TDMQ for CKafka is a distributed message queue service based on open-source Apache Kafka, offering high throughput and scalability. It is fully compatible with Apache Kafka 2.4, 2.8, and 3.2 APIs, with superior advantages in performance, scalability, security, and operations, allowing you to enjoy low-cost, powerful features without complex maintenance.
Introduces key concepts, advantages, coverage regions, and availability zones of TDMQ for CKafka to help you understand the product.
Introducing the basic concepts, core features (message decoupling, peak shifting, sequential read and write, asynchronous communication), high availability, and data compression of CKafka to help you quickly understand its positioning and capabilities.
Introducing the usage methods and practices of CKafka in seven typical application scenarios such as web tracking, log aggregation, big data processing, monitoring linkage, IoT data collection, and SCF triggers.
Introducing the core advantages of CKafka compared to open-source Kafka: 100% compatible with Apache Kafka, high performance (improved by 10%-20%), 7x24 high availability, 50% disk fault tolerance, automatic scale-out, and enterprise-grade security protection.
Detailed comparison of the differences and advantages between CKafka and self-built Kafka from dimensions such as basic features, Ops management, isolation and multi-tenancy, monitoring and alarm, high availability and disaster recovery, and security and compliance.
Introducing CKafka's dual-layer security protection system: the control plane implements account-level permission management through CAM; the data plane implements resource-level access control through SASL/SCRAM authentication combined with ACL policies.
Provides step-by-step guidance on how to quickly connect to and start using TDMQ for CKafka.
Introducing the two access methods supported by CKafka instances: Virtual Private Cloud (VPC) and public network, along with their access procedures, to help users select the appropriate access solution based on their specific network environment.
Introducing the operational steps for creating a CKafka instance, including configuring key parameters such as product type, billing mode, cluster type, bandwidth, disk, and partitions, as well as the method for binding to a VPC network.
Introduce how to configure and use TDMQ for CKafka, including daily operation and maintenance management to help you perform hands-on operation.
Introducing the method for manually creating Topics in the console and enabling the client automatic creation feature, including name rules, number of partitions, number of replicas, retention time, and advanced parameter configurations.
Introducing the operational steps for upgrading and downgrading CKafka instance specifications, explaining data migration, Leader switching, and transient disconnection risks during the change process, along with recommendations for selecting change modes.
This mainly introduces feasible solutions for migrating self-built or other cloud vendors' Kafka clusters to TDMQ for CKafka clusters. You can choose the most suitable migration method based on actual business needs.
Introducing the method for migrating self-built or other cloud vendors' Kafka to Tencent Cloud CKafka using open-source tools, including target instance planning and purchase, Topic metadata synchronization, and historical data migration.
Introducing two core plugins of CKafka Connector: Kafka-to-Kafka (cross-cluster replication/active-active disaster recovery) and Kafka-to-ES (log monitoring/Behavior Analytics/IoT analytics), supporting ETL cleansing and dynamic intelligent routing.
Learn about TDMQ for CKafka practical scenarios to help you use TDMQ for CKafka more efficiently.
Introducing how to conduct capacity planning by combining bandwidth utilization rate and cluster load metrics, providing safety thresholds reference for different AZ deployment modes, along with calculation formulas and practical cases for scale-out specifications.
Introducing best practices for the producer side including retry mechanism, acknowledgment mechanism, batch processing, and sticky partitioning; solutions for the consumer side covering Rebalance handling, Offset management, and backlog resolution; along with implementation methods for ordered messaging.
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