Spark with Python
Apache Spark is one of the hottest new trends in the technology domain. It is the framework with probably the highest potential to realize the fruit of the marriage between Big Data and Machine Learning. It runs fast (up to x faster than traditional Hadoop MapReduce due to in-memory operation, offers robust, distributed, fault-tolerant data objects (called RDD), and integrates beautifully with the world of machine learning and graph analytics through supplementary packages like Mlib and GraphX.
Spark is implemented on Hadoop/HDFS and written mostly in Scala, a functional programming language, similar to Java. In fact, Scala needs the latest Java installation on your system and runs on JVM. However, for most of the beginners, Scala is not a language that they learn first to venture into the world of data science. Fortunately, Spark provides a wonderful Python integration, called PySpark, which lets Python programmers to interface with the Spark framework and learn how to manipulate data at scale and work with objects and algorithms over a distributed file system.
RDD and basics
Setting up Apache Spark with Python 3 and Jupyter notebook
Unlike most Python libraries, getting PySpark to start working properly is not as straightforward as and Most of us with Python-based data science and Jupyter/IPython background take this workflow as granted for all popular Python packages. We tend to just head over to our CMD or BASH shell, type the pip install command, launch a Jupyter notebook and import the library to start practicing.
But, PySpark+Jupyter combo needs a little bit more love :-)
Check which version of Python is running. Python + is needed.
Install pip3 (or pip for Python3)
Install Jupyter for Python3
Augment the PATH variable to launch Jupyter notebook
Java 8 is shown to work with UBUNTU LTS/SPARKBIN-HADOOP
Set Java related PATH variables
Install py4j for Python-Java integration
Download latest Apache Spark (with pre-built Hadoop) from Apache download server. Unpack Apache Spark after downloading
Set variables to launch PySpark with Python3 and enable it to be called from Jupyter notebook. Add all the following lines to the end of your .bashrc file
Resilient Distributed Datasets (RDD) is a fundamental data structure of Spark. It is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster. RDDs can contain any type of Python, Java, or Scala objects, including user-defined classes.
Spark makes use of the concept of RDD to achieve faster and efficient MapReduce operations.
Formally, an RDD is a read-only, partitioned collection of records. RDDs can be created through deterministic operations on either data on stable storage or other RDDs. RDD is a fault-tolerant collection of elements that can be operated on in parallel.
There are two ways to create RDDs,
- parallelizing an existing collection in your driver program,
- referencing a dataset in an external storage system, such as a shared file system, HDFS, HBase, or any data source offering a Hadoop Input Format.
Basics of the
In Apache Spark, a DataFrame is a distributed collection of rows under named columns. It is conceptually equivalent to a table in a relational database, an Excel sheet with Column headers, or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs. It also shares some common characteristics with RDD:
- Immutable in nature : We can create DataFrame / RDD once but can’t change it. And we can transform a DataFrame / RDD after applying transformations.
- Lazy Evaluations: Which means that a task is not executed until an action is performed.
- Distributed: RDD and DataFrame both are distributed in nature.
Advantages of the Dataframe
- DataFrames are designed for processing large collection of structured or semi-structured data.
- Observations in Spark DataFrame are organised under named columns, which helps Apache Spark to understand the schema of a DataFrame. This helps Spark optimize execution plan on these queries.
- DataFrame in Apache Spark has the ability to handle petabytes of data.
- DataFrame has a support for wide range of data format and sources.
- It has API support for different languages like Python, R, Scala, Java.
Spark SQL provides a DataFrame API that can perform relational operations on both external data sources and Spark's built-in distributed collections—at scale!
To support a wide variety of diverse data sources and algorithms in Big Data, Spark SQL introduces a novel extensible optimizer called Catalyst, which makes it easy to add data sources, optimization rules, and data types for advanced analytics such as machine learning. Essentially, Spark SQL leverages the power of Spark to perform distributed, robust, in-memory computations at massive scale on Big Data.
Spark SQL provides state-of-the-art SQL performance and also maintains compatibility with all existing structures and components supported by Apache Hive (a popular Big Data warehouse framework) including data formats, user-defined functions (UDFs), and the metastore. Besides this, it also helps in ingesting a wide variety of data formats from Big Data sources and enterprise data warehouses like JSON, Hive, Parquet, and so on, and performing a combination of relational and procedural operations for more complex, advanced analytics.
Speed of Spark SQL
Spark SQL has been shown to be extremely fast, even comparable to C++ based engines such as Impala.
Following graph shows a nice benchmark result of DataFrames vs. RDDs in different languages, which gives an interesting perspective on how optimized DataFrames can be.
Why is Spark SQL so fast and optimized? The reason is because of a new extensible optimizer, Catalyst, based on functional programming constructs in Scala.
Catalyst's extensible design has two purposes.
- Makes it easy to add new optimization techniques and features to Spark SQL, especially to tackle diverse problems around Big Data, semi-structured data, and advanced analytics
- Ease of being able to extend the optimizer—for example, by adding data source-specific rules that can push filtering or aggregation into external storage systems or support for new data types
PySpark Example Project
This document is designed to be read in parallel with the code in the repository. Together, these constitute what we consider to be a 'best practices' approach to writing ETL jobs using Apache Spark and its Python ('PySpark') APIs. This project addresses the following topics:
- how to structure ETL code in such a way that it can be easily tested and debugged;
- how to pass configuration parameters to a PySpark job;
- how to handle dependencies on other modules and packages; and,
- what constitutes a 'meaningful' test for an ETL job.
ETL Project Structure
The basic project structure is as follows:
The main Python module containing the ETL job (which will be sent to the Spark cluster), is . Any external configuration parameters required by are stored in JSON format in . Additional modules that support this job can be kept in the folder (more on this later). In the project's root we include , which is a bash script for building these dependencies into a zip-file to be sent to the cluster (). Unit test modules are kept in the folder and small chunks of representative input and output data, to be used with the tests, are kept in folder.
Structure of an ETL Job
In order to facilitate easy debugging and testing, we recommend that the 'Transformation' step be isolated from the 'Extract' and 'Load' steps, into its own function - taking input data arguments in the form of DataFrames and returning the transformed data as a single DataFrame. Then, the code that surrounds the use of the transformation function in the job function, is concerned with Extracting the data, passing it to the transformation function and then Loading (or writing) the results to their ultimate destination. Testing is simplified, as mock or test data can be passed to the transformation function and the results explicitly verified, which would not be possible if all of the ETL code resided in and referenced production data sources and destinations.
More generally, transformation functions should be designed to be idempotent. This is a technical way of saying that the repeated application of the transformation function should have no impact on the fundamental state of output data, until the moment the input data changes. One of the key advantages of idempotent ETL jobs, is that they can be set to run repeatedly (e.g. by using to trigger the command above, on a pre-defined schedule), rather than having to factor-in potential dependencies on other ETL jobs completing successfully.
Passing Configuration Parameters to the ETL Job
Although it is possible to pass arguments to , as you would for any generic Python module running as a 'main' program - by specifying them after the module's filename and then parsing these command line arguments - this can get very complicated, very quickly, especially when there are lot of parameters (e.g. credentials for multiple databases, table names, SQL snippets, etc.). This also makes debugging the code from within a Python interpreter extremely awkward, as you don't have access to the command line arguments that would ordinarily be passed to the code, when calling it from the command line.
A much more effective solution is to send Spark a separate file - e.g. using the flag with - containing the configuration in JSON format, which can be parsed into a Python dictionary in one line of code with . Testing the code from within a Python interactive console session is also greatly simplified, as all one has to do to access configuration parameters for testing, is to copy and paste the contents of the file - e.g.,
For the exact details of how the configuration file is located, opened and parsed, please see the function in (also discussed further below), which in addition to parsing the configuration file sent to Spark (and returning it as a Python dictionary), also launches the Spark driver program (the application) on the cluster and retrieves the Spark logger at the same time.
Packaging ETL Job Dependencies
In this project, functions that can be used across different ETL jobs are kept in a module called and referenced in specific job modules using, for example,
This package, together with any additional dependencies referenced within it, must be copied to each Spark node for all jobs that use to run. This can be achieved in one of several ways:
- send all dependencies as a archive together with the job, using with Spark submit;
- formally package and upload to somewhere like the archive (or a private version) and then run on each node; or,
- a combination of manually copying new modules (e.g. ) to the Python path of each node and using for additional dependencies (e.g. for ).
Option (1) is by far the easiest and most flexible approach, so we will make use of this for now. To make this task easier, especially when modules such as have additional dependencies (e.g. the package), we have provided the bash script for automating the production of , given a list of dependencies documented in and managed by the python application (discussed below).
Running the ETL job
Assuming that the environment variable points to your local Spark installation folder, then the ETL job can be run from the project's root directory using the following command from the terminal,
Briefly, the options supplied serve the following purposes:
- - the address of the Spark cluster to start the job on. If you have a Spark cluster in operation (either in single-executor mode locally, or something larger in the cloud) and want to send the job there, then modify this with the appropriate Spark IP - e.g. ;
- - Maven coordinates for any JAR dependencies required by the job (e.g. JDBC driver for connecting to a relational database);
- - the (optional) path to any config file that may be required by the ETL job;
- - archive containing Python dependencies (modules) referenced by the job; and,
- - the Python module file containing the ETL job to execute.
Full details of all possible options can be found here. Note, that we have left some options to be defined within the job (which is actually a Spark application) - e.g. and are defined in the Python script as it is felt that the job should explicitly contain the requests for the required cluster resources.
Debugging Spark Jobs Using
It is not practical to test and debug Spark jobs by sending them to a cluster using and examining stack traces for clues on what went wrong. A more productive workflow is to use an interactive console session (e.g. IPython) or a debugger (e.g. the package in the Python standard library or the Python debugger in Visual Studio Code). In practice, however, it can be hard to test and debug Spark jobs in this way, as they implicitly rely on arguments that are sent to , which are not available in a console or debug session.
We wrote the function - found in - to facilitate the development of Spark jobs that are aware of the context in which they are being executed - i.e. as jobs or within an IPython console, etc. The expected location of the Spark and job configuration parameters required by the job, is contingent on which execution context has been detected. The docstring for gives the precise details,
For example, the following code snippet,
Will use the arguments provided to to setup the Spark job if executed from an interactive console session or debugger, but will look for the same arguments sent via if that is how the job has been executed.
In order to test with Spark, we use the Python package, which is bundled with the Spark JARs required to programmatically start-up and tear-down a local Spark instance, on a per-test-suite basis (we recommend using the and methods in to do this once per test-suite). Note, that using to run Spark is an alternative way of developing with Spark as opposed to using the PySpark shell or .
Given that we have chosen to structure our ETL jobs in such a way as to isolate the 'Transformation' step into its own function (see 'Structure of an ETL job' above), we are free to feed it a small slice of 'real-world' production data that has been persisted locally - e.g. in or some easily accessible network directory - and check it against known results (e.g. computed manually or interactively within a Python interactive console session).
To execute the example unit test for this project run,
If you're wondering what the command is, then read the next section.
Managing Project Dependencies using Pipenv
We use pipenv for managing project dependencies and Python environments (i.e. virtual environments). All direct packages dependencies (e.g. NumPy may be used in a User Defined Function), as well as all the packages used during development (e.g. PySpark, flake8 for code linting, IPython for interactive console sessions, etc.), are described in the . Their precise downstream dependencies are described in .
To get started with Pipenv, first of all download it - assuming that there is a global version of Python available on your system and on the PATH, then this can be achieved by running the following command,
Pipenv is also available to install from many non-Python package managers. For example, on OS X it can be installed using the Homebrew package manager, with the following terminal command,
For more information, including advanced configuration options, see the official pipenv documentation.
Installing this Projects' Dependencies
Make sure that you're in the project's root directory (the same one in which the resides), and then run,
This will install all of the direct project dependencies as well as the development dependencies (the latter a consequence of the flag).
Running Python and IPython from the Project's Virtual Environment
In order to continue development in a Python environment that precisely mimics the one the project was initially developed with, use Pipenv from the command line as follows,
The command could just as well be , for example,
This will fire-up an IPython console session where the default Python 3 kernel includes all of the direct and development project dependencies - this is our preference.
Prepending to every command you want to run within the context of your Pipenv-managed virtual environment can get very tedious. This can be avoided by entering into a Pipenv-managed shell,
This is equivalent to 'activating' the virtual environment; any command will now be executed within the virtual environment. Use to leave the shell session.
Automatic Loading of Environment Variables
Pipenv will automatically pick-up and load any environment variables declared in the file, located in the package's root directory. For example, adding,
Will enable access to these variables within any Python program -e.g. via a call to . Note, that if any security credentials are placed here, then this file must be removed from source control - i.e. add to the file to prevent potential security risks.
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Example github pyspark
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