8000 more notebook updates · codeaudit/pipeline-1@9cb99fc · GitHub
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16 files changed

+337
-227
lines changed

16 files changed

+337
-227
lines changed

‎config/.vim/.netrwhist

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@@ -1,11 +1,2 @@
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let g:netrw_dirhistmax =10
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let g:netrw_dirhist_cnt =9
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let g:netrw_dirhist_1='/root/pipeline/config/logstash'
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let g:netrw_dirhist_2='/root/pipeline/flows/flowfile_repository'
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let g:netrw_dirhist_3='/root/pipeline/myapps/ml/src/main/scala/com/advancedspark/ml/graph'
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let g:netrw_dirhist_4='/root/pipeline/config/zeppelin'
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let g:netrw_dirhist_5='/root/pipeline/myapps/ml/src/main/scala/com/advancedspark/ml/graph'
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let g:netrw_dirhist_6='/root/spark-1.6.0-bin-fluxcapacitor/tachyon'
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let g:netrw_dirhist_7='/root/spark-1.6.0-bin-fluxcapacitor/tachyon/bin'
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let g:netrw_dirhist_8='/root/pipeline/myapps/streaming'
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let g:netrw_dirhist_9='/root/pipeline/myapps/streaming/src/main/scala/com/advancedspark/streaming/rating/approx'
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let g:netrw_dirhist_cnt =0

‎config/zeppelin/interpreter.json

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@@ -28,9 +28,9 @@
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"spark.app.name": "Zeppelin",
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"spark.executor.memory": "2g",
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"zeppelin.spark.useHiveContext": "true",
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"spark.home": "/root/spark-1.6.0-bin-fluxcapacitor",
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"zeppelin.spark.concurrentSQL": "false",
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"args": "",
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"zeppelin.spark.concurrentSQL": "false",
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"spark.home": "/root/spark-1.6.0-bin-fluxcapacitor",
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"zeppelin.pyspark.python": "python",
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"zeppelin.dep.additionalRemoteRepository": "spark-packages,http://dl.bintray.com/spark-packages/maven,false;"
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},
@@ -64,8 +64,8 @@
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"default.password": "",
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"default.user": "hive",
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"hive.hiveserver2.url": "jdbc:hive2://127.0.0.1:10000",
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"default.driver": "org.apache.hive.jdbc.HiveDriver",
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"default.url": "jdbc:hive2://127.0.0.1:10000",
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"default.driver": "org.apache.hive.jdbc.HiveDriver",
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"common.max_count": "1000",
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"hive.hiveserver2.password": "",
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"hive.hiveserver2.user": "hiveuser"
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"2AS9P7JSA",
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"2ARR8UZDJ",
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"2AR33ZMZJ"
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],
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"2BCGC211S": [
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"2AS9P7JSA",
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"2ARR8UZDJ",
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"2AR33ZMZJ"
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]
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}
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}
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}

‎data_persist/zeppelin/2ASEWJ19K/note.json

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Original file line numberDiff line numberDiff line change
@@ -149,11 +149,11 @@
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{
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"title": "Extract and Enrich the Top Influencers",
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"text": "// Get the Top 10 Influencers\nval topInfluencers \u003d pageRank.vertices.top(5)(Ordering.by(rank \u003d\u003e rank._2))\nval topInfluencersDF \u003d sc.parallelize(topInfluencers).toDF(\"itemId\", \"rank\")\n\nval enrichedTopInfluencersDF \u003d topInfluencersDF.join(usersDF, $\"id\" \u003d\u003d\u003d $\"itemId\")\n .select($\"id\", $\"name\", $\"rank\")\n .sort($\"rank\" desc)\n \nz.show(enrichedTopInfluencersDF)",
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"dateUpdated": "Jan 16, 2016 5:23:57 AM",
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"dateUpdated": "Feb 11, 2016 9:27:12 PM",
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"config": {
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"colWidth": 12.0,
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"graph": {
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"mode": "table",
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"mode": "pieChart",
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"height": 300.0,
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"optionOpen": false,
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"keys": [

‎data_persist/zeppelin/2AUUDPT56/note.json

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@@ -192,6 +192,18 @@
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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{
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"config": {},
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"settings": {
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"params": {},
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"forms": {}
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},
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"jobName": "paragraph_1455251808245_-631760587",
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"id": "20160212-043648_845375799",
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"dateCreated": "Feb 12, 2016 4:36:48 AM",
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"status": "READY",
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"progressUpdateIntervalMs": 500
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},
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{
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"title": "TODOOOOO: Distribution of Ratings",
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"text": "z.show(joinedDF.describe(\"count\"))",

‎data_persist/zeppelin/2AUYFSKXN/note.json

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Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
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{
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"title": "Collaborative Filtering: Alternating Least Squares Matrix Factorization",
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"text": "%md ![Alternating Least Squares - Matrix Factorization](https://raw.githubusercontent.com/cfregly/spark-after-dark/master/img/ALS.png)",
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"dateUpdated": "Feb 4, 2016 3:14:56 AM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
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"msg": "\u003cp\u003e\u003cimg src\u003d\"https://raw.githubusercontent.com/cfregly/spark-after-dark/master/img/ALS.png\" alt\u003d\"Alternating Least Squares - Matrix Factorization\" /\u003e\u003c/p\u003e\n"
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},
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"dateCreated": "Jul 4, 2015 2:49:13 AM",
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"dateStarted": "Jan 16, 2016 4:40:54 PM",
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"dateFinished": "Jan 16, 2016 4:40:54 PM",
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"dateStarted": "Feb 12, 2016 4:38:33 AM",
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"dateFinished": "Feb 12, 2016 4:38:33 AM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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{
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"text": "%md ![User-to-Item Similarity with Facebook](http://fluxcapacitor.com/img/netflix-facebook-integration.png)",
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"dateUpdated": "Feb 4, 2016 3:15:46 AM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
@@ -66,15 +66,15 @@
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"msg": "\u003cp\u003e\u003cimg src\u003d\"http://fluxcapacitor.com/img/netflix-facebook-integration.png\" alt\u003d\"User-to-Item Similarity with Facebook\" /\u003e\u003c/p\u003e\n"
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},
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"dateCreated": "Feb 4, 2016 3:14:53 AM",
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"dateStarted": "Feb 4, 2016 3:15:46 AM",
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"dateFinished": "Feb 4, 2016 3:15:46 AM",
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"dateStarted": "Feb 12, 2016 4:38:33 AM",
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"dateFinished": "Feb 12, 2016 4:38:33 AM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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{
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"title": "Train The Model Using The Historical Training Split Of The Historical Data",
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"text": "import org.apache.spark.ml.recommendation.ALS\n\nval rank \u003d 10\nval maxIterations \u003d 20\nval convergenceThreshold \u003d 0.01\n\nval als \u003d new ALS()\n .setRank(rank)\n .setRegParam(convergenceThreshold)\n .setUserCol(\"userId\")\n .setItemCol(\"itemId\")\n .setRatingCol(\"rating\")\n\nval model \u003d als.fit(itemRatingsDF)\n\nmodel.setPredictionCol(\"confidence\")",
77-
"dateUpdated": "Jan 16, 2016 4:40:54 PM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
@@ -98,20 +98,20 @@
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"jobName": "paragraph_1435978256373_-160526409",
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"id": "20150704-025056_169923529",
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"result": {
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"code": "SUCCESS",
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"code": "ERROR",
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"type": "TEXT",
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"msg": "import org.apache.spark.ml.recommendation.ALS\nrank: Int \u003d 10\nmaxIterations: Int \u003d 20\nconvergenceThreshold: Double \u003d 0.01\nals: org.apache.spark.ml.recommendation.ALS \u003d als_9a2aa41f1d28\nmodel: org.apache.spark.ml.recommendation.ALSModel \u003d als_9a2aa41f1d28\nres29: model.type \u003d als_9a2aa41f1d28\n"
103+
"msg": "import org.apache.spark.ml.recommendation.ALS\nrank: Int \u003d 10\nmaxIterations: Int \u003d 20\nconvergenceThreshold: Double \u003d 0.01\nals: org.apache.spark.ml.recommendation.ALS \u003d als_0713aff1e6f6\n\u003cconsole\u003e:35: error: not found: value itemRatingsDF\n val model \u003d als.fit(itemRatingsDF)\n ^\n"
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},
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"dateCreated": "Jul 4, 2015 2:50:56 AM",
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"dateStarted": "Jan 16, 2016 4:40:54 PM",
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"dateFinished": "Jan 16, 2016 4:40:58 PM",
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"status": "FINISHED",
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"dateStarted": "Feb 12, 2016 4:38:33 AM",
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"dateFinished": "Feb 12, 2016 4:38:33 AM",
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"status": "ERROR",
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"progressUpdateIntervalMs": 500
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},
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{
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"title": "Generate Personalized Recommendations For Each Distinct User",
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"text": "val recommendationsDF \u003d model.transform(itemRatingsDF.select($\"userId\", $\"itemId\"))\n\nval enrichedRecommendationsDF \u003d \n recommendationsDF.join(itemsDF, $\"itemId\" \u003d\u003d\u003d $\"id\")\n .select($\"userId\", $\"itemId\", $\"title\", $\"description\", $\"tags\", $\"img\", $\"confidence\")\n .sort($\"userId\", $\"confidence\" desc)",
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"dateUpdated": "Jan 16, 2016 4:40:54 PM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
@@ -161,17 +161,17 @@
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"result": {
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"code": "ERROR",
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"type": "TEXT",
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"msg": "java.lang.IllegalArgumentException: Field \"lyrics\" does not exist.\n\tat org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:212)\n\tat org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:212)\n\tat scala.collection.MapLike$class.getOrElse(MapLike.scala:128)\n\tat scala.collection.AbstractMap.getOrElse(Map.scala:58)\n\tat org.apache.spark.sql.types.StructType.apply(StructType.scala:211)\n\tat org.apache.spark.ml.UnaryTransformer.transformSchema(Transformer.scala:106)\n\tat org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:301)\n\tat org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:301)\n\tat scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)\n\tat scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)\n\tat scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)\n\tat org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:301)\n\tat org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:68)\n\tat org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:296)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:71)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:76)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:78)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:80)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:82)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:84)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:86)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:88)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:90)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:92)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:94)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:96)\n\tat $iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:98)\n\tat $iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:100)\n\tat $iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:102)\n\tat $iwC.\u003cinit\u003e(\u003cconsole\u003e:104)\n\tat \u003cinit\u003e(\u003cconsole\u003e:106)\n\tat .\u003cinit\u003e(\u003cconsole\u003e:110)\n\tat .\u003cclinit\u003e(\u003cconsole\u003e)\n\tat .\u003cinit\u003e(\u003cconsole\u003e:7)\n\tat .\u003cclinit\u003e(\u003cconsole\u003e)\n\tat $print(\u003cconsole\u003e)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:497)\n\tat org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)\n\tat org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346)\n\tat org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)\n\tat org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)\n\tat org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)\n\tat org.apache.zeppelin.spark.SparkInterpreter.interpretInput(SparkInterpreter.java:709)\n\tat org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:674)\n\tat org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:667)\n\tat org.apache.zeppelin.interpreter.ClassloaderInterpreter.interpret(ClassloaderInterpreter.java:57)\n\tat org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93)\n\tat org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:300)\n\tat org.apache.zeppelin.scheduler.Job.run(Job.java:169)\n\tat org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:134)\n\tat java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)\n\tat java.util.concurrent.FutureTask.run(FutureTask.java:266)\n\tat java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)\n\tat java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\n\n"
164+
"msg": "\u003cconsole\u003e:28: error: not found: value model\n val recommendationsDF \u003d model.transform(itemRatingsDF.select($\"userId\", $\"itemId\"))\n ^\n"
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},
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"dateCreated": "Jul 4, 2015 2:51:32 AM",
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"dateStarted": "Jan 16, 2016 4:40:54 PM",
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"dateFinished": "Jan 16, 2016 4:40:59 PM",
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"status": "FINISHED",
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"dateStarted": "Feb 12, 2016 4:38:33 AM",
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"dateFinished": "Feb 12, 2016 4:38:33 AM",
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"status": "ERROR",
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"progressUpdateIntervalMs": 500
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},
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{
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"text": "z.show(enrichedRecommendationsDF.select($\"userId\", $\"itemId\", $\"confidence\", $\"title\", $\"img\").limit(10))",
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"dateUpdated": "Jan 16, 2016 4:40:54 PM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
@@ -216,19 +216,19 @@
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"jobName": "paragraph_1443377582412_2075344434",
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"id": "20150927-181302_1140885708",
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"result": {
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"code": "SUCCESS",
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"type": "TABLE",
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"msg": "userId\titemId\tconfidence\ttitle\timg\n22\t43\t0.9967797\tJava\timg/software/java.png\n22\t66\t0.99488777\tRedshift\timg/software/redshift.png\n22\t49\t0.99466026\tAmazon Web Services\timg/software/aws.png\n22\t77\t0.9943304\tS3\timg/software/s3.png\n22\t52\t0.9943008\tJSON\timg/software/json.png\n22\t21\t0.99397665\tElasticSearch\timg/software/elasticsearch.png\n22\t64\t0.9937968\tCSV\timg/software/csv.png\n22\t39\t0.99280584\tTableau\timg/software/tableau.png\n22\t68\t0.9927822\tDynamoDB\timg/software/dynamodb.png\n22\t60\t0.9926167\tMySQL\timg/software/mysql.png\n"
219+
"code": "ERROR",
220+
"type": "TEXT",
221+
"msg": "\u003cconsole\u003e:31: error: not found: value enrichedRecommendationsDF\n z.show(enrichedRecommendationsDF.select($\"userId\", $\"itemId\", $\"confidence\", $\"title\", $\"img\").limit(10))\n ^\n"
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},
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"dateCreated": "Sep 27, 2015 6:13:02 PM",
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"dateStarted": "Jan 16, 2016 4:40:59 PM",
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"dateFinished": "Jan 16, 2016 4:41:04 PM",
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"status": "FINISHED",
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"dateStarted": "Feb 12, 2016 4:38:33 AM",
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"dateFinished": "Feb 12, 2016 4:38:33 AM",
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"status": "ERROR",
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"progressUpdateIntervalMs": 500
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},
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{
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"text": "import org.elasticsearch.spark.sql._ \nimport org.apache.spark.sql.SaveMode\n\nval esConfig \u003d Map(\"pushdown\" -\u003e \"true\", \"es.nodes\" -\u003e \"127.0.0.1\", \"es.port\" -\u003e \"9200\")\nenrichedRecommendationsDF.write.format(\"org.elasticsearch.spark.sql\").mode(SaveMode.Overwrite).options(esConfig)\n .save(\"advancedspark/personalized-als\")",
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"dateUpdated": "Jan 16, 2016 4:40:54 PM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
@@ -251,18 +251,18 @@
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"jobName": "paragraph_1438113388648_-491234562",
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"id": "20150728-195628_1365871289",
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"result": {
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"code": "SUCCESS",
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"code": "ERROR",
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"type": "TEXT",
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"msg": "import org.elasticsearch.spark.sql._\nimport org.apache.spark.sql.SaveMode\nesConfig: scala.collection.immutable.Map[String,String] \u003d Map(pushdown -\u003e true, es.nodes -\u003e 127.0.0.1, es.port -\u003e 9200)\n"
256+
"msg": "import org.elasticsearch.spark.sql._\nimport org.apache.spark.sql.SaveMode\nesConfig: scala.collection.immutable.Map[String,String] \u003d Map(pushdown -\u003e true, es.nodes -\u003e 127.0.0.1, es.port -\u003e 9200)\n\u003cconsole\u003e:35: error: not found: value enrichedRecommendationsDF\n enrichedRecommendationsDF.write.format(\"org.elasticsearch.spark.sql\").mode(SaveMode.Overwrite).options(esConfig)\n ^\n"
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},
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"dateCreated": "Jul 28, 2015 7:56:28 PM",
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"dateStarted": "Jan 16, 2016 4:40:59 PM",
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"dateFinished": "Jan 16, 2016 4:41:27 PM",
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"status": "FINISHED",
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"dateStarted": "Feb 12, 2016 4:38:33 AM",
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"dateFinished": "Feb 12, 2016 4:38:34 AM",
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"status": "ERROR",
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"progressUpdateIntervalMs": 500
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},
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{
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"dateUpdated": "Jan 16, 2016 4:40:54 PM",
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"dateUpdated": "Feb 12, 2016 4:38:33 AM",
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"config": {
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"colWidth": 12.0,
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"graph": {
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"type": "TEXT"
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},
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"dateCreated": "Dec 25, 2015 7:08:18 PM",
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"dateStarted": "Jan 16, 2016 4:41:04 PM",
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"dateFinished": "Jan 16, 2016 4:41:27 PM",
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"dateStarted": "Feb 12, 2016 4:38:34 AM",
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"dateFinished": "Feb 12, 2016 4:38:34 AM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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}

‎data_persist/zeppelin/2B68NAUVG/note.json

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Original file line numberDiff line numberDiff line change
@@ -573,7 +573,7 @@
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"progressUpdateIntervalMs": 500
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}
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],
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"name": "TODO: Live Recs/04: Generate Item-to-Item Tag-Similarity Graph Recs",
576+
"name": "TODO: Live Recs/04: Generate Item-to-Item Tag-Similarity Graph-based Recs",
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"id": "2B68NAUVG",
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"angularObjects": {
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"2ARR8UZDJ": [],

‎data_persist/zeppelin/2B7RXDS6A/note.json

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‎data_persist/zeppelin/2B8DEJYQS/note.json

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{
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"title": "Tokenize Song Lyrics",
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"text": "import org.apache.spark.ml.feature.RegexTokenizer\n\nval tokenizer \u003d new RegexTokenizer()\n .setInputCol(\"lyrics\")\n .setOutputCol(\"words\")\n .setGaps(false)\n .setPattern(\"\\\\p{L}+\")",
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"dateUpdated": "Feb 4, 2016 6:37:29 AM",
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"dateUpdated": "Feb 5, 2016 9:48:53 PM",
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"config": {
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"colWidth": 12.0,
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"editorMode": "ace/mode/scala",

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