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add more description of tutorial note

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Jeff Zhang 2017-01-02 20:56:06 +08:00
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4
docs/interpreter/pig.md
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@ -99,4 +99,6 @@ c = group b by Category;
foreach c generate group as category, COUNT($1) as count;
```
Data is shared between `%pig` and `%pig.query`, so that you can do some common work in `%pig`, and do different kinds of query based on the data of `%pig`. There's one pig tutorial note in zeppelin for your reference.
Data is shared between `%pig` and `%pig.query`, so that you can do some common work in `%pig`, and do different kinds of query based on the data of `%pig`.
Besides, we recommend you to specify alias explicitly so that the visualization can display the column name correctly. Here, we name `COUNT($1)` as `count`, if you don't do this,
then we will name it using position, here we will use `col_1` to represent `COUNT($1)` if you don't specify alias for it. There's one pig tutorial note in zeppelin for your reference.

316
notebook/2C57UKYWR/note.json Normal file
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@ -0,0 +1,316 @@
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{
"text": "%md\n\n\n### [Apache Pig](http://pig.apache.org/) is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.\n\nPig\u0027s language layer currently consists of a textual language called Pig Latin, which has the following key properties:\n\n* Ease of programming. It is trivial to achieve parallel execution of simple, \"embarrassingly parallel\" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.\n* Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.\n* Extensibility. Users can create their own functions to do special-purpose processing.\n",
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"data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003ch3\u003e\u003ca href\u003d\"http://pig.apache.org/\"\u003eApache Pig\u003c/a\u003e is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.\u003c/h3\u003e\n\u003cp\u003ePig\u0026rsquo;s language layer currently consists of a textual language called Pig Latin, which has the following key properties:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEase of programming. It is trivial to achieve parallel execution of simple, \u0026ldquo;embarrassingly parallel\u0026rdquo; data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.\u003c/li\u003e\n \u003cli\u003eOptimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.\u003c/li\u003e\n \u003cli\u003eExtensibility. Users can create their own functions to do special-purpose processing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e"
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"text": "%md\n\nThis pig tutorial use pig to do the same thing as spark tutorial. The default mode is mapreduce, you can also use other modes like local/tez_local/tez. For mapreduce mode, you need to have hadoop installed and export `HADOOP_CONF_DIR` in `zeppelin-env.sh`\n\nThe tutorial consists of 3 steps.\n\n* Use shell interpreter to download bank.csv and upload it to hdfs\n* use `%pig` to process the data\n* use `%pig.query` to query the data",
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"data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThis pig tutorial use pig to do the same thing as spark tutorial. The default mode is mapreduce, you can also use other modes like local/tez_local/tez. For mapreduce mode, you need to have hadoop installed and export \u003ccode\u003eHADOOP_CONF_DIR\u003c/code\u003e in \u003ccode\u003ezeppelin-env.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe tutorial consists of 3 steps.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eUse shell interpreter to download bank.csv and upload it to hdfs\u003c/li\u003e\n \u003cli\u003euse \u003ccode\u003e%pig\u003c/code\u003e to process the data\u003c/li\u003e\n \u003cli\u003euse \u003ccode\u003e%pig.query\u003c/code\u003e to query the data\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e"
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"text": "%pig\n\nbankText \u003d load \u0027bank.csv\u0027 using PigStorage(\u0027;\u0027);\nbank \u003d foreach bankText generate $0 as age, $1 as job, $2 as marital, $3 as education, $5 as balance; \nbank \u003d filter bank by age !\u003d \u0027\"age\"\u0027;\nbank \u003d foreach bank generate (int)age, REPLACE(job,\u0027\"\u0027,\u0027\u0027) as job, REPLACE(marital, \u0027\"\u0027, \u0027\u0027) as marital, (int)(REPLACE(balance, \u0027\"\u0027, \u0027\u0027)) as balance;\n\n-- The following statement is optional, it depends on whether your needs.\n-- store bank into \u0027clean_bank.csv\u0027 using PigStorage(\u0027;\u0027);\n\n\n",
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"text": "%pig.query\n\nbank_data \u003d filter bank by age \u003c 30;\nb \u003d group bank_data by age;\nforeach b generate group, COUNT($1);\n\n",
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"dateUpdated": "Jan 6, 2017 3:57:15 PM",
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"text": "%pig.query\n\nbank_data \u003d filter bank by age \u003c ${maxAge\u003d40};\nb \u003d group bank_data by age;\nforeach b generate group, COUNT($1);",
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"text": "%pig.query\n\nbank_data \u003d filter bank by marital\u003d\u003d\u0027${marital\u003dsingle,single|divorced|married}\u0027;\nb \u003d group bank_data by age;\nforeach b generate group, COUNT($1) as c;\n\n\n",
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326
notebook/2C7BDKAHN/note.json
View file

@ -1,326 +0,0 @@
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"text": "%md\n\nThis pig tutorial use pig to do the same thing as spark tutorial.\n",
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"text": "%sh\n\nwget https://s3.amazonaws.com/apache-zeppelin/tutorial/bank/bank.csv\nhadoop fs -put bank.csv .\n\n",
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"dateUpdated": "Jan 1, 2017 9:46:58 PM",
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"text": "%pig\n\nbankText \u003d load \u0027bank.csv\u0027 using PigStorage(\u0027;\u0027);\nbank \u003d foreach bankText generate $0 as age, $1 as job, $2 as marital, $3 as education, $5 as balance; \nbank \u003d filter bank by age !\u003d \u0027\"age\"\u0027;\nbank \u003d foreach bank generate (int)age, REPLACE(job,\u0027\"\u0027,\u0027\u0027) as job, REPLACE(marital, \u0027\"\u0027, \u0027\u0027) as marital, (int)(REPLACE(balance, \u0027\"\u0027, \u0027\u0027)) as balance;\n\n-- The following statement is optional, it depends on your needs.\n-- store bank into \u0027clean_bank.csv\u0027 using PigStorage(\u0027;\u0027);\n",
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"dateUpdated": "Jan 2, 2017 11:41:21 AM",
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{
"text": "%pig.query\n\nbank_data \u003d filter bank by age \u003c 30;\nb \u003d group bank_data by age;\nforeach b generate group as age, COUNT($1) as count;\n\n",
"user": "user1",
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"text": "%pig.query\n\nbank_data \u003d filter bank by age \u003c ${maxAge\u003d40};\nb \u003d group bank_data by age;\nforeach b generate group as age, COUNT($1) as count;",
"user": "user1",
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},
{
"text": "%pig.query\n\nbank_data \u003d filter bank by marital\u003d\u003d\u0027${marital\u003dsingle,single|divorced|married}\u0027;\nb \u003d group bank_data by age;\nforeach b generate group as age, COUNT($1) as count;\n\n\n\n",
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