Confluent Cloud Availability and Resilience

1/1/2026

This post introduces the overarching engineering philosophy and significant investment behind Confluent Cloud, positioning it as a 10x improvement over self-managed Kafka. It details the architectural shift required for a true cloud-native, elastic utility, emphasizing multi-tenancy, optimized cloud environments, elastic scaling, self-protective limits, and automated operations. It also introduces Cluster Linking as a key feature for unified data fabrics across diverse environments and announces a series of upcoming posts detailing specific engineering advancements in elasticity, storage, and reliability.

10/28/2025

This post details Confluent Cloud's engineering for 99.99% uptime, including multi-zone availability, automated failure remediation, and client-side best practices for resilience.

9/30/2025

This post provides a deep dive into testing and maintaining Apache Kafka® High Availability (HA) and Disaster Recovery (DR) readiness. It explains the role of unclean leader election, recommending it be kept disabled due to data loss risks. It outlines strategies for handling network partitions using replication factors, ISRs, min.insync.replicas, and acks=all, along with architectural best practices like rack awareness and monitoring. The importance of time synchronization via NTP for data consistency and coordinated operations is highlighted. Key JMX metrics for monitoring leader election health (LeaderElectionRateAndTimeMs, UncleanLeaderElectionsPerSec, OfflinePartitionsCount, UnderReplicatedPartitions, ActiveControllerCount) and DR mechanisms (Cross-Cluster Replication Lag, Timestamp Skew) are detailed. It also emphasizes proactive management through replication, min.insync.replicas, acks=all, keeping unclean.leader.election.enable=false, using preferred replica elections, and addressing root causes of instability.

9/4/2025

This post elaborates on Confluent Cloud's cost-efficiency and operational benefits, specifically highlighting how it reduces infrastructure costs by 60% or more through its cloud-native Kora Engine, elastic autoscaling from zero to massive workloads, Freight Clusters to cut cross-AZ networking fees by up to 90%, and Private Networking Interface (PNI). It also details the 'truly fully managed' aspect with instant provisioning, automated zero-downtime upgrades, per-client quotas for tenant isolation, and a comprehensive Terraform provider. The post reinforces the 99.99% uptime SLA covering the entire service, the fault-tolerant Kora Engine's decoupling of compute from storage, and the use of Cluster Linking and Schema Linking for multi-region disaster recovery. It contrasts these with the cost and operational burdens of hyperscaler-hosted Kafka services.

7/16/2025

This post details Confluent Cloud's engineering for cost-efficient scaling and broker resilience, contrasting it with hosted Kafka offerings. Key innovations discussed include diskless storage (Freight), elastic compute (eCKUs), Private Networking Interfaces (PNI) for cost-effective and secure networking, and self-healing mechanisms for proactive node replacement and automatic rebalancing. It emphasizes how these architectural changes reduce operational burden and total cost of ownership compared to provisioned Kafka.

7/10/2025

Introduces public and Private Link networking on Jio Cloud, adds the Jio India Central region for multi-region resilience and disaster recovery, and enables simplified procurement via Azure Marketplace for Confluent Cloud on Jio Cloud.

5/10/2023

This post elaborates on Confluent Cloud's engineering for reliability, durability, scalability, and security, providing quantitative metrics and insights into operational capabilities. It highlights the management of over 30,000 Kafka clusters, 3 trillion messages written daily, and 7,700+ monitors for reliability. Durability is supported by checks on over 80 trillion messages daily. Scalability is emphasized with 10x elasticity and 2,500+ cluster expansions/contractions annually. Security is demonstrated through 2 billion daily RBAC authorization checks and adherence to industry compliance frameworks.

9/7/2022

This post elaborates on Confluent Cloud's availability and resilience engineering, detailing how it achieves 99.99% uptime. It contrasts the complexity and downtime of self-managed Kafka with Confluent Cloud's built-in multi-zone availability, proactive monitoring, and automated failure handling. The post also introduces Cluster Linking and Schema Linking as solutions for multi-region disaster recovery to mitigate regional outages, aiming for even higher availability.

6/7/2022

This post introduces the self-serve capability for elastic scaling of Confluent Cloud dedicated Kafka clusters, allowing users to expand and shrink clusters based on Confluent Units for Kafka (CKUs). It details the user experience through the UI, CLI, and public APIs, including the two-step process of capacity adjustment and data rebalancing. Safeguards are in place to prevent adverse impacts during shrinking operations, and billing adjustments are explained. Future plans for autoscaling based on policies are also mentioned.

7/29/2021

This post details how Confluent Cloud leverages Apache Kafka's existing multi-tenancy features (authentication, authorization, quotas) to build a cloud-native multi-tenant system. It explains how logical Kafka clusters are used as tenants in Confluent Cloud, with isolation provided through security, namespace conventions (prefixed ACLs, CreateTopicPolicy), and performance quotas (bandwidth, request rate, topic operations). The post also discusses the challenges and requirements of cloud-native multi-tenancy, such as abstraction, pay-as-you-go models, stringent SLOs, and cost-efficiency, and how Confluent Cloud addresses these through automated capacity planning and auto-scaling.

7/26/2021

This post introduces the concept of cloud-native data systems and how Apache Kafka, particularly in Confluent Cloud, embodies these principles. It details the key capabilities expected of cloud-native data systems: Elasticity, Infinite scale, Resiliency, Multi-tenancy, Pay per use, Cost-effectiveness, and Global reach. It elaborates on the engineering efforts for Elasticity, highlighting the Confluent Cloud control plane, event-driven microservices, Self Balancing Clusters (SBC), and tiered storage. For Performance and scalability, it emphasizes understanding user workloads, optimizing infrastructure, and production observability. For Multi-tenancy, it discusses access isolation, namespace isolation, and performance isolation, leveraging Kafka's inherent building blocks. Finally, it mentions a Storage Durability Audit capability.

4/27/2021

This post details strategies for handling Apache Kafka broker failures and cluster outages, focusing on application-level resilience and recovery mechanisms. It outlines best practices for building resilient applications that interact with Kafka, including proper configuration, monitoring, and upgrade cadences. The post categorizes applications based on their susceptibility to outages and discusses how the timing of message sends relative to business operations impacts outage handling strategies. It also describes the characteristics of extended outages and the symptoms that applications might exhibit, providing guidance on how to manage unsent messages and client library behavior during such events.

11/10/2020

Introduces Proactive Support for Confluent Platform, which uses the Confluent Telemetry Reporter to send cluster metadata to Confluent for analysis, enabling proactive issue detection and faster support resolution for self-managed Kafka deployments. Details the importance of context and experience in Kafka support, using hung transactions as an example of proactive mitigation.

6/12/2020

This post details Confluent Cloud's ability to scale Kafka clusters to over 10 GBps of aggregate throughput without downtime. It explains the underlying mechanisms for elastic scaling, including the concept of Confluent Units for Kafka (CKUs) which encapsulate cloud resources. The process involves provisioning new CKUs, which adds brokers and storage, followed by an automated, sophisticated partition rebalancing strategy. This strategy involves identifying candidate partitions for movement based on broker utilization, incrementing ISR counts, placing new replicas on new brokers, and then carefully managing failovers to new leaders. The post highlights the use of Kubernetes and the Confluent Operator for infrastructure automation and discusses the algorithm for rebalancing, including optimizations and the impact of leader changes on client connections.

4/1/2020

This post details Confluent's rigorous testing and release process for ensuring the stability and reliability of Confluent Cloud, focusing on community contributions to Apache Kafka, comprehensive testing suites (JUnit, system, cloud system, performance, soak tests), and a multi-environment release train (local, development, staging, production) with canary releases. It emphasizes the importance of automated testing, performance regression tests, and continuous monitoring to prevent regressions and ensure high availability.

3/24/2020

This post details the architecture of Confluent Cloud, breaking it down into the control plane (mothership and satellites managing physical clusters via Kubernetes custom resources and Confluent Operator) and the data plane (network architecture with load balancers for broker abstraction and dynamic scaling). It also highlights improvements made to Apache Kafka, including TLS certificate renewals without restarts, multi-tenant Kafka with quotas, and client-side DNS re-resolution for improved fault tolerance and infrastructure flexibility.

9/30/2019

Introduces Confluent Cloud as a 'serverless' Kafka service, emphasizing 'outcomes, not clusters', elastic scaling, and consumption-based pricing. Details the pricing model based on data in, data out, and data retained. Guarantees 99.95% uptime SLA and mentions tiered support plans. Highlights availability on AWS, Azure, and GCP.

9/20/2019

This post elaborates on the benefits of Confluent Cloud as a fully managed Kafka service, contrasting it with hosted and partially managed solutions. It details the complexities of self-managing Kafka in the cloud and highlights how a true managed service simplifies operations by abstracting away infrastructure concerns like compute instance selection, storage sizing, security, high availability, recoverability, observability, and data regulations. It emphasizes that a fully managed service allows developers to focus on coding by handling these operational aspects transparently. The post also defines key characteristics of a trustworthy fully managed service, including minimal user involvement in hard design decisions, a fast and painless user experience (e.g., 90-second setup), and the absence of co-responsibility models where users are asked to perform complex tasks like network setup or broker-level authentication. It contrasts this with partially managed services that may require users to manage Kafka ecosystem components like Schema Registry, Kafka Connect, Kafka Streams, and ksqlDB.

11/28/2017

This post announces the general availability of Confluent Cloud on AWS, marking a significant milestone in offering a hosted, enterprise-ready Apache Kafka service. It establishes a baseline uptime guarantee of 99.95% and details the core value proposition of abstracting away Kafka cluster operations for users. It also outlines the basic user interaction model, including private clusters, network access, and administrative CLI usage, along with essential configuration parameters like throughput, availability zones, and data retention.

9/22/2017

This post details Apache Kafka disaster recovery best practices, focusing on multi-region deployments. It introduces Confluent Cloud features like Cluster Linking and Schema Linking for building multi-region, multi-cloud, and hybrid cloud deployments. It also highlights Confluent Replicator for managing cross-datacenter replication, timestamp preservation, preventing cyclic topic repetition, resetting consumer offsets, and centralized schema management. Architecture designs for multi-datacenter disaster recovery, including active-passive and active-active patterns, are discussed.

5/8/2017

This post announces the launch of Confluent Cloud, a fully managed Apache Kafka as a Service. It highlights the simplification of running Kafka in the public cloud, abstracting away operational complexities like setup, configuration, upgrades, backups, monitoring, and security. The service offers Kafka's core capabilities with selectable throughput, retention, and replication tiers, provisioned in seconds. It also emphasizes the inclusion of the entire open-source Kafka ecosystem, including clients, REST proxy, and Schema Registry. The post stresses the operational expertise of the Confluent team, built over years of running Kafka as a service, and the commitment to avoiding vendor lock-in by making Confluent Cloud available on multiple public cloud providers (starting with AWS, followed by Azure and Google Cloud).