Spark Modularized View (SMV)

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Spark Modularized View enables users to build enterprise scale applications on Apache Spark platform.

SMV Quickstart

Installation

Pip Install

SMV is now distributed as a package on PyPi. It comes in two flavors -- with and without a dependnecy on pyspark. The first is for consumers who might be installing to a machine outside of a cluster that does not already have pyspark installed, while the second is targeted for those installing to a gateway machine in a cluster that already has Spark available in the environment.

Without Pyspark

pip install smv

With Pyspark

pip install smv[pyspark]

Docker

We avidly recommend using Docker to install SMV. Using Docker, start an SMV container with

docker run -it --rm tresamigos/smv

If Docker is not an option on your system, see the installation guide.

Create Example App

SMV provides a shell script to easily create template applications. We will use a simple example app to explore SMV.

$ smv-init -s MyApp

Run Example App

Run the entire application with

$ smv-run --run-app

This command must be run from the root of the project.

The output csv file and schema can be found in the data/output directory. Note that 'XXXXXXXX' here substitutes for a number which is like the version of the module.

$ cat data/output/stage1.employment.EmploymentByState_XXXXXXXX.csv/part-* | head -5
"50",245058
"51",2933665
"53",2310426
"54",531834
"55",2325877

$ cat data/output/stage1.employment.EmploymentByState_XXXXXXXX.schema/part-*
@delimiter = ,
@has-header = false
@quote-char = "
ST: String[,_SmvStrNull_]
EMP: Long

Edit Example App

The EmploymentByState module is defined in src/python/stage1/employment.py:

class EmploymentByState(SmvModule, SmvOutput):
    """Python ETL Example: employment by state"""

    def requiresDS(self):
        return [inputdata.Employment]

    def run(self, i):
        df = i[inputdata.Employment]
        df1 = df.groupBy(col("ST")).agg(sum(col("EMP")).alias("EMP"))
        return df1

The run method of a module defines the operations needed to get the output based on the input. We would like to filter the table based on if each row's state is greater or less than 1,000,000. To accomplish this, we need to add a filter to the run method:

  def run(self, i):
      df = i[inputdata.Employment]
      df1 = df.groupBy(col("ST")).agg(sum(col("EMP")).alias("EMP"))
      df2 = df1.filter((col("EMP") > lit(1000000)))
      return df2

Now run the module again with

smv-run --run-app

(make sure you run this from the from the root of the project)

Inspect the new output to see the changes.

$ cat data/output/stage1.employment.EmploymentByState_XXXXXXXX.csv/part-* | head -5
"51",2933665
"53",2310426
"55",2325877
"01",1501148
"04",2027240

$ cat data/output/stage1.employment.EmploymentByState_XXXXXXXX.schema/part-*
@delimiter = ,
@has-header = false
@quote-char = "
ST: String[,_SmvStrNull_]
EMP: Long

Publish to Hive Table

If you would like to publish your module to a hive table, add a tableName method to EmploymentByState. It should return the name of the Hive table as a string.

class EmploymentByState(SmvModule, SmvOutput):
    ...
    def tableName(self):
        return "myTableName"
    def requiresDS(self): ...
    def run(self, i): ...

Then use

$ smv-run --publish-hive -m stage1.employment.EmploymentByState

smv-pyshell

We can also view the results in the smv-pyshell. To start the shell, run

$ smv-pyshell

To get the DataFrame of EmploymentByState,

>>> x = df('stage1.employment.EmploymentByState')

To peek at the first row of results,

>>> x.peek(1)
ST:String            = 50
EMP:Long             = 245058
cat_high_emp:Boolean = false

See the user guide for further examples and documentation.

Contributions

Please see SMV Development Best Practices.