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url](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/db/week3/04_SparkSQLIntro/007_SparkSQLIntroBasics.html) of this databricks notebook and its recorded Uji ![Image of Uji, Dogen's Time-Being](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/images/UjiTimeBeingDogen.png \"uji\"):\n\n[![sds/uji/week3/04_SparkSQLIntro/007_SparkSQLIntroBasics](http://img.youtube.com/vi/6NoPvmTBVz0/0.jpg)](https://www.youtube.com/v/6NoPvmTBVz0?rel=0&autoplay=1&modestbranding=1&start=0&end=1473)\n","commandVersion":0,"state":"error","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"03875f03-453c-482a-a9d8-fcbee47af98f"},{"version":"CommandV1","origId":50709,"guid":"636a1f3d-e1e9-4df5-b187-3fa2f49f9fa3","subtype":"command","commandType":"auto","position":0.75,"command":"%md\n#Introduction 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[https://en.wikipedia.org/wiki/Apache_Hive](https://en.wikipedia.org/wiki/Apache_Hive)\n* [http://www.infoq.com/articles/apache-spark-sql](http://www.infoq.com/articles/apache-spark-sql)\n* [https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html](https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html)\n* **READ**: [https://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf](https://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf)\n\nSome of them are embedded below in-place for your convenience.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"4fdb2cb6-1231-482f-8e4b-4af82c17bf1c"},{"version":"CommandV1","origId":50717,"guid":"3c68842b-6636-4818-a3d0-744d5f47c967","subtype":"command","commandType":"auto","position":0.9375,"command":"//This allows easy embedding of publicly available information into any other notebook\n//when viewing in git-book just ignore this block - you may have to manually chase the URL in 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sql-progamming-guide](http://spark.apache.org/docs/latest/sql-programming-guide.html).\n\n# Getting Started\n\n## Starting Point: SQLContext\n\nThe entry point into all functionality in Spark SQL is \n* the [`SQLContext`](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.SQLContext) class, \n* or one of its descendants, e.g. [`HiveContext`](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.hive.HiveContext). \n\nTo create a basic `SQLContext`, all you need is a `SparkContext`. \n\nConveniently, in Databricks notebook (similar to `spark-shell`) `SQLContext` is already created for you and is available as 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for now)\n\nUsually, when building application (running on production-like on-premises cluster) you would manually create `SQLContext` using something like this:\n```scala\n// An existing SparkContext\nval sc: SparkContext = ...\nval sqlContext = new org.apache.spark.sql.SQLContext(sc)\n\n// This is used to implicitly convert an RDD to a DataFrame (see examples below)\nimport sqlContext.implicits._\n```\n\nNote that SQLContext in notebook is actually HiveContext. The difference is that HiveContext provides richer functionality over standard SQLContext, e.g. window functions were only available with HiveContext up to Spark 1.5.2, or usage of Hive user-defined functions. This originates from the fact that Spark SQL parser was built based on HiveQL parser, so only HiveContext was supporting full HiveQL syntax. Now it is changing, and SQLContext supports most of the functionality of the descendant (window functions should be available in SQLContext in 1.6+).\n\n> Note that you do not need Hive installation when working with SQLContext or HiveContext, Spark comes with built-in derby datastore.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"8260e565-a257-4809-935c-f04b82c9369a"},{"version":"CommandV1","origId":50737,"guid":"3d0fb3bd-f5cf-47ac-a315-4ab3ab7f699e","subtype":"command","commandType":"auto","position":0.99267578125,"command":"%md\n## Creating DataFrames\n\nWith a [`SQLContext`](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.SQLContext), applications can create [`DataFrame`](https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.DataFrame) \n* from an existing `RDD`, \n* from a Hive table, or \n* from various other data sources.\n\n#### Just to recap: \n* A DataFrame is a distributed collection of data organized into named columns. \n* You can think of it as being organized into table RDD of case class `Row` (which is not exactly true). \n* DataFrames, in comparison to RDDs, are backed by rich optimizations, including:\n  * tracking their own schema, \n  * adaptive query execution, \n  * code generation including whole stage codegen, \n  * extensible Catalyst optimizer, and \n  * project [Tungsten](https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html). \n\n> Note that performance for DataFrames is the same across languages Scala, Java, Python, and R. This is due to the fact that the only planning phase is language-specific (logical + physical SQL plan), not the actual execution of the SQL plan.\n\n![DF speed across languages](https://databricks.com/wp-content/uploads/2015/02/Screen-Shot-2015-02-16-at-9.46.39-AM-1024x457.png)","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"efeb8394-017d-437e-a982-fc4bcf99bf10"},{"version":"CommandV1","origId":50736,"guid":"7b001874-08de-425f-9682-8758c5fda638","subtype":"command","commandType":"auto","position":0.9951171875,"command":"%md\n## DataFrame Basics\n\n#### 1. An empty DataFrame\n#### 2. DataFrame from a range\n#### 3. DataFrame from an RDD\n#### 4. DataFrame from a table","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"790a6db2-4729-43b1-8233-5cfd889d91e6"},{"version":"CommandV1","origId":50739,"guid":"16f9f72d-b53c-4dc3-b62d-4d6ffc6b25e5","subtype":"command","commandType":"auto","position":0.996337890625,"command":"%md\n#### 1. Making an empty DataFrame\nSpark has some of the pre-built methods to create simple DataFrames\n* let us make an Empty DataFrame","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"d564af95-ec97-4e4b-a9fe-bc0b1a3b1909"},{"version":"CommandV1","origId":50738,"guid":"2fbbb381-5ec3-401e-828f-70f1627866a2","subtype":"command","commandType":"auto","position":0.99755859375,"command":"val emptyDF = sqlContext.emptyDataFrame // Ctrl+Enter to make an empty DataFrame","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">emptyDF: org.apache.spark.sql.DataFrame = []\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581327924E12,"submitTime":1.457581288774E12,"finishTime":1.457581327973E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"308f67b6-2fa6-4941-8e64-b4208caec47e"},{"version":"CommandV1","origId":50740,"guid":"7f3911be-6955-4d5a-aa86-c83103ee4983","subtype":"command","commandType":"auto","position":0.998779296875,"command":"%md\nNot really interesting, or is it?\n\n**You Try!**\n\nPut your cursor after `emptyDF.` below and hit Tab to see what can be done with `emptyDF`.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"851f547c-f952-450f-9664-5db7403716cb"},{"version":"CommandV1","origId":50741,"guid":"9feb3e22-a13d-4510-a798-90e8e03d63ee","subtype":"command","commandType":"auto","position":0.9993896484375,"command":"emptyDF.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"8e93edd6-dcca-4db3-a005-5d6bb98689a0"},{"version":"CommandV1","origId":50742,"guid":"22e42a89-58cd-46b7-a325-d60d94731427","subtype":"command","commandType":"auto","position":0.99969482421875,"command":"%md\n#### 2. Making a DataFrame from a range\nLet us make a DataFrame next\n* from a range of numbers, as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"027e15a0-eee7-4139-9dfc-23b9f80a4060"},{"version":"CommandV1","origId":50743,"guid":"3f2e4d8e-07e7-4e74-a113-6294fec85ead","subtype":"command","commandType":"auto","position":0.999847412109375,"command":"val rangeDF = sqlContext.range(0, 3) // Ctrl+Enter to make DataFrame with 0,1,2","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">rangeDF: org.apache.spark.sql.DataFrame = [id: bigint]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581371006E12,"submitTime":1.457581331871E12,"finishTime":1.457581371071E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"bd64b0e9-da67-4558-b878-51bec404c5d3"},{"version":"CommandV1","origId":50744,"guid":"70bfe2b2-99ad-45b3-8632-45aa49a2f619","subtype":"command","commandType":"auto","position":0.9999237060546875,"command":"%md\nNote that Spark automatically names column as `id` and casts integers to type `bigint` for big integer or Long.\n\nIn order to get a preview of data in DataFrame use `show()` as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1e728f9e-5816-46d1-bed2-62d8725d4009"},{"version":"CommandV1","origId":50745,"guid":"11807584-162b-4899-890b-b9fdaea4bf48","subtype":"command","commandType":"auto","position":0.9999618530273438,"command":"rangeDF.show() // Ctrl+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+---+\n| id|\n+---+\n|  0|\n|  1|\n|  2|\n+---+\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581390837E12,"submitTime":1.457581351695E12,"finishTime":1.457581390933E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1feb1a13-d42a-4072-8cdb-3df1f573e07a"},{"version":"CommandV1","origId":50747,"guid":"49984d57-6d7a-4ba7-92a8-368c6d391cfc","subtype":"command","commandType":"auto","position":0.9999713897705078,"command":"%md\n#### 3. Making a DataFrame from an RDD\n\n* Make an RDD\n* Conver the RDD into a DataFrame using the defualt `.toDF()` method\n* Conver the RDD into a DataFrame using the non-default `.toDF(...)` method\n* Do it all in one 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org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[1018] at map at &lt;console&gt;:36\nres19: Array[(Int, Int)] = Array((1,2), (2,4), (3,6), (4,8), (5,10))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.45758146554E12,"submitTime":1.457581426381E12,"finishTime":1.457581465661E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"ee67d4c9-f85d-4ceb-bc9d-98b7ad4565d7"},{"version":"CommandV1","origId":50751,"guid":"a1b8c7ed-6a01-4dab-906a-7ec889929aae","subtype":"command","commandType":"auto","position":0.999995231628418,"command":"%md\nNext, let us convert the RDD into DataFrame using the `.toDF()` method, as 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Let us see its content using the `.show()` method next.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"38eefec7-d1fe-49e3-a771-a8aaf5dc61c3"},{"version":"CommandV1","origId":50754,"guid":"d2cc14a6-7ee3-40fd-9ed3-42f5f508d683","subtype":"command","commandType":"auto","position":0.9999988079071045,"command":"df1.show() // Ctrl+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+---+---+\n| _1| _2|\n+---+---+\n|  1|  2|\n|  2|  4|\n|  3|  6|\n|  4|  8|\n|  5| 10|\n+---+---+\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581517349E12,"submitTime":1.457581478202E12,"finishTime":1.457581517449E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"7792f840-50ca-4352-83c4-38acfa97599e"},{"version":"CommandV1","origId":50756,"guid":"09b5480d-90a7-4cc2-966e-d3cea2d3d9bc","subtype":"command","commandType":"auto","position":0.9999991059303284,"command":"%md\nNote that by default, i.e. without specifying any options as in `toDF()`, the column names are given by `_1` and `_2`.\n\nWe can easily specify column names as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"564e579a-4a4d-4d75-bc0b-b9cc9ffd8bb9"},{"version":"CommandV1","origId":50755,"guid":"a68ecedc-98dc-4049-befe-156493063c49","subtype":"command","commandType":"auto","position":0.9999994039535522,"command":"val df1 = rdd1.toDF(\"single\", \"double\") // Ctrl+Enter\ndf1.show()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+------+------+\n|single|double|\n+------+------+\n|     1|     2|\n|     2|     4|\n|     3|     6|\n|     4|     8|\n|     5|    10|\n+------+------+\n\ndf1: org.apache.spark.sql.DataFrame = [single: int, double: 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class=\"ansiout\">+------+------+\n|single|double|\n+------+------+\n|     1|     2|\n|     2|     4|\n|     3|     6|\n|     4|     8|\n|     5|    10|\n+------+------+\n\ndf1: org.apache.spark.sql.DataFrame = [single: int, double: int]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581568113E12,"submitTime":1.457581528967E12,"finishTime":1.457581568275E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"06f0a061-40c2-4fc6-823a-bf4dc4b07eb0"},{"version":"CommandV1","origId":50759,"guid":"c20ad25d-f866-4c00-b8ad-a7294507d165","subtype":"command","commandType":"auto","position":0.9999998509883881,"command":"%md\n**You Try!**\n\nFill in the `???` below to get a DataFrame `df2` whose first two columns are the same as `df1` and whose third column named triple has values that are three times the values in the first column.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"0298372c-858b-41e5-a0e4-cfab20283d84"},{"version":"CommandV1","origId":50761,"guid":"f7c6382f-d9bb-4343-ba3e-5e12f841ff6d","subtype":"command","commandType":"auto","position":0.999999888241291,"command":"val df2 = sc.parallelize(1 to 5).map(i => (i, i*2, ???)).toDF(\"single\", \"double\", \"triple\") // Ctrl+enter after editing ???\ndf2.show()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\"></div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"scala.MatchError: Nothing (of class scala.reflect.internal.Types$TypeRef$$anon$6)","error":"<div class=\"ansiout\">\tat org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:658)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:693)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:691)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:691)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:630)\n\tat org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)\n\tat org.apache.spark.sql.SQLContext.createDataFrame(SQLContext.scala:413)\n\tat org.apache.spark.sql.SQLImplicits.rddToDataFrameHolder(SQLImplicits.scala:94)</div>","startTime":1.457581724272E12,"submitTime":1.457581685107E12,"finishTime":1.457581724319E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c76becfe-ca75-4b72-b6da-452f5593a9f6"},{"version":"CommandV1","origId":50760,"guid":"b60606ab-24d0-494d-bab2-6468a05e0156","subtype":"command","commandType":"auto","position":0.999999925494194,"command":"%md\n#### 4. Making a DataFrame from a table\nWe can load existing tables as DataFrames.  We will later see how to create tables from RDDs or other sources of raw data, including csv files, etc.\n\nFirst let's see what tables are available to us.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1c4a8c32-8ddd-4ab7-b86f-103c0e054acb"},{"version":"CommandV1","origId":50762,"guid":"a5a233c5-c953-465a-aaee-680f9ea5f2b8","subtype":"command","commandType":"auto","position":0.999999962747097,"command":"sqlContext.tables.show() // Ctrl+Enter to see available tables","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+------------------+-----------+\n|         tableName|isTemporary|\n+------------------+-----------+\n|          diamonds|      false|\n|      simple_range|      false|\n|social_media_usage|      false|\n+------------------+-----------+\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581746681E12,"submitTime":1.457581707529E12,"finishTime":1.457581746767E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"137bf4b3-1b39-4b72-ab87-a9be458b5562"},{"version":"CommandV1","origId":50763,"guid":"5d188508-f6da-415c-9081-10b9ac7852d8","subtype":"command","commandType":"auto","position":0.9999999813735485,"command":"%md\nLet us load the table with `tableName` `diamonds` as a DataFrame (assuming it exixts!, if not don't worry as diamonds is our next stop!), as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"126ad2fc-6af3-4361-b038-0ff8adc6627a"},{"version":"CommandV1","origId":50764,"guid":"f77e8eba-f6c0-4713-abf8-87fc9e94cdbe","subtype":"command","commandType":"auto","position":0.9999999906867743,"command":"val diamondsDF = sqlContext.table(\"diamonds\") // Shift+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">diamondsDF: org.apache.spark.sql.DataFrame = [carat: double, cut: string, color: string, clarity: string, depth: double, table: double, price: int, x: double, y: double, z: double]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457581819986E12,"submitTime":1.457581780779E12,"finishTime":1.457581820054E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"bf53e9d2-39f6-4b15-8ed3-35298d718ba8"},{"version":"CommandV1","origId":50765,"guid":"1f93b26d-d872-4e9c-837b-509e271f8d32","subtype":"command","commandType":"auto","position":0.9999999953433871,"command":"display(diamondsDF)","commandVersion":0,"state":"finished","results":{"type":"table","data":[[0.23,"Ideal","E","SI2",61.5,55.0,326.0,3.95,3.98,2.43],[0.21,"Premium","E","SI1",59.8,61.0,326.0,3.89,3.84,2.31],[0.23,"Good","E","VS1",56.9,65.0,327.0,4.05,4.07,2.31],[0.29,"Premium","I","VS2",62.4,58.0,334.0,4.2,4.23,2.63],[0.31,"Good","J","SI2",63.3,58.0,335.0,4.34,4.35,2.75],[0.24,"Very Good","J","VVS2",62.8,57.0,336.0,3.94,3.96,2.48],[0.24,"Very Good","I","VVS1",62.3,57.0,336.0,3.95,3.98,2.47],[0.26,"Very Good","H","SI1",61.9,55.0,337.0,4.07,4.11,2.53],[0.22,"Fair","E","VS2",65.1,61.0,337.0,3.87,3.78,2.49],[0.23,"Very Good","H","VS1",59.4,61.0,338.0,4.0,4.05,2.39],[0.3,"Good","J","SI1",64.0,55.0,339.0,4.25,4.28,2.73],[0.23,"Ideal","J","VS1",62.8,56.0,340.0,3.93,3.9,2.46],[0.22,"Premium","F","SI1",60.4,61.0,342.0,3.88,3.84,2.33],[0.31,"Ideal","J","SI2",62.2,54.0,344.0,4.35,4.37,2.71],[0.2,"Premium","E","SI2",60.2,62.0,345.0,3.79,3.75,2.27],[0.32,"Premium","E","I1",60.9,58.0,345.0,4.38,4.42,2.68],[0.3,"Ideal","I","SI2",62.0,54.0,348.0,4.31,4.34,2.68],[0.3,"Good","J","SI1",63.4,54.0,351.0,4.23,4.29,2.7],[0.3,"Good","J","SI1",63.8,56.0,351.0,4.23,4.26,2.71],[0.3,"Very Good","J","SI1",62.7,59.0,351.0,4.21,4.27,2.66],[0.3,"Good","I","SI2",63.3,56.0,351.0,4.26,4.3,2.71],[0.23,"Very Good","E","VS2",63.8,55.0,352.0,3.85,3.92,2.48],[0.23,"Very 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Next we will play with data\n\nThe data here is **semi-structured tabular data** (Tab-delimited text file in dbfs).\nLet us see what Anthony Joseph in BerkeleyX/CS100.1x had to say about such data.\n\n### Key Data Management Concepts: Semi-Structured Tabular Data\n\n**(watch now 1:26)**:\n\n[![Semi-Structured Tabular Data by Anthony Joseph in BerkeleyX/CS100.1x](http://img.youtube.com/vi/G_67yUxdDbU/0.jpg)](https://www.youtube.com/v/G_67yUxdDbU?rel=0&autoplay=1&modestbranding=1&start=1)\n\n***","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"54e1830a-b062-434e-8026-b8520872b1d3"},{"version":"CommandV1","origId":63100,"guid":"6215e60b-bbeb-4a9a-adad-e7fa44aca769","subtype":"command","commandType":"auto","position":1.5,"command":"%md\nThis week's recommended homework and extra-work linked below will help further your understanding beyond the three example notebooks we will see next at: \n* [Workspace -> scalable-data-science -> week3 -> 05_SparkSQLETLEDA](/#workspace/scalable-data-science/week3/05_SparkSQLETLEDA).\n\n### Recommended Homework\nThis week's recommended homework is a deep dive into the [SparkSQL programming guide](http://spark.apache.org/docs/latest/sql-programming-guide.html) via a \"databricksified\" set of scala notebooks at: \n* [Workspace -> scalable-data-science -> xtraResources -> ProgGuides1_6 -> sqlProgrammingGuide](/#workspace/scalable-data-science/xtraResources/ProgGuides1_6/sqlProgrammingGuide).\n\n### Recommended Extra-work\nThose who want to understand SparkSQL functionalities in more detail can see:\n* [video lectures in Module 3 of Anthony Joseph's Introduction to Big Data edX course](/#workspace/scalable-data-science/xtraResources/edXBigDataSeries2015/CS100-1x/Module 3: Lectures) from the Community Edition (CE) of databricks \n  * NOTE on June 18 2016: AJ's 2015 course is now already in databricks CE so won't be re-fielded here in html/git-booked md, except in the .dbc archive of this 2016 instance of the scalable-data-science course - remarked (you should really see the 2016 version of Anthony Joseph + Ameet Talwarkar + Jon Bates edX course now... and Spark 2.0 will be another story I am sure...). \n\nAnthony Joseph's Introduction to Big Data edX course (in python using pySpark) has been added to this databricks shard at:\n* [Workspace -> scalable-data-science -> xtraResources -> edXBigDataSeries2015 -> CS100-1x](/#workspace/scalable-data-science/xtraResources/edXBigDataSeries2015/CS100-1x), \nas an extra resource for this project-focussed course [Scalable Data Science](http://www.math.canterbury.ac.nz/~r.sainudiin/courses/ScalableDataScience/).\n","commandVersion":0,"state":"error","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b4e53a81-bd9f-4600-806e-75b8eb80344d"},{"version":"CommandV1","origId":50707,"guid":"c14ad880-976d-439b-b685-06853f71c6f9","subtype":"command","commandType":"auto","position":2.0,"command":"%md\n\n# [Scalable Data Science](http://www.math.canterbury.ac.nz/~r.sainudiin/courses/ScalableDataScience/)\n\n\n### prepared by [Raazesh Sainudiin](https://nz.linkedin.com/in/raazesh-sainudiin-45955845) and [Sivanand Sivaram](https://www.linkedin.com/in/sivanand)\n\n*supported by* [![](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/images/databricks_logoTM_200px.png)](https://databricks.com/)\nand \n[![](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/images/AWS_logoTM_200px.png)](https://www.awseducate.com/microsite/CommunitiesEngageHome)","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"215f4aac-de99-49fc-8bfe-90f47a7b5d79"}],"dashboards":[],"guid":"a994df4a-3c46-4471-bede-19fab6147b71","globalVars":{},"iPythonMetadata":null,"inputWidgets":{}};</script>
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