising them means the evidence outlives the pod.
Pattern grouping collapses near-identical log lines into a single pattern with a count and a trend, so millions of records become a few hundred distinct events. It makes a genuinely new error visible on its first occurrence instead of hiding it in volume.
By tiering retention rather than deleting: recent data is fully indexed, older data moves to cheaper storage that remains searchable, and audit-relevant classes are retained separately. Ingest-time filtering removes classes nobody ever queries.
Yes. Redaction rules apply at ingest, so tokens, card numbers and personal data are removed before the record is stored rather than discovered in it later.
It can, or it can read from an existing Loki or Elasticsearch deployment. Teams commonly keep their collector and use DevOpsArk for cross-cluster search, pattern grouping and correlation with metrics and traces.
Records carry the trace and span identifiers where the application emits them, so a log line links to the request it belongs to and a slow span links to the lines emitted during it.
Retention is configured per tier and per log class. A common arrangement is short full-index retention for debug output, longer searchable retention for application and access logs, and a separate multi-year schedule for audit classes.
See Log Management against your own environment
A 30-minute walkthrough with a platform engineer, not a sales deck. Bring a cluster and a problem.