Wednesday, July 29, 2015

Deploy edx spark environment to DigitalOcean

This summer I took the Spark courses at edx CS100 and CS190, and had wonderful experience.
The two classes apply a Vagrant virtual machine containing Spark and all teaching materials. There are two challenges with the virtual machine —
  1. The labs usually take long time to finish, say 8-10 hours. If the host machine is closed, the RDDs will be lost and the pipeline has to be run again.
  2. Some RDD operations take a lot computation/communication powers, such as groupByKey and distinct. Many of my 50k classmates complained about the waiting time. And my most used laptop is a Chromebook and doesn’t even have options to install Virtual Box.
To deploy the learning environment to a cloud may be an alternative. DigitalOcean is a good choice because it uses mirrors for most packages, and the network speed is amazingly fast that is almost 100MB/s (thanks to the SSD infrastructure DigitalOcean implements for the cloud, otherwise the hard disk may not stand this rapid IO; see my deployment records GitHub).

I found that a Linux box with 1 GB memory and 1 CPU at DigitalOcean that costs 10 dollars a month will handle most labs fairly easy with IPython and Spark. A 2 GB memory and 2 CPU droplet will be ideal since it is the minimal requirement for a simulated cluster. It costs 20 dollars a month, but is still much cheaper than the cost to earn the big data certificate that is $100 (50 for each). I just need to write Python scripts to install IPython notebook with SSL, and download Spark and the course materials.
  • The DevOps tool is Fabric and the fabfile is at GitHub.
  • The deployment pipeline is also at GitHub

Friday, July 17, 2015

Transform SAS files to Parquet through Spark

The demo pipeline is at GitHub.
Since the version 1.3, Spark has introduced the new data structure DataFrame. A data analyst now could easily scale out the exsiting codes based on the DataFrame from Python or R to a cluster hosting Hadoop and Spark.
There are quite a few practical scenarios that DataFrame fits well. For example, a lot of data files including the hardly read SAS files want to merge into a single data store. Apache Parquet is a popular column store in a distributed environment, and especially friendly to structured or semi-strucutred data. It is an ideal candidate for a univeral data destination.
I copy three SAS files called prdsale, prdsal2 and prdsal3, which are about a simulated sales record, from the SASHELP library to a Linux directory. And then I launch the SQL context from Spark 1.4.
The three SAS files now have the size of 4.2MB. My overall strategy is to build a pipeline to realize my purpose such as SAS --> Python --> Spark --> Parquet.
import os
    import sas7bdat
    import pandas
except ImportError:
    print('try to install the packags first')

print('Spark verion is {}'.format(sc.version))

if type(sqlContext) != pyspark.sql.context.HiveContext:
    print('reset the Spark SQL context')


def print_bytes(filename):
    print('{} has {:,} bytes'.format(filename, os.path.getsize(filename)))


!du -ch --exclude=test_parquet

Spark verion is 1.4.0
prdsale.sas7bdat has 148,480 bytes
prdsal2.sas7bdat has 2,790,400 bytes
prdsal3.sas7bdat has 1,401,856 bytes
4.2M    .
4.2M    total

1. Test DataFrame in Python and Spark

First I transform a SAS sas7bdat file to a pandas DataFrame. The great thing in Spark is that a Python/pandas DataFrame could be translated to Spark DataFrame by the createDataFrame method. Now I have two DataFrames: one is a pandas DataFrame and the other is a Spark DataFrame.
with sas7bdat.SAS7BDAT('prdsale.sas7bdat') as f:
     pandas_df = f.to_data_frame()
print('-----Data in Pandas dataframe-----')

print('-----Data in Spark dataframe-----')
spark_df = sqlContext.createDataFrame(pandas_df)

-----Data in Pandas dataframe-----
0     925  CANADA  EDUCATION  12054      850  FURNITURE    SOFA        1   
1     999  CANADA  EDUCATION  12085      297  FURNITURE    SOFA        1   
2     608  CANADA  EDUCATION  12113      846  FURNITURE    SOFA        1   
3     642  CANADA  EDUCATION  12144      533  FURNITURE    SOFA        2   
4     656  CANADA  EDUCATION  12174      646  FURNITURE    SOFA        2   

0   EAST  1993  
1   EAST  1993  
2   EAST  1993  
3   EAST  1993  
4   EAST  1993  
-----Data in Spark dataframe-----
| 925.0| CANADA|EDUCATION|12054.0|  850.0|FURNITURE|   SOFA|    1.0|  EAST|1993.0|
| 999.0| CANADA|EDUCATION|12085.0|  297.0|FURNITURE|   SOFA|    1.0|  EAST|1993.0|
| 608.0| CANADA|EDUCATION|12113.0|  846.0|FURNITURE|   SOFA|    1.0|  EAST|1993.0|
| 642.0| CANADA|EDUCATION|12144.0|  533.0|FURNITURE|   SOFA|    2.0|  EAST|1993.0|
| 656.0| CANADA|EDUCATION|12174.0|  646.0|FURNITURE|   SOFA|    2.0|  EAST|1993.0|
The two should be the identical length. Here both show 1,440 rows.


2. Automate the transformation

I write a pipeline function to automate the transformation. As the result, the all three SAS files are saved to the same directory as Parquet format.
def sas_to_parquet(filelist, destination):
    """Save SAS file to parquet
        filelist (list): the list of sas file names
        destination (str): the path for parquet
    rows = 0
    for i, filename in enumerate(filelist):
        with sas7bdat.SAS7BDAT(filename) as f:
            pandas_df = f.to_data_frame()
            rows += len(pandas_df)
        spark_df = sqlContext.createDataFrame(pandas_df)"{0}/key={1}".format(destination, i), "parquet")
    print('{0} rows have been transformed'.format(rows))

sasfiles = [x for x in os.listdir('.') if x[-9:] == '.sas7bdat']

sas_to_parquet(sasfiles, '/root/playground/test_parquet')

['prdsale.sas7bdat', 'prdsal2.sas7bdat', 'prdsal3.sas7bdat']
36000 rows has been transformed
Then I read from the newly created Parquet data store. The query shows that the data has been successfully saved.
df = sqlContext.load("/root/playground/test_parquet", "parquet")
df.filter(df.key == 0).show(5)

| 925.0| CANADA|  null|null|12054.0|  850.0|FURNITURE|   SOFA|    1.0| null|1993.0| null|EDUCATION|  EAST|  0|
| 999.0| CANADA|  null|null|12085.0|  297.0|FURNITURE|   SOFA|    1.0| null|1993.0| null|EDUCATION|  EAST|  0|
| 608.0| CANADA|  null|null|12113.0|  846.0|FURNITURE|   SOFA|    1.0| null|1993.0| null|EDUCATION|  EAST|  0|
| 642.0| CANADA|  null|null|12144.0|  533.0|FURNITURE|   SOFA|    2.0| null|1993.0| null|EDUCATION|  EAST|  0|
| 656.0| CANADA|  null|null|12174.0|  646.0|FURNITURE|   SOFA|    2.0| null|1993.0| null|EDUCATION|  EAST|  0|

3. Conclusion

There are multiple advantages to tranform data from various sources to Parquet.
  1. It is an open format that could be read and written by major softwares.
  2. It could be well distributed to HDFS.
  3. It compresses data.
For example, the original SAS files add up to 4.2 megabyte. Now as Parquet, it only weighs 292KB and achieves 14X compression ratio.
!du -ahc 

4.0K    ./key=2/._metadata.crc
4.0K    ./key=2/._SUCCESS.crc
0    ./key=2/_SUCCESS
4.0K    ./key=2/_common_metadata
4.0K    ./key=2/.part-r-00001.gz.parquet.crc
4.0K    ./key=2/._common_metadata.crc
4.0K    ./key=2/_metadata
60K    ./key=2/part-r-00001.gz.parquet
88K    ./key=2
4.0K    ./key=0/._metadata.crc
4.0K    ./key=0/._SUCCESS.crc
0    ./key=0/_SUCCESS
4.0K    ./key=0/_common_metadata
4.0K    ./key=0/.part-r-00001.gz.parquet.crc
4.0K    ./key=0/._common_metadata.crc
4.0K    ./key=0/_metadata
12K    ./key=0/part-r-00001.gz.parquet
40K    ./key=0
4.0K    ./key=1/._metadata.crc
4.0K    ./key=1/._SUCCESS.crc
0    ./key=1/_SUCCESS
4.0K    ./key=1/_common_metadata
4.0K    ./key=1/.part-r-00001.gz.parquet.crc
4.0K    ./key=1/._common_metadata.crc
4.0K    ./key=1/_metadata
132K    ./key=1/part-r-00001.gz.parquet
160K    ./key=1
292K    .
292K    total
A bar plot visualizes the signifcant size difference between the two formats. It shows an order of magnitude space deduction.
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
index = np.arange(2)
bar_width = 0.35
data = [4200, 292]
header = ['SAS files', 'Parquet'], data)
plt.grid(b=True, which='major', axis='y')
plt.ylabel('File Size by KB')
plt.xticks(index + bar_width, header)

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