[MINOR][DOCS] Fix minor typos in python example code
## What changes were proposed in this pull request? Fix minor typos python example code in streaming programming guide ## How was this patch tested? N/A Author: Dmitriy Sokolov <silentsokolov@gmail.com> Closes #14805 from silentsokolov/fix-typos.
This commit is contained in:
Dmitriy Sokolov
Sean Owen
parent
befab9c1c6
commit
d4eee9932e
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@ -104,7 +104,7 @@ dv2 = [1.0, 0.0, 3.0]
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# Create a SparseVector.
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sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0])
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# Use a single-column SciPy csc_matrix as a sparse vector.
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sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape = (3, 1))
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sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape=(3, 1))
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{% endhighlight %}
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</div>
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@ -517,12 +517,12 @@ from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
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# Create an RDD of indexed rows.
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# - This can be done explicitly with the IndexedRow class:
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indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
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IndexedRow(1, [4, 5, 6]),
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IndexedRow(2, [7, 8, 9]),
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indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
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IndexedRow(1, [4, 5, 6]),
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IndexedRow(2, [7, 8, 9]),
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IndexedRow(3, [10, 11, 12])])
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# - or by using (long, vector) tuples:
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indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]),
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indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]),
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(2, [7, 8, 9]), (3, [10, 11, 12])])
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# Create an IndexedRowMatrix from an RDD of IndexedRows.
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@ -731,15 +731,15 @@ from pyspark.mllib.linalg import Matrices
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from pyspark.mllib.linalg.distributed import BlockMatrix
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# Create an RDD of sub-matrix blocks.
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blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
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blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
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((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
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# Create a BlockMatrix from an RDD of sub-matrix blocks.
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mat = BlockMatrix(blocks, 3, 2)
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# Get its size.
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m = mat.numRows() # 6
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n = mat.numCols() # 2
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m = mat.numRows() # 6
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n = mat.numCols() # 2
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# Get the blocks as an RDD of sub-matrix blocks.
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blocksRDD = mat.blocks
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@ -445,7 +445,7 @@ Similarly to text files, SequenceFiles can be saved and loaded by specifying the
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classes can be specified, but for standard Writables this is not required.
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{% highlight python %}
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>>> rdd = sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x ))
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>>> rdd = sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x))
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>>> rdd.saveAsSequenceFile("path/to/file")
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>>> sorted(sc.sequenceFile("path/to/file").collect())
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[(1, u'a'), (2, u'aa'), (3, u'aaa')]
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@ -459,10 +459,12 @@ Elasticsearch ESInputFormat:
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{% highlight python %}
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$ SPARK_CLASSPATH=/path/to/elasticsearch-hadoop.jar ./bin/pyspark
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>>> conf = {"es.resource" : "index/type"} # assume Elasticsearch is running on localhost defaults
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>>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",\
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"org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
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>>> rdd.first() # the result is a MapWritable that is converted to a Python dict
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>>> conf = {"es.resource" : "index/type"} # assume Elasticsearch is running on localhost defaults
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>>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",
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"org.apache.hadoop.io.NullWritable",
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"org.elasticsearch.hadoop.mr.LinkedMapWritable",
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conf=conf)
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>>> rdd.first() # the result is a MapWritable that is converted to a Python dict
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(u'Elasticsearch ID',
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{u'field1': True,
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u'field2': u'Some Text',
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@ -797,7 +799,6 @@ def increment_counter(x):
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rdd.foreach(increment_counter)
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print("Counter value: ", counter)
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{% endhighlight %}
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</div>
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@ -1455,13 +1456,14 @@ The code below shows an accumulator being used to add up the elements of an arra
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{% highlight python %}
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>>> accum = sc.accumulator(0)
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>>> accum
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Accumulator<id=0, value=0>
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>>> sc.parallelize([1, 2, 3, 4]).foreach(lambda x: accum.add(x))
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...
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10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
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scala> accum.value
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>>> accum.value
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10
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{% endhighlight %}
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@ -74,10 +74,10 @@ Spark's primary abstraction is a distributed collection of items called a Resili
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RDDs have _[actions](programming-guide.html#actions)_, which return values, and _[transformations](programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
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{% highlight python %}
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>>> textFile.count() # Number of items in this RDD
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>>> textFile.count() # Number of items in this RDD
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126
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>>> textFile.first() # First item in this RDD
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>>> textFile.first() # First item in this RDD
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u'# Apache Spark'
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{% endhighlight %}
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@ -90,7 +90,7 @@ Now let's use a transformation. We will use the [`filter`](programming-guide.htm
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We can chain together transformations and actions:
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{% highlight python %}
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>>> textFile.filter(lambda line: "Spark" in line).count() # How many lines contain "Spark"?
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>>> textFile.filter(lambda line: "Spark" in line).count() # How many lines contain "Spark"?
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15
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{% endhighlight %}
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@ -195,8 +195,8 @@ Next, we discuss how to use this approach in your streaming application.
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for o in offsetRanges:
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print "%s %s %s %s" % (o.topic, o.partition, o.fromOffset, o.untilOffset)
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directKafkaStream\
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.transform(storeOffsetRanges)\
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directKafkaStream \
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.transform(storeOffsetRanges) \
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.foreachRDD(printOffsetRanges)
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</div>
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</div>
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@ -930,7 +930,7 @@ JavaPairDStream<String, Integer> cleanedDStream = wordCounts.transform(
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<div data-lang="python" markdown="1">
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{% highlight python %}
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spamInfoRDD = sc.pickleFile(...) # RDD containing spam information
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spamInfoRDD = sc.pickleFile(...) # RDD containing spam information
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# join data stream with spam information to do data cleaning
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cleanedDStream = wordCounts.transform(lambda rdd: rdd.join(spamInfoRDD).filter(...))
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@ -1495,16 +1495,15 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_
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</div>
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<div data-lang="python" markdown="1">
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{% highlight python %}
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def getWordBlacklist(sparkContext):
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if ('wordBlacklist' not in globals()):
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globals()['wordBlacklist'] = sparkContext.broadcast(["a", "b", "c"])
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return globals()['wordBlacklist']
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if ("wordBlacklist" not in globals()):
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globals()["wordBlacklist"] = sparkContext.broadcast(["a", "b", "c"])
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return globals()["wordBlacklist"]
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def getDroppedWordsCounter(sparkContext):
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if ('droppedWordsCounter' not in globals()):
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globals()['droppedWordsCounter'] = sparkContext.accumulator(0)
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return globals()['droppedWordsCounter']
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if ("droppedWordsCounter" not in globals()):
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globals()["droppedWordsCounter"] = sparkContext.accumulator(0)
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return globals()["droppedWordsCounter"]
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def echo(time, rdd):
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# Get or register the blacklist Broadcast
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@ -1626,12 +1625,12 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_
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# Lazily instantiated global instance of SparkSession
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def getSparkSessionInstance(sparkConf):
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if ('sparkSessionSingletonInstance' not in globals()):
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globals()['sparkSessionSingletonInstance'] = SparkSession\
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.builder\
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.config(conf=sparkConf)\
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if ("sparkSessionSingletonInstance" not in globals()):
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globals()["sparkSessionSingletonInstance"] = SparkSession \
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.builder \
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.config(conf=sparkConf) \
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.getOrCreate()
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return globals()['sparkSessionSingletonInstance']
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return globals()["sparkSessionSingletonInstance"]
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...
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@ -1829,11 +1828,11 @@ This behavior is made simple by using `StreamingContext.getOrCreate`. This is us
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{% highlight python %}
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# Function to create and setup a new StreamingContext
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def functionToCreateContext():
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sc = SparkContext(...) # new context
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ssc = new StreamingContext(...)
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lines = ssc.socketTextStream(...) # create DStreams
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sc = SparkContext(...) # new context
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ssc = StreamingContext(...)
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lines = ssc.socketTextStream(...) # create DStreams
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...
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ssc.checkpoint(checkpointDirectory) # set checkpoint directory
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ssc.checkpoint(checkpointDirectory) # set checkpoint directory
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return ssc
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# Get StreamingContext from checkpoint data or create a new one
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@ -59,9 +59,9 @@ from pyspark.sql import SparkSession
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from pyspark.sql.functions import explode
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from pyspark.sql.functions import split
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spark = SparkSession\
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.builder()\
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.appName("StructuredNetworkWordCount")\
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spark = SparkSession \
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.builder() \
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.appName("StructuredNetworkWordCount") \
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.getOrCreate()
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{% endhighlight %}
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@ -124,22 +124,22 @@ This `lines` DataFrame represents an unbounded table containing the streaming te
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{% highlight python %}
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# Create DataFrame representing the stream of input lines from connection to localhost:9999
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lines = spark\
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.readStream\
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.format('socket')\
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.option('host', 'localhost')\
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.option('port', 9999)\
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lines = spark \
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.readStream \
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.format("socket") \
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.option("host", "localhost") \
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.option("port", 9999) \
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.load()
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# Split the lines into words
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words = lines.select(
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explode(
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split(lines.value, ' ')
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).alias('word')
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split(lines.value, " ")
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).alias("word")
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)
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# Generate running word count
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wordCounts = words.groupBy('word').count()
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wordCounts = words.groupBy("word").count()
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{% endhighlight %}
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This `lines` DataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named "value", and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have used two built-in SQL functions - split and explode, to split each line into multiple rows with a word each. In addition, we use the function `alias` to name the new column as "word". Finally, we have defined the `wordCounts` DataFrame by grouping by the unique values in the Dataset and counting them. Note that this is a streaming DataFrame which represents the running word counts of the stream.
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@ -180,10 +180,10 @@ query.awaitTermination();
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{% highlight python %}
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# Start running the query that prints the running counts to the console
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query = wordCounts\
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.writeStream\
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.outputMode('complete')\
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.format('console')\
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query = wordCounts \
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.writeStream \
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.outputMode("complete") \
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.format("console") \
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.start()
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query.awaitTermination()
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@ -488,7 +488,7 @@ spark = SparkSession. ...
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# Read text from socket
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socketDF = spark \
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.readStream() \
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.readStream() \
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.format("socket") \
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.option("host", "localhost") \
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.option("port", 9999) \
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@ -504,7 +504,7 @@ csvDF = spark \
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.readStream() \
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.option("sep", ";") \
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.schema(userSchema) \
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.csv("/path/to/directory") # Equivalent to format("csv").load("/path/to/directory")
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.csv("/path/to/directory") # Equivalent to format("csv").load("/path/to/directory")
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{% endhighlight %}
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</div>
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@ -596,8 +596,7 @@ ds.groupByKey(new MapFunction<DeviceData, String>() { // using typed API
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<div data-lang="python" markdown="1">
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{% highlight python %}
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df = ... # streaming DataFrame with IOT device data with schema { device: string, type: string, signal: double, time: DateType }
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df = ... # streaming DataFrame with IOT device data with schema { device: string, type: string, signal: double, time: DateType }
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# Select the devices which have signal more than 10
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df.select("device").where("signal > 10")
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@ -653,11 +652,11 @@ Dataset<Row> windowedCounts = words.groupBy(
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</div>
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<div data-lang="python" markdown="1">
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{% highlight python %}
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words = ... # streaming DataFrame of schema { timestamp: Timestamp, word: String }
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words = ... # streaming DataFrame of schema { timestamp: Timestamp, word: String }
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# Group the data by window and word and compute the count of each group
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windowedCounts = words.groupBy(
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window(words.timestamp, '10 minutes', '5 minutes'),
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window(words.timestamp, "10 minutes", "5 minutes"),
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words.word
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).count()
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{% endhighlight %}
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@ -704,7 +703,7 @@ streamingDf.join(staticDf, "type", "right_join"); // right outer join with a st
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{% highlight python %}
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staticDf = spark.read. ...
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streamingDf = spark.readStream. ...
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streamingDf.join(staticDf, "type") # inner equi-join with a static DF
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streamingDf.join(staticDf, "type") # inner equi-join with a static DF
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streamingDf.join(staticDf, "type", "right_join") # right outer join with a static DF
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{% endhighlight %}
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@ -907,25 +906,25 @@ spark.sql("select * from aggregates").show(); // interactively query in-memory
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noAggDF = deviceDataDf.select("device").where("signal > 10")
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# Print new data to console
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noAggDF\
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.writeStream()\
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.format("console")\
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noAggDF \
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.writeStream() \
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.format("console") \
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.start()
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# Write new data to Parquet files
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noAggDF\
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.writeStream()\
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.parquet("path/to/destination/directory")\
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noAggDF \
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.writeStream() \
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.parquet("path/to/destination/directory") \
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.start()
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# ========== DF with aggregation ==========
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aggDF = df.groupBy("device").count()
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# Print updated aggregations to console
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aggDF\
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.writeStream()\
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.outputMode("complete")\
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.format("console")\
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aggDF \
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.writeStream() \
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.outputMode("complete") \
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.format("console") \
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.start()
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# Have all the aggregates in an in memory table. The query name will be the table name
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@ -1072,11 +1071,11 @@ spark.streams().awaitAnyTermination(); // block until any one of them terminat
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{% highlight python %}
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spark = ... # spark session
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spark.streams().active # get the list of currently active streaming queries
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spark.streams().active # get the list of currently active streaming queries
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spark.streams().get(id) # get a query object by its unique id
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spark.streams().get(id) # get a query object by its unique id
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spark.streams().awaitAnyTermination() # block until any one of them terminates
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spark.streams().awaitAnyTermination() # block until any one of them terminates
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{% endhighlight %}
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</div>
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@ -1116,11 +1115,11 @@ aggDF
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<div data-lang="python" markdown="1">
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{% highlight python %}
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aggDF\
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.writeStream()\
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.outputMode("complete")\
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.option("checkpointLocation", "path/to/HDFS/dir")\
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.format("memory")\
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aggDF \
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.writeStream() \
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.outputMode("complete") \
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.option("checkpointLocation", "path/to/HDFS/dir") \
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.format("memory") \
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.start()
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{% endhighlight %}
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