KLogic
📊 Log Analysis

Kafka Log Analysis

Aggregate broker logs from across your entire Kafka cluster, search them with full-text queries, filter by log level and time range, and drill down to specific hosts and services—all powered by ClickHouse for sub-second query performance at any scale.

Scattered Broker Logs Slow Down Every Incident Investigation

When logs live on individual broker hosts, finding the one relevant line takes longer than fixing the issue

SSH-and-Grep Is Not an Incident Response Strategy

When an alert fires, engineers SSH into individual broker nodes and grep through multi-gigabyte log files. With a 12-broker cluster, that means 12 separate sessions and no unified view of what happened cluster-wide.

No Full-Text Search Across the Cluster

Searching for a specific exception or error code across all brokers simultaneously is impossible without centralized log storage. The result is incomplete investigations and missed correlations between broker log entries.

Logs Rolled and Lost Before Investigation

Kafka broker logs roll frequently on busy clusters. Without centralized aggregation, logs from the exact time window of an incident are often overwritten before engineers get a chance to analyze them.

Centralized Log Analysis Powered by ClickHouse

Vector aggregates, ClickHouse stores, and KLogic makes every log instantly searchable

Vector-Powered Aggregation with ClickHouse Storage

Centralized Log Aggregation via Vector

KLogic ships with a pre-configured Vector pipeline that collects logs from all broker nodes and streams them into ClickHouse—no custom log shipper setup required

ClickHouse-Powered Sub-Second Search

ClickHouse's columnar storage delivers full-text search across billions of log lines in under a second, even with multi-dimensional filters applied simultaneously

Configurable TTL Retention

Set log retention independently from your topic retention—configure TTL policies per service or host to balance storage costs against investigation needs

Log Search1.2M entries • 0.4s
ReplicationManager offset out of range
ERROR14:02:31broker-3ReplicationManager: offset out of range for partition orders-12
ERROR14:02:33broker-1ReplicationManager: offset out of range for partition orders-7
WARN14:02:35broker-3ReplicationManager: consumer group payment-svc reset to earliest
ERROR14:02:38broker-2ReplicationManager: offset out of range for partition orders-4
Showing 4 of 247 matches
Log Filters
ERRORWARNINFODEBUG
broker-1, broker-2, broker-3
kafka-prod-*
2025-03-27 14:00 — 15:00 UTC
247 results across 3 brokers198 ERRORs

Log Level Filtering and Service Drill-Down

ERROR, WARN, INFO, DEBUG Level Filtering

Isolate only the log levels you care about during an incident—filter to ERROR and WARN to cut through INFO noise when time matters most

Service and Host Filtering

Filter logs to specific broker services or hostnames—use wildcards to select a group of hosts or pick individual brokers for targeted analysis

Precise Time-Range Scoping

Scope any log query to a custom time window down to the second—essential for correlating broker log entries with the exact moment of an alert or metric anomaly

Frequently Asked Questions

KLogic uses Vector as the log aggregation agent. Vector is deployed alongside your Kafka brokers and streams log lines to KLogic's ClickHouse storage in real time. Configuration is minimal—KLogic ships with a ready-made Vector config that covers all standard Kafka log outputs.

KLogic supports filtering by ERROR, WARN, INFO, and DEBUG log levels. You can combine multiple levels in a single query, making it easy to see all ERROR and WARN entries without the noise of INFO and DEBUG lines during an active incident investigation.

KLogic stores logs in ClickHouse, a columnar database purpose-built for analytical queries. Full-text search across millions of log lines typically returns results in under a second, even when filtering by service, host, time range, and log level simultaneously.

Yes. The log analysis interface supports filtering by service name, host, log level, and time range simultaneously. You can search all brokers at once or drill into a single broker to narrow down a service-specific issue.

Log retention is controlled by a configurable TTL policy in ClickHouse. You can set retention independently from your Kafka topic retention—common configurations range from 7 to 90 days depending on your compliance and debugging requirements.

Yes. KLogic allows you to create log-based alert rules that trigger when a specific keyword or pattern appears in the log stream. Common use cases include alerting on FATAL errors, authentication failures, or specific exception class names.

Find the Log Line That Explains Your Incident in Seconds

Stop SSHing into brokers during incidents. KLogic aggregates all your Kafka broker logs into a searchable, filterable interface so your team can investigate faster and recover sooner.

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