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| {"paragraphs":[{"text":"%md\n\n##Welcome to Zeppelin.\n####This is a live tutorial, you can run the code yourself. (Shift-Enter to Run)\n","dateUpdated":"2017-02-28T13:48:48+0000","config":{"tableHide":false,"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691068_-996268266","id":"20170224-141003_1862458520","result":{"code":"SUCCESS","type":"HTML","msg":"<h2>Welcome to Zeppelin.</h2>\n<h4>This is a live tutorial, you can run the code yourself. (Shift-Enter to Run)</h4>\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:226","dateFinished":"2017-02-28T13:48:49+0000","dateStarted":"2017-02-28T13:48:48+0000","focus":true},{"text":"%md \n\nIn this short tutorial we will load some data from Python and query it from SQL & Scala. Compared to Jupyter, [Zeppelin](https://zeppelin.apache.org/) provides a better integration with Spark, in particular it\n- allows multiple languages (i.e. Python, Scala & SQL) to share the same Spark context within a notebook;\n- comes out of the box with a Spark progress bar;\n- allows the current Spark job to be cancelled;\n- comes with simple plotting tools that are particularly attractive when combined with SQL. \n\nJupyter is still king in other areas, like auto-completion and matplotlib support, for which Zeppelin has only a basic support. Furthermore, neither RTMO nor Github can render Zeppelin notebooks yet. \n","dateUpdated":"2017-02-28T14:03:20+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorHide":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691068_-996268266","id":"20170224-141147_107877359","result":{"code":"SUCCESS","type":"HTML","msg":"<p>In this short tutorial we will load some data from Python and query it from SQL & Scala. Compared to Jupyter, <a href=\"https://zeppelin.apache.org/\">Zeppelin</a> provides a better integration with Spark, in particular it</p>\n<ul>\n<li>allows multiple languages (i.e. Python, Scala & SQL) to share the same Spark context within a notebook;</li>\n<li>comes out of the box with a Spark progress bar;</li>\n<li>allows the current Spark job to be cancelled;</li>\n<li>comes with simple plotting tools that are particularly attractive when combined with SQL.</li>\n</ul>\n<p>Jupyter is still king in other areas, like auto-completion and matplotlib support, for which Zeppelin has only a basic support. Furthermore, neither RTMO nor Github can render Zeppelin notebooks yet.</p>\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:227","focus":true,"dateFinished":"2017-02-28T14:03:16+0000","dateStarted":"2017-02-28T14:03:16+0000"},{"text":"%pyspark \n\nfrom moztelemetry import get_pings_properties\nfrom moztelemetry.dataset import Dataset\n\npings = Dataset.from_source(\"telemetry\") \\\n .where(docType='main') \\\n .where(submissionDate=\"20170224\") \\\n .where(appUpdateChannel=\"nightly\") \\\n .records(sc, sample=0.1)\n \nsubset = get_pings_properties(pings, [\"environment/system/os/name\",\n \"payload/simpleMeasurements/firstPaint\"])\n \nsubset.toDF().createOrReplaceTempView(\"nightly_sample\") ","dateUpdated":"2017-02-28T13:55:06+0000","config":{"colWidth":12,"editorMode":"ace/mode/scala","tableHide":false,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691069_-996653015","id":"20170224-135041_118327965","result":{"code":"SUCCESS","type":"TEXT","msg":"Unable to parse whitelist (/home/hadoop/anaconda2/lib/python2.7/site-packages/moztelemetry/histogram-whitelists.json). Assuming all histograms are acceptable.\n/usr/lib/spark/python/pyspark/sql/session.py:336: UserWarning:\n\nUsing RDD of dict to inferSchema is deprecated. Use pyspark.sql.Row instead\n\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:228","dateFinished":"2017-02-28T13:52:22+0000","dateStarted":"2017-02-28T13:51:21+0000","focus":true},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/markdown","editorHide":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488290136438_1538793270","id":"20170228-135536_141565591","dateCreated":"2017-02-28T13:55:36+0000","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:1002","text":"%md\nDisplaying matplotlib plots is different than with Jupyter and requires to invoke the `show` function.","dateUpdated":"2017-02-28T13:56:27+0000","dateFinished":"2017-02-28T13:56:24+0000","dateStarted":"2017-02-28T13:56:24+0000","result":{"code":"SUCCESS","type":"HTML","msg":"<p>Displaying matplotlib plots is different than with Jupyter and requires to invoke the <code>show</code> function.</p>\n"}},{"text":"%pyspark\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfrom moztelemetry.zeppelin import show\n\nfig = plt.figure()\nframe = pd.DataFrame(subset.collect())\nnp.log(frame[\"payload/simpleMeasurements/firstPaint\"]).plot(kind=\"hist\", bins=100)\nshow(plt)","dateUpdated":"2017-02-28T13:56:33+0000","config":{"colWidth":12,"editorMode":"ace/mode/scala","graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691069_-996653015","id":"20170224-145456_1997683137","result":{"code":"SUCCESS","type":"HTML","msg":"<div style='width:600px'><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with 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xlink:href=\"#BitstreamVeraSans-Roman-79\"/>\n </g>\n </g>\n </g>\n </g>\n </g>\n <defs>\n <clipPath id=\"p9e575f5172\">\n <rect height=\"345.6\" width=\"446.4\" x=\"72.0\" y=\"43.2\"/>\n </clipPath>\n </defs>\n</svg>\n</div>\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:229","dateFinished":"2017-02-28T13:57:03+0000","dateStarted":"2017-02-28T13:56:33+0000","focus":true},{"text":"%md\n\nAs the SparkContext is shared between different interpreters once a temporary view has been registered it can be accessed by SparkSQL and Scala. A visualization toolbox will appear automatically when running a SparkSQL query.","dateUpdated":"2017-02-28T14:00:46+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorHide":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691071_-995883517","id":"20170224-143145_1378628353","result":{"code":"SUCCESS","type":"HTML","msg":"<p>As the SparkContext is shared between different interpreters once a temporary view has been registered it can be accessed by SparkSQL and Scala. A visualization toolbox will appear automatically when running a SparkSQL query.</p>\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:230","dateFinished":"2017-02-28T14:00:43+0000","dateStarted":"2017-02-28T14:00:43+0000","focus":true},{"text":"%sql \n\nselect `environment/system/os/name`, count(*)\nfrom nightly_sample\ngroup by 1","dateUpdated":"2017-02-28T14:00:47+0000","config":{"colWidth":12,"editorMode":"ace/mode/scala","graph":{"mode":"multiBarChart","height":300,"optionOpen":false,"keys":[{"name":"environment/system/os/name","index":0,"aggr":"sum"}],"values":[{"name":"count(1)","index":1,"aggr":"sum"}],"groups":[],"scatter":{"xAxis":{"name":"environment/system/os/name","index":0,"aggr":"sum"}}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691071_-995883517","id":"20170224-135047_511050306","result":{"code":"SUCCESS","type":"TABLE","msg":"environment/system/os/name\tcount(1)\nLinux\t924\nDarwin\t482\nWindows_NT\t10855\n","comment":"","msgTable":[[{"key":"count(1)","value":"Linux"},{"key":"count(1)","value":"924"}],[{"value":"Darwin"},{"value":"482"}],[{"value":"Windows_NT"},{"value":"10855"}]],"columnNames":[{"name":"environment/system/os/name","index":0,"aggr":"sum"},{"name":"count(1)","index":1,"aggr":"sum"}],"rows":[["Linux","924"],["Darwin","482"],["Windows_NT","10855"]]},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:231","dateFinished":"2017-02-28T14:01:19+0000","dateStarted":"2017-02-28T14:00:47+0000","focus":true},{"text":"sqlContext\n .sql(\"select * from nightly_sample\")\n .groupBy(\"environment/system/os/name\")\n .count()\n .collect()","dateUpdated":"2017-02-28T14:01:29+0000","config":{"colWidth":12,"editorMode":"ace/mode/scala","graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691071_-995883517","id":"20170224-135639_2049260364","result":{"code":"SUCCESS","type":"TEXT","msg":"\nres0: Array[org.apache.spark.sql.Row] = Array([Linux,924], [Darwin,482], [Windows_NT,10855])\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:232","focus":true,"dateFinished":"2017-02-28T14:01:52+0000","dateStarted":"2017-02-28T14:01:29+0000"},{"text":"%md \n\nJust as we can access temporary views any other table registered within the Hive metastore can be access as well, like the *longitudinal* one.","dateUpdated":"2017-02-28T14:02:19+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorHide":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691072_-1010119226","id":"20170224-143309_1430894078","result":{"code":"SUCCESS","type":"HTML","msg":"<p>Just as we can access temporary views any other table registered within the Hive metastore can be access as well, like the <em>longitudinal</em> one.</p>\n"},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:233","dateFinished":"2017-02-28T14:02:15+0000","dateStarted":"2017-02-28T14:02:15+0000","focus":true},{"text":"%sql\nwith submissions as (\n select explode(build) as build\n from longitudinal\n where build is not null\n), grouped as (\n select build.architecture as arch, count(*) as total\n from submissions\n group by 1\n)\nselect arch, 100 * total / sum(total) over () as percentage\nfrom grouped\norder by 2 desc\nlimit 2","dateUpdated":"2017-02-28T14:02:39+0000","config":{"colWidth":12,"editorMode":"ace/mode/sql","graph":{"mode":"table","height":300,"optionOpen":false,"keys":[{"name":"arch","index":0,"aggr":"sum"}],"values":[{"name":"percentage","index":1,"aggr":"sum"}],"groups":[],"scatter":{"xAxis":{"name":"arch","index":0,"aggr":"sum"},"yAxis":null}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691072_-1010119226","id":"20170224-142611_475781068","result":{"code":"SUCCESS","type":"TABLE","msg":"arch\tpercentage\nx86\t91.96796532613196\nx86-64\t8.02839171660672\n","comment":"","msgTable":[[{"key":"percentage","value":"x86"},{"key":"percentage","value":"91.96796532613196"}],[{"value":"x86-64"},{"value":"8.02839171660672"}]],"columnNames":[{"name":"arch","index":0,"aggr":"sum"},{"name":"percentage","index":1,"aggr":"sum"}],"rows":[["x86","91.96796532613196"],["x86-64","8.02839171660672"]]},"dateCreated":"2017-02-28T13:48:11+0000","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:234","dateFinished":"2017-02-28T14:04:23+0000","dateStarted":"2017-02-28T14:02:39+0000","focus":true},{"text":"","dateUpdated":"2017-02-28T13:48:11+0000","config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1488289691072_-1010119226","id":"20170224-142502_226596480","dateCreated":"2017-02-28T13:48:11+0000","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:235"}],"name":"Tutorial","id":"2CCE7549C","angularObjects":{"2BRWU4WXC:shared_process":[],"2AJXGMUUJ:shared_process":[],"2ANGGHHMQ:shared_process":[],"2AKK3QQXU:shared_process":[]},"config":{"looknfeel":"default"},"info":{}} 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