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Created March 31, 2015 05:29
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Spark training 1
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"spark.app.name" : "Notebook",
"spark.master" : "local[8]",
"spark.executor.memory" : "1G"
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"cell_type" : "markdown",
"source" : "# Exercise 1 \n## A Scala script we will use interactively in the Spark Shell.\n\nIf you're using the Spark Shell or our build process, the following three\nstatements, two import statements and constructing the SparkContext variable\n`sc`, are executed automatically for you. Otherwise, our scripts would need\nthem explicitly:\n```\n import org.apache.spark.SparkContext\n import org.apache.spark.SparkContext._\n val sc = new SparkContext(\"local\", \"Intro\")\n```"
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "Load the plays of Shakespeare, then convert each line to lower case.\nBoth input and lines will be RDDs."
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"cell_type" : "code",
"source" : "val sc = sparkContext\nval input = sc.textFile(\"/Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt\")\nval lines = input.map(line => line.toLowerCase)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "sc: org.apache.spark.SparkContext = org.apache.spark.SparkContext@46824958\ninput: org.apache.spark.rdd.RDD[String] = /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[14] at textFile at <console>:39\nlines: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[15] at map at <console>:40\n"
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"text/html" : "MapPartitionsRDD[15] at map at &lt;console&gt;:40"
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"execution_count" : 24
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"metadata" : { },
"cell_type" : "markdown",
"source" : "Cache the data in memory for faster, repeated retrieval."
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "lines.cache",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res17: lines.type = MapPartitionsRDD[15] at map at <console>:40\n"
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"execution_count" : 26
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"metadata" : { },
"cell_type" : "markdown",
"source" : "Find all verses that mention \"hamlet\".\nNote the shorthand for the function literal:\n`_contains(\"hamlet\")` behaves the same as `line => line.contains(\"hamlet\")`."
}, {
"metadata" : {
"trusted" : true,
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},
"cell_type" : "code",
"source" : "val hamlet = lines.filter(_.contains(\"hamlet\"))\nhamlet.take(10)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "hamlet: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[5] at filter at <console>:42\nres4: Array[String] = Array(\"\thamlet\", hamlet\tson to the late, and nephew to the present king., horatio\tfriend to hamlet., gertrude\tqueen of denmark, and mother to hamlet., \"\tghost of hamlet's father. (ghost:)\", \"\thamlet\", \"\tdared to the combat; in which our valiant hamlet--\", \"\this fell to hamlet. now, sir, young fortinbras,\", \"\tunto young hamlet; for, upon my life,\", \"\thamlet\")\n"
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}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "The `()` are optional in Scala for no-argument methods"
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val count = hamlet.count() // How many occurrences of hamlet?",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "count: Long = 489\n"
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"data" : {
"text/html" : "489"
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"output_type" : "execute_result",
"execution_count" : 7
} ]
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"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val array = hamlet.collect() // Convert the RDD into a collection (array)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "array: Array[String] = Array(\"\thamlet\", hamlet\tson to the late, and nephew to the present king., horatio\tfriend to hamlet., gertrude\tqueen of denmark, and mother to hamlet., \"\tghost of hamlet's father. (ghost:)\", \"\thamlet\", \"\tdared to the combat; in which our valiant hamlet--\", \"\this fell to hamlet. now, sir, young fortinbras,\", \"\tunto young hamlet; for, upon my life,\", \"\thamlet\", \"\t[enter king claudius, queen gertrude, hamlet,\", king claudius\tthough yet of hamlet our dear brother's death, \"\tbut now, my cousin hamlet, and my son,--\", hamlet\t[aside] a little more than kin, and less than kind., hamlet\tnot so, my lord; i am too much i' the sun., queen gertrude\tgood hamlet, cast thy nighted colour off,, hamlet\tay, madam, it is common., hamlet\tseems, madam! nay it is; i know not 'seems.', k..."
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"execution_count" : 8
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"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "array.take(20) foreach println // Take the first 20, and print them 1/line.",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "\thamlet\nhamlet\tson to the late, and nephew to the present king.\nhoratio\tfriend to hamlet.\ngertrude\tqueen of denmark, and mother to hamlet.\n\tghost of hamlet's father. (ghost:)\n\thamlet\n\tdared to the combat; in which our valiant hamlet--\n\this fell to hamlet. now, sir, young fortinbras,\n\tunto young hamlet; for, upon my life,\n\thamlet\n\t[enter king claudius, queen gertrude, hamlet,\nking claudius\tthough yet of hamlet our dear brother's death\n\tbut now, my cousin hamlet, and my son,--\nhamlet\t[aside] a little more than kin, and less than kind.\nhamlet\tnot so, my lord; i am too much i' the sun.\nqueen gertrude\tgood hamlet, cast thy nighted colour off,\nhamlet\tay, madam, it is common.\nhamlet\tseems, madam! nay it is; i know not 'seems.'\nking claudius\t'tis sweet and commendable in your nature, hamlet,\nqueen gertrude\tlet not thy mother lose her prayers, hamlet:\n"
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"output_type" : "execute_result",
"execution_count" : 9
} ]
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"metadata" : {
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},
"cell_type" : "code",
"source" : "hamlet.take(20) foreach println // ... but you don't have to \"collect\" first.",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "\thamlet\nhamlet\tson to the late, and nephew to the present king.\nhoratio\tfriend to hamlet.\ngertrude\tqueen of denmark, and mother to hamlet.\n\tghost of hamlet's father. (ghost:)\n\thamlet\n\tdared to the combat; in which our valiant hamlet--\n\this fell to hamlet. now, sir, young fortinbras,\n\tunto young hamlet; for, upon my life,\n\thamlet\n\t[enter king claudius, queen gertrude, hamlet,\nking claudius\tthough yet of hamlet our dear brother's death\n\tbut now, my cousin hamlet, and my son,--\nhamlet\t[aside] a little more than kin, and less than kind.\nhamlet\tnot so, my lord; i am too much i' the sun.\nqueen gertrude\tgood hamlet, cast thy nighted colour off,\nhamlet\tay, madam, it is common.\nhamlet\tseems, madam! nay it is; i know not 'seems.'\nking claudius\t'tis sweet and commendable in your nature, hamlet,\nqueen gertrude\tlet not thy mother lose her prayers, hamlet:\n"
}, {
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"data" : {
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"output_type" : "execute_result",
"execution_count" : 10
} ]
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "Create a separate filter function instead and pass it as an argument to the\nfilter method. `filterFunc` is a value that's a function of type\n`String` to `Boolean`."
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"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val filterFunc: String => Boolean = \n s => s.contains(\"claudius\") || s.contains(\"gertrude\")",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "filterFunc: String => Boolean = <function1>\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "&lt;function1&gt;"
},
"output_type" : "execute_result",
"execution_count" : 11
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : true
},
"cell_type" : "markdown",
"source" : "Equivalent, due to type inference:\n`s => s.contains(\"claudius\") || s.contains(\"gertrude\")` or\n`(s:String) => s.contains(\"claudius\") || s.contains(\"gertrude\")`"
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "Filter the hamlets for the verses that mention God or Christ (lowercase)"
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val hamletPlusClaudiusOrGertrude = hamlet filter filterFunc\nval hamletWithGodOrChrist = hamlet.filter(s => s.contains(\"god\") || s.contains(\"christ\"))\nprintln(hamletPlusClaudiusOrGertrude.count())\nprintln(hamletWithGodOrChrist.count())",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "27\n8\nhamletPlusClaudiusOrGertrude: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[10] at filter at <console>:48\nhamletWithGodOrChrist: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[11] at filter at <console>:49\n"
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"data" : {
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"output_type" : "execute_result",
"execution_count" : 15
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "hamletWithGodOrChrist foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "hamlet\tfor god's love, let me hear.\nhamlet\to god!\nhamlet\twell, god-a-mercy.\nhamlet\to god, i could be bounded in a nut shell and count\nhamlet\tgod's bodykins, man, much better: use every man\nhamlet\tay, so, god be wi' ye;\nhamlet\ti have heard of your paintings too, well enough; god\nhamlet\to god, your only jig-maker. what should a man do\n"
}, {
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"data" : {
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},
"output_type" : "execute_result",
"execution_count" : 16
} ]
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "Count how many we found. (Note we dropped the parentheses after \"count\")"
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val count2 = hamletPlusClaudiusOrGertrude.count",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "count2: Long = 27\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "27"
},
"output_type" : "execute_result",
"execution_count" : 17
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "hamletPlusClaudiusOrGertrude foreach println",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "gertrude\tqueen of denmark, and mother to hamlet.\n\t[enter king claudius, queen gertrude, hamlet,\nking claudius\tthough yet of hamlet our dear brother's death\nqueen gertrude\tgood hamlet, cast thy nighted colour off,\nking claudius\t'tis sweet and commendable in your nature, hamlet,\nqueen gertrude\tlet not thy mother lose her prayers, hamlet:\nqueen gertrude\tcame this from hamlet to her?\nking claudius\thow fares our cousin hamlet?\nking claudius\ti have nothing with this answer, hamlet; these words\nqueen gertrude\tcome hither, my dear hamlet, sit by me.\nqueen gertrude\thamlet, thou hast thy father much offended.\nqueen gertrude\twhy, how now, hamlet!\nqueen gertrude\to hamlet, speak no more:\nqueen gertrude\to hamlet, thou hast cleft my heart in twain.\nking claudius\twhat, gertrude? how does hamlet?\nking claudius\tnow, hamlet, where's polonius?\nking claudius\thamlet, this deed, for thine especial safety,--\nking claudius\tay, hamlet.\nking claudius\tthy loving father, hamlet.\nking claudius\tfrom hamlet! who brought them?\nking claudius\t'tis hamlets character. 'naked!\nqueen gertrude\thamlet, hamlet!\nking claudius\tcome, hamlet, come, and take this hand from me.\n\t[king claudius puts laertes' hand into hamlet's]\nking claudius\tgive them the foils, young osric. cousin hamlet,\nking claudius\tstay; give me drink. hamlet, this pearl is thine;\nqueen gertrude\tno, no, the drink, the drink,--o my dear hamlet,--\n"
}, {
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"data" : {
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},
"output_type" : "execute_result",
"execution_count" : 18
} ]
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "This *method* creates a filter function using the two input strings:"
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "def makeAndFilter(s1: String, s2: String): String => Boolean =\n s => s.contains(s1) && s.contains(s2)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "makeAndFilter: (s1: String, s2: String)String => Boolean\n"
}, {
"metadata" : { },
"data" : {
"text/html" : ""
},
"output_type" : "execute_result",
"execution_count" : 19
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val rottenDenmark = lines filter (makeAndFilter(\"rotten\", \"denmark\"))\nval countGoodEvil = rottenDenmark.count\nrottenDenmark foreach println",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "marcellus\tsomething is rotten in the state of denmark.\nrottenDenmark: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[12] at filter at <console>:42\ncountGoodEvil: Long = 1\n"
}, {
"metadata" : { },
"data" : {
"text/html" : ""
},
"output_type" : "execute_result",
"execution_count" : 20
} ]
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "A few other useful tools:"
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "rottenDenmark.toDebugString",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res13: String = \n(2) MapPartitionsRDD[12] at filter at <console>:42 []\n | MapPartitionsRDD[2] at map at <console>:36 []\n | CachedPartitions: 2; MemorySize: 17.8 MB; TachyonSize: 0.0 B; DiskSize: 0.0 B\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[1] at textFile at <console>:35 []\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt HadoopRDD[0] at textFile at <console>:35 []\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "(2) MapPartitionsRDD[12] at filter at &lt;console&gt;:42 []\n | MapPartitionsRDD[2] at map at &lt;console&gt;:36 []\n | CachedPartitions: 2; MemorySize: 17.8 MB; TachyonSize: 0.0 B; DiskSize: 0.0 B\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[1] at textFile at &lt;console&gt;:35 []\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt HadoopRDD[0] at textFile at &lt;console&gt;:35 []"
},
"output_type" : "execute_result",
"execution_count" : 21
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "println(lines.flatMap(_.split(\"\\\\W+\")).count() / 500)\nlines.toDebugString",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "2215\nres30: String = \n(2) MapPartitionsRDD[15] at map at <console>:40 [Memory Deserialized 1x Replicated]\n | CachedPartitions: 2; MemorySize: 17.8 MB; TachyonSize: 0.0 B; DiskSize: 0.0 B\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[14] at textFile at <console>:39 [Memory Deserialized 1x Replicated]\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt HadoopRDD[13] at textFile at <console>:39 [Memory Deserialized 1x Replicated]\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "(2) MapPartitionsRDD[15] at map at &lt;console&gt;:40 [Memory Deserialized 1x Replicated]\n | CachedPartitions: 2; MemorySize: 17.8 MB; TachyonSize: 0.0 B; DiskSize: 0.0 B\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[14] at textFile at &lt;console&gt;:39 [Memory Deserialized 1x Replicated]\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt HadoopRDD[13] at textFile at &lt;console&gt;:39 [Memory Deserialized 1x Replicated]"
},
"output_type" : "execute_result",
"execution_count" : 42
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "input.toDebugString",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res15: String = \n(2) /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[1] at textFile at <console>:35 []\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt HadoopRDD[0] at textFile at <console>:35 []\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "(2) /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt MapPartitionsRDD[1] at textFile at &lt;console&gt;:35 []\n | /Users/huitseeker/Spark/spark-workshop/exercises/data/all-shakespeare.txt HadoopRDD[0] at textFile at &lt;console&gt;:35 []"
},
"output_type" : "execute_result",
"execution_count" : 23
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : true
},
"cell_type" : "markdown",
"source" : "Clean up nicely with `sc.stop`"
}, {
"metadata" : { },
"cell_type" : "markdown",
"source" : "# Exercise 2"
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "import org.apache.spark.rdd._\n\ndef load_file(filename: String) : RDD[String] = {\n val fileHandle = sparkContext.textFile(s\"/Users/huitseeker/Spark/spark-workshop/exercises/data/$filename\")\n return fileHandle.map(_.toLowerCase)\n}\nval lines_shakespeare = load_file(\"all-shakespeare.txt\").cache()",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "import org.apache.spark.rdd._\nload_file: (filename: String)org.apache.spark.rdd.RDD[String]\nlines_shakespeare: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[20] at map at <console>:38\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "MapPartitionsRDD[20] at map at &lt;console&gt;:38"
},
"output_type" : "execute_result",
"execution_count" : 35
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val acts = load_file(\"apodat.txt\")\nacts.take(4) foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "\nes1|1|1|and josias held the feast of the passover in jerusalem unto his lord, and offered the passover the fourteenth day of the first month; \nes1|1|2|having set the priests according to their daily courses, being arrayed in long garments, in the temple of the lord. \nes1|1|3|and he spake unto the levites, the holy ministers of israel, that they should hallow themselves unto the lord, to set the holy ark of the lord in the house that king solomon the son of david had built: \nacts: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[41] at map at <console>:38\n"
}, {
"metadata" : { },
"data" : {
"text/html" : ""
},
"output_type" : "execute_result",
"execution_count" : 40
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val l = lines_shakespeare.flatMap(_.split(\"\"\"\\W+\"\"\")).map(w => (w,1)).reduceByKey(_ + _)\nl.take(4) foreach (println)\nl.lookup(\"hamlet\").head",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(pinnace,3)\n(bone,21)\n(lug,3)\n(vailing,3)\nl: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[63] at reduceByKey at <console>:43\nres38: Int = 494\n"
}, {
"metadata" : { },
"data" : {
"text/html" : "494"
},
"output_type" : "execute_result",
"execution_count" : 52
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "def simpleTimeStamp(): String = {\n import java.util.Date\n import java.text.SimpleDateFormat\n val fmt = new SimpleDateFormat (\"yyyy.MM.dd-kk.mm.ss\")\n\n fmt.format(new Date())\n}",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "simpleTimeStamp: ()String\n"
}, {
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"text/html" : "2015.03.30-16.01.15"
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"cell_type" : "code",
"source" : "simpleTimeStamp()",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res39: String = 2015.03.30-16.01.32\n"
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},
"cell_type" : "code",
"source" : "l.saveAsTextFile(s\"/tmp/wc${simpleTimeStamp()}.txt\")",
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}, {
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},
"cell_type" : "code",
"source" : "l.take(10) foreach (print _)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(pinnace,3)(bone,21)(lug,3)(vailing,3)(bombast,4)(gaping,11)(hem,10)(stinks,1)(forsooth,48)(been,738)"
}, {
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"metadata" : {
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},
"cell_type" : "code",
"source" : "val count_driver = lines_shakespeare.flatMap(_.split(\"\"\"\\W+\"\"\")).countByValue()\nprintln(count_driver(\"hamlet\"))",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "494\ncount_driver: scala.collection.Map[String,Long] = Map(professed -> 5, chary -> 1, purifies -> 1, incident -> 5, haberdasher -> 6, confesseth -> 1, serious -> 25, garboils -> 2, brink -> 2, flibbertigibbet -> 2, youthful -> 32, sinister -> 6, comply -> 3, ebb -> 17, breaks -> 35, marr -> 12, forgotten -> 15, precious -> 82, caithness -> 6, leer -> 6, inkhorn -> 3, compliment -> 12, ascension -> 5, unlettered -> 2, hourly -> 24, tripe -> 2, respecting -> 6, uncropped -> 1, alarums -> 28, marchioness -> 4, gamester -> 9, friendless -> 1, ecclesiastic -> 1, cadent -> 1, accosted -> 1, lover -> 72, tokened -> 1, plentiful -> 4, malignant -> 8, pasture -> 12, speaker -> 10, dapples -> 1, cxxiv -> 1, terrible -> 28, lion -> 123, drudges -> 1, unfelt -> 4, rate -> 35, pepper -> 3, inevitable ->..."
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},
"cell_type" : "code",
"source" : "val kjBible = load_file(\"kjvdat.txt\").cache()",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "kjBible: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[82] at map at <console>:38\n"
}, {
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},
"cell_type" : "code",
"source" : "kjBible.take(2) foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "gen|1|1| in the beginning god created the heaven and the earth.~\ngen|1|2| and the earth was without form, and void; and darkness was upon the face of the deep. and the spirit of god moved upon the face of the waters.~\n"
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}, {
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},
"cell_type" : "code",
"source" : "\"a | b | c this is text\".split(\"\"\"\\s*\\|\\s*\"\"\").last",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res51: String = c this is text\n"
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}, {
"metadata" : {
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},
"cell_type" : "code",
"source" : "val kjBibleText = kjBible.map(_.split(\"\"\"\\s*\\|\\s*\"\"\").last).cache()\nkjBibleText.take(2) foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "in the beginning god created the heaven and the earth.~\nand the earth was without form, and void; and darkness was upon the face of the deep. and the spirit of god moved upon the face of the waters.~\nkjBibleText: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[85] at map at <console>:43\n"
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}, {
"metadata" : {
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},
"cell_type" : "code",
"source" : "val kjBibleCounts = kjBibleText.flatMap(_.split(\"\"\"\\W+\"\"\")).map(w => (w,1)).reduceByKey(_ + _)\nval kjBibleVerily = kjBibleCounts.filter(x => x._1 == \"verily\")\nkjBibleVerily.count()\nkjBibleVerily foreach (println)\nkjBibleVerily.lookup(\"verily\").head",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(verily,140)\nkjBibleCounts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[100] at reduceByKey at <console>:47\nkjBibleVerily: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[101] at filter at <console>:48\nres61: Int = 140\n"
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"text/html" : "140"
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"output_type" : "execute_result",
"execution_count" : 83
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val kjBibleCountsDesc = kjBibleCounts.keyBy(x => x._2).sortByKey(false).map(_._2)\n\nkjBibleCountsDesc.take(10) foreach (s => println (s._1))",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "the\nand\nof\nto\nthat\nin\nhe\nshall\nunto\nfor\nkjBibleCountsDesc: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[119] at map at <console>:47\n"
}, {
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}, {
"metadata" : {
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},
"cell_type" : "code",
"source" : "kjBibleCountsDesc.collect().toSeq",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res66: Seq[(String, Int)] = WrappedArray((the,63924), (and,51696), (of,34617), (to,13562), (that,12912), (in,12667), (he,10420), (shall,9838), (unto,8997), (for,8971), (i,8854), (his,8473), (a,8177), (lord,7964), (they,7376), (be,7013), (is,6989), (him,6659), (not,6596), (them,6430), (it,6129), (with,6012), (all,5620), (thou,5474), (thy,4600), (was,4522), (god,4472), (which,4413), (my,4368), (me,4096), (said,3999), (but,3992), (ye,3982), (their,3942), (have,3904), (will,3836), (thee,3827), (from,3642), (as,3520), (are,2950), (when,2834), (this,2785), (out,2775), (were,2772), (upon,2748), (man,2735), (by,2624), (you,2617), (israel,2575), (king,2540), (son,2392), (up,2380), (there,2299), (hath,2264), (then,2169), (people,2143), (came,2093), (had,2026), (house,2024), (into,2015), (on,2011)..."
}, {
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}, {
"metadata" : {
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},
"cell_type" : "code",
"source" : "kjBibleCountsDesc.filter(_._1.length >= 8).take(10) foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(children,1821)\n(therefore,1237)\n(jerusalem,814)\n(according,793)\n(offering,724)\n(brethren,564)\n(thousand,520)\n(answered,492)\n(together,484)\n(servants,480)\n"
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}, {
"metadata" : {
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"cell_type" : "code",
"source" : "val kjBibleWords = kjBibleText.flatMap(_.split(\"\"\"\\W+\"\"\"))\nval kjBibleWordCount = kjBibleWords.count()\nimport org.apache.spark.mllib.rdd.RDDFunctions._\nval kjBibleBigrams = kjBibleWords.sliding(2).map(a => s\"${a(0)} ${a(1)}\")\nkjBibleBigrams.take(4) foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "in the\nthe beginning\nbeginning god\ngod created\nkjBibleWords: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[124] at flatMap at <console>:50\nkjBibleWordCount: Long = 791512\nimport org.apache.spark.mllib.rdd.RDDFunctions._\nkjBibleBigrams: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[126] at map at <console>:53\n"
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}, {
"metadata" : {
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"cell_type" : "code",
"source" : "import scala.reflect.ClassTag\n\ndef orderByOccur[T:ClassTag](r:RDD[T]) = r.map(w => (w,1)).reduceByKey(_ + _).keyBy(_._2).sortByKey(false).map(_._2)\norderByOccur(kjBibleBigrams).take(20) foreach (println)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(of the,11528)\n(the lord,7035)\n(and the,6268)\n(in the,5031)\n(and he,2791)\n(shall be,2461)\n(all the,2144)\n(to the,2142)\n(and they,2086)\n(unto the,2032)\n(i will,1922)\n(of israel,1697)\n(for the,1675)\n(the king,1656)\n(said unto,1649)\n(son of,1602)\n(out of,1502)\n(the son,1500)\n(the children,1421)\n(children of,1393)\nimport scala.reflect.ClassTag\norderByOccur: [T](r: org.apache.spark.rdd.RDD[T])(implicit evidence$1: scala.reflect.ClassTag[T])org.apache.spark.rdd.RDD[(T, Int)]\n"
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"metadata" : {
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"cell_type" : "code",
"source" : "val orderedBigrams = orderByOccur(kjBibleBigrams).values.collect().toSeq",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "orderedBigrams: Seq[Int] = WrappedArray(11528, 7035, 6268, 5031, 2791, 2461, 2144, 2142, 2086, 2032, 1922, 1697, 1675, 1656, 1649, 1602, 1502, 1500, 1421, 1393, 1300, 1260, 1256, 1250, 1222, 1172, 1157, 1128, 1110, 1062, 1049, 1048, 1045, 1014, 987, 968, 966, 960, 960, 955, 947, 946, 933, 915, 911, 906, 895, 892, 883, 882, 855, 849, 806, 786, 768, 759, 753, 747, 745, 742, 734, 725, 714, 706, 702, 689, 683, 679, 676, 674, 673, 669, 663, 659, 655, 643, 640, 639, 638, 618, 607, 594, 591, 586, 580, 579, 573, 570, 568, 562, 561, 559, 548, 538, 532, 532, 528, 527, 525, 520, 515, 513, 504, 501, 494, 483, 482, 480, 471, 470, 468, 465, 464, 463, 461, 459, 454, 444, 427, 425, 421, 421, 419, 416, 416, 412, 408, 406, 400, 400, 398, 398, 398, 396, 395, 394, 393, 391, 390, 390, 383, 381, 379, 375, 37..."
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"source" : "val kjBibleTrigrams = kjBibleWords.sliding(3).map(a => s\"${a(0)} ${a(1)} ${a(2)}\")\norderByOccur(kjBibleTrigrams).take(20) foreach (println)",
"outputs" : [ {
"name" : "stdout",
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"text" : "(of the lord,1775)\n(the son of,1450)\n(the children of,1355)\n(the house of,883)\n(saith the lord,854)\n(the lord and,816)\n(out of the,805)\n(and i will,672)\n(children of israel,647)\n(the land of,617)\n(and the lord,571)\n(and all the,561)\n(the sons of,560)\n(and he said,510)\n(unto the lord,509)\n(the lord god,479)\n(the king of,472)\n(came to pass,457)\n(it came to,454)\n(said unto him,454)\nkjBibleTrigrams: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[143] at map at <console>:54\n"
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"source" : "val orderedTrigrams = orderByOccur(kjBibleTrigrams).values.collect().toSeq",
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"text" : "orderedTrigrams: Seq[Int] = WrappedArray(1775, 1450, 1355, 883, 854, 816, 805, 672, 647, 617, 571, 561, 560, 510, 509, 479, 472, 457, 454, 454, 430, 425, 417, 398, 394, 378, 365, 351, 349, 349, 340, 339, 327, 324, 315, 315, 312, 312, 304, 300, 297, 296, 294, 291, 287, 285, 283, 280, 279, 269, 266, 264, 256, 253, 248, 245, 245, 244, 240, 232, 227, 227, 227, 221, 221, 219, 208, 205, 203, 203, 203, 203, 201, 197, 191, 188, 188, 185, 185, 183, 181, 180, 178, 177, 177, 176, 174, 173, 173, 172, 171, 170, 170, 168, 168, 167, 166, 165, 165, 164, 163, 163, 162, 161, 161, 159, 159, 159, 158, 157, 157, 156, 156, 156, 156, 155, 155, 154, 152, 151, 149, 149, 149, 149, 148, 148, 147, 146, 146, 145, 145, 144, 144, 144, 144, 142, 142, 141, 140, 140, 137, 137, 136, 135, 135, 135, 134, 134, 131, 131, 131..."
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"cell_type" : "code",
"source" : "kjBibleCounts.keyBy(w => w._1.length).sortByKey(true).map(_._2).take(10) foreach println",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(,71)\n(a,8177)\n(s,1790)\n(i,8854)\n(o,1065)\n(go,1492)\n(as,3520)\n(so,1689)\n(my,4368)\n(we,1844)\n"
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"source" : "val kjBibleElemsPerFrequency = kjBibleCounts.groupBy(_._2).sortByKey(true).mapValues(_.map(_._1)).mapValues(_.size)\nkjBibleElemsPerFrequency.take(10) foreach println",
"outputs" : [ {
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"text" : "(1,3950)\n(2,1739)\n(3,973)\n(4,621)\n(5,498)\n(6,405)\n(7,312)\n(8,286)\n(9,229)\n(10,192)\nkjBibleElemsPerFrequency: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[177] at mapValues at <console>:54\n"
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"source" : "kjBibleElemsPerFrequency.values.collect.toSeq",
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"text" : "res74: Seq[Int] = WrappedArray(3950, 1739, 973, 621, 498, 405, 312, 286, 229, 192, 159, 146, 130, 120, 132, 96, 98, 74, 83, 78, 72, 64, 49, 57, 51, 49, 35, 38, 43, 44, 31, 39, 46, 26, 17, 27, 43, 32, 48, 20, 21, 30, 24, 14, 24, 24, 12, 19, 24, 20, 29, 13, 14, 14, 18, 16, 12, 11, 15, 17, 17, 18, 8, 14, 9, 16, 7, 9, 9, 11, 14, 9, 11, 10, 9, 11, 8, 5, 7, 7, 11, 10, 1, 3, 4, 9, 3, 6, 7, 4, 7, 3, 1, 7, 4, 8, 6, 9, 2, 9, 9, 5, 1, 10, 5, 7, 7, 7, 4, 4, 3, 1, 7, 1, 2, 6, 2, 5, 6, 3, 2, 4, 4, 3, 3, 7, 4, 2, 4, 2, 3, 7, 4, 1, 6, 5, 2, 1, 2, 4, 2, 5, 1, 7, 1, 3, 2, 2, 2, 1, 1, 3, 2, 1, 3, 3, 4, 2, 4, 1, 5, 1, 1, 2, 2, 2, 2, 4, 3, 2, 1, 3, 2, 2, 3, 2, 3, 5, 3, 3, 1, 2, 2, 2, 3, 1, 2, 4, 2, 2, 1, 3, 1, 1, 1, 2, 3, 1, 1, 3, 1, 1, 1, 1, 2, 2, 2, 3, 2, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 2, 2, 3, 1, 1, 2,..."
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},
"output_type" : "execute_result",
"execution_count" : 99
} ]
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"metadata" : {
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"collapsed" : false
},
"cell_type" : "code",
"source" : "val kjBibleFrequencies = kjBibleCounts.map(w => (w._1, w._2.toFloat / kjBibleWordCount))\nval bibleFrequencies = kjBibleFrequencies.collectAsMap()",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "kjBibleFrequencies: org.apache.spark.rdd.RDD[(String, Float)] = MapPartitionsRDD[179] at map at <console>:56\nbibleFrequencies: scala.collection.Map[String,Float] = Map(ophel -> 6.3170237E-6, jude -> 1.2634047E-6, arts -> 1.2634047E-6, moseroth -> 2.5268093E-6, sounding -> 8.843833E-6, baali -> 1.2634047E-6, slippeth -> 3.7902141E-6, follow -> 1.0865281E-4, diseases -> 1.6424261E-5, establish -> 5.5589808E-5, calebephratah -> 1.2634047E-6, demand -> 5.0536187E-6, sighing -> 8.843833E-6, unperfect -> 1.2634047E-6, weakness -> 8.843833E-6, reaia -> 1.2634047E-6, sister -> 1.4655494E-4, palti -> 1.2634047E-6, manahath -> 3.7902141E-6, disquiet -> 1.2634047E-6, committing -> 2.5268093E-6, bashanhavothjair -> 1.2634047E-6, former -> 6.3170235E-5, itching -> 1.2634047E-6, labour -> 1.1244302E-..."
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3.7902141E-6, haste -&gt; 6.948726E-5, curtain -&gt; 3.2848522E-5, unjustly -&gt; 2.5268093E-6, snare -&gt; 5.8116617E-5, exorcists -&gt; 1.2634047E-6, willeth -&gt; 1.2634047E-6, aforehand -&gt; 1.2634047E-6, malchishua -&gt; 3.7902141E-6, creditor -&gt; 3.7902141E-6, enjoined -&gt; 3.7902141E-6, joezer -&gt; 1.2634047E-6, stings -&gt; 1.2634047E-6, trickleth -&gt; 1.2634047E-6, wealthy -&gt; 2.5268093E-6, correcteth -&gt; 2.5268093E-6, cherethites -&gt; 1.1370643E-5, reputed -&gt; 2.5268093E-6, shittah -&gt; 1.2634047E-6, vultures -&gt; 1.2634047E-6, seize -&gt; 5.0536187E-6, girgashite -&gt; 1.2634047E-6, gahar -&gt; 2.5268093E-6, hebron -&gt; 9.222855E-5, troop -&gt; 1.6424261E-5, renewing -&gt; 2.5268093E-6, ebed -&gt; 7.5804282E-6, begged -&gt; 3.7902141E-6, jeiel -&gt; 1.3897452E-5, ethiopia -&gt; 2.5268095E-5, rejected -&gt; 3.6638736E-5, hezro -&gt; 1.2634047E-6, forty -&gt; 1.9961795E-4, lest -&gt; 3.0321712E-4, abiah -&gt; 5.0536187E-6, pleasantness -&gt; 1.2634047E-6, unrighteousness -&gt; 2.65315E-5, cast -&gt; 6.3296576E-4, woollen -&gt; 7.5804282E-6, rags -&gt; 5.0536187E-6, homer -&gt; 1.3897452E-5, parents -&gt; 2.65315E-5, ammonites -&gt; 2.9058308E-5, fellowhelpers -&gt; 1.2634047E-6, ispah -&gt; 1.2634047E-6, striketh -&gt; 3.7902141E-6, bakemeats -&gt; 1.2634047E-6, imna -&gt; 1.2634047E-6, fiftieth -&gt; 5.0536187E-6, withheldest -&gt; 1.2634047E-6, forefathers -&gt; 2.5268093E-6, answering -&gt; 3.9165545E-5, relief -&gt; 1.2634047E-6, alphaeus -&gt; 6.3170237E-6, conveyed -&gt; 1.2634047E-6, dangerous -&gt; 1.2634047E-6, business -&gt; 3.6638736E-5, kingdoms -&gt; 7.201407E-5, black -&gt; 2.2741286E-5, eliphelet -&gt; 7.5804282E-6, kine -&gt; 3.0321713E-5, rescue -&gt; 3.7902141E-6, repenting -&gt; 1.2634047E-6, equals -&gt; 1.2634047E-6, ant -&gt; 1.2634047E-6, diggedst -&gt; 1.2634047E-6, enough -&gt; 4.042895E-5, act -&gt; 5.0536187E-6, little -&gt; 3.0574395E-4, haziel -&gt; 1.2634047E-6, madian -&gt; 1.2634047E-6, asaiah -&gt; 7.5804282E-6, squared -&gt; 1.2634047E-6, horem -&gt; 1.2634047E-6, evermore -&gt; 3.2848522E-5, as -&gt; 0.0044471845, chedorlaomer -&gt; 6.3170237E-6, forest -&gt; 4.800938E-5, nebuchadnezzar -&gt; 7.580428E-5, cheerfully -&gt; 1.2634047E-6, stoodest -&gt; 3.7902141E-6, given -&gt; 6.2917557E-4, under -&gt; 4.9525464E-4, absent -&gt; 1.3897452E-5, lance -&gt; 1.2634047E-6, neesings -&gt; 1.2634047E-6, sparingly -&gt; 2.5268093E-6, entice -&gt; 1.0107237E-5, zeredathah -&gt; 1.2634047E-6, slingstones -&gt; 1.2634047E-6, jemuel -&gt; 2.5268093E-6, reasoned -&gt; 1.51608565E-5, tillage -&gt; 3.7902141E-6, enhaddah -&gt; 1.2634047E-6, print -&gt; 5.0536187E-6, pallu -&gt; 5.0536187E-6, arioch -&gt; 8.843833E-6, wist -&gt; 1.6424261E-5, stamped -&gt; 1.1370643E-5, selvedge -&gt; 2.5268093E-6, ammah -&gt; 1.2634047E-6, grayheaded -&gt; 2.5268093E-6, liars -&gt; 1.0107237E-5, thefts -&gt; 3.7902141E-6, berith -&gt; 1.2634047E-6, nest -&gt; 1.895107E-5, spoons -&gt; 1.51608565E-5, withereth -&gt; 1.0107237E-5, zarah -&gt; 2.5268093E-6, sabtecha -&gt; 1.2634047E-6, although -&gt; 2.0214475E-5, footsteps -&gt; 5.0536187E-6, held -&gt; 6.5697044E-5, flowing -&gt; 1.51608565E-5, scorn -&gt; 2.0214475E-5, whirlwind -&gt; 3.4111927E-5, bore -&gt; 3.7902141E-6, head -&gt; 4.5987932E-4, caused -&gt; 1.1876004E-4, colour -&gt; 1.7687666E-5, assuredly -&gt; 1.1370643E-5, titles -&gt; 2.5268093E-6, samos -&gt; 1.2634047E-6, polled -&gt; 3.7902141E-6, swifter -&gt; 7.5804282E-6, goats -&gt; 1.2255026E-4, bethhoglah -&gt; 2.5268093E-6, jahaziel -&gt; 7.5804282E-6, schoolmaster -&gt; 2.5268093E-6, betharabah -&gt; 3.7902141E-6, fetcht -&gt; 1.2634047E-6, wandered -&gt; 1.2634047E-5, shoham -&gt; 1.2634047E-6, staves -&gt; 6.190683E-5, arelites -&gt; 1.2634047E-6, abase -&gt; 5.0536187E-6, hazor -&gt; 2.400469E-5, stony -&gt; 8.843833E-6, chased -&gt; 1.6424261E-5, thirst -&gt; 3.9165545E-5, frontlets -&gt; 3.7902141E-6, transgressors -&gt; 2.5268095E-5, harosheth -&gt; 3.7902141E-6, bondage -&gt; 4.9272785E-5, entering -&gt; 5.8116617E-5, helm -&gt; 1.2634047E-6, prised -&gt; 1.2634047E-6, esteemed -&gt; 1.3897452E-5, tabering -&gt; 1.2634047E-6, born -&gt; 1.9961795E-4, overflown -&gt; 3.7902141E-6, withdrawest -&gt; 1.2634047E-6, hen -&gt; 3.7902141E-6, lambs -&gt; 1.0233578E-4, rumours -&gt; 2.5268093E-6, mibzar -&gt; 2.5268093E-6, vanities -&gt; 1.6424261E-5, straiteneth -&gt; 1.2634047E-6, berachiah -&gt; 1.2634047E-6, zif -&gt; 2.5268093E-6, hewers -&gt; 1.1370643E-5, shimeathites -&gt; 1.2634047E-6, esau -&gt; 1.2634047E-4, reserveth -&gt; 2.5268093E-6, reaping -&gt; 3.7902141E-6, rie -&gt; 2.5268093E-6, garmite -&gt; 1.2634047E-6, ebronah -&gt; 2.5268093E-6, comforted -&gt; 4.548257E-5, fatter -&gt; 1.2634047E-6, philistines -&gt; 3.209048E-4, hew -&gt; 1.51608565E-5, denounce -&gt; 1.2634047E-6, inscription -&gt; 1.2634047E-6, foreknew -&gt; 1.2634047E-6, fan -&gt; 1.0107237E-5, rendered -&gt; 5.0536187E-6, joahaz -&gt; 1.2634047E-6, pineth -&gt; 1.2634047E-6, nineteenth -&gt; 5.0536187E-6, affected -&gt; 2.5268093E-6, lovely -&gt; 5.0536187E-6, grind -&gt; 8.843833E-6, consecration -&gt; 1.1370643E-5, ammihud -&gt; 1.2634047E-5, denieth -&gt; 5.0536187E-6, ramathlehi -&gt; 1.2634047E-6, eliud -&gt; 2.5268093E-6, misrephothmaim -&gt; 2.5268093E-6, gibeonite -&gt; 2.5268093E-6, jonan -&gt; 1.2634047E-6, carnal -&gt; 1.3897452E-5, besides -&gt; 1.0107237E-5, miracles -&gt; 3.4111927E-5, extinct -&gt; 2.5268093E-6, loss -&gt; 1.2634047E-5, zealously -&gt; 2.5268093E-6, anon -&gt; 2.5268093E-6, rising -&gt; 4.9272785E-5, abiezrites -&gt; 2.5268093E-6, spun -&gt; 2.5268093E-6, abound -&gt; 2.400469E-5, soever -&gt; 2.0214475E-5, wintered -&gt; 1.2634047E-6, consolations -&gt; 3.7902141E-6, rewards -&gt; 6.3170237E-6, shaulites -&gt; 1.2634047E-6, zealous -&gt; 1.0107237E-5, makheloth -&gt; 2.5268093E-6, pavilion -&gt; 5.0536187E-6, hazarsusim -&gt; 1.2634047E-6, hindermost -&gt; 2.5268093E-6, aunt -&gt; 1.2634047E-6, daubed -&gt; 8.843833E-6, failed -&gt; 1.51608565E-5, prevailest -&gt; 1.2634047E-6, lodgings -&gt; 1.2634047E-6, crystal -&gt; 6.3170237E-6, commit -&gt; 9.4755356E-5, evening -&gt; 7.580428E-5, sopater -&gt; 1.2634047E-6, hungerbitten -&gt; 1.2634047E-6, prating -&gt; 3.7902141E-6, jashobeam -&gt; 3.7902141E-6, hananeel -&gt; 5.0536187E-6, troops -&gt; 8.843833E-6, torches -&gt; 3.7902141E-6, sinew -&gt; 3.7902141E-6, vials -&gt; 6.3170237E-6, foal -&gt; 3.7902141E-6, fadeth -&gt; 8.843833E-6, vipers -&gt; 5.0536187E-6, plowing -&gt; 5.0536187E-6, nehelamite -&gt; 3.7902141E-6, emerods -&gt; 1.0107237E-5, reason -&gt; 8.970174E-5, marketplaces -&gt; 1.2634047E-6, plaister -&gt; 8.843833E-6, galilee -&gt; 9.096514E-5, manifested -&gt; 1.2634047E-5, jediael -&gt; 7.5804282E-6, pursuers -&gt; 6.3170237E-6, orderings -&gt; 1.2634047E-6, obed -&gt; 1.6424261E-5, healing -&gt; 1.7687666E-5, hor -&gt; 1.51608565E-5, albeit -&gt; 2.5268093E-6, trusteth -&gt; 2.1477881E-5, mortality -&gt; 1.2634047E-6, nun -&gt; 3.6638736E-5, discharge -&gt; 1.2634047E-6, scorner -&gt; 1.3897452E-5, coney -&gt; 2.5268093E-6, shadows -&gt; 3.7902141E-6, withersoever -&gt; 1.2634047E-6, nephew -&gt; 2.5268093E-6, infinite -&gt; 3.7902141E-6, eliphaz -&gt; 1.895107E-5, ulla -&gt; 1.2634047E-6, mede -&gt; 1.2634047E-6, cyrene -&gt; 5.0536187E-6, eshtaol -&gt; 8.843833E-6, sand -&gt; 3.537533E-5, abolish -&gt; 1.2634047E-6, sodden -&gt; 7.5804282E-6, wax -&gt; 3.0321713E-5, bruit -&gt; 2.5268093E-6, ishiah -&gt; 1.2634047E-6, blains -&gt; 2.5268093E-6, religious -&gt; 2.5268093E-6, loammi -&gt; 1.2634047E-6, pestilences -&gt; 2.5268093E-6, marsena -&gt; 1.2634047E-6, permission -&gt; 1.2634047E-6, restingplace -&gt; 1.2634047E-6, forsookest -&gt; 2.5268093E-6, constrain -&gt; 1.2634047E-6, forgave -&gt; 1.1370643E-5, furbish -&gt; 1.2634047E-6, shetharboznai -&gt; 5.0536187E-6, passing -&gt; 1.6424261E-5, receipt -&gt; 3.7902141E-6, tobiah -&gt; 1.895107E-5, tahanites -&gt; 1.2634047E-6, regions -&gt; 5.0536187E-6, feared -&gt; 9.349195E-5, assembly -&gt; 6.190683E-5, gamul -&gt; 1.2634047E-6, dreams -&gt; 2.65315E-5, bravery -&gt; 1.2634047E-6, woundeth -&gt; 1.2634047E-6, backbiting -&gt; 1.2634047E-6, dead -&gt; 4.5987932E-4, withdrawn -&gt; 7.5804282E-6, zebina -&gt; 1.2634047E-6, quiet -&gt; 3.9165545E-5, hypocrisies -&gt; 1.2634047E-6, chancellor -&gt; 3.7902141E-6, julia -&gt; 1.2634047E-6, languishing -&gt; 1.2634047E-6, shemaah -&gt; 1.2634047E-6, banqueting -&gt; 1.2634047E-6, caphthorim -&gt; 1.2634047E-6, disannulling -&gt; 1.2634047E-6, zohar -&gt; 5.0536187E-6, worms -&gt; 1.0107237E-5, refraineth -&gt; 1.2634047E-6, joha -&gt; 2.5268093E-6, burnings -&gt; 3.7902141E-6, planets -&gt; 1.2634047E-6, tahrea -&gt; 1.2634047E-6, madai -&gt; 2.5268093E-6, treasurest -&gt; 1.2634047E-6, locks -&gt; 1.895107E-5, troubled -&gt; 8.5911524E-5, soap -&gt; 2.5268093E-6, mibhar -&gt; 1.2634047E-6, unrighteous -&gt; 1.1370643E-5, gad -&gt; 9.096514E-5, corban -&gt; 1.2634047E-6, extended -&gt; 2.5268093E-6, knives -&gt; 6.3170237E-6, vanish -&gt; 5.0536187E-6, ephrath -&gt; 6.3170237E-6, laodicea -&gt; 6.3170237E-6, shewest -&gt; 6.3170237E-6, tammuz -&gt; 1.2634047E-6, skies -&gt; 6.3170237E-6, mine -&gt; 8.1994967E-4, sendeth -&gt; 1.895107E-5, lodging -&gt; 7.5804282E-6, hopeth -&gt; 1.2634047E-6, mice -&gt; 5.0536187E-6, gainsay -&gt; 1.2634047E-6, ono -&gt; 6.3170237E-6, thongs -&gt; 1.2634047E-6, bidkar -&gt; 1.2634047E-6, urgent -&gt; 2.5268093E-6, gift -&gt; 7.4540876E-5, thickness -&gt; 5.0536187E-6, roarings -&gt; 1.2634047E-6, esaias -&gt; 2.65315E-5, ordinance -&gt; 3.790214E-5, consecrations -&gt; 5.0536187E-6, regemmelech -&gt; 1.2634047E-6, elihu -&gt; 1.3897452E-5, overturn -&gt; 5.0536187E-6, ahira -&gt; 6.3170237E-6, aholah -&gt; 6.3170237E-6, warreth -&gt; 1.2634047E-6, abundance -&gt; 8.5911524E-5, skin -&gt; 9.7282165E-5, jahleelites -&gt; 1.2634047E-6, pillows -&gt; 5.0536187E-6, nurse -&gt; 1.2634047E-5, -&gt; 8.970174E-5, liest -&gt; 6.3170237E-6, amiable -&gt; 1.2634047E-6)"
},
"output_type" : "execute_result",
"execution_count" : 100
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val kjBibleTextPerBook = kjBible.map(s => {val r = s.split(\"\"\"\\s*\\|\\s*\"\"\"); (r.head,r.last)})\nkjBibleTextPerBook.take(2) foreach println ",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "(gen,in the beginning god created the heaven and the earth.~)\n(gen,and the earth was without form, and void; and darkness was upon the face of the deep. and the spirit of god moved upon the face of the waters.~)\nkjBibleTextPerBook: org.apache.spark.rdd.RDD[(String, String)] = MapPartitionsRDD[180] at map at <console>:48\n"
}, {
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"data" : {
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"execution_count" : 101
} ]
}, {
"metadata" : {
"trusted" : true,
"collapsed" : false
},
"cell_type" : "code",
"source" : "val lengthPerBook = kjBibleTextPerBook.mapValues(_.size).reduceByKey(_ + _).cache()\nval bibleBookLength = lengthPerBook.collectAsMap()",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "lengthPerBook: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[184] at reduceByKey at <console>:52\nbibleBookLength: scala.collection.Map[String,Int] = Map(jam -> 12137, co2 -> 32004, sa1 -> 129259, rom -> 50228, sol -> 13687, gen -> 196810, th1 -> 9663, pro -> 79971, eze -> 205261, pe2 -> 8700, jo3 -> 1594, zep -> 8501, ecc -> 28365, lam -> 18084, kg2 -> 120745, mal -> 9193, tit -> 5131, hab -> 7922, gal -> 16232, num -> 175610, psa -> 223712, mic -> 16329, mar -> 79123, nah -> 6912, co1 -> 48934, ti2 -> 9190, kg1 -> 126978, jon -> 6629, joe -> 10715, jde -> 3523, col -> 10720, ch2 -> 139054, joh -> 98035, rut -> 13005, hos -> 27114, jo2 -> 1553, pe1 -> 13459, job -> 94234, mat -> 124421, luk -> 134677, exo -> 169368, hag -> 5734, dan -> 61875, est -> 30217, phi -> 11558, sa2 -> 1..."
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"text/html" : "Map(jam -&gt; 12137, co2 -&gt; 32004, sa1 -&gt; 129259, rom -&gt; 50228, sol -&gt; 13687, gen -&gt; 196810, th1 -&gt; 9663, pro -&gt; 79971, eze -&gt; 205261, pe2 -&gt; 8700, jo3 -&gt; 1594, zep -&gt; 8501, ecc -&gt; 28365, lam -&gt; 18084, kg2 -&gt; 120745, mal -&gt; 9193, tit -&gt; 5131, hab -&gt; 7922, gal -&gt; 16232, num -&gt; 175610, psa -&gt; 223712, mic -&gt; 16329, mar -&gt; 79123, nah -&gt; 6912, co1 -&gt; 48934, ti2 -&gt; 9190, kg1 -&gt; 126978, jon -&gt; 6629, joe -&gt; 10715, jde -&gt; 3523, col -&gt; 10720, ch2 -&gt; 139054, joh -&gt; 98035, rut -&gt; 13005, hos -&gt; 27114, jo2 -&gt; 1553, pe1 -&gt; 13459, job -&gt; 94234, mat -&gt; 124421, luk -&gt; 134677, exo -&gt; 169368, hag -&gt; 5734, dan -&gt; 61875, est -&gt; 30217, phi -&gt; 11558, sa2 -&gt; 106388, isa -&gt; 193993, rev -&gt; 62072, th2 -&gt; 5442, neh -&gt; 57117, deu -&gt; 146431, ti1 -&gt; 12697, jos -&gt; 100130, lev -&gt; 126860, ezr -&gt; 40386, heb -&gt; 37207, jer -&gt; 223708, jdg -&gt; 98687, amo -&gt; 21862, zac -&gt; 32954, jo1 -&gt; 12754, eph -&gt; 16282, oba -&gt; 3579, act -&gt; 129841, plm -&gt; 2320, ch1 -&gt; 110958)"
},
"output_type" : "execute_result",
"execution_count" : 103
} ]
}, {
"metadata" : {
"trusted" : true,
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},
"cell_type" : "code",
"source" : "lengthPerBook.keyBy(_._2).sortByKey(false).map(_._2).collect().toSeq",
"outputs" : [ {
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"text" : "res78: Seq[(String, Int)] = WrappedArray((psa,223712), (jer,223708), (eze,205261), (gen,196810), (isa,193993), (num,175610), (exo,169368), (deu,146431), (ch2,139054), (luk,134677), (act,129841), (sa1,129259), (kg1,126978), (lev,126860), (mat,124421), (kg2,120745), (ch1,110958), (sa2,106388), (jos,100130), (jdg,98687), (joh,98035), (job,94234), (pro,79971), (mar,79123), (rev,62072), (dan,61875), (neh,57117), (rom,50228), (co1,48934), (ezr,40386), (heb,37207), (zac,32954), (co2,32004), (est,30217), (ecc,28365), (hos,27114), (amo,21862), (lam,18084), (mic,16329), (eph,16282), (gal,16232), (sol,13687), (pe1,13459), (rut,13005), (jo1,12754), (ti1,12697), (jam,12137), (phi,11558), (col,10720), (joe,10715), (th1,9663), (mal,9193), (ti2,9190), (pe2,8700), (zep,8501), (hab,7922), (nah,6912), (jo..."
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<tbody><tr><td>psa</td><td>223712</td></tr><tr><td>jer</td><td>223708</td></tr><tr><td>eze</td><td>205261</td></tr><tr><td>gen</td><td>196810</td></tr><tr><td>isa</td><td>193993</td></tr><tr><td>num</td><td>175610</td></tr><tr><td>exo</td><td>169368</td></tr><tr><td>deu</td><td>146431</td></tr><tr><td>ch2</td><td>139054</td></tr><tr><td>luk</td><td>134677</td></tr><tr><td>act</td><td>129841</td></tr><tr><td>sa1</td><td>129259</td></tr><tr><td>kg1</td><td>126978</td></tr><tr><td>lev</td><td>126860</td></tr><tr><td>mat</td><td>124421</td></tr><tr><td>kg2</td><td>120745</td></tr><tr><td>ch1</td><td>110958</td></tr><tr><td>sa2</td><td>106388</td></tr><tr><td>jos</td><td>100130</td></tr><tr><td>jdg</td><td>98687</td></tr><tr><td>joh</td><td>98035</td></tr><tr><td>job</td><td>94234</td></tr><tr><td>pro</td><td>79971</td></tr><tr><td>mar</td><td>79123</td></tr><tr><td>...</td></tr>\n </tbody>\n </table></div>\n </div><div class=\"tab-pane\" id=\"tab812920054-1\">\n <div>\n <svg 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},
"output_type" : "execute_result",
"execution_count" : 106
} ]
}, {
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"cell_type" : "code",
"source" : "val kjBibleWordsPerBook = kjBibleTextPerBook.reduceByKey(_ + \" \" + _).mapValues(_.split(\"\"\"\\W+\"\"\")).cache()\nkjBibleWordsPerBook.take(1) foreach (x => {println(x._1); x._2.take(10) foreach println}) ",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "pe1\npeter\nan\napostle\nof\njesus\nchrist\nto\nthe\nstrangers\nscattered\nkjBibleWordsPerBook: org.apache.spark.rdd.RDD[(String, Array[String])] = MapPartitionsRDD[200] at mapValues at <console>:52\n"
}, {
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} ]
}, {
"metadata" : {
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},
"cell_type" : "code",
"source" : "kjBibleWordsPerBook.lookup(\"gen\").head.take(10)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res81: Array[String] = Array(in, the, beginning, god, created, the, heaven, and, the, earth)\n"
}, {
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"data" : {
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"metadata" : {
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},
"cell_type" : "code",
"source" : "val invDocumentFrequencies = kjBibleWordsPerBook.map{\n case (book, bkText) => (book, bkText.groupBy(word => word).mapValues(bibleBookLength(book) / _.size.toFloat).toSeq)\n }.cache()\n//kjBibleWordsPerBook.map(bkwds => (bkwds._1.toString,bkwds._2.groupBy(word => word).mapValues(bibleBookLength(bkwds._1) / _.size.toFloat).toSeq)).cache()\ninvDocumentFrequencies.lookup(\"gen\").head.sortBy(_._2)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "invDocumentFrequencies: org.apache.spark.rdd.RDD[(String, Seq[(String, Float)])] = MapPartitionsRDD[206] at map at <console>:58\nres84: Seq[(String, Float)] = ArrayBuffer((and,53.51006), (the,80.06916), (of,144.18315), (his,301.39355), (he,301.85583), (to,321.58496), (in,328.01666), (unto,329.1137), (that,378.48077), (i,406.63223), (said,411.7364), (him,489.57712), (my,573.7901), (a,577.1554), (for,603.7117), (was,620.85175), (it,643.1699), (with,671.7065), (me,674.00684), (thou,692.993), (is,707.94965), (thy,707.94965), (thee,734.36566), (shall,759.88416), (be,765.79767), (s,768.78906), (they,771.8039), (all,787.24), (them,826.9328), (god,844.6781), (not,863.2018), (lord,932.74884), (father,979.15424), (which,988.995), (will,1009.28204), (land,1052.4598), (jacob,1087.348), (came,1118.23..."
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},
"output_type" : "execute_result",
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} ]
}, {
"metadata" : {
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},
"cell_type" : "code",
"source" : "val tfIdf = invDocumentFrequencies.mapValues(_.map(wd => (wd._1, wd._2 * bibleFrequencies(wd._1))))",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "tfIdf: org.apache.spark.rdd.RDD[(String, Seq[(String, Float)])] = MapPartitionsRDD[209] at mapValues at <console>:70\n"
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},
"cell_type" : "code",
"source" : "tfIdf.lookup(\"gen\").head.sortBy(_._2)",
"outputs" : [ {
"name" : "stdout",
"output_type" : "stream",
"text" : "res85: Seq[(String, Float)] = ArrayBuffer((rouse,0.24865067), (pildash,0.24865067), (butler,0.24865067), (methusael,0.24865067), (kenizzites,0.24865067), (sabtah,0.24865067), (jidlaph,0.24865067), (ashkenaz,0.24865067), (resen,0.24865067), (ford,0.24865067), (pilled,0.24865067), (lasha,0.24865067), (longedst,0.24865067), (shinab,0.24865067), (vowedst,0.24865067), (peniel,0.24865067), (gutters,0.24865067), (tebah,0.24865067), (shepho,0.24865067), (ehi,0.24865067), (huz,0.24865067), (jehovahjireh,0.24865067), (alvah,0.24865067), (irad,0.24865067), (venison,0.24865067), (wounding,0.24865067), (shaveh,0.24865067), (allonbachuth,0.24865067), (replenish,0.24865067), (fugitive,0.24865067), (dinah,0.24865067), (sevens,0.24865067), (bera,0.24865067), (belah,0.24865067), (ashteroth,0.24865067), (..."
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