4/5/2023 0 Comments Filebeats to kafka![]() ![]() Run multiple heterogeneous clusters on a shared hardware pool.Modify resource requirements without changing hardware, e.g., more compute for one cluster and less storage for another.Easily scale up or down cluster resources (compute or storage) as needed and in a self-service manner.The alternative is custom infrastructure silos for each team, all configured and managed slightly differently. Log analytics as a service, so each team and project can create and operate independently with just the resources they need.The PortWorx storage can be backed by local drives, FlashArray volumes, or FlashBlade NFS. The diagram below illustrates the deployed pipeline architecture:Ĭonfluent and Elasticsearch PersistentVolumes orchestrated by Portworx while also using S3 buckets for long-term shared storage. This blog post describes a helm meta-chart that demonstrates how to automatically deploy a full pipeline that automates the deployment and configuration of a disaggregated log analytics pipeline based on Kafka and Elasticsearch. Support for fast historical searches with the predictable all-flash performance of FlashBlade. ![]() With small, bounded amounts of storage attached to a pod, rebalance operations are orders of magnitude faster. Faster failure handling by making pods (brokers or data nodes) near-stateless.More efficient resource usage by avoiding deploying extra nodes just to increase storage and no longer needing full replicas for data protection.More importantly, recently released features for both applications, Confluent Tiered Storage and Elastic Searchable Snapshots, use object store to fully disaggregate compute and storage in log analytics.Ī cloud-native disaggregated pipeline architecture with fast object storage means: Kubernetes makes deploying log pipelines as a service easy, with CSI dynamic volume provisioning allowing for easy scaling and adjusting of resources. Kubernetes and disaggregated storage simplify Kafka and Elasticsearch clusters and are essential for scaling and operating log pipelines in production. ![]() The time it takes to deploy a new log pipeline is a key factor in if a new data project will be successful. The most commonly deployed log analytics combines Apache Kafka ® and Elasticsearch to create a reliable, scalable, and performant system to ingest and query data. A log analytics pipeline allows teams to debug and troubleshoot issues, track historical trends, or investigate security incidents. Collecting and indexing logs from servers, applications, and devices enables crucial visibility into running systems. ![]()
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