Member since
10-08-2015
108
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62
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7
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My Accepted Solutions
Title | Views | Posted |
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4207 | 06-03-2017 12:11 AM | |
5483 | 01-24-2017 01:02 PM | |
6000 | 12-27-2016 11:38 AM | |
3254 | 12-20-2016 09:52 AM | |
2362 | 12-07-2016 02:15 AM |
01-24-2017
01:02 PM
> So what do you think is the actual problem here? It is hard to say what is the problem. It depends on your cluster size and data size. According my experience, 512M for executor is too small usually. You might need to increase it to 4G, 4 cores for each executor. That means you can run at most 4 task per executor, each consume 1g memory. And you may also need to set the executor number. IIRC, the default is 2. But if you want it to be shared by many users, then just increase it. > does it make any difference or will be okay if sparkContext and executors are correctly set? The question is a little confusing. Do you use hive thrift server or spark thrift server ? If you are using hive thrift server, then it is not related to spark.
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01-24-2017
09:12 AM
It depends on whether these users share the same SparkContext. Because zeppelin only support yarn-client for spark interpreter which means the driver will run on the same host as zeppelin server. And if you run spark interpreter in shared mode, then all the user share the same SparkContext and you can increase executor size and executor number for the query needs. But if you run isolated mode (each user own its own sparkcontext, launching a new spark app). That would almost impossible for your cluster. Because there would be 30-50 drivers process on your zeppelin server which will eat up your resources.
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01-23-2017
02:01 PM
Do you have the full stacktrace ?
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01-23-2017
01:33 PM
It works for me. Can you check the interpreter log ?
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01-23-2017
11:44 AM
You should use show tables For the error you see, this is a bug of livy interpreter IIRC. The workaround is that always run paragraph %livy first just as you did above.
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01-23-2017
09:34 AM
4 Kudos
Introduction Apache Zeppelin is a web-based notebook that enables interactive data analytics while Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs. Pig-latin is a very powerful languages for data flow processing. One drawback of pig community complains about is that pig-latin is not a standard language like sql so very few BI tools integrate with it. So it is pretty to hard to visualize the result from pig. Now the good news is that Pig is integrated in zeppelin 0.7 where you can write pig latin and visualize the result. Use pig interpreter Pig interpreter is supported from zeppelin 0.7.0, so first you need to install zeppelin, you can refer this link for how to install and start zeppelin. Zeppelin supports 2 kinds of pig interpreters for now. %pig (default interpreter) %pig.query %pig is like the pig grunt shell. Anything you can run in pig grunt shell can be run in %pig.script interpreter, it is used for running pig script where you don’t need to visualize the data, it is suitable for data munging. %pig.query is a little different compared with %pig.script. It is used for exploratory data analysis by using pig latin where you can leverage zeppelin’s visualization ability. There're 2 minor differences in the last statement between %pig and %pig.query No pig alias in the last statement in %pig.query (read the examples below). The last statement must be in single line in %pig.query Here I will give 4 simple examples to illustrate how to use these 2 interpreters. These 4 examples are another implementation of zeppelin tutorial where spark is used. We just do the same thing by using pig instead. This script do the data preprocessing %pig
bankText = load 'bank.csv' using PigStorage(';');
bank = foreach bankText generate $0 as age, $1 as job, $2 as marital, $3 as education, $5 as balance;
bank = filter bank by age != '"age"';
bank = foreach bank generate (int)age, REPLACE(job,'"','') as job, REPLACE(marital, '"', '') as marital, (int)(REPLACE(balance, '"', '')) as balance;
store bank into 'clean_bank.csv' using PigStorage(';'); -- this statement is optional, it just show you that most of time %pig.script is used for data munging before querying the data. Get the number of each age where age is less than 30 %pig.query
bank_data = filter bank by age < 30;
b = group bank_data by age;
foreach b generate group, COUNT($1); The same as above, but use dynamic text form so that use can specify the variable maxAge in textbox. (See screenshot below). Dynamic form is a very cool feature of zeppelin, you can refer this link for details. %pig.query
bank_data = filter bank by age < ${maxAge=40};
b = group bank_data by age;
foreach b generate group, COUNT($1); Get the number of each age for specific marital type, also use dynamic form here. User can choose the marital type in the dropdown list (see screenshot below). %pig.query
bank_data = filter bank by marital=='${marital=single,single|divorced|married}';
b = group bank_data by age;
foreach b generate group, COUNT($1); The following is a screenshot of these 4 examples. You can also check pig tutorial note which contains all the code of this blog in zeppelin. Configuration Pig interpreter in zeppelin supports all the execution engine that pig supports. Local Mode
Nothing needs to be done for local mode MapReduce Mode
HADOOP_CONF_DIR needs to be specified in ZEPPELIN_CONF_DIR/zeppelin-env.sh Tez Local Mode
Nothing needs to be done for tez local mode Tez Mode
HADOOP_CONF_DIR and TEZ_CONF_DIR needs to be specified in ZEPPELIN_CONF_DIR/zeppelin-env.sh The default mode is mapreduce, but you can change that in interpreter setting. You can also set any pig configuration in the interpreter setting page. Here's one screenshot of that. Future work This is the first phase work to integrate pig into zeppelin. There’s lots of work needs to do in future. Here’s my current to-do list Integrate spark engine so that we can use spark sql together with pig-latin Integrate spark mllib so that we can use pig-latin to do machine learning Add new interpreter %pig.udf to allow user to write java udf in zeppelin Integrate more closely with datafu If you have any other new ideas, please contact me at jzhang@hortonworks.com or you can file ticket in apache zeppelin jira https://issues.apache.org/jira/browse/ZEPPELIN
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12-27-2016
11:38 AM
This means you can run at most 10 queries parallelly in each paragraph rather than in the same paragraph. This is because the interpreter process can at most allocate 10 threads for query execution.
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12-20-2016
10:57 AM
That is scala code.
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12-20-2016
09:52 AM
1 Kudo
I don't think currently there's any java interpreter for zeppelin.
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12-20-2016
01:31 AM
The hive metastore security issue should be fixed in HDP 2.5. (Please use yarn-cluster mode, yarn-client mode still has this issue) Could you attach the logs and configs ?
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