HBase and Elasticsearch are both distributed, open-source NoSQL databases. However, they have different strengths and use cases.
HBase is a column-oriented database that is built on top of Hadoop’s distributed file system (HDFS). It is designed to handle big data workloads and supports random read/write access. HBase is often used for real-time processing, such as storing and analyzing log data or social media feeds.
Elasticsearch is a full-text search engine that is optimized for searching large volumes of unstructured data. It is built on top of the Apache Lucene library and provides fast search capabilities through a RESTful API. Elasticsearch is often used for text-based search applications, such as e-commerce product search or website search.
In some cases, HBase and Elasticsearch can be used together to provide a comprehensive big data solution. For example, HBase could be used to store raw data while Elasticsearch is used for indexing and searching that data. This combined approach offers the benefits of both systems, including real-time processing and fast search capabilities.
![/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)
