Kubernetes (K8s) is an open-source container orchestration system that is widely used to deploy, manage and scale containerized applications. Hadoop, on the other hand, is a distributed computing platform that is used for storing and processing large volumes of data across clusters of computers.
Using Kubernetes to manage Hadoop clusters can provide several benefits, including:
Scalability: Kubernetes can automatically scale up or down the number of Hadoop nodes based on workload demands.
Resilience: Kubernetes can help ensure high availability of Hadoop services by monitoring and restarting failed containers and nodes.
Resource management: Kubernetes can allocate resources such as CPU and memory efficiently among different Hadoop workloads.
Simplified deployment: Kubernetes can simplify the deployment of Hadoop clusters by providing a declarative way to specify configurations and dependencies.
Some popular tools for running Hadoop on Kubernetes include Apache Spark, MapReduce, and HDFS. These tools can be deployed as containers and managed by Kubernetes in a scalable and efficient manner.
![/data # iw --debug dev wlan0 connect -w "lucky-5g" auth open key 0:1234567890
Usage: iw [options] dev connect [-w] [] [] [auth open|shared] [key 0:abcde d:1:6162636465] [mfp:req/opt/no]
Join the network with ...](https://linuxcpp.0voice.com/zb_users/upload/2023/05/202305162239148267954.png)
![驱动代码
void kalRxTaskletSchedule(struct GLUE_INFO *pr)
{
static unsigned int num = 0;
tasklet_hi_schedule(&pr->rRxTask[(num++)%NR_CPUS]);
// tasklet_hi_schedule(&pr->rRxTask);
DBGLOG(HAL, ERROR,](https://linuxcpp.0voice.com/zb_users/upload/2023/05/202305162226144313964.png)
