Release 89.3: Optional Log Cleaner Check Disable
Release Info
Kpow can be found on Dockerhub
docker pull operatr/kpow:89.3View our Docker quick start guide for help getting started.
Kpow can be found on ArtifactHub
Helm version: 1.0.38
helm repo add kpow https://charts.kpow.io
helm repo update
helm install --namespace factorhouse --create-namespace my-kpow kpow/kpow --version 1.0.38 \
--set env.LICENSE_ID="00000000-0000-0000-0000-000000000001" \
--set env.LICENSE_CODE="KPOW_CREDIT" \
--set env.LICENSEE="Your Corp\, Inc." \
--set env.LICENSE_EXPIRY="2024-01-01" \
--set env.LICENSE_SIGNATURE="638......A51" \
--set env.BOOTSTRAP="127.0.0.1:9092\,127.0.0.1:9093\,127.0.0.1:9094" \
--set env.SECURITY_PROTOCOL="SASL_PLAINTEXT" \
--set env.SASL_MECHANISM="PLAIN" \
--set env.SASL_JAAS_CONFIG="org.apache.kafka.common.security.plain.PlainLoginModule required username=\"user\" password=\"secret\";"
--set env.LICENSE_CREDITS="7"View our Helm instructions for help getting started.
Kpow can be found on the AWS Marketplace
View our AWS Marketplace documentation for help getting started.
Kpow can be downloaded and installed as a Java JAR file. This JAR is compatible with Java versions 11+.
View our JAR quick start guide for help getting started.
Kpow can be downloaded and installed as a Java JAR file. This JAR is compatible with Java 8.
View our JAR quick start guide for help getting started.
For more information, read the Kpow accessibility documentation.
Kpow v89.3 introduces the ability to disable the log.cleaner.enable=true broker configuration check that Kpow requires by default.
What is Log Cleaner Enable?
log.cleaner.enable is a Kafka broker configuration setting that governs data deletion, it is true by default.
When the log cleaner is enabled your Kafka brokers will periodically delete TTL'd data. If you change this configuration to false then no data will ever be physically deleted from your cluster, regardless of whether it has expired or not.
Why Does Kpow Require Log Cleaner Enable?
Kpow writes data into internal topics in your primary cluster, this data helps Kpow perform long-term analysis on the health of your clusters. We drastically reduced the amount of data written to internal topics in a recent release, but Kpow still relies on TTLd data being periodically deleted from your cluster.
Given a long enough period of operation without any data deletion Kpow's internal topics could grow to a considerable size. Being conservative, previous releases of Kpow will refuse to start if the brokers of your primary cluster are not configured to delete data.
How Do I Disable the Log Cleaner Check?
Update to v89.3 and start Kpow with the new environment variable setting: DISABLE_LOG_CLEANER_CHECK=true.
Is It Safe to Disable the Log Cleaner Check?
In the short-term, we believe so. Kpow has significantly reduced the amount of data written to internal topics such that this has become a less significant issue. The next major release of Kpow (v90) has an ephemeral mode where you can choose to run Kpow without writing any data to internal topics.
From v90, Kpow will detect where your primary cluster does not support data deletion and automatically switch into ephemeral mode.
Release v89.3 Changelog
See the Factor House Product Roadmap to understand current delivery priorities.
Kpow v89.3 Changelog
See the full Kpow Changelog for information on previous releases
- Provide DISABLE_LOG_CLEANER_CHECK configuration option
Release 94.6: Factor Platform, Ververica Integration, and kJQ Enhancements
The first Factor Platform release candidate is here, a major milestone toward a unified control plane for real-time data streaming technologies. This release also introduces Ververica Platform integration in Flex, plus support for Kafka Clients 4.1 / Confluent 8.0.0 and new kJQ operators for richer stream inspection.
Release 94.5: New Factor House docs, enhanced data inspection and URP & KRaft improvements
This release introduces a new unified documentation hub - Factor House Docs. It also introduces major data inspection enhancements, including comma-separated kJQ Projection expressions, in-browser search, and over 15 new kJQ transforms and functions. Further improvements include more reliable cluster monitoring with improved Under-Replicated Partition (URP) detection, support for KRaft improvements, the flexibility to configure custom serializers per-cluster, and a resolution for a key consumer group offset reset issue.
Release 94.4: Auto SerDes improvements
This minor hotfix release from Factor House resolves a bug when using Auto SerDes without Data policies, and adds support for UTF-8 String Auto SerDes inference.
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