pyspark-hdfs数据操作函数介绍

谁借莪１个温暖的怀抱￠ 2022-05-21 20:47 256阅读 0赞

# 一、SparkContext API #

## 1、读取hdfs数据转成numpy ##

#!/usr/bin/python # -*- coding: UTF-8 -*- from pyspark import SparkContext,SparkConf
    import numpy as np
    import pickle
    
    
    dirPath='hdfs://xxx/user/root/data_16/11/labels/part-00199' # 注该数据为pickle格式
    
    sc = SparkContext(conf=SparkConf().setAppName("The first example"))
    # textFiles=sc.textFile(dirPath) textFiles=sc.pickleFile(dirPath)
    
    data=textFiles.collect()
    
    # print(data[:5]) print(type(data)) # <type 'list'> print(data[0].dtype) # float16 data=np.array(data,np.float32) # 转成array np.save('123.npy',data) # 保存数据到本地 np.load('123.npy') # 加载数据

## 2、wholeTextFiles 读取目录下的所有数据（本地 或 hdfs） ##

\[python\]  [view plain][]   [copy][view plain]

1.  wholeTextFiles(path, minPartitions=None, use\_unicode=True)

For example, if you have the following files:

hdfs://a-hdfs-path/part-00000
    hdfs://a-hdfs-path/part-00001
    ...
    hdfs://a-hdfs-path/part-nnnnn

Do rdd = sparkContext.wholeTextFiles(“hdfs://a-hdfs-path”), then rdd contains:

(a-hdfs-path/part-00000, its content)
    (a-hdfs-path/part-00001, its content)
    ...
    (a-hdfs-path/part-nnnnn, its content)

#!/usr/bin/python # -*- coding: UTF-8 -*- from pyspark import SparkContext,SparkConf
    import os
    # from pyspark.context import SparkContext # from pyspark.conf import SparkConf #from pyspark.sql import DataFrame,SQLContext sc = SparkContext(conf=SparkConf().setAppName("The first example"))
    
    dirPath = os.path.join('./', "files") # dirPath 也可以是hdfs上的文件 os.mkdir(dirPath)
    with open(os.path.join(dirPath, "1.txt"), "w") as file1:
        file1.write("10")
    with open(os.path.join(dirPath, "2.txt"), "w") as file2:
        file2.write("20")
    textFiles = sc.wholeTextFiles(dirPath)
    # sorted(textFiles.collect()) print(type(textFiles)) # <class 'pyspark.rdd.RDD'> print(textFiles.collect())
    print(type(textFiles.collect())) # list # [(u'.../1.txt', u'10'), (u'.../2.txt', u'20')] print(len(textFiles.collect())) # 2

## 3、addFile(path) ##

Add a file to be downloaded with this Spark job on every node. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

To access the file in Spark jobs, use SparkFiles.get(fileName) with the filename to find its download location.

#!/usr/bin/python
    # -*- coding: UTF-8 -*-
    from pyspark import SparkFiles
    from pyspark import SparkContext, SparkConf
    import os
    sc = SparkContext(conf=SparkConf().setAppName("The first example"))
    path = os.path.join('./', "test.txt") # 也可以说hdfs路径
    print path
    with open(path, "w") as testFile:
        testFile.write("100")
    sc.addFile(path)  # Add a file to be downloaded with this Spark job on every node
    def func(iterator):
        with open(SparkFiles.get("test.txt")) as testFile:
            fileVal = int(testFile.readline())
        return [float(x) * fileVal for x in iterator]
    
    result=sc.parallelize([1, 2, 3, 4]).mapPartitions(func)#.collect()) # [100, 200, 300, 400]
    print result.count()
    print result.collect()

*执行：spark-submit test2.py,输出如下：*

*./test.txt  
4  
\[100.0, 200.0, 300.0, 400.0\]*

## 4、addPyFile(path)[¶][Link 1] ##

Add a .py or .zip dependency for all tasks to be executed on this SparkContext in the future. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

import pyspark_csv as pycsv
    sc.addPyFile('pyspark_csv.py')

#!/usr/bin/python # -*- coding: UTF-8 -*- from pyspark import SparkContext,SparkConf
    from pyspark import SparkFiles
    import pyspark_csv as pycsv
    import os
    
    sc = SparkContext(conf=SparkConf().setAppName("The first example"))
    sc.addPyFile('pyspark_csv.py')
    
    # print(SparkFiles.get("pyspark_csv.py")) # 返回文件的绝对路径 os.popen("python "+SparkFiles.get("pyspark_csv.py")) # 执行脚本

## 5、binaryFiles(path, minPartitions=None) ##

:: Experimental

Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file.

Note: Small files are preferred, large file is also allowable, but may cause bad performance.

#!/usr/bin/python # -*- coding: UTF-8 -*- from pyspark import SparkFiles
    from pyspark import SparkContext,SparkConf
    import os
    
    sc = SparkContext(conf=SparkConf().setAppName("The first example"))
    dirPath='hdfs://xxx/user/root/data_16/11/labels/part-00199'
    
    data=sc.binaryFiles(dirPath) # Read a directory of binary files from HDFS print(data) # org.apache.spark.api.java.JavaPairRDD@27a22ddc

## 6、clearFiles()[¶][Link 2] ##

Clear the job’s list of files added by [addFile][] or [addPyFile][Link 1] so that they do not get downloaded to any new nodes.

# 二、RDD API #

## 1、保存文件 ##

### saveAsPickleFile ###

saveAsPickleFile ( path ,   batchSize=10 ) [¶][Link 3]

>>> tmpFile = NamedTemporaryFile(delete=True)
    >>> tmpFile.close()
    >>> sc.parallelize([1, 2, 'spark', 'rdd']).saveAsPickleFile(tmpFile.name, 3)
    >>> sorted(sc.pickleFile(tmpFile.name, 5).collect())
    [1, 2, 'rdd', 'spark']

### saveAsTextFile ###

Save this RDD as a Sequence File of serialized objects.

The serializer used is pyspark.serializers.PickleSerializer, default batch size is 10.

saveAsTextFile ( path )

Save this RDD as a text file, using string representations of elements.

>>> tempFile = NamedTemporaryFile(delete=True)
    >>> tempFile.close()
    >>> sc.parallelize(range(10)).saveAsTextFile(tempFile.name)
    >>> from fileinput import input
    >>> from glob import glob
    >>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
    '0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n'

Empty lines are tolerated when saving to text files.

>>> tempFile2 = NamedTemporaryFile(delete=True)
    >>> tempFile2.close()
    >>> sc.parallelize(['', 'foo', '', 'bar', '']).saveAsTextFile(tempFile2.name)
    >>> ''.join(sorted(input(glob(tempFile2.name + "/part-0000*"))))
    '\n\n\nbar\nfoo\n'

# 三、SparkFiles #

Resolves paths to files added through [SparkContext.addFile()][addFile]

classmethod  get ( filename )   \# 获取文件的绝对路径

Get the absolute path of a file added through [SparkContext.addFile()][addFile].

classmethod  getRootDirectory ( ) [¶][Link 4]

Get the root directory that contains files added through [SparkContext.addFile()][addFile].

#!/usr/bin/python # -*- coding: UTF-8 -*-

from pyspark import SparkFiles
    from pyspark import SparkContext,SparkConf
    import os
    
    sc = SparkContext(conf=SparkConf().setAppName("The first example"))
    path = os.path.join('./', "test.txt") # 也可以说hdfs路径 with open(path, "w") as testFile:
        testFile.write("100")
    sc.addFile(path) # Add a file to be downloaded with this Spark job on every node def func(iterator):
        with open(SparkFiles.get("test.txt")) as testFile: # SparkFiles.get(path)
            fileVal = int(testFile.readline())
            return [x * fileVal for x in iterator]
    print(sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect())
    # [100, 200, 300, 400]

# 四、DataFrameReader #

## csv ##

>>> df = spark.read.csv('python/test_support/sql/ages.csv')
    >>> df.dtypes
    [('_c0', 'string'), ('_c1', 'string')]

## format(source) ##

Specifies the input data source format.

<table style="border-spacing:0px;width:851.25px;color:rgb(51,51,51);font-size:14px;background-color:rgb(255,255,255);">     
 <tbody> 
  <tr> 
   <th style="padding-top:1px;padding-bottom:1px;padding-left:5px;margin:0px;border-width:0px;">Parameters:</th> 
   <td style="padding-top:1px;padding-bottom:1px;padding-left:5px;margin:0px;border-width:0px;"><span style="font-weight:700;">source</span>&nbsp;– string, name of the data source, e.g. ‘json’, ‘parquet’.</td> 
  </tr> 
 </tbody> 
</table>

>>> df = spark.read.format('json').load('python/test_support/sql/people.json')
    >>> df.dtypes
    [('age', 'bigint'), ('name', 'string')]

## json ##

Loads JSON files and returns the results as a [DataFrame][].

>>> df1 = spark.read.json('python/test_support/sql/people.json')
    >>> df1.dtypes
    [('age', 'bigint'), ('name', 'string')]
    >>> rdd = sc.textFile('python/test_support/sql/people.json')
    >>> df2 = spark.read.json(rdd)
    >>> df2.dtypes
    [('age', 'bigint'), ('name', 'string')]

## load(path=None, format=None, schema=None, \*\*options) ##

>>> df = spark.read.load('python/test_support/sql/parquet_partitioned', opt1=True,
    ...     opt2=1, opt3='str')
    >>> df.dtypes
    [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]

>>> df = spark.read.format('json').load(['python/test_support/sql/people.json',
    ...     'python/test_support/sql/people1.json'])
    >>> df.dtypes
    [('age', 'bigint'), ('aka', 'string'), ('name', 'string')]

## orc(path) ##

Loads ORC files, returning the result as a   [DataFrame][] .

Note

Currently ORC support is only available together with Hive support.

>>> df = spark.read.orc('python/test_support/sql/orc_partitioned')
    >>> df.dtypes
    [('a', 'bigint'), ('b', 'int'), ('c', 'int')]

## parquet(\*paths)[¶][Link 5] ##

Loads Parquet files, returning the result as a [DataFrame][].

>>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
    >>> df.dtypes
    [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]

## text(paths) ##

>>> df = spark.read.text('python/test_support/sql/text-test.txt')
    >>> df.collect()
    [Row(value=u'hello'), Row(value=u'this')]

#!/usr/bin/python # -*- coding: UTF-8 -*- from pyspark import SparkFiles
    from pyspark import SparkContext,SparkConf
    from pyspark.sql import DataFrame,SQLContext,DataFrameReader
    import os
    from pyspark.sql import SparkSession
    
    # sc = SparkContext(conf=SparkConf().setAppName("The first example")) path = os.path.join('./', "dna_seq.txt") # 也可以是hdfs路径 spark = SparkSession.builder \
         .master("local") \
         .appName("Word Count") \
         .config("spark.some.config.option", "some-value") \
         .getOrCreate()
    
    df=spark.read.text(path)
    # spark.read.json("hdfs://localhost:9000/testdata/person.json") # spark.read.csv() print(type(df)) # <class 'pyspark.sql.dataframe.DataFrame'>

# 五、DataFrameWriter #

Use [DataFrame.write()][DataFrame.write] to access this.

## csv ##

>>> df.write.csv(os.path.join(tempfile.mkdtemp(), 'data'))

## format(source) ##

Specifies the underlying output data source.

<table style="border-spacing:0px;width:851.25px;margin-bottom:10px;color:rgb(51,51,51);font-size:14px;background-color:rgb(255,255,255);border:0px;">     
 <tbody> 
  <tr> 
   <th style="padding-top:1px;padding-bottom:1px;padding-left:5px;margin:0px;border-width:0px;">Parameters:</th> 
   <td style="padding-top:1px;padding-bottom:1px;padding-left:5px;margin:0px;border-width:0px;"><span style="font-weight:700;">source</span>&nbsp;– string, name of the data source, e.g. ‘json’, ‘parquet’.</td> 
  </tr> 
 </tbody> 
</table>

>>> df.write.format('json').save(os.path.join(tempfile.mkdtemp(), 'data'))

## json ##

>>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))

## mode(saveMode) ##

Options include:

*  append: Append contents of this [DataFrame][] to existing data.
 *  overwrite: Overwrite existing data.
 *  error: Throw an exception if data already exists.
 *  ignore: Silently ignore this operation if data already exists.

>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))

## save(path=None, format=None, mode=None, partitionBy=None, \*\*options) ##

>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))

## text(path, compression=None) ##

Saves the content of the DataFrame in a text file at the specified path

Specifies the behavior when data or table already exists.

# 六、DataStreamReader #

Use spark.readStream() to access this

## csv ##

>>> csv_sdf = spark.readStream.csv(tempfile.mkdtemp(), schema = sdf_schema)
    >>> csv_sdf.isStreaming
    True
    >>> csv_sdf.schema == sdf_schema
    True

## format(source)[\[source\]][source] ##

Specifies the input data source format.

Note

Evolving.

>>> s = spark.readStream.format("text")

## json ##

>>> json_sdf = spark.readStream.json(tempfile.mkdtemp(), schema = sdf_schema)
    >>> json_sdf.isStreaming
    True
    >>> json_sdf.schema == sdf_schema
    True

## load(path=None, format=None, schema=None, \*\*options) ##

>>> json_sdf = spark.readStream.format("json") \
    ...     .schema(sdf_schema) \
    ...     .load(tempfile.mkdtemp())
    >>> json_sdf.isStreaming
    True
    >>> json_sdf.schema == sdf_schema
    True

## text(path) ##

>>> text_sdf = spark.readStream.text(tempfile.mkdtemp())
    >>> text_sdf.isStreaming
    True
    >>> "value" in str(text_sdf.schema)
    True

# 七、附加 hadoop 文件操作命令 #

hdfs dfs -ls # 显示目录 hdfs dfs -ls xxx/|wc -l # 显示xxx目录下的文件和文件夹个数 hdfs dfs -mkdir xxx # 新建目录 hdfs dfs -rm -r xxx # 删除文件或目录 hdfs dfs -put  xxx data # 将xxx 上传到 hdfs的data目录 hdfs dfs -get xxx ./ # 将hdfs的xxx（文件或文件夹）复制到本地 yarn application -kill application_1502181070712_0574  # 杀掉进程

spark-submit test.py  # 执行脚本 test.py

参考: https://blog.csdn.net/wc781708249/article/details/78251701

[view plain]: https://blog.csdn.net/wc781708249/article/details/78251701#
[Link 1]: http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.SparkContext.addPyFile
[Link 2]: http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.SparkContext.clearFiles
[addFile]: http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.SparkContext.addFile
[Link 3]: http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.saveAsPickleFile
[Link 4]: http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.SparkFiles.getRootDirectory
[DataFrame]: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame
[Link 5]: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.parquet
[DataFrame.write]: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.write
[source]: http://spark.apache.org/docs/latest/api/python/_modules/pyspark/sql/streaming.html#DataStreamReader.format