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Hadoop_minicluster.py
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Hadoop_minicluster.py
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# This file was generated from Hadoop_minicluster.ipynb with nbconvert
# Source: https://github.com/groda/big_data
#!/usr/bin/env python
# coding: utf-8
# <a href="https://colab.research.google.com/github/groda/big_data/blob/master/Hadoop_minicluster.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# <a href="https://github.com/groda/big_data"><div><img src="https://github.com/groda/big_data/blob/master/logo_bdb.png?raw=true" align=right width="90"></div></a>
#
# # Hadoop MiniCluster
# <br>
# <br>
#
# The Hadoop MiniCluster is a lightweight, single-node Hadoop cluster that is primarily used for testing and development purposes. It provides a way to simulate a distributed Hadoop environment on a single machine, allowing developers to experiment with and test their Hadoop applications without the need for a full-scale, multi-node cluster.
#
# Quote from [the official Hadoop documentation](https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/CLIMiniCluster.html):
#
#
# > *Using the CLI MiniCluster, users can simply start and stop a single-node Hadoop cluster with a single command, and without the need to set any environment variables or manage configuration files. The CLI MiniCluster starts both a YARN/MapReduce & HDFS clusters.*
#
#
# In this notebook we download the Hadoop core and guide you through the steps required to launch the MiniCluster.
# In[1]:
# @title
from IPython.core.display import HTML
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<b>Note:</b>
While the MiniCluster is useful for many development and testing scenarios, it's important to note that it does not fully replicate the complexities and challenges of a true multi-node Hadoop cluster. For production-scale testing or performance evaluations, a larger, more representative cluster setup is recommended.
</div>
""")
# ## About this tutorial
#
# This tutorial dives deep to help you really get the hang of things, explaining every step in Big Data processing, even if it takes a bit of time, so just hang in there and be patient! <img 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wC/v8DAwICAgP8AAAD/AP//AAAA//8A/wD//////wSGkMhJq7046827/yABAGEHPGOpnQ9KqhbbonAlz+k35uLs07yMbHf7jWib4s94eq2WOCYyCVUeg0Jjdcgpso4tcDi583m9T6jZZRSWlahw2y3Ficfk8tKqU8fNXSRwcoBpdzeITheJUWGIVGuEf3grYkONREQxXo5fenMTmaIioqU1p6ipFxEAIfkECQcABAAsEAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIaQyEmrvTjrzbv/YAWEHfCM5IRS5pkS5iq1Ltmu9FODNJrrspIO+BsGN8WhcudJLoG8p1F51CQBv2qmyA1hsU/w6ftFSsXEpXb2PRnJV2sWLWVi4Glg+DOPqTlYMHVuVGZjVIiFW1Q9b4qLiYSSUHKNepdwZSJZRDdteyyZZDCimS+nqKkbEQAh+QQJBwAEACwQAQAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8EhpDISau9OOvNu/8gAQBhBzxjqZ0PSqoW26JwJc/pN+bi7NO8jGx3+41om+LPeHqtljgmMglVHoNCY3XIKbKOLXA4ufN5vU+o2WUUlpWocNstxYnH5PLSqlPHzV0kcHKAaXc3iE4XiVFhiFRrhH94K2JDjUREMV6OX3pzE5miIqKlNaeoqRcRACH5BAkHAAQALBABAAAgACAAgwAAAL8AAAC/AL+/AAAAv78AvwC/v8DAwICAgP8AAAD/AP//AAAA//8A/wD//////wSGkMhJq7046827/2AFhB3wjOSEUuaZEuYqtS7ZrvRTgzSa67KSDvgbBjfFoXLnSS6BvKdRedQkAb9qpsgNYbFP8On7RUrFxKV29j0ZyVdrFi1lYuBpYPgzj6k5WDB1blRmY1SIhVtUPW+Ki4mEklByjXqXcGUiWUQ3bXssmWQwopkvp6ipGxEAIfkECQcABAAsEAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIGQyEmrvTjrzbufwBV+FvCM4ElWpiq16Nq6xBx/s5rfXX4CPt7G9ygai0KN7XgyJjPLo03mZE5JRCaSCoQhgTSc1rpKjcPlrhNYFjnbbimckn32zuheKLtl339SUjAcO1GAb0pWVUhkSmCMkE1fXX50QZORX4gglJ17nqBzoqOkFxEAIfkECRkABAAsEAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIOQyEmrvTjrzbufwBV+FvCM4ElWpiq16Nq6xBx/s5rfXX4CPt7GBAwCaZ7dafloCjUwp80pOzJtQFnTyd0ie95oeFXbiqnksnScfll/Ty26TQnSU9552scmZflmNXEsa2ZhMBw7U4V6InmLTIFQb5BYRVksQVJnjDeXn3+gonekpaYTEQAh+QQJMgAEACwQAQAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8EeJDISau9OOvNu/9gKI5kaZ4joK6oBbSU6rJp9kp3t9JTDjy52u4HLP2IwIcyCCIOlUDmZgVNIq06ZzW6PU6h121XWkGGxWByr3pGR9Xhdhd7OR9VYi08zzaDo3V8c2x0ZWhtd1x7eUNUXEUYjYx4SYBZjTiYMJsdEQAh+QQJGQAEACwQAQAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8EgpDISau9OOvNuwZgCHgkMZbbeKIpy6mVuJLzVXcyGLufyP9AyS1oegxpsYfxB1hOmsrjKypSGnU0aNRo3UopIG433OV+i9uympqyatdiLOb9VmtD8zK0mm7683qBbl6AY3CBcmB2VHeDfxZ1aWthlIVjOWSZGTl2IX1nT5hCOUSlLBEAIfkECTIABAAsEAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BHiQyEmrvTjrzbv/YCiOZGmeI6CuqAW0lOqyafZKd7fSUw48udruByz9iMCHMggiDpVA5mYFTSKtOmc1uj1OoddtV1pBhsVgcq96RkfV4XYXezkfVWItPM82g6N1fHNsdGVobXdce3lDVFxFGI2MeEmAWY04mDCbHREAIfkECTIABAAsEAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIKQyEmrvTjrzbsGYAh4JDGW23iiKcuplbiS81V3Mhi7n8j/QMktaHoMabGH8QdYTprK4ysqUhp1NGjUaN1KKSBuN9zlfovbspqasmrXYizm/VZrQ/MytJpu+vN6gW5egGNwgXJgdlR3g38WdWlrYZSFYzlkmRk5diF9Z0+YQjlEpSwRACH5BAlkAAQALBABAAAgACAAgwAAAL8AAAC/AL+/AAAAv78AvwC/v8DAwICAgP8AAAD/AP//AAAA//8A/wD//////wSVkMhJq7046z0Bx54WdtdImNkIPOgaoqK0st1Di/Bp37oNAyZgZebrFTu502vnAzJpwg/x+UymdlMWFgRsYatbWVf0ZRKtIKoaXWI5tW7XZ6g2zy1Z+x3/jlPYV0c1gHhVNTxzeVCCiXVwhGJGVC6QM31wTYxpWGeceoFlmFlsXaGdTWiXj2+jVl2vr0qwsXu1tre4GREAIfkECQcABAAsMAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIOQyEmrvTjrzbsFoJcBFfA8pFimk4myqglLLqoS7kyftlfruxNwxXthfkNcsQisCUMU0BIl/EyfrWuudGUqtdVol+nswspjspR3TruX7fc7LteCZmh2Gkq0u/licGtzOjl3h3mDRmKIUI2IZFGHHY1seElHh1g3Hyl3nKChoqOkpaajEQAh+QQJBwAEACxQAQAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8Ei5DISau9OOvNu59A+HHhY4qjVZrsg6YE0M4nHNM0kMp4ruqqHg4IcgWFQ8mKqDz1eK0QlHk7QWfXa61Y07K8WAoPCHYiyduyedg1VoXqcYwZj65c22Pua81Sm1gyalEXS1J4SHkVS3yBYYWHLnd9c1IZUmOBLx0ogpiYNkovoKGiIJulqaqrrK2uBBEAIfkECQcABAAscAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIOQyEmrvTjrzbsFoJcBFfA8pFimk4myqglLLqoS7kyftlfruxNwxXthfkNcsQisCUMU0BIl/EyfrWuudGUqtdVol+nswspjspR3TruX7fc7LteCZmh2Gkq0u/licGtzOjl3h3mDRmKIUI2IZFGHHY1seElHh1g3Hyl3nKChoqOkpaajEQAh+QQJBwAEACyQAQAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8Ei5DISau9OOvNu59A+HHhY4qjVZrsg6YE0M4nHNM0kMp4ruqqHg4IcgWFQ8mKqDz1eK0QlHk7QWfXa61Y07K8WAoPCHYiyduyedg1VoXqcYwZj65c22Pua81Sm1gyalEXS1J4SHkVS3yBYYWHLnd9c1IZUmOBLx0ogpiYNkovoKGiIJulqaqrrK2uBBEAIfkECQcABAAssAEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIOQyEmrvTjrzbsFoJcBFfA8pFimk4myqglLLqoS7kyftlfruxNwxXthfkNcsQisCUMU0BIl/EyfrWuudGUqtdVol+nswspjspR3TruX7fc7LteCZmh2Gkq0u/licGtzOjl3h3mDRmKIUI2IZFGHHY1seElHh1g3Hyl3nKChoqOkpaajEQAh+QQJBwAEACzQAQAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8Ei5DISau9OOvNu59A+HHhY4qjVZrsg6YE0M4nHNM0kMp4ruqqHg4IcgWFQ8mKqDz1eK0QlHk7QWfXa61Y07K8WAoPCHYiyduyedg1VoXqcYwZj65c22Pua81Sm1gyalEXS1J4SHkVS3yBYYWHLnd9c1IZUmOBLx0ogpiYNkovoKGiIJulqaqrrK2uBBEAIfkECQcABAAs8AEAACAAIACDAAAAvwAAAL8Av78AAAC/vwC/AL+/wMDAgICA/wAAAP8A//8AAAD//wD/AP//////BIOQyEmrvTjrzbsFoJcBFfA8pFimk4myqglLLqoS7kyftlfruxNwxXthfkNcsQisCUMU0BIl/EyfrWuudGUqtdVol+nswspjspR3TruX7fc7LteCZmh2Gkq0u/licGtzOjl3h3mDRmKIUI2IZFGHHY1seElHh1g3Hyl3nKChoqOkpaajEQAh+QQJBwAEACwQAgAAIAAgAIMAAAC/AAAAvwC/vwAAAL+/AL8Av7/AwMCAgID/AAAA/wD//wAAAP//AP8A//////8Ei5DISau9OOvNu59A+HHhY4qjVZrsg6YE0M4nHNM0kMp4ruqqHg4IcgWFQ8mKqDz1eK0QlHk7QWfXa61Y07K8WAoPCHYiyduyedg1VoXqcYwZj65c22Pua81Sm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alt="Running cat" />
# # Preamble
# ## Download Hadoop
# In[2]:
import urllib.request
import os
import shutil
import tarfile
import logging
import subprocess
import time
import sys
############
# COSTANTS #
############
# URL for downloading Hadoop (archive site https://archive.apache.org/dist/)
HADOOP_URL = "https://archive.apache.org/dist/hadoop/core/hadoop-3.4.0/hadoop-3.4.0.tar.gz"
# logging level (should be one of: DEBUG, INFO, WARNING, ERROR, CRITICAL)
LOGGING_LEVEL = "INFO" #@param ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
# setup logging
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
logging_level = getattr(logging, LOGGING_LEVEL.upper(), 10)
logging.basicConfig(level=logging_level, \
format='%(asctime)s - %(levelname)s: %(message)s', \
datefmt='%d-%b-%y %I:%M:%S %p')
logger = logging.getLogger('my_logger')
JAVA_PATH = '/usr/lib/jvm/java-11-openjdk-amd64'
# true if running on Google Colab
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
from google.colab import output
# setup logging
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
logging_level = getattr(logging, LOGGING_LEVEL.upper(), 10)
logging.basicConfig(level=logging_level, \
format='%(asctime)s - %(levelname)s: %(message)s', \
datefmt='%d-%b-%y %I:%M:%S %p')
logger = logging.getLogger('my_logger')
# set variable JAVA_HOME (install Java if necessary)
def is_java_installed():
os.environ['JAVA_HOME'] = os.path.realpath(shutil.which("java")).split('/bin')[0]
return os.environ['JAVA_HOME']
def install_java():
# Uncomment and modify the desired version
# java_version= 'openjdk-11-jre-headless'
# java_version= 'default-jre'
# java_version= 'openjdk-17-jre-headless'
# java_version= 'openjdk-18-jre-headless'
java_version= 'openjdk-19-jre-headless'
print(f"Java not found. Installing {java_version} ... (this might take a while)")
try:
cmd = f"apt install -y {java_version}"
subprocess_output = subprocess.run(cmd, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
stdout_result = subprocess_output.stdout
# Process the results as needed
logger.info("Done installing Java {}".format(java_version))
os.environ['JAVA_HOME'] = os.path.realpath(shutil.which("java")).split('/bin')[0]
logger.info("JAVA_HOME is {}".format(os.environ['JAVA_HOME']))
except subprocess.CalledProcessError as e:
# Handle the error if the command returns a non-zero exit code
logger.warn("Command failed with return code {}".format(e.returncode))
logger.warn("stdout: {}".format(e.stdout))
# Install Java if not available
if is_java_installed():
logger.info("Java is already installed: {}".format(os.environ['JAVA_HOME']))
else:
logger.info("Installing Java")
install_java()
# download Hadoop
file_name = os.path.basename(HADOOP_URL)
if os.path.isfile(file_name):
logger.info("{} already exists, not downloading".format(file_name))
else:
logger.info("Downloading {}".format(file_name))
urllib.request.urlretrieve(HADOOP_URL, file_name)
# uncompress archive
dir_name = file_name[:-7]
if os.path.exists(dir_name):
logger.info("{} is already uncompressed".format(file_name))
else:
logger.info("Uncompressing {}".format(file_name))
tar = tarfile.open(file_name)
tar.extractall()
tar.close()
# environment variables
os.environ['HADOOP_HOME'] = os.path.join(os.getcwd(), dir_name)
logger.info("HADOOP_HOME is {}".format(os.environ['HADOOP_HOME']))
os.environ['PATH'] = ':'.join([os.path.join(os.environ['HADOOP_HOME'], 'bin'), os.environ['PATH']])
logger.info("PATH is {}".format(os.environ['PATH']))
# ### Check environment variables used by `mapred`
#
# The following steps are not needed but they might be useful to get familiar the `mapred` command.
#
# `mapred minicluster` is the command that we are going to use to start the MiniCluster once a few variables and libraries are taken care of.
# In[3]:
get_ipython().system('mapred -h')
# In[4]:
get_ipython().system('mapred envvars')
# ## Set `HADOOP_TOOLS_LIB_JARS_DIR`
#
# This variable needs to point to the folder containing Hadoop libraries. As you can see in the output of `mapred envvars`, by default this is set incorrectly to `share/hadoop/tools/lib`.
# In[5]:
os.environ['HADOOP_TOOLS_LIB_JARS_DIR'] = os.path.join(os.environ['HADOOP_HOME'], 'share/hadoop/tools/lib/') #IMPORTANT
# ## Install the `mockito` library
#
# To figure out which version of `mockito` is compatible with the current version of Hadoop, check this page for library dependency analysis: https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/dependency-analysis.html
#
# We need Mockito 2.28.2.
# After a new Hadoop installation, the mockito library is not yet there!
# In[6]:
get_ipython().system('find hadoop-3.4.0 -name "mockito*"')
# Download it from the Maven repository.
# In[7]:
get_ipython().system('wget --no-clobber https://repo1.maven.org/maven2/org/mockito/mockito-core/2.28.2/mockito-core-2.28.2.jar')
# Install the library in a location where it can be found.
# In[8]:
shutil.copy('mockito-core-2.28.2.jar', os.path.join(os.environ['HADOOP_HOME'],'share/hadoop/mapreduce/'))
# In[9]:
os.listdir(os.path.join(os.environ['HADOOP_HOME'],'share/hadoop/mapreduce/'))
# ## Create folders for the datanodes
#
# These folders are needed for the correct functioning of the MiniCluster.
# In[10]:
get_ipython().system('mkdir -p ./target/test/data/dfs/{name-0-1,name-0-2}')
# In[11]:
get_ipython().system('ls ./target/test/data/dfs/')
# # Start the MiniCluster
#
#
# To see a full list of options run `mapred minicluster -help`.
# In[12]:
get_ipython().system('mapred minicluster -help')
# If you are not running this notebook for the first time or have edited the `core-site.xml` file you should now empty it to get the default initial configuration.
#
# **Note:** the file `core-site.xml` needs to exist and contain the lines
#
# ```
# <configuration>
#
# </configuration>
# ```
#
# In[13]:
# check if the file is there
get_ipython().system('find $HADOOP_HOME -name "core-site.xml"')
# In[14]:
# view the contents of the file
get_ipython().system('cat $(find $HADOOP_HOME -name "core-site.xml")')
# In[15]:
with open(os.environ['HADOOP_HOME']+'/etc/hadoop/core-site.xml', 'w') as file:
file.write("<configuration>
</configuration>")
# In[16]:
get_ipython().system('cat $(find $HADOOP_HOME -name "core-site.xml")')
# ## `mapred minicluster -format`
#
# Finally, we are all set up to start the MiniCluster.
#
# Make sure to include the `-format` option to initialize and format the filesystem.
#
# Other than that, we are using the defaults for all the other options.
#
# Note that this process runs forever thus blocking the notebook. In order to proceed with the rest of the notebook, just interrupt the running cell.
#
# We'll see later how to run the MiniCluster as a subprocess without blocking the notebook's cells execution.
#
# Uncomment the next cell to launch the MiniCluster!
# In[17]:
#!mapred minicluster -format
# If the MiniMRCluster started correctly, you should see a line like this at the bottom:
#
# 2024-01-14 13:53:15,112 INFO mapreduce.MiniHadoopClusterManager: Started MiniMRCluster
# # Stop the MiniCluster process
#
# To continue to work with this notebook, you need to stop the MiniCluster by terminating the execution of the previous cell.
# 
# # Start the MiniCluster as a subprocess
#
# It is convenient to start the MiniCluster as a subprocess in order prevent it from blocking the execution of other notebook cells.
#
# The MiniCluster is a Java process with multiple listening ports.
#
#
# ## Use `lsof` to show listening ports
#
# To check ports that have listening services use `lsof`.
#
# The output should look like this:
#
# ```
# COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME
# node 7 root 21u IPv6 19666 0t0 TCP *:8080 (LISTEN)
# kernel_manager_ 20 root 3u IPv4 18322 0t0 TCP 172.28.0.12:6000 (LISTEN)
# colab-fileshim. 61 root 3u IPv4 19763 0t0 TCP 127.0.0.1:3453 (LISTEN)
# jupyter-noteboo 79 root 7u IPv4 19989 0t0 TCP 172.28.0.12:9000 (LISTEN)
# python3 428 root 21u IPv4 25506 0t0 TCP 127.0.0.1:38217 (LISTEN)
# python3 467 root 3u IPv4 26425 0t0 TCP 127.0.0.1:43729 (LISTEN)
# python3 467 root 5u IPv4 26426 0t0 TCP 127.0.0.1:60229 (LISTEN)
# ```
# In[18]:
get_ipython().system('lsof -n -i -P +c0 -sTCP:LISTEN')
# Options used in `lsof`:
#
# * `-i` specifies that you want to display only network files, that is open network connections
# * `-n` and `-P` tell `lsof` to show IP addresses (`-n`) and ports (`-P`) in numeric form. This makes `lsof` faster as it saves the time for name lookups.
# * the option `+c0` is used to show a longer substring of the name of the UNIX command associated with the process (https://linux.die.net/man/8/lsof)
# * `-sTCP:LISTEN` filters for TCP connections in state `LISTEN`
#
# See also [lsof and listening ports](https://unix.stackexchange.com/questions/26887/lsof-and-listening-ports) on Stackexchange.
# ## Start the subprocess
#
# Start the MiniCluster as a subprocess using Python's [`subprocess` library](https://docs.python.org/3/library/subprocess.html).
#
# The files `out.txt` and `err.txt` will contain respectively the standard output and the standard error emitted by the `mapred minicluster` command.
# In[19]:
import subprocess
with open('out.txt', "w") as stdout_file, open('err.txt', "w") as stderr_file:
process = subprocess.Popen(
["mapred", "minicluster", "-format"],
stdout=stdout_file,
stderr=stderr_file
)
# Wait for a couple of seconds because the services might not be available immediately.
# In[20]:
if not IN_COLAB:
time.sleep(30)
else:
time.sleep(10)
# Now check for listening ports again (you can also refresh the next cell with ctrl-enter).
# In[21]:
get_ipython().system('lsof -n -i -P +c0 -sTCP:LISTEN')
# You should have gotten something like this (a total of $18$ listening ports associated with the MiniCluster process):
#
# ```
# COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME
# node 6 root 21u IPv6 17373 0t0 TCP *:8080 (LISTEN)
# kernel_manager_ 20 root 3u IPv4 17180 0t0 TCP 172.28.0.12:6000 (LISTEN)
# colab-fileshim. 58 root 3u IPv4 19499 0t0 TCP 127.0.0.1:3453 (LISTEN)
# jupyter-noteboo 75 root 7u IPv4 19658 0t0 TCP 172.28.0.12:9000 (LISTEN)
# python3 4081 root 21u IPv4 108431 0t0 TCP 127.0.0.1:44519 (LISTEN)
# python3 4108 root 3u IPv4 109755 0t0 TCP 127.0.0.1:46699 (LISTEN)
# python3 4108 root 5u IPv4 109756 0t0 TCP 127.0.0.1:51813 (LISTEN)
# java 17261 root 347u IPv4 390097 0t0 TCP 127.0.0.1:38817 (LISTEN)
# java 17261 root 357u IPv4 391184 0t0 TCP 127.0.0.1:41631 (LISTEN)
# java 17261 root 367u IPv4 390801 0t0 TCP 127.0.0.1:34651 (LISTEN)
# java 17261 root 370u IPv4 390804 0t0 TCP 127.0.0.1:35015 (LISTEN)
# java 17261 root 399u IPv4 390814 0t0 TCP 127.0.0.1:46503 (LISTEN)
# java 17261 root 400u IPv4 390817 0t0 TCP 127.0.0.1:44665 (LISTEN)
# java 17261 root 423u IPv4 401418 0t0 TCP *:8031 (LISTEN)
# java 17261 root 440u IPv4 395796 0t0 TCP *:10033 (LISTEN)
# java 17261 root 450u IPv4 400174 0t0 TCP *:19888 (LISTEN)
# java 17261 root 455u IPv4 396244 0t0 TCP 127.0.0.1:43877 (LISTEN)
# java 17261 root 465u IPv4 400367 0t0 TCP *:8088 (LISTEN)
# java 17261 root 470u IPv4 400420 0t0 TCP *:8033 (LISTEN)
# java 17261 root 490u IPv4 401422 0t0 TCP *:8030 (LISTEN)
# java 17261 root 500u IPv4 400426 0t0 TCP 127.0.0.1:37335 (LISTEN)
# java 17261 root 510u IPv4 400470 0t0 TCP 127.0.0.1:42337 (LISTEN)
# java 17261 root 520u IPv4 401450 0t0 TCP 127.0.0.1:40401 (LISTEN)
# java 17261 root 530u IPv4 401454 0t0 TCP *:42359 (LISTEN)
# java 17261 root 531u IPv4 401457 0t0 TCP 127.0.0.1:38543 (LISTEN)
# ```
#
# The `java` process is the one responsible for providing the MiniCluster services by listening on several ports.
#
# There are two known ports for the Web interfaces (see [https://hadoop.apache.org/docs/.../ClusterSetup.html#Web_Interfaces](https://hadoop.apache.org/docs/r3.0.1/hadoop-project-dist/hadoop-common/ClusterSetup.html#Web_Interfaces)):
# * $8088$ the port to the ResourceManager
# * $19888$ the port to the MapReduce JobHistory Server
#
#
# ### Access the Web UI
# Let us check the Web interface at port $8088$.
# In[22]:
get_ipython().system('wget http://localhost:8088')
# We can serve the ResourceManager UI in the browser through Google Colab.
# In[23]:
if IN_COLAB:
# serve the Web UI on Colab
print("Click on the link below to open the Resource Manager Web UI 🚀")
output.serve_kernel_port_as_window(8088, path='/node')
# The port $19888$ is redirected to the same page as port $8088$, so it won't be very useful. I'm not sure if this is due to a missing configuration parameter or if it's a bug.
# In[24]:
get_ipython().system('wget http://localhost:19888')
# In[25]:
if IN_COLAB:
# serve the Web UI on Colab
print("Click on the link below to open the MapReduce JobHistory Server Web UI 🚀")
output.serve_kernel_port_as_window(19888, path='/node')
# ### Use ngrok to access the Web UI
# In the free tier of Google Colab this functionality might not be available (see https://research.google.com/colaboratory/faq.html#limitations-and-restrictions). As an alternative, you can use [ngrok](https://ngrok.com/) after signing up for a free account.
#
# Check the NGROK box below if you want to use ngrok.
#
#
# In[26]:
# you should set this to True
NGROK = False #@param {type:"boolean"}
# We are going to use the Python ngrok client `pyngrok` (see the [Colab example](https://pyngrok.readthedocs.io/en/latest/integrations.html#colab-http-example)).
# In[27]:
if NGROK:
get_ipython().system('pip install pyngrok')
from pyngrok import ngrok, conf
import getpass
print("Enter your authtoken, which can be copied from https://dashboard.ngrok.com/get-started/your-authtoken")
conf.get_default().auth_token = getpass.getpass()
# After entering the ngrok authorization token, you can open a connection.
# In[28]:
if NGROK:
# Open a ngrok tunnel to the HTTP server
public_url = ngrok.connect(19888).public_url
# In[29]:
if NGROK:
print(f'Click on {public_url} to open the MapReduce JobHistory Server Web UI')
# You can safely ignore the warning since we are not disclosing any confidential information and proceed with clicking on the "Visit site" button.
# 
# ## Stop the MiniCluster subprocess
#
# To stop the MiniCluster subprocess use `process.kill()` (remember: `process` is the variable name for the MiniCluster subprocess).
# In[30]:
process.kill()
# The Java process should now be gone.
# In[31]:
get_ipython().system('lsof -n -i -P +c0 -sTCP:LISTEN')
# In case there are still some `java` processes lingering around, kill them with
# In[32]:
get_ipython().system('pkill -f java')
# Verify that the Java processes are gone.
# # Start the MiniCluster with your own ports
#
# This time we will also set the ports for various services:
#
# mapred minicluster -format -jhsport 8900 -nnhttpport 8901 -nnport 8902 -rmport 8903
#
# Ports:
#
# port number | description
# --- | ---
# 8900 | JobHistoryServer port
# 8901 | NameNode HTTP port
# 8902 | NameNode port
# 8903 | ResourceManager port
#
#
# **Note:** we chose these port numbers (8900, 8901, 8902, 8903) arbitrarily, you can pick other numbers as long as they do not conflict with ports that are already is use.
# In[33]:
import subprocess
with open('out.txt', "w") as stdout_file, open('err.txt', "w") as stderr_file:
process = subprocess.Popen(
["mapred", "minicluster", "-format", "-jhsport", "8900", "-nnhttpport", "8901", "-nnport", "8902", "-rmport", "8903"],
stdout=stdout_file,
stderr=stderr_file
)
# In[34]:
if not IN_COLAB:
time.sleep(30)
else:
time.sleep(10)
# List the Java ports. These should be all ports associated with the MIniCluster `java` process.
#
# Note: `grep "^COMMAND\|java"` means "filter out the lines that begin with the string `COMMAND` or that contain the string `java`". This is to preserve the header line.
# In[35]:
get_ipython().system('lsof -n -i -P +c0 -sTCP:LISTEN | grep "^COMMAND\|java"')
# You should now see
#
# ```
# COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME
# java 30246 root 347u IPv4 671659 0t0 TCP 127.0.0.1:8901 (LISTEN)
# java 30246 root 357u IPv4 672029 0t0 TCP 127.0.0.1:8902 (LISTEN)
# java 30246 root 367u IPv4 674008 0t0 TCP 127.0.0.1:44721 (LISTEN)
# java 30246 root 370u IPv4 672082 0t0 TCP 127.0.0.1:36789 (LISTEN)
# java 30246 root 399u IPv4 674082 0t0 TCP 127.0.0.1:37975 (LISTEN)
# java 30246 root 400u IPv4 674085 0t0 TCP 127.0.0.1:37675 (LISTEN)
# java 30246 root 423u IPv4 681319 0t0 TCP *:8031 (LISTEN)
# java 30246 root 440u IPv4 680955 0t0 TCP *:10033 (LISTEN)
# java 30246 root 450u IPv4 681014 0t0 TCP *:19888 (LISTEN)
# java 30246 root 455u IPv4 681987 0t0 TCP 127.0.0.1:8900 (LISTEN)
# java 30246 root 465u IPv4 682035 0t0 TCP *:8088 (LISTEN)
# java 30246 root 470u IPv4 681313 0t0 TCP *:8033 (LISTEN)
# java 30246 root 490u IPv4 681325 0t0 TCP *:8030 (LISTEN)
# java 30246 root 500u IPv4 682046 0t0 TCP 127.0.0.1:8903 (LISTEN)
# java 30246 root 510u IPv4 681370 0t0 TCP 127.0.0.1:34521 (LISTEN)
# java 30246 root 520u IPv4 682057 0t0 TCP 127.0.0.1:36981 (LISTEN)
# java 30246 root 530u IPv4 681373 0t0 TCP *:46657 (LISTEN)
# java 30246 root 531u IPv4 682061 0t0 TCP 127.0.0.1:39897 (LISTEN)
# ```
#
# Our ports $8900$, $8901$, $8902$, and $8903$ are included in the list.
# The log messages are in the file `err.txt`. The last 10 lines should look like this:
#
# ```
# 2023-12-27 21:36:08,820 INFO server.MiniYARNCluster: All Node Managers connected in MiniYARNCluster
# 2023-12-27 21:36:08,820 INFO v2.MiniMRYarnCluster: MiniMRYARN ResourceManager address: localhost:8903
# 2023-12-27 21:36:08,821 INFO v2.MiniMRYarnCluster: MiniMRYARN ResourceManager web address: 0.0.0.0:8088
# 2023-12-27 21:36:08,821 INFO v2.MiniMRYarnCluster: MiniMRYARN HistoryServer address: localhost:8900
# 2023-12-27 21:36:08,822 INFO v2.MiniMRYarnCluster: MiniMRYARN HistoryServer web address: 26769af38ddc:19888
# 2023-12-27 21:36:08,823 INFO mapreduce.MiniHadoopClusterManager: Started MiniMRCluster
# ```
# In[36]:
get_ipython().system('tail err.txt')
# Check the NameNode's HTTP port.
# In[37]:
get_ipython().system('wget http://localhost:8901')
# ## Start the NameNode Web UI
#
# Serve the NameNode UI in the browser through Google Colab (path should be set to `/index.html` as in the output of `wget` otherwise the URL won't work).
# In[38]:
if IN_COLAB and not NGROK:
# serve the Web UI on Colab
print("Click on the link below to open the NameNode Web UI 🚀")
output.serve_kernel_port_as_window(8901, path='/index.html')
else:
if NGROK:
# disconnect previous tunnels (note: you can have max 3 tunnels open!)
# see: https://pyngrok.readthedocs.io/en/latest/index.html#get-active-tunnels
tunnels = ngrok.get_tunnels()
for t in tunnels:
ngrok.disconnect(t.public_url)
# Open a ngrok tunnel to the HTTP server on port 8901
public_url = ngrok.connect(8901).public_url
print(f'Click on {public_url} to open the NameNode Web UI 🚀')
# By clicking on the above link you should see the NameNode's Web UI in your browser:
#
#
# 
# # Test the HDFS filesystem
# ## Create a directory
#
# **Note:** in order to use the MiniCluster's Hadoop filesystem you need to specify the full path prepending `hdfs://localhost:8902/` otherwise `hdfs` will write to the local filesystem.
# In[39]:
get_ipython().run_cell_magic('bash', '', '# create a folder my_dir
hdfs dfs -mkdir hdfs://localhost:8902/my_dir
')
# List the contents of `my_dir` (should be empty).
# In[40]:
get_ipython().system('hdfs dfs -ls hdfs://localhost:8902/my_dir')
# Upload the local folder `sample_data` to `my_dir` on HDFS
# In[41]:
get_ipython().system('ls -lh sample_data')
# Check the total size of the local folder `sample_data` using the command `du` ("du" stands for "disk usage" and the `-h` option stands for "human" as it will format file sizes in a “human-readable” fashion, e.g 55M instead of 55508) .
# In[42]:
get_ipython().system('du -h sample_data')
# Upload `sample_data` to HDFS.
# In[43]:
get_ipython().system('hdfs dfs -put sample_data hdfs://localhost:8902/my_dir/')
# Check
# In[44]:
get_ipython().system('hdfs dfs -ls hdfs://localhost:8902/my_dir')
# Check the size of `my_dir` on HDFS using the [HDFS equivalent of `du`](https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/FileSystemShell.html#du).
# In[45]:
get_ipython().system('hdfs dfs -du -h hdfs://localhost:8902/my_dir')
# Check the contents of the HDFS folder `my_dir`
# In[46]:
get_ipython().system('hdfs dfs -ls -R -h hdfs://localhost:8902/my_dir')
# You should now see in the Web interface that the "DFS used" has increased (you might need to refresh the NameNode UI Web page):
# 
# Remove the folder `my_dir`
# In[47]:
get_ipython().system('hdfs dfs -rm -r hdfs://localhost:8902/my_dir')
# Now the DFS used should be back to ~$4$MB.
#
# 
#
# ## The default filesystem
#
# By default, `hdfs` will use the local filesystem so you need to prepend `hdfs://...` if you want to use the HDFS filesystem.
#
# If you do not want to use the prefix `hdfs://localhost:8902/` in the filenames, you could set the property `fs.defaultFS` in `core-site.xml` or else use the option `-fs` like this:
#
# hdfs dfs -fs hdfs://localhost:8902/
# In[48]:
get_ipython().system('hdfs dfs -fs hdfs://localhost:8902/ -ls /')
# This is the same as
# In[49]:
get_ipython().system('hdfs dfs -ls hdfs://localhost:8902/')
# And also the same as
# In[50]:
get_ipython().system('hdfs dfs -D fs.defaultFS=hdfs://localhost:8902/ -ls /')
# With the option `-D` we can set any variable on the fly (in this case we set `fs.defaultFS` to be the HDFS filesystem).
#
# **Note:** the `-D` option should come before any other option.
# If you configure the property `fs.defaultFS` in `core-site.xml`, you can also use `hdfs dfs -ls /`.
# In[51]:
with open(os.environ['HADOOP_HOME']+'/etc/hadoop/core-site.xml', 'w') as file:
file.write("""<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:8902/</value>
</property>
</configuration>""")
# In[52]:
get_ipython().system('cat $HADOOP_HOME/etc/hadoop/core-site.xml')
# In[53]:
get_ipython().system('hdfs dfs -ls /')
# Let us set the local filesystem as default (`file:///` means the local filesystem `file://` and the extra slash `/` indicates the root folder).
# In[54]:
with open(os.environ['HADOOP_HOME']+'/etc/hadoop/core-site.xml', 'w') as file:
file.write("""
<configuration>
<property>
<name>fs.defaultFS</name>
<value>file:///</value>
</property>
</configuration>""")
# Now run `!hdfs dfs -ls /` as before. This time we are going to be listing the local filesystem and not the HDFS.
# In[55]:
get_ipython().system('hdfs dfs -ls /')
# I advise to get used to the fact that Hadoop interprets a file path as HDFS (`hdfs://`) vs. local (`file://`) depending on the setting of the variable `fs.defaultFS`, since this is often a source of confusion.
# ## Show status of HDFS with `hdfs dfsadmin`
#
# The command `hdfs dfsadmin` allows to run administration tasks on the Hadoop filesystem.
# In[56]:
get_ipython().system('hdfs dfsadmin -h')
# The comand `hdfs dfsadmin -report` shows the current status of the Hadoop filesystem. In order to run it on our MiniCluster HDFS we need to pass the option
#
# -fs hdfs://localhost:8902/
#
#
#
# Alternatively, we can configure the default filesystem (the URI of the namenode) in `core-site.xml` (see discussion in [The default filesystem)](#scrollTo=The_default_filesystem).
# In[57]:
get_ipython().system('hdfs dfsadmin -fs hdfs://localhost:8902/ -report')
# The information displayed by `hdfs dfsadmin` corresponds to what is presented in the [NameNode Web UI](#scrollTo=Start_the_NameNode_Web_UI).
# # Test MapReduce
#
# Find the MapReduce examples that come with the Hadoop distribution.
# ## Run the `pi` example
# In[58]:
get_ipython().system('find . -name "*examples*.jar"')
# Check if the cluster is still running, if not you will need to restart it! (from the cell cell [Configure the MiniCluster's ports](#scrollTo=Configure_the_MiniCluster_s_ports)).
# In[59]:
get_ipython().system('lsof -n -i -P +c0 -sTCP:LISTEN -ac java')
# Here's ChatGPT 3.5's poem inspired by
# `lsof -n -i -P +c0 -sTCP:LISTEN`:
# In[60]:
# @title
from IPython.core.display import HTML
HTML("""
<div style="background-color:rgb(16, 163, 127,.2);border:2px solid rgb(16, 163, 127,.3);padding:3px;">
<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 320" style="width:32px;height:32px;">
<g fill="currentColor">
<path d="m297.06 130.97c7.26-21.79 4.76-45.66-6.85-65.48-17.46-30.4-52.56-46.04-86.84-38.68-15.25-17.18-37.16-26.95-60.13-26.81-35.04-.08-66.13 22.48-76.91 55.82-22.51 4.61-41.94 18.7-53.31 38.67-17.59 30.32-13.58 68.54 9.92 94.54-7.26 21.79-4.76 45.66 6.85 65.48 17.46 30.4 52.56 46.04 86.84 38.68 15.24 17.18 37.16 26.95 60.13 26.8 35.06.09 66.16-22.49 76.94-55.86 22.51-4.61 41.94-18.7 53.31-38.67 17.57-30.32 13.55-68.51-9.94-94.51zm-120.28 168.11c-14.03.02-27.62-4.89-38.39-13.88.49-.26 1.34-.73 1.89-1.07l63.72-36.8c3.26-1.85 5.26-5.32 5.24-9.07v-89.83l26.93 15.55c.29.14.48.42.52.74v74.39c-.04 33.08-26.83 59.9-59.91 59.97zm-128.84-55.03c-7.03-12.14-9.56-26.37-7.15-40.18.47.28 1.3.79 1.89 1.13l63.72 36.8c3.23 1.89 7.23 1.89 10.47 0l77.79-44.92v31.1c.02.32-.13.63-.38.83l-64.41 37.19c-28.69 16.52-65.33 6.7-81.92-21.95zm-16.77-139.09c7-12.16 18.05-21.46 31.21-26.29 0 .55-.03 1.52-.03 2.2v73.61c-.02 3.74 1.98 7.21 5.23 9.06l77.79 44.91-26.93 15.55c-.27.18-.61.21-.91.08l-64.42-37.22c-28.63-16.58-38.45-53.21-21.95-81.89zm221.26 51.49-77.79-44.92 26.93-15.54c.27-.18.61-.21.91-.08l64.42 37.19c28.68 16.57 38.51 53.26 21.94 81.94-7.01 12.14-18.05 21.44-31.2 26.28v-75.81c.03-3.74-1.96-7.2-5.2-9.06zm26.8-40.34c-.47-.29-1.3-.79-1.89-1.13l-63.72-36.8c-3.23-1.89-7.23-1.89-10.47 0l-77.79 44.92v-31.1c-.02-.32.13-.63.38-.83l64.41-37.16c28.69-16.55 65.37-6.7 81.91 22 6.99 12.12 9.52 26.31 7.15 40.1zm-168.51 55.43-26.94-15.55c-.29-.14-.48-.42-.52-.74v-74.39c.02-33.12 26.89-59.96 60.01-59.94 14.01 0 27.57 4.92 38.34 13.88-.49.26-1.33.73-1.89 1.07l-63.72 36.8c-3.26 1.85-5.26 5.31-5.24 9.06l-.04 89.79zm14.63-31.54 34.65-20.01 34.65 20v40.01l-34.65 20-34.65-20z"></path>
</svg>
<p>
In the realm of <span style="color: #00f;">networks</span>, where processes twine,<br>
A <code>command</code> unfolds, a <em>symphony</em> of lines.<br>
"<strong>Lsof</strong>," it whispers, with a mystic hum,<br>
A dance of flags, each one has its own drum.<br>
<p>
"<code>-n -i -P</code>," the conductor commands,<br>
Navigate swiftly, across distant lands.<br>
"<code>+c0</code>" echoes softly, a chorus of glee,<br>
Embrace all processes, as far as eyes can see.<br>
<p>
"<code>-sTCP:LISTEN</code>," a stanza profound,<br>
Seeking the echoes of ports, a network's sound.<br>
Processes in repose, in a state so keen,<br>
A tapestry of <span style="font-style: italic;">LISTEN</span>, a poetic scene.<br>
</div>
""")
# Apart from this poetic digression, I consider `lsof` an exceptionally valuable command.
# Use the following command to get the list of available examples in the jar file.
# In[61]:
get_ipython().system('yarn jar ./hadoop-3.4.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.4.0.jar')
# Let us run the `pi` example (here we call it without arguments in order to get a usage message) through `yarn`.
# In[62]:
get_ipython().system('yarn jar ./hadoop-3.4.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.4.0.jar pi')
# The command takes `[genericOptions]` and `[commandOptions]`.
#
# The command options are:
#
# * `nMaps`, the number of mappers
# * `nSamples`, the number of iterations per mapper
#
# In[63]:
get_ipython().system('yarn jar ./hadoop-3.4.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.4.0.jar pi 5 1000')
# The job completed successfully, however it did not run on the MiniCluster because we did not specify the MiniCluster's Yarn Resource Manager address.
#
#
# In fact, `yarn application -list` returns no apps (neither running nor finished).
#
# In[64]:
get_ipython().system('yarn application -D yarn.resourcemanager.address=localhost:8903 -list -appStates ALL')
# ## Local configuration files
# In order to be able to submit the job to the MiniCluster with YARN we need to edit three files:
#
#
# * `mapred-site.xml`
# * `core-site.xml`
# * `yarn-site.xml`
#
# (see https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SingleCluster.html#YARN_on_a_Single_Node)
#
# In[65]:
file_mapred_site = os.path.join(os.environ['HADOOP_HOME'],'etc/hadoop/mapred-site.xml')
file_core_site = os.path.join(os.environ['HADOOP_HOME'],'etc/hadoop/core-site.xml')
file_yarn_site = os.path.join(os.environ['HADOOP_HOME'],'etc/hadoop/yarn-site.xml')