What Are the Advantages of CS?
CS has the following advantages:
- Distributed real-time stream computing
CS supports large-scale distributed clusters and cluster auto-scaling. You can adjust your cluster capacity based on the resources required by your jobs, minimizing the costs.
- Easy to use
You can use the online SQL editor to compile Stream SQL statements to define the source stream, sink stream, and data processing logic to rapidly implement business logic. With CS, you can analyze streaming data without managing clusters and learning more programming skills.
- Support of exclusive clusters
CS supports auto scaling and is fully managed, which frees you from managing clusters, big data frameworks, and resource scheduling frameworks. It also visualizes the running status of your submitted jobs. You can run your jobs in a shared cluster or exclusive cluster. Exclusive clusters are physically isolated from shared clusters and other tenants' clusters. You can also manage the quota of exclusive clusters.
- Secure isolation
Security protection mechanisms for tenants ensure secure job running. Tenants' computing clusters are physically isolated from each other and protected by independent security configurations.
- Pay-per-use billing mode
You are only charged for the stream processing unit (SPU) resources you use by usage duration (seconds). An SPU contains one core and 4 GB memory.
- High throughput and low latency
CS uses the Dataflow model of Apache Flink, a real-time computing framework. High-performance computing resources are used to consume data from your created Kafka, DMS Kafka, and MRS Kafka clusters. A single SPU processes about 10,000 messages per second.
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