It is painful to play with Redshift.
You may have run into the error that there is no space left on the disk for shuffle RDD data although you seems having much than enough disk space in fact.
It happens because usually we allocate a not-so-large space for system dir /tmp, while SPARK by default use /tmp for shuffle RDD data which might be quite large. (There are some posts questioning whether SPARK never clean temporary data – which can be a severe problem that I personally did not confirm). Anyway, as you can guess now, the SPARK_LOCAL_DIRS is designed for this purpose that specifies the location for temporary data.
You could configure this variable in conf/spark-env.sh, e.g. use hdfs
There is spark.local.dirs in conf/spark-default.conf for the same purpose, which however will be overwritten by SPARK_LCOAL_DIRS.
Here is a small chunk of code for testing Spark RDD join function.
a=[(1, 'a'), (2, 'b')] b=[(1, 'c'), (4, 'd')] ardd = sc.parallelize(a) brdd = sc.parallelize(b) def merge(a, b): if a is None: return b if b is None: return a return a+b ardd.fullOuterJoin(brdd).map(lambda x: (x, merge(x, x))).collect()
This code works fine. But when I apply this to my real data (reading from HDFS and Join and write it back). I ran into the PYTHONHASHSEED problem again! YES AGAIN. I did not get chance to fix this problem before.
This problem happens for Python 3.3+. The line of code responsible for this trouble is pythont/pyspark/rdd.py, line 74.
if sys.version >= '3.3' and 'PYTHONHASHSEED' not in os.environ: raise Exception("Randomness of hash of string should be disabled via PYTHONHASHSEED")
After searching around and trying many different proposals, I really got frustrated about this. It seems the community knows well this issue and Spark Github seems having fixed it (2015), while my version (2016) still does not work
A few options I found:
- put export PYTHONOHASHSEED=0 .bashrc
- Failed. In a notebook, I could get out the os.environ[‘PYTHONHASHSEE’] and it was correctly set. This is the correct way for standalone python program, but not for spark cluster.
- A possible reason is pyspark has a different set of environment variables. It is not about propagating this variable across workers either because even if all workers has this variable exported in .bashrc, it still will complain.
- Doesn’t work. Some suggested to pass this to pyspark when starting notebook. Unfortunately, nothing fortunate happened. and I don’t think I am even using yarn.
Anyway, in the end, I find the solution from this link. Most of pssh can be ignored. The only line matters is place ‘Export PYTHONHASHSEED=0’ in to conf/spark-env.sh for each worker, which confirms the statement that PYTHONHASHSEED=0 should be somehow placed into the Spark Run-time Environment.
Thanks to this post, which saves my ass: http://comments.gmane.org/gmane.comp.lang.scala.spark.user/24459
Most of time, I get the impression that machine learning is statistic under beautiful costume. Here is an article that gives some ideas of difference between two subjects.
Ran into a error ” Module ‘urllib’ has no attribute ‘request’ ”
The script runs well before I threw it into a parallel mode by calling sc.parallelize(data, 8). The spark log shows the above error. So far, I could not find any solution by googling. I have printed the the python version used, which is 3.5. Have no clue where goes wrong.
after a few exploration, I finally found the solution, i.e. put a statement import urllib.request right before I use urllib.request.urlopen(…). Is this caused by the fact that I am using Jupyter, in which, the import statement was in another cell.
With Spark How to install Spark? and HDFS How to install HDFS?, we are moving to form a cluster. Oh, I hope you are using virtual machine if you are following this series. It matters because you don’t have go through every step again by just cloning a VM!
So clone completely a copy of virtual machine with Spark and HDFS installed. Boot it and run ifconfig to get the IP, e.g. 192.168.11.138. Let us call this copied vm the slave, and the original vm the master with IP 192.168.11.136.
- In the master, reformat namenode giving a cluster name, whatever you want to call it
$ hdfs namenode -format <cluster name>
- In the slave machine, edit your core-site.xml to replace localhost with your real IP address, e.g. 192.168.11.136. (you can always use ifconfig to find it out).
- In the master, edit etc/hadoop/workers and add one line for the additional worker, i.e. 192.168.11.138. If you are looking at hadoop-2.7, it should be etc/hadoop/slaves. (No idea why they change the filename. Personally, I think slaves is better as it says.)
- Start the services
- Check by run ‘jps’ in 192.168.11.138 to see whether DataNode service has been started. Also, in your slave machine, you can put README.md file to HDFS and list from master. Also, try the Spark script using the hdfs url: hdfs://184.108.40.206:9000/README.md
That’s it! Goddamn it! These Hadoop guys really make the basic deployment easy!
Spark has a similar framework as HDFS, i.e. master-slave mode.
Launch Spark monitoring: http://localhost:8080/
copy SSPARK_HOME/conf/slaves.template to SPARK_HOME/conf/slaves. Add 192.168.11.138 at the end of the file
From the Spark Monitor, I can see the local worker (in the master machine) is alive, and the worker in slave machine (192.168. 11.138) never shows up. Not that easy, hah!
I login to the slave and run jps. Surprisely enough, it shows that the worker is running. I check the log – nothing!!!
I noticed the parameters in the worker thread by ‘ps -aux’. There is an option ‘host’, which is specified as a hostname. Since I copied the virtual machine, so my slave, i.e. 192.168.11.138, has the same dignity name as my master. That’s why!!!!
But even after I run start-slave.sh at my slave, trying to connect my master, it fails either. However, the worker in the master always shows up in the Spark monitoring.
$ $SPARK_HOME/sbin/start-slave.sh spark://192.168.11.136:7077
Why? Because my Spark master is running on localhost!!! So, I need to start my master on its IP, instead of hostname (in fact, it should be FQDN). So I went back to shutdown the master. and restart it by the following command
$ SPARK_MASTER_HOST=192.168.11.136 $SPARK_HOME/sbin/start-master.sh
Now, I run start-slave.sh on both machines. Two workers show up. For the start-slaves.sh, instead of localhost, I need to put the real IP of my master if I want my master is also a worker.
There is a script conf/spark-env.sh, which is used to make all these problem goes away. The idea is that you can configure all these environment variables in the script and run it in all nodes where you want turn it to be a worker. Easy, ah!
Now, let us connect jupyter to Spark master so we can parallelize our data processing but still have the power of jupyter. How?
$ pyspark –master spark://192.168.11.136:7077
That is it?! Yes, that’s it!