从多个分区读取多个镶木地板文件

Posted 2021-05-01

我试图通过pyspark从多个分区读取多个镶木地板文件，并将它们连接到一个大数据框架。文件看起来像，

 hdfs dfs -ls /data/customers/odysseyconsultants/logs_ch_blade_fwvpn
Found 180 items
drwxrwxrwx   - impala impala          0 2018-03-01 10:31 /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/_impala_insert_staging
drwxr-xr-x   - impala impala          0 2017-08-23 17:55 /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/cdateint=20170822
drwxr-xr-x   - impala impala          0 2017-08-24 05:57 /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/cdateint=20170823
drwxr-xr-x   - impala impala          0 2017-08-25 06:00 /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/cdateint=20170824
drwxr-xr-x   - impala impala          0 2017-08-26 06:04 /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/cdateint=20170825

每个分区都有一个或多个镶木地板文件，即

hdfs dfs -ls /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/cdateint=20170822
Found 1 items
-rw-r--r--   2 impala impala   72252308 2017-08-23 17:55 /data/customers/odysseyconsultants/logs_ch_blade_fwvpn/cdateint=20170822/5b4bb1c5214fdffd-cc8dbcf600000008_1393229110_data.0.parq

我想要创建的是一个通用函数，它将采用from - to参数并加载并连接大数据框中该时间范围的所有镶木地板文件。

我可以创建要读取的文件，

def read_files(table,from1,to):
     s1 = ', '.join('/data/customers/odysseyconsultants/' + table + '/' + 'cdateint=' + str(i) for i in range(from1, to+1))
     return s1.split(', ')

如果我尝试读取文件，如下所示，我得到一个例外

for i in read_files('logs_ch_blade_fwvpn', 20170506, 20170510):
...  sqlContext.read.parquet(i).show()

如果我试着读它

x = read_files('logs_cs_blade_fwvpn', 20180109, 20180110)
d1 = sqlContext.read.parquet(*x)

我收到错误

pyspark.sql.utils.AnalysisException：u'Path不存在：hdfs：// nameservice1 / data / customers / odysseyconsultants / logs_cs_blade_fwvpn / cdateint = 20180109;'

答案

将目录名称用作分区怎么样？例如：

table = 'logs_ch_blade_fwvpn'
sqlContext.read.parquet('/data/customers/odysseyconsultants/' + table) \
    .where(col('cdateint').between('20170822', '20170825')).show()

另一答案

这是一种做法，尽管我对替代方案持开放态度

import subprocess
from datetime import date, timedelta
from pyspark.sql import SQLContext


def read_data(customer, table, start_date, end_date):
    def run_cmd(args_list):
        #Run linux commands
        print('Running system command: {0}'.format(' '.join(args_list)))
        proc = subprocess.Popen(args_list, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        s_output, s_error = proc.communicate()
        s_return = proc.returncode
        return s_return, s_output, s_error

    #Generate a list with the dates to access the parquet files
    d1 = date(int(start_date[0:4]), int(start_date[4:6]), int(start_date[6:8]))
    d2 = date(int(end_date[0:4]), int(end_date[4:6]), int(end_date[6:8]))
    dates = [d1 + timedelta(days=x) for x in range((d2-d1).days + 1)]
    #Loop through the dates and load the parquet files
    files = []
    for i in dates:
        path = '/data/customers/' + customer + '/' + table + '/cdateint=' + str(i).replace('-','')
        (ret, out, err) = run_cmd(['hdfs','dfs','-find',path,'-name','*.parq'])
        files.append(out.split('\n'))
    c=0
    for i in files:
        print(c)
        for j in i:
            print j
            if c == 0:
                if len(j) > 0:
                    df = sqlContext.read.parquet(j)
            else:
                if len(j) > 0:
                    df_temp = sqlContext.read.parquet(j)
                    df = df.union(df_temp)
                    del(df_temp)
            c += 1
    return df