Last updated: 2019-11-06 16:49:08PDF
CKafka is widely used in big data scenarios, such as webpage tracking, behavior analysis, log aggregation, monitoring, streaming data processing, and online and offline data analysis.
You can make data integration easier by:
- Importing the messages in CKafka to data warehouses in Tencent Cloud such as COS and Oceanus.
- Connecting to other Tencent Cloud products via SCF triggers.
CKafka processes website activities (such as PV, search, and other user behaviors) in real time and then publishes them to topics by type. These information flows can be used for real-time monitoring or offline statistical analysis.
Since a large amount of activity information is generated in each user's page views, website activity tracking requires a very high throughput. CKafka can perfectly meet the requirements of high throughput and offline processing.
The low-latency processing capability of CKafka makes it easier to sustain distributed processing (consumption) of data from multiple sources. Compared to a centralized log aggregation system, CKafka can achieve stronger persistence guarantee and lower end-to-end latency while providing the same performance.
The features of CKafka make it an ideal "log collection center". Multiple servers/applications can send operation logs to a CKafka cluster "in batches" and "asynchronously" with no need to store them locally or in a database. CKafka can submit/compress messages in batches, so that the producer can hardly perceive the performance overhead. In this case, the consumer can use systematic storage and analysis systems such as Hadoop to perform statistical analysis on the pulled logs.
Big Data Scenarios
In some business scenarios involving big data, massive amounts of concurrent data need to be processed and aggregated, so high cluster processing performance and scalability are required. Thanks to its advantages in data distribution mechanism, allocation of disk storage space, processing of message formats, server selection, and data compression, CKafka is suitable for processing high numbers of real-time messages and can aggregate the data generated by distributed applications for easier system OPS.
Specifically, CKafka can process offline data or streaming data effectively and aggregate and analyze data easily.
CKafka can be used as SCF function triggers, and when a message is received, a function can be triggered and the message will be passed to the function as event content. For example, when CKafka triggers a function, the function can transform the message structure, filter the message contents, or deliver the message to Elasticsearch Service (ES).
For more information on the availability of SCF, see Service Level Agreement for SCF.