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在 Node1 (4CPU, 8GB) 上启动 Dask 调度程序：

Dask调度程序：dask-scheduler --host 0.0.0.0 --port 8786

在 Node2（8CPU，32GB）和 Node3（8CPU，32GB）上启动 Worker： Dask Worker：

dask-worker tcp://http://xxx.xxx.xxx.xxx:8786 --nanny-port 3000:3004 --worker-port 3100:3104 --dashboard-address :8789

这是我的原型，编辑some_private_processing和some_processing方法：

import glob
import pandas as pd

from dask.distributed import Client

N_CORES = 16
THREADS_PER_WORKER = 2
dask_cluster = Client(
    '127.0.0.1:8786'
)

def get_clean_str1(str1):
    ret_tuple = None, False, True, None, False
    if not str1:
        return ret_tuple
    if string_validators(str1) is not True:
        return ret_tuple

    data = some_processing(str1)
    match_flag = False
    if str1 == data.get('formated_str1'):
        match_flag = True

    private_data = some_private_processing(str1)
    private_match_flag = False
    if str1 == private_data.get('formated_private_str1'):
        private_match_flag = True
    ret_tuple = str1, match_flag, False, private_str1, private_match_flag
    return ret_tuple

files = [
    'part-00000-abcd.gz.parquet',
    'part-00001-abcd.gz.parquet',
    'part-00002-abcd.gz.parquet',
]
print('Starting...')
for idx, each_file in enumerate(files):
    dask_cluster.restart()
    print(f'Processing file {idx}: {each_file}')
    all_str1s_df = pd.read_parquet(
        each_file,
        engine='pyarrow'
    )
    print(f'Read file {idx}: {each_file}')
    all_str1s_df = dd.from_pandas(all_str1s_df, npartitions=16000)
    print(f'Starting file processing {idx}: {each_file}')
    str1_res_tuple = all_str1s_df.map_partitions(
        lambda part: part.apply(
            lambda x: get_clean_str1(x['str1']),
            axis=1
        ),
        meta=tuple
    )

    (clean_str1,
     match_flag,
     bad_str1_flag,
     private_str1,
     private_match_flag) = zip(*str1_res_tuple)

    all_str1s_df = all_str1s_df.assign(
        clean_str1=pd.Series(clean_str1)
    )
    all_str1s_df = all_str1s_df.assign(
        match_flag=pd.Series(match_flag)
    )
    all_str1s_df = all_str1s_df.assign(
        bad_str1_flag=pd.Series(bad_str1_flag)
    )
    all_str1s_df = all_str1s_df.assign(
        private_str1=pd.Series(private_str1)
    )
    all_str1s_df = all_str1s_df.assign(
        private_match_flag=pd.Series(private_match_flag)
    )
    all_str1s_df = all_str1s_df[
        all_str1s_df['match_flag'] == False
    ]
    all_str1s_df = all_str1s_df.repartition(npartitions=200)
    all_str1s_df.to_csv(
        f'results-str1s-{idx}-*.csv'
    )
    print(f'Finished file {idx}: {each_file}')

此处理需要 8 多个小时，我看到所有数据仅在 Node2 或 Node3 上的一个节点上处理，而不是在 Node2 和 Node3 上处理。

需要帮助来理解洞察力并了解我做错了什么以使这个简单的数据转换运行超过 8 小时但仍未完成。

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1 回答 1

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超时增加，内存增加。之后，它开始工作，没有失败和挂起。

timeouts:
  connect: 180s          # time before connecting fails
  tcp: 180s              # time before calling an unresponsive connection dead
于 2020-09-23T17:38:44.720 回答