def scrape_cluster_data()

in edas-demo/script/scrape-data-from-prometheus/scrape-data-from-prometheus.py [0:0]

• 17 lines of code
• 3 McCabe index (conditional complexity)

def scrape_cluster_data(prom_url: str, prom_token: str) -> dict:
    data = {}
    data["load15"] = query_and_process_data(prom_url, prom_token, PROMQL_CLUSTER_LOAD15)
    data["节点CPU使用率"] = query_and_process_data(prom_url, prom_token, PROMQL_CLUSTER_CPU_USAGE)
    data["节点内存使用率"] = query_and_process_data(prom_url, prom_token, PROMQL_CLUSTER_MEM_USAGE)
    data["节点磁盘读取(Bytes)"] = query_and_process_data(prom_url, prom_token, PROMQL_CLUSTER_DISK_READ)
    data["节点磁盘写入(Bytes)"] = query_and_process_data(prom_url, prom_token, PROMQL_CLUSTER_DISK_WRITE)

    worker_data = {}
    for type, values in data.items():
        for instance, v_tuple in values.items():
            d = worker_data.get(instance, {})
            min, max, avg = v_tuple
            d[f"{type}-最小值"] = min
            d[f"{type}-最大值"] = max
            d[f"{type}-平均值"] = avg
            worker_data[instance] = d
    return worker_data