What is real-time stream data?
Log data is generated continuously as a user browses a website, navigates through an application, or plays a game. Such data is known as real-time stream data. It allows you to monitor your business and find new opportunities in a timely manner.
What are the advantages of stream computing over batch computing?
Batch computing requires the collection of all data before starting various complex computations which may last from hours to one day. Stream computing processes stream data in quasi-real time, without requiring full data collection, which is beneficial to real-time business monitoring and new opportunity discovery.
What is a CU?
A compute unit (CU) is the unit of compute resources provided by Stream Compute Service. Specifically, a CU contains one CPU core and 4 GB memory.
Can the JAR job mode be connected to self-built services?
Yes. You can access your self-built services such as Druid and Kafka through peering connection.
What are fine-grained resources?
Fine-grained resources are resources whose compute unit can be smaller than 1 CU (1 CPU core and 4 GB memory). Stream Compute Service currently supports four types of compute units: 0.25 CU, 0.5 CU, 1 CU, and 2 CUs. You can set different compute units for JobManager and TaskManager. If you want to use 0.25 CU or 0.5 CU resources, please contact us. Why does the actual job parallelism fail to reach the maximum parallelism configured?
If fine-grained resources are used, there is a remote possibility that resource fragmentation affects the job running, leading to an actual job parallelism smaller than the maximum one. We recommend you select appropriate resource specs to avoid resource fragmentation as much as possible. If you need any help, please contact us.