recommend-model/recommend-model-produce/src/main/python/models/dssm/milvus_import.py

import os
import sys

__dir__ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
#sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__,"tools"))

import json
from concurrent.futures import ThreadPoolExecutor
import utils.compress as compress
from utils.my_hdfs_client import MyHDFSClient
from utils.oss_client import HangZhouOSSClient

# Hadoop 安装目录和配置信息
hadoop_home = "/app/env/hadoop-3.2.4"
configs = {
    "fs.defaultFS": "hdfs://192.168.141.208:9000",
    "hadoop.job.ugi": ""
}
hdfs_client = MyHDFSClient(hadoop_home, configs)
oss_client = HangZhouOSSClient("tz-milvus")


def process_file(file_path):
    """处理单个文件"""
    ret, out = hdfs_client._run_cmd(f"text {file_path}")
    result=[]
    for line in out:
        sample_values = line.rstrip('\n').split('\t')
        if(len(sample_values) == 2):
            vid, vec = sample_values
            result.append({
                "vid":int(vid),
                "vec":json.loads(vec)
            })
        else :
            # 过滤PaxHeader
            print(f"{sample_values}")
    return result

def write_results(data, output_file):
    """将结果写入文件"""
    with open(output_file, 'w') as json_file:
            json_file.write(data)

def thread_task(name, file_list):
    """线程任务"""
    print(f"Thread {name}: starting file_list:{file_list}")
    i=0
    for file_path in file_list:
        i=i+1
        count=len(file_list)
        print(f"Thread {name}: starting file:{file_path} {i}/{count}")
        result=process_file(file_path)
        file_name, file_suffix = os.path.splitext(os.path.basename(file_path))
        output_file = f"/app/milvus-{file_name}.json"
        write_results(json.dumps({"rows":result}, indent=4), output_file)

        oss_object_name=f"dyp/milvus/{file_name}.json"
        oss_client.put_object_from_file(oss_object_name, output_file)

        #compress.compress_file_tar(output_file, f"{output_file}.tar.gz")
        # hdfs_client.delete(f"/dyp/milvus/{file_name}.gz")
        # hdfs_client.upload(f"{output_file}.tar.gz", f"/dyp/milvus/{file_name}.gz", multi_processes=1, overwrite=True)
        # print(f"Thread {name}: ending file:{file_path} {i}/{count}")
    print(f"Thread {name}: finishing")

def main():
    sub_dirs,file_list = hdfs_client.ls_dir('/dw/recommend/model/56_dssm_i2i_itempredData/20241212')   
    all_file=[]
    file_extensions=[".gz"]
    for file in file_list:
        # 扩展名过滤
        if file_extensions and not any(file.endswith(ext) for ext in file_extensions):
            continue    
        all_file.append(file)
    print(f"File list : {all_file}")

    max_workers = 8
    chunk_size = len(all_file) // max_workers
    remaining = len(all_file) % max_workers

    # 分割列表
    split_file_list = []
    for i in range(max_workers):
        # 计算每份的起始和结束索引
        start = i * chunk_size + min(i, remaining)
        end = start + chunk_size + (1 if i < remaining else 0)
        # 添加分割后的子列表
        split_file_list.append(all_file[start:end])

    future_list = []
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        for i, file_list in enumerate(split_file_list):
            future_list.append(executor.submit(thread_task, f"thread{i}", file_list))

    for future in future_list:
        future.result()

    print("Main program ending")

if __name__ == "__main__":
    main()