how do i download a large list of urls in parallel in pyspark?

I can't test your data, but it should work with code like this:

import concurrent.futures, requests

def get_one(url):
   resp = requests.get(url)
resp.raise_for_status()
return resp.text

def get_all():
   with concurrent.futures.ThreadPoolExecutor(max_workers = 20) as executor:
   futures = [executor.submit(get_one, url)
      for url in urls.toLocalIterator()
   ]
# the end of the "with"
block will automatically wait
#
for all of the executor 's tasks to complete

for fut in futures:
   if fut.exception() is not None:
   print('{}: {}'.format(fut.exception(), 'ERR')
      else:
         print('{}: {}'.format(fut.result(), 'OK')

The naming convention for the patched methods is methodNameAsync, for example:

RDD.count⇒ RDD.countAsync
DataFrame.take⇒ RDD.takeAsync
DataFrameWriter.save⇒ DataFrameWriter.saveAsync

Usage To patch existing classes just import the package:

>>> import asyncactions
>>> from pyspark.sql import SparkSession
>>>
>>> spark = SparkSession.builder.getOrCreate()
All *Async methods return concurrent.futures.Future:
>>> rdd = spark.sparkContext.range(100)
>>> f = rdd.countAsync()
>>> f
<Future at ... state=running>
   >>> type(f)
   concurrent.futures._base.Future
   >>> f.add_done_callback(lambda f: print(f.result()))
   100

Suggestion : 2

1 week ago Apr 25, 2019 · shutil.copyfileobj (urldata, out_file) With the downloader () function complete, the remaining work uses Spark to create an RDD and then parallelize the download operations. I assume we start with a list of URLs so we need to create an RDD from that list of URLs with the parallelize () function: # Convert URL list to an RDD in order to ... , 1 week ago Download multiple files in parallel with Python. To start, create a function ( download_parallel) to handle the parallel download. The function ( download_parallel) will take one argument, an iterable containing URLs and associated filenames (the inputs variable we created earlier). Next, get the number of CPUs available for processing. , 1 week ago How do I download a large list of URLs in parallel in pyspark?(如何在pyspark中并行下载大量URL？) - IT ... think of PySpark has a way to handle parallel processing without the need for the threading or multiprocessing modules Pyspark Parallelize For Loop The dataframe can be derived from a dataset which can be delimited text files ... , 1 week ago Nov 20, 2018 · I have a text file containing several million URLs and I have to run a POST request for each of those URLs. I tried to do it on my machine but it is taking forever so I would like to use my Spark cluster instead. I wrote this PySpark code:

list = ['http://SDFKHSKHGKLHSKLJHGSDFKSJH.com', 'http://google.com', 'http://twitter.com'] urls = sc.parallelize(list)

list = ['http://SDFKHSKHGKLHSKLJHGSDFKSJH.com', 'http://google.com', 'http://twitter.com'] urls = sc.parallelize(list)

import asyncio
import concurrent.futures
import requests async def get(url): with concurrent.futures.ThreadPoolExecutor() as executor: loop = asyncio.get_event_loop() futures = [loop.run_in_executor(executor, requests.get, i) for i in url]
return futures async def get_response(futures): response = await asyncio.gather(futures, return_exceptions = True) return (response) tasks = urls.map(lambda query: get(query)) # Method returns http call response as a Future[String] results = tasks.map(lambda task: get_response(task)) results = results.map(lambda response: 'ERR'
   if isinstance(response, Exception)
   else 'OK') results.collect()

['OK', 'OK', 'OK']

import asyncio
import concurrent.futures
import requests async def get(): with concurrent.futures.ThreadPoolExecutor(max_workers = 20) as executor: loop = asyncio.get_event_loop() futures = [loop.run_in_executor(executor, requests.get, i) for i in urls.toLocalIterator()]
for response in await asyncio.gather( * futures, return_exceptions = True): print('{}: {}'.format(response, 'ERR'
   if isinstance(response, Exception)
   else 'OK')) pass loop = asyncio.get_event_loop() loop.run_until_complete(get())

HTTPConnectionPool(host='SDFKHSKHGKLHSKLJHGSDFKSJH.com', port=80): Max retries exceeded with url: / (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x12c834210>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known')): ERR <Response [200]>: OK <Response [200]>: OK

Suggestion : 3

Automating file downloads can save a lot of time. There are several ways for automating file downloads in Python. The easiest way to download files is using a simple Python loop to iterate through a list of URLs to download. This serial approach can work well with a few small files, but if you are downloading many files or large files, you’ll want to use a parallel approach to maximize your computational resources. ,To download the list of URLs to the associated files, loop through the iterable (inputs) that we created, passing each element to download_url. After each download is complete we will print the downloaded URL and the time it took to download.,The download_url function is the meat of our code. It does the actual work of downloading and file creation. We can now use this function to download files in serial (using a loop) and in parallel. Let’s go through those examples.,Here, I specify the URLs to four files in a list. In other applications, you may programmatically generate a list of files to download.

We’ll also import the time module to keep track of how long it takes to download individual files and compare performance between the serial and parallel download routines. The time module is also part of the Python standard library.

import requests
import time
from multiprocessing
import cpu_count
from multiprocessing.pool
import ThreadPool

2._

urls = ['https://www.northwestknowledge.net/metdata/data/pr_1979.nc',
   'https://www.northwestknowledge.net/metdata/data/pr_1980.nc',
   'https://www.northwestknowledge.net/metdata/data/pr_1981.nc',
   'https://www.northwestknowledge.net/metdata/data/pr_1982.nc'
]

Each URL must be associated with its download location. Here, I’m downloading the files to the Windows ‘Downloads’ directory. I’ve hardcoded the filenames in a list for simplicity and transparency. Given your application, you may want to write code that will parse the input URL and download it to a specific directory.

fns = [r 'C:\Users\konrad\Downloads\pr_1979.nc',
   r 'C:\Users\konrad\Downloads\pr_1980.nc',
   r 'C:\Users\konrad\Downloads\pr_1981.nc',
   r 'C:\Users\konrad\Downloads\pr_1982.nc'
]

The download_url function is the meat of our code. It does the actual work of downloading and file creation. We can now use this function to download files in serial (using a loop) and in parallel. Let’s go through those examples.

def download_url(args):
   t0 = time.time()
url, fn = args[0], args[1]
try:
r = requests.get(url)
with open(fn, 'wb') as f:
   f.write(r.content)
return (url, time.time() - t0)
except Exception as e:
   print('Exception in download_url():', e)

6._

t0 = time.time()
for i in inputs:
   result = download_url(i)
print('url:', result[0], 'time:', result[1])
print('Total time:', time.time() - t0)

Suggestion : 4

The following python 3 program downloads a list of urls to a list of local files. However the download may take sometime since it is executed sequentially.,The following python 3 program downloads a given url to a local file. The following example assumes that the url contains the name of the file at the end and uses it as the name for the locally saved file.,Python has a very powerful library called requests for initiating http requests programmatically. You can use requests for downloading files hosted over http protocol. Run the following command to install requests python library. This assumes that you already have python 3 installed on your system.,After running the above program, you will find a file named "posts" in the same folder where you have the script saved.

The following python 3 program downloads a given url to a local file. The following example assumes that the url contains the name of the file at the end and uses it as the name for the locally saved file.

import requests

def download_url(url):
   # assumes that the last segment after the / represents the file name
#
if the url is http: //abc.com/xyz/file.txt, the file name will be file.txt
   file_name_start_pos = url.rfind("/") + 1
file_name = url[file_name_start_pos: ]

r = requests.get(url, stream = True)
if r.status_code == requests.codes.ok:
   with open(file_name, 'wb') as f:
   for data in r:
   f.write(data)

# download a sngle url
# the file name at the end is used as the local file name
download_url("https://jsonplaceholder.typicode.com/posts")

The following python 3 program downloads a list of urls to a list of local files. However the download may take sometime since it is executed sequentially.

import requests

def download_url(url):
   print("downloading: ", url)
# assumes that the last segment after the / represents the file name
#
if url is abc / xyz / file.txt, the file name will be file.txt
file_name_start_pos = url.rfind("/") + 1
file_name = url[file_name_start_pos: ]

r = requests.get(url, stream = True)
if r.status_code == requests.codes.ok:
   with open(file_name, 'wb') as f:
   for data in r:
   f.write(data)

# download a sngle url
# the file name at the end is used as the local file name
download_url("https://jsonplaceholder.typicode.com/posts")
download_url("https://jsonplaceholder.typicode.com/comments")
download_url("https://jsonplaceholder.typicode.com/photos")
download_url("https://jsonplaceholder.typicode.com/todos")
download_url("https://jsonplaceholder.typicode.com/albums")

The download program above can be substantially speeded up by running them in parallel. The following python program shows how to download multiple files concurrently by using multiprocessing library which has support for thread pools. Note the use of results list which forces python to continue execution until all the threads are complete. Without the iteration of the results list, the program will terminate even before the threads are started. Also note that we are running 5 threads concurrently in the script below and you may want to increase it if you have a large number of files to download. However, this puts substantial load on the server and you need to be sure that the server can handle such concurrent loads.

import requests
from multiprocessing.pool
import ThreadPool

def download_url(url):
   print("downloading: ", url)
# assumes that the last segment after the / represents the file name
#
if url is abc / xyz / file.txt, the file name will be file.txt
file_name_start_pos = url.rfind("/") + 1
file_name = url[file_name_start_pos: ]

r = requests.get(url, stream = True)
if r.status_code == requests.codes.ok:
   with open(file_name, 'wb') as f:
   for data in r:
   f.write(data)
return url

urls = ["https://jsonplaceholder.typicode.com/posts",
   "https://jsonplaceholder.typicode.com/comments",
   "https://jsonplaceholder.typicode.com/photos",
   "https://jsonplaceholder.typicode.com/todos",
   "https://jsonplaceholder.typicode.com/albums"
]

# Run 5 multiple threads.Each call will take the next element in urls list
results = ThreadPool(5).imap_unordered(download_url, urls)
for r in results:
   print(r)

Suggestion : 5

PySpark shell provides SparkContext variable “sc”, use sc.parallelize() to create an RDD.,Now, use sparkContext.parallelize() to create rdd from a list or collection.,`sparkContext.parallelize([1,2,3,4,5])` shoud be `rdd = sparkContext.parallelize([1,2,3,4,5])`,Below is an example of how to create an RDD using a parallelize method from Sparkcontext. sparkContext.parallelize([1,2,3,4,5,6,7,8,9,10]) creates an RDD with a list of Integers.

1._

rdd = sc.parallelize([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

Since PySpark 2.0, First, you need to create a SparkSession which internally creates a SparkContext for you.

import pyspark
from pyspark.sql
import SparkSession

spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
sparkContext = spark.sparkContext

Now, use sparkContext.parallelize() to create rdd from a list or collection.

rdd = sparkContext.parallelize([1, 2, 3, 4, 5])
rddCollect = rdd.collect()
print("Number of Partitions: " + str(rdd.getNumPartitions()))
print("Action: First element: " + str(rdd.first()))
print(rddCollect)

5._

emptyRDD = sparkContext.emptyRDD()
emptyRDD2 = rdd = sparkContext.parallelize([])

print("is Empty RDD : " + str(emptyRDD2.isEmpty()))

Suggestion : 6

06/15/2022

The examples in this article do not include usernames and passwords in JDBC URLs. Instead it expects that you follow the Secret management user guide to store your database credentials as secrets, and then leverage them in a notebook to populate your credentials in a java.util.Properties object. For example:

val jdbcUsername = dbutils.secrets.get(scope = "jdbc", key = "username")
val jdbcPassword = dbutils.secrets.get(scope = "jdbc", key = "password")

val jdbcUsername = dbutils.secrets.get(scope = "jdbc", key = "username")
val jdbcPassword = dbutils.secrets.get(scope = "jdbc", key = "password")

Step 1: Check that the JDBC driver is available

Class.forName("com.microsoft.sqlserver.jdbc.SQLServerDriver")

Step 2: Create the JDBC URL

val jdbcHostname = "<hostname>"
val jdbcPort = 1433
val jdbcDatabase = "<database>"

// Create the JDBC URL without passing in the user and password parameters.
val jdbcUrl = s"jdbc:sqlserver://${jdbcHostname}:${jdbcPort};database=${jdbcDatabase}"

// Create a Properties() object to hold the parameters.
import java.util.Properties
val connectionProperties = new Properties()

connectionProperties.put("user", s"${jdbcUsername}")
connectionProperties.put("password", s"${jdbcPassword}")

For the client public key certificate, client_cert.pem.
For the client private key, client_key.pem.
For the server certificate, server_ca.pem.

%sh
# Copy the PEM files to a folder within /dbfs so that all nodes can read them.
mkdir -p <target-folder>
   cp <source-files> <target-folder>

%sh
# Copy the PEM files to a folder within /dbfs so that all nodes can read them.
mkdir -p <target-folder>
cp <source-files> <target-folder>

%sh
# Convert the PEM files to PK8 and DER format.
cd <target-folder>
   openssl pkcs8 -topk8 -inform PEM -in client_key.pem -outform DER -out client_key.pk8 -nocrypt
   openssl x509 -in server_ca.pem -out server_ca.der -outform DER
   openssl x509 -in client_cert.pem -out client_cert.der -outform DER

Suggestion : 7

The default parallelism of Spark SQL leaf nodes that produce data, such as the file scan node, the local data scan node, the range node, etc. The default value of this config is 'SparkContext#defaultParallelism'.,The max number of entries to be stored in queue to wait for late epochs. If this parameter is exceeded by the size of the queue, stream will stop with an error., When running in YARN cluster mode, this file will also be localized to the remote driver for dependency resolution within SparkContext#addJar ,When true, some predicates will be pushed down into the Hive metastore so that unmatching partitions can be eliminated earlier.

val conf = new SparkConf()
   .setMaster("local[2]")
   .setAppName("CountingSheep")
val sc = new SparkContext(conf)

25 ms(milliseconds)
5 s(seconds)
10 m or 10 min(minutes)
3 h(hours)
5 d(days)
1 y(years)

1 b(bytes)
1 k or 1 kb(kibibytes = 1024 bytes)
1 m or 1 mb(mebibytes = 1024 kibibytes)
1 g or 1 gb(gibibytes = 1024 mebibytes)
1 t or 1 tb(tebibytes = 1024 gibibytes)
1 p or 1 pb(pebibytes = 1024 tebibytes)

val sc = new SparkContext(new SparkConf())

. / bin / spark - submit--name "My app"--master local[4]--conf spark.eventLog.enabled = false
   --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps"
myApp.jar

spark.master spark: //5.6.7.8:7077
   spark.executor.memory 4 g
spark.eventLog.enabled true
spark.serializer org.apache.spark.serializer.KryoSerializer