/
Hadoop_minicluster.py
1142 lines (718 loc) · 575 KB
/
Hadoop_minicluster.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# 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[2]:
# @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>
<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 src="data:image/gif;base64,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" alt="Running cat" />
# # Preamble
# ## Download Hadoop
# In[3]:
import urllib.request
import os
import shutil
import tarfile
import logging
import subprocess
import time
import sys
############
# COSTANTS #
############
# URL for downloading Hadoop
HADOOP_URL = "https://dlcdn.apache.org/hadoop/common/stable/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[4]:
get_ipython().system('mapred -h')
# In[5]:
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[6]:
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[7]:
get_ipython().system('find hadoop-3.4.0 -name "mockito*"')
# Download it from the Maven repository.
# In[8]:
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[9]:
shutil.copy('mockito-core-2.28.2.jar', os.path.join(os.environ['HADOOP_HOME'],'share/hadoop/mapreduce/'))
# In[10]:
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[11]:
get_ipython().system('mkdir -p ./target/test/data/dfs/{name-0-1,name-0-2}')
# In[12]:
get_ipython().system('ls ./target/test/data/dfs/')
# # Start the MiniCluster
#
#
# To see a full list of options run `mapred minicluster -help`.
# In[13]:
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[14]:
# check if the file is there
get_ipython().system('find $HADOOP_HOME -name "core-site.xml"')
# In[15]:
# view the contents of the file
get_ipython().system('cat $(find $HADOOP_HOME -name "core-site.xml")')
# In[16]:
with open(os.environ['HADOOP_HOME']+'/etc/hadoop/core-site.xml', 'w') as file:
file.write("<configuration>
</configuration>")
# In[17]:
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[18]:
#!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[19]:
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 ports and IP addresses in numeric form
# * 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[20]:
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[21]:
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[22]:
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
#
#
# Let us check the Web interface at port $8088$.
# In[23]:
get_ipython().system('wget http://localhost:8088')
# We can serve the ResourceManager UI in the browser through Google Colab.
# In[24]:
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[25]:
get_ipython().system('wget http://localhost:19888')
# In[26]:
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')
# ## Stop the MiniCluster subprocess
#
# To stop the MiniCluster subprocess use `process.kill()` (remember: `process` is the variable name for the MiniCluster subprocess).
# In[27]:
process.kill()
# The Java process should now be gone.
# In[28]:
get_ipython().system('lsof -n -i -P +c0 -sTCP:LISTEN')
# In case there are still some `java` processes lingering around, kill them with
# In[29]:
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[30]:
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[31]:
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[32]:
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[33]:
get_ipython().system('tail err.txt')
# Check the NameNode's HTTP port.
# In[34]:
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[35]:
if IN_COLAB:
# 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')
# 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[36]:
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[37]:
get_ipython().system('hdfs dfs -ls hdfs://localhost:8902/my_dir')
# Upload the local folder `sample_data` to `my_dir` on HDFS
# In[38]:
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[39]:
get_ipython().system('du -h sample_data')
# Upload `sample_data` to HDFS.
# In[40]:
get_ipython().system('hdfs dfs -put sample_data hdfs://localhost:8902/my_dir/')
# Check
# In[41]:
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[42]:
get_ipython().system('hdfs dfs -du -h hdfs://localhost:8902/my_dir')
# Check the contents of the HDFS folder `my_dir`
# In[43]:
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[44]:
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[45]:
get_ipython().system('hdfs dfs -fs hdfs://localhost:8902/ -ls /')
# This is the same as
# In[46]:
get_ipython().system('hdfs dfs -ls hdfs://localhost:8902/')
# And also the same as
# In[47]:
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[48]:
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[49]:
get_ipython().system('cat $HADOOP_HOME/etc/hadoop/core-site.xml')
# In[50]:
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[51]:
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[52]:
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[53]:
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[54]:
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[55]:
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[56]:
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[57]:
# @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[58]:
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[59]:
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[60]:
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[61]:
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[62]:
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')
# In[63]:
get_ipython().run_cell_magic('bash', '', "cat > $HADOOP_HOME/'etc/hadoop/mapred-site.xml' << 🐸
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.application.classpath</name>
<value>${HADOOP_HOME}/share/hadoop/mapreduce/*:${HADOOP_HOME}/share/hadoop/mapreduce/lib/*</value>
</property>
</configuration>
🐸
")
# In[64]:
get_ipython().system("cat $HADOOP_HOME/'etc/hadoop/mapred-site.xml'")
# Set the default filesystem as the MiniCluster's filesystem in `core-site.xml`. This is necessary in order to allow YARN to save the applications logs to HDFS and this is why we create the directory `/tmp/logs` on HDFS.
# In[65]:
with open(file_core_site, 'w') as file:
file.write("""
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:8902/</value>
</property>
</configuration>""")
# Set `yarn.resourcemanager.address=localhost:8903` in `yarn-site.xml`.
# In[66]:
with open(file_yarn_site, 'w') as file:
file.write("""
<configuration>
<property>
<name>yarn.resourcemanager.address</name>
<value>localhost:8903</value>
</property>
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
</configuration>""")
# It might be necessary at this point to re-start the cluster.