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
|
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>
Examples | Apache Spark
</title>
<!-- Bootstrap core CSS -->
<link href="/css/cerulean.min.css" rel="stylesheet">
<link href="/css/custom.css" rel="stylesheet">
<!-- Code highlighter CSS -->
<link href="/css/pygments-default.css" rel="stylesheet">
<script type="text/javascript">
<!-- Google Analytics initialization -->
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-32518208-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
<!-- Adds slight delay to links to allow async reporting -->
function trackOutboundLink(link, category, action) {
try {
_gaq.push(['_trackEvent', category , action]);
} catch(err){}
setTimeout(function() {
document.location.href = link.href;
}, 100);
}
</script>
<!-- HTML5 shim and Respond.js IE8 support of HTML5 elements and media queries -->
<!--[if lt IE 9]>
<script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
<script src="https://oss.maxcdn.com/libs/respond.js/1.3.0/respond.min.js"></script>
<![endif]-->
</head>
<body>
<script src="https://code.jquery.com/jquery.js"></script>
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.3/js/bootstrap.min.js"></script>
<script src="/js/lang-tabs.js"></script>
<script src="/js/downloads.js"></script>
<div class="container" style="max-width: 1200px;">
<div class="masthead">
<p class="lead">
<a href="/">
<img src="/images/spark-logo-trademark.png"
style="height:100px; width:auto; vertical-align: bottom; margin-top: 20px;"></a><span class="tagline">
Lightning-fast cluster computing
</span>
</p>
</div>
<nav class="navbar navbar-default" role="navigation">
<!-- Brand and toggle get grouped for better mobile display -->
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse"
data-target="#navbar-collapse-1">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
</div>
<!-- Collect the nav links, forms, and other content for toggling -->
<div class="collapse navbar-collapse" id="navbar-collapse-1">
<ul class="nav navbar-nav">
<li><a href="/downloads.html">Download</a></li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">
Libraries <b class="caret"></b>
</a>
<ul class="dropdown-menu">
<li><a href="/sql/">SQL and DataFrames</a></li>
<li><a href="/streaming/">Spark Streaming</a></li>
<li><a href="/mllib/">MLlib (machine learning)</a></li>
<li><a href="/graphx/">GraphX (graph)</a></li>
<li class="divider"></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects">Third-Party Packages</a></li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">
Documentation <b class="caret"></b>
</a>
<ul class="dropdown-menu">
<li><a href="/docs/latest/">Latest Release (Spark 2.0.2)</a></li>
<li><a href="/documentation.html">Older Versions and Other Resources</a></li>
</ul>
</li>
<li><a href="/examples.html">Examples</a></li>
<li class="dropdown">
<a href="/community.html" class="dropdown-toggle" data-toggle="dropdown">
Community <b class="caret"></b>
</a>
<ul class="dropdown-menu">
<li><a href="/community.html">Mailing Lists</a></li>
<li><a href="/community.html#events">Events and Meetups</a></li>
<li><a href="/community.html#history">Project History</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark">Powered By</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Committers">Project Committers</a></li>
<li><a href="https://issues.apache.org/jira/browse/SPARK">Issue Tracker</a></li>
</ul>
</li>
<li><a href="/faq.html">FAQ</a></li>
</ul>
<ul class="nav navbar-nav navbar-right">
<li class="dropdown">
<a href="http://www.apache.org/" class="dropdown-toggle" data-toggle="dropdown">
Apache Software Foundation <b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="http://www.apache.org/">Apache Homepage</a></li>
<li><a href="http://www.apache.org/licenses/">License</a></li>
<li><a href="http://www.apache.org/foundation/sponsorship.html">Sponsorship</a></li>
<li><a href="http://www.apache.org/foundation/thanks.html">Thanks</a></li>
<li><a href="http://www.apache.org/security/">Security</a></li>
</ul>
</li>
</ul>
</div>
<!-- /.navbar-collapse -->
</nav>
<div class="row">
<div class="col-md-3 col-md-push-9">
<div class="news" style="margin-bottom: 20px;">
<h5>Latest News</h5>
<ul class="list-unstyled">
<li><a href="/news/spark-2-0-2-released.html">Spark 2.0.2 released</a>
<span class="small">(Nov 14, 2016)</span></li>
<li><a href="/news/spark-1-6-3-released.html">Spark 1.6.3 released</a>
<span class="small">(Nov 07, 2016)</span></li>
<li><a href="/news/spark-2-0-1-released.html">Spark 2.0.1 released</a>
<span class="small">(Oct 03, 2016)</span></li>
<li><a href="/news/spark-2-0-0-released.html">Spark 2.0.0 released</a>
<span class="small">(Jul 26, 2016)</span></li>
</ul>
<p class="small" style="text-align: right;"><a href="/news/index.html">Archive</a></p>
</div>
<div class="hidden-xs hidden-sm">
<a href="/downloads.html" class="btn btn-success btn-lg btn-block" style="margin-bottom: 30px;">
Download Spark
</a>
<p style="font-size: 16px; font-weight: 500; color: #555;">
Built-in Libraries:
</p>
<ul class="list-none">
<li><a href="/sql/">SQL and DataFrames</a></li>
<li><a href="/streaming/">Spark Streaming</a></li>
<li><a href="/mllib/">MLlib (machine learning)</a></li>
<li><a href="/graphx/">GraphX (graph)</a></li>
</ul>
<a href="https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects">Third-Party Packages</a>
</div>
</div>
<div class="col-md-9 col-md-pull-3">
<h2>Apache Spark Examples</h2>
<p>These examples give a quick overview of the Spark API.
Spark is built on the concept of <em>distributed datasets</em>, which contain arbitrary Java or
Python objects. You create a dataset from external data, then apply parallel operations
to it. The building block of the Spark API is its <a href="http://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds">RDD API</a>.
In the RDD API,
there are two types of operations: <em>transformations</em>, which define a new dataset based on previous ones,
and <em>actions</em>, which kick off a job to execute on a cluster.
On top of Spark’s RDD API, high level APIs are provided, e.g.
<a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#dataframes">DataFrame API</a> and
<a href="http://spark.apache.org/docs/latest/mllib-guide.html">Machine Learning API</a>.
These high level APIs provide a concise way to conduct certain data operations.
In this page, we will show examples using RDD API as well as examples using high level APIs.</p>
<h2>RDD API Examples</h2>
<h3>Word Count</h3>
<p>In this example, we use a few transformations to build a dataset of (String, Int) pairs called <code>counts</code> and then save it to a file.</p>
<ul class="nav nav-tabs">
<li class="lang-tab lang-tab-python active"><a href="#">Python</a></li>
<li class="lang-tab lang-tab-scala"><a href="#">Scala</a></li>
<li class="lang-tab lang-tab-java"><a href="#">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">text_file</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"hdfs://..."</span><span class="p">)</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">text_file</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">" "</span><span class="p">))</span> \
<span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">word</span><span class="p">:</span> <span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> \
<span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>
<span class="n">counts</span><span class="o">.</span><span class="n">saveAsTextFile</span><span class="p">(</span><span class="s">"hdfs://..."</span><span class="p">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">textFile</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">)</span>
<span class="k">val</span> <span class="n">counts</span> <span class="k">=</span> <span class="n">textFile</span><span class="o">.</span><span class="n">flatMap</span><span class="o">(</span><span class="n">line</span> <span class="k">=></span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="s">" "</span><span class="o">))</span>
<span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">word</span> <span class="k">=></span> <span class="o">(</span><span class="n">word</span><span class="o">,</span> <span class="mi">1</span><span class="o">))</span>
<span class="o">.</span><span class="n">reduceByKey</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)</span>
<span class="n">counts</span><span class="o">.</span><span class="n">saveAsTextFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">textFile</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">);</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">words</span> <span class="o">=</span> <span class="n">textFile</span><span class="o">.</span><span class="na">flatMap</span><span class="o">(</span><span class="k">new</span> <span class="n">FlatMapFunction</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Iterable</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> <span class="k">return</span> <span class="n">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">" "</span><span class="o">));</span> <span class="o">}</span>
<span class="o">});</span>
<span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="n">pairs</span> <span class="o">=</span> <span class="n">words</span><span class="o">.</span><span class="na">mapToPair</span><span class="o">(</span><span class="k">new</span> <span class="n">PairFunction</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> <span class="k">return</span> <span class="k">new</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>(</span><span class="n">s</span><span class="o">,</span> <span class="mi">1</span><span class="o">);</span> <span class="o">}</span>
<span class="o">});</span>
<span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="n">counts</span> <span class="o">=</span> <span class="n">pairs</span><span class="o">.</span><span class="na">reduceByKey</span><span class="o">(</span><span class="k">new</span> <span class="n">Function2</span><span class="o"><</span><span class="n">Integer</span><span class="o">,</span> <span class="n">Integer</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Integer</span> <span class="nf">call</span><span class="o">(</span><span class="n">Integer</span> <span class="n">a</span><span class="o">,</span> <span class="n">Integer</span> <span class="n">b</span><span class="o">)</span> <span class="o">{</span> <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="o">;</span> <span class="o">}</span>
<span class="o">});</span>
<span class="n">counts</span><span class="o">.</span><span class="na">saveAsTextFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">);</span></code></pre></figure>
</div>
</div>
</div>
<h3>Pi Estimation</h3>
<p>Spark can also be used for compute-intensive tasks. This code estimates <span style="font-family: serif; font-size: 120%;">π</span> by "throwing darts" at a circle. We pick random points in the unit square ((0, 0) to (1,1)) and see how many fall in the unit circle. The fraction should be <span style="font-family: serif; font-size: 120%;">π / 4</span>, so we use this to get our estimate.</p>
<ul class="nav nav-tabs">
<li class="lang-tab lang-tab-python active"><a href="#">Python</a></li>
<li class="lang-tab lang-tab-scala"><a href="#">Scala</a></li>
<li class="lang-tab lang-tab-java"><a href="#">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">random</span><span class="p">(),</span> <span class="n">random</span><span class="p">()</span>
<span class="k">return</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="o">*</span><span class="n">y</span> <span class="o"><</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">count</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="nb">xrange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">NUM_SAMPLES</span><span class="p">))</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span> \
<span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"Pi is roughly </span><span class="si">%f</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="mf">4.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="n">NUM_SAMPLES</span><span class="p">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">count</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="mi">1</span> <span class="n">to</span> <span class="nc">NUM_SAMPLES</span><span class="o">).</span><span class="n">map</span><span class="o">{</span><span class="n">i</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">x</span> <span class="k">=</span> <span class="nc">Math</span><span class="o">.</span><span class="n">random</span><span class="o">()</span>
<span class="k">val</span> <span class="n">y</span> <span class="k">=</span> <span class="nc">Math</span><span class="o">.</span><span class="n">random</span><span class="o">()</span>
<span class="k">if</span> <span class="o">(</span><span class="n">x</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="o">*</span><span class="n">y</span> <span class="o"><</span> <span class="mi">1</span><span class="o">)</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span>
<span class="o">}.</span><span class="n">reduce</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Pi is roughly "</span> <span class="o">+</span> <span class="mf">4.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="nc">NUM_SAMPLES</span><span class="o">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">List</span><span class="o"><</span><span class="n">Integer</span><span class="o">></span> <span class="n">l</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><</span><span class="n">Integer</span><span class="o">>(</span><span class="n">NUM_SAMPLES</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">NUM_SAMPLES</span><span class="o">;</span> <span class="n">i</span><span class="o">++)</span> <span class="o">{</span>
<span class="n">l</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">i</span><span class="o">);</span>
<span class="o">}</span>
<span class="kt">long</span> <span class="n">count</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">l</span><span class="o">).</span><span class="na">filter</span><span class="o">(</span><span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">Integer</span><span class="o">,</span> <span class="n">Boolean</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Boolean</span> <span class="nf">call</span><span class="o">(</span><span class="n">Integer</span> <span class="n">i</span><span class="o">)</span> <span class="o">{</span>
<span class="kt">double</span> <span class="n">x</span> <span class="o">=</span> <span class="n">Math</span><span class="o">.</span><span class="na">random</span><span class="o">();</span>
<span class="kt">double</span> <span class="n">y</span> <span class="o">=</span> <span class="n">Math</span><span class="o">.</span><span class="na">random</span><span class="o">();</span>
<span class="k">return</span> <span class="n">x</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="o">*</span><span class="n">y</span> <span class="o"><</span> <span class="mi">1</span><span class="o">;</span>
<span class="o">}</span>
<span class="o">}).</span><span class="na">count</span><span class="o">();</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Pi is roughly "</span> <span class="o">+</span> <span class="mf">4.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="n">NUM_SAMPLES</span><span class="o">);</span></code></pre></figure>
</div>
</div>
</div>
<h2>DataFrame API Examples</h2>
<p>
In Spark, a <a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#dataframes">DataFrame</a>
is a distributed collection of data organized into named columns.
Users can use DataFrame API to perform various relational operations on both external
data sources and Spark’s built-in distributed collections without providing specific procedures for processing data.
Also, programs based on DataFrame API will be automatically optimized by Spark’s built-in optimizer, Catalyst.
</p>
<h3>Text Search</h3>
<p>In this example, we search through the error messages in a log file.</p>
<ul class="nav nav-tabs">
<li class="lang-tab lang-tab-python active"><a href="#">Python</a></li>
<li class="lang-tab lang-tab-scala"><a href="#">Scala</a></li>
<li class="lang-tab lang-tab-java"><a href="#">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">textFile</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"hdfs://..."</span><span class="p">)</span>
<span class="c"># Creates a DataFrame having a single column named "line"</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">textFile</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="n">Row</span><span class="p">(</span><span class="n">r</span><span class="p">))</span><span class="o">.</span><span class="n">toDF</span><span class="p">([</span><span class="s">"line"</span><span class="p">])</span>
<span class="n">errors</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s">"line"</span><span class="p">)</span><span class="o">.</span><span class="n">like</span><span class="p">(</span><span class="s">"</span><span class="si">%E</span><span class="s">RROR%"</span><span class="p">))</span>
<span class="c"># Counts all the errors</span>
<span class="n">errors</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="c"># Counts errors mentioning MySQL</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s">"line"</span><span class="p">)</span><span class="o">.</span><span class="n">like</span><span class="p">(</span><span class="s">"%MySQL%"</span><span class="p">))</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="c"># Fetches the MySQL errors as an array of strings</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s">"line"</span><span class="p">)</span><span class="o">.</span><span class="n">like</span><span class="p">(</span><span class="s">"%MySQL%"</span><span class="p">))</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">textFile</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">)</span>
<span class="c1">// Creates a DataFrame having a single column named "line"</span>
<span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">textFile</span><span class="o">.</span><span class="n">toDF</span><span class="o">(</span><span class="s">"line"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">errors</span> <span class="k">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="n">like</span><span class="o">(</span><span class="s">"%ERROR%"</span><span class="o">))</span>
<span class="c1">// Counts all the errors</span>
<span class="n">errors</span><span class="o">.</span><span class="n">count</span><span class="o">()</span>
<span class="c1">// Counts errors mentioning MySQL</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="n">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="n">count</span><span class="o">()</span>
<span class="c1">// Fetches the MySQL errors as an array of strings</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="n">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="n">collect</span><span class="o">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Creates a DataFrame having a single column named "line"</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">textFile</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">);</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Row</span><span class="o">></span> <span class="n">rowRDD</span> <span class="o">=</span> <span class="n">textFile</span><span class="o">.</span><span class="na">map</span><span class="o">(</span>
<span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Row</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Row</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">line</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
<span class="k">return</span> <span class="n">RowFactory</span><span class="o">.</span><span class="na">create</span><span class="o">(</span><span class="n">line</span><span class="o">);</span>
<span class="o">}</span>
<span class="o">});</span>
<span class="n">List</span><span class="o"><</span><span class="n">StructField</span><span class="o">></span> <span class="n">fields</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><</span><span class="n">StructField</span><span class="o">>();</span>
<span class="n">fields</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">DataTypes</span><span class="o">.</span><span class="na">createStructField</span><span class="o">(</span><span class="s">"line"</span><span class="o">,</span> <span class="n">DataTypes</span><span class="o">.</span><span class="na">StringType</span><span class="o">,</span> <span class="kc">true</span><span class="o">));</span>
<span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="n">DataTypes</span><span class="o">.</span><span class="na">createStructType</span><span class="o">(</span><span class="n">fields</span><span class="o">);</span>
<span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rowRDD</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>
<span class="n">DataFrame</span> <span class="n">errors</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="na">like</span><span class="o">(</span><span class="s">"%ERROR%"</span><span class="o">));</span>
<span class="c1">// Counts all the errors</span>
<span class="n">errors</span><span class="o">.</span><span class="na">count</span><span class="o">();</span>
<span class="c1">// Counts errors mentioning MySQL</span>
<span class="n">errors</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="na">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="na">count</span><span class="o">();</span>
<span class="c1">// Fetches the MySQL errors as an array of strings</span>
<span class="n">errors</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="na">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="na">collect</span><span class="o">();</span></code></pre></figure>
</div>
</div>
</div>
<h3>Simple Data Operations</h3>
<p>
In this example, we read a table stored in a database and calculate the number of people for every age.
Finally, we save the calculated result to S3 in the format of JSON.
A simple MySQL table "people" is used in the example and this table has two columns,
"name" and "age".
</p>
<ul class="nav nav-tabs">
<li class="lang-tab lang-tab-python active"><a href="#">Python</a></li>
<li class="lang-tab lang-tab-scala"><a href="#">Scala</a></li>
<li class="lang-tab lang-tab-java"><a href="#">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Creates a DataFrame based on a table named "people"</span>
<span class="c"># stored in a MySQL database.</span>
<span class="n">url</span> <span class="o">=</span> \
<span class="s">"jdbc:mysql://yourIP:yourPort/test?user=yourUsername;password=yourPassword"</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span> \
<span class="o">.</span><span class="n">read</span> \
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s">"jdbc"</span><span class="p">)</span> \
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s">"url"</span><span class="p">,</span> <span class="n">url</span><span class="p">)</span> \
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s">"dbtable"</span><span class="p">,</span> <span class="s">"people"</span><span class="p">)</span> \
<span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="c"># Looks the schema of this DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
<span class="c"># Counts people by age</span>
<span class="n">countsByAge</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s">"age"</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c"># Saves countsByAge to S3 in the JSON format.</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s">"json"</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">"s3a://..."</span><span class="p">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// Creates a DataFrame based on a table named "people"</span>
<span class="c1">// stored in a MySQL database.</span>
<span class="k">val</span> <span class="n">url</span> <span class="k">=</span>
<span class="s">"jdbc:mysql://yourIP:yourPort/test?user=yourUsername;password=yourPassword"</span>
<span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span>
<span class="o">.</span><span class="n">read</span>
<span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="s">"jdbc"</span><span class="o">)</span>
<span class="o">.</span><span class="n">option</span><span class="o">(</span><span class="s">"url"</span><span class="o">,</span> <span class="n">url</span><span class="o">)</span>
<span class="o">.</span><span class="n">option</span><span class="o">(</span><span class="s">"dbtable"</span><span class="o">,</span> <span class="s">"people"</span><span class="o">)</span>
<span class="o">.</span><span class="n">load</span><span class="o">()</span>
<span class="c1">// Looks the schema of this DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
<span class="c1">// Counts people by age</span>
<span class="k">val</span> <span class="n">countsByAge</span> <span class="k">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="s">"age"</span><span class="o">).</span><span class="n">count</span><span class="o">()</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">show</span><span class="o">()</span>
<span class="c1">// Saves countsByAge to S3 in the JSON format.</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="s">"json"</span><span class="o">).</span><span class="n">save</span><span class="o">(</span><span class="s">"s3a://..."</span><span class="o">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Creates a DataFrame based on a table named "people"</span>
<span class="c1">// stored in a MySQL database.</span>
<span class="n">String</span> <span class="n">url</span> <span class="o">=</span>
<span class="s">"jdbc:mysql://yourIP:yourPort/test?user=yourUsername;password=yourPassword"</span><span class="o">;</span>
<span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span>
<span class="o">.</span><span class="na">read</span><span class="o">()</span>
<span class="o">.</span><span class="na">format</span><span class="o">(</span><span class="s">"jdbc"</span><span class="o">)</span>
<span class="o">.</span><span class="na">option</span><span class="o">(</span><span class="s">"url"</span><span class="o">,</span> <span class="n">url</span><span class="o">)</span>
<span class="o">.</span><span class="na">option</span><span class="o">(</span><span class="s">"dbtable"</span><span class="o">,</span> <span class="s">"people"</span><span class="o">)</span>
<span class="o">.</span><span class="na">load</span><span class="o">();</span>
<span class="c1">// Looks the schema of this DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="na">printSchema</span><span class="o">();</span>
<span class="c1">// Counts people by age</span>
<span class="n">DataFrame</span> <span class="n">countsByAge</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="na">groupBy</span><span class="o">(</span><span class="s">"age"</span><span class="o">).</span><span class="na">count</span><span class="o">();</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="na">show</span><span class="o">();</span>
<span class="c1">// Saves countsByAge to S3 in the JSON format.</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="na">write</span><span class="o">().</span><span class="na">format</span><span class="o">(</span><span class="s">"json"</span><span class="o">).</span><span class="na">save</span><span class="o">(</span><span class="s">"s3a://..."</span><span class="o">);</span></code></pre></figure>
</div>
</div>
</div>
<h2>Machine Learning Example</h2>
<p>
<a href="http://spark.apache.org/docs/latest/mllib-guide.html">MLlib</a>, Spark’s Machine Learning (ML) library, provides many distributed ML algorithms.
These algorithms cover tasks such as feature extraction, classification, regression, clustering,
recommendation, and more.
MLlib also provides tools such as ML Pipelines for building workflows, CrossValidator for tuning parameters,
and model persistence for saving and loading models.
</p>
<h3>Prediction with Logistic Regression</h3>
<p>
In this example, we take a dataset of labels and feature vectors.
We learn to predict the labels from feature vectors using the Logistic Regression algorithm.
</p>
<ul class="nav nav-tabs">
<li class="lang-tab lang-tab-python active"><a href="#">Python</a></li>
<li class="lang-tab lang-tab-scala"><a href="#">Scala</a></li>
<li class="lang-tab lang-tab-java"><a href="#">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Every record of this DataFrame contains the label and</span>
<span class="c"># features represented by a vector.</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">])</span>
<span class="c"># Set parameters for the algorithm.</span>
<span class="c"># Here, we limit the number of iterations to 10.</span>
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c"># Fit the model to the data.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="c"># Given a dataset, predict each point's label, and show the results.</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// Every record of this DataFrame contains the label and</span>
<span class="c1">// features represented by a vector.</span>
<span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="o">(</span><span class="n">data</span><span class="o">).</span><span class="n">toDF</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">)</span>
<span class="c1">// Set parameters for the algorithm.</span>
<span class="c1">// Here, we limit the number of iterations to 10.</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">().</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="c1">// Fit the model to the data.</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">df</span><span class="o">)</span>
<span class="c1">// Inspect the model: get the feature weights.</span>
<span class="k">val</span> <span class="n">weights</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">weights</span>
<span class="c1">// Given a dataset, predict each point's label, and show the results.</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">df</span><span class="o">).</span><span class="n">show</span><span class="o">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Every record of this DataFrame contains the label and</span>
<span class="c1">// features represented by a vector.</span>
<span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StructType</span><span class="o">(</span><span class="k">new</span> <span class="n">StructField</span><span class="o">[]{</span>
<span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="n">DataTypes</span><span class="o">.</span><span class="na">DoubleType</span><span class="o">,</span> <span class="kc">false</span><span class="o">,</span> <span class="n">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">()),</span>
<span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">VectorUDT</span><span class="o">(),</span> <span class="kc">false</span><span class="o">,</span> <span class="n">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">()),</span>
<span class="o">});</span>
<span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">data</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>
<span class="c1">// Set parameters for the algorithm.</span>
<span class="c1">// Here, we limit the number of iterations to 10.</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">().</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span>
<span class="c1">// Fit the model to the data.</span>
<span class="n">LogisticRegressionModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">df</span><span class="o">);</span>
<span class="c1">// Inspect the model: get the feature weights.</span>
<span class="n">Vector</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">weights</span><span class="o">();</span>
<span class="c1">// Given a dataset, predict each point's label, and show the results.</span>
<span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">df</span><span class="o">).</span><span class="na">show</span><span class="o">();</span></code></pre></figure>
</div>
</div>
</div>
<p><a name="additional"></a></p>
<h1>Additional Examples</h1>
<p>Many additional examples are distributed with Spark:</p>
<ul>
<li>Basic Spark: <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples">Scala examples</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples">Java examples</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/python">Python examples</a></li>
<li>Spark Streaming: <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming">Scala examples</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples/streaming">Java examples</a></li>
</ul>
</div>
</div>
<footer class="small">
<hr>
Apache Spark, Spark, Apache, and the Spark logo are <a href="/trademarks.html">trademarks</a> of
<a href="http://www.apache.org">The Apache Software Foundation</a>.
</footer>
</div>
</body>
</html>
|
