Target audience: Intermediate
Estimated reading time: 5'
Ever grappled with piecing together a data pipeline from diverse, sometimes mismatched components or processes? Apache Airflow might just be the remedy you have been looking for!
This article delves into the orchestration design and implementation of a pipeline geared towards testing an Apache Spark application crafted in Scala.
Table of contents
Notes:
- In order to demonstrate the flexibility of Airflow orchestration capability, our workflow combines process implemented in Python, Bash and Spark/Scala.
- The implementation of the Spark application used in the article is included in the Appendix for reference.
- Environments: Python 3.10, Apache Airflow 2.7.0, Apache Spark 3.3.1, Scala 2.13.2
- To enhance the readability of the algorithm implementations, we have omitted non-essential code elements like error checking, comments, exceptions, validation of class and method arguments, scoping qualifiers, and import statements.
Introduction
Organizations often develop and implement scripts in multiple languages (like Batch, SQL, Python) to handle data pipelines. This approach can rapidly become unmanageable. Using an orchestration tool like Airflow, data and MLOps engineers can unify these scripts into one integrated platform.
Airflow is an orchestration platform that support modular architecture, dynamic pipeline generation, editable execution graph (DAG), customizable and reusable tasks [ref 1].
Key concepts
Airflow's design was influenced by very specific considerations, specifically:
- Purpose: Airflow serves as a platform designed for the programmatic creation, scheduling, and monitoring of workflows. With its ability to define workflows via Python code, it offers significant flexibility and adaptability.
- Components: The core components of Airflow include a visualization web server, a scheduler that oversees job execution based on triggers or schedules, and workers responsible for task execution.
- Use cases: Typical applications of Airflow encompass ETL (Extract, Transform, Load) processes, data analysis workflows, and regularly occurring tasks.
- Execution graph: Workflows in Airflow are framed using Directed Acyclic Graphs (DAG). Within these DAGs, each node denotes a task, while the connecting edges symbolize task dependencies.
The implementation and resources for Apache Airflow framework is available on Github [ref 2].
Setup
Here is the steps required to install and deploy Airflow using a simple Bash script:
export AIRFLOW_HOME=~/airflow
AIRFLOW_VERSION=2.7.0
PYTHON_VERSION="$(python --version | cut -d " " -f 2 | cut -d "." -f 1-2)"
CONSTRAINT_URL=https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_ VERSION}/constraints-${PYTHON_VERSION}.txt
pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL"}
airflow standalone
Notes:
- Airflow uses SQLite database by default. For production deployment uses MySQL or PostgreSQL database (not SQLite) and do not use SequentialExecutor.
- Default users and password are defined in $AIRFLOW_HOME/standalone_admin_passwords.txt
Operations
Start airflow: airflow webserver –port 8082
Stop airflow: Ctrl-C inside the terminal running airflow
pkill -f “airflow-scheduler”
Stop airflow: Ctrl-C inside the terminal running airflow
pkill -f “airflow-scheduler”
Testing a task: airflow tasks test dag_id task_id start_date
Example: airflow tasks test spark_eval_v1 spark_job 2023-10-09
Use case
The goal is to create and orchestrate a pipeline for testing a Spark application that cleanses an employee dataset of anomalies before feeding it into a deep learning model.
Each 'Employee' entry is characterized by a 'name', a 'role' within the company (e.g., engineer), a 'score' (rating) from their most recent performance evaluation, and 'tenure' represented as the number of years they've been with the organization.
The objective is to design and manage a pipeline to test a Spark application: cleanse a data set of employees from outliers prior to input to a deep learning model.
An instance of Employee is defined by a name, a role in the organization (i.e. engineer), a score from his/her last performance review and tenure as number of years in the organization.
valid_employee_role = ['intern', 'engineer', 'manager', 'director', 'VP']
"""
Data class for employee record
"""
@dataclass
class Employee:
name: AnyStr
role: AnyStr
score: int
tenure: floatThe three orchestration tasks are
- Generate employee records with random attributes for position, rating and seniority to the stored on HDFS (generate_to_hdfs)
- Load records from HDFS (load_from_hdfs)
- Remove outliers records using Apache Spark defined by condition: Valid role, 0 < score <= 10 and 0.0 < tenure < 50.0 (spark_job)
Airflow operators for Spark application
Orchestration
There are 3 components in creating an Airflow orchestration:
- Define the directed acyclic graph (DAG) for execution with the appropriate scheduling, retries,... arguments
- Invoke the appropriate operators which define each of the 3 tasks
- Specify the order of tasks in the workflow
We reference the following Python code of an Airflow DAG for our scenario to highlight these three elements. This source code serves as the basis for our discussion in this section.
from airflow.decorators import task
from airflow import DAG
from datetime import datetime, timedelta
from typing import List, Optional
from airflow.operators.bash import BashOperator
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
import logging
# Step 1: Define the DAG with appropriate arguments
# Step 2: Create the 3 operators
with dag:
"""
Task 1 generates randomized employees
"""
loaded = BashOperator(
task_id='generate_to_hdfs',
bash_command=f'python3 {data_source_executor} generate'
)
"""
Task 2 loads the randomly generated data from HDFS
"""
employees = BashOperator(
task_id='load_from_hdfs',
bash_command=f'python3 {data_source_executor} load'
)
"""
Task 3 Launch the Spark job to process
"""
spark_job = SparkSubmitOperator(
task_id='spark_job',
conn_id='spark_default',
application=spark_jar_path, java_class='org.batcheval.Cleanser', conf={ # To be loaded from file 'master: 'local[8]', 'spark.driver.memory': '18g',
'spark.executor.memory': '24g',
'spark.default.parallelism': '16',
'spark.local.dir': 'temp',
'spark.network.timeout': '2100',
'spark.driver.maxResultSize': '524288',
'spark.driver.cores': 4,
'spark.executor.cores': 3
},
application_args=[file_name]
)
# Step 3: Specify the workflow loaded >> employees >> spark_job
Defining the DAG
The first step is to define the execution graph. The DAG is a data pipeline crafted in Python. Every DAG depicts a series of tasks structured as a graph. The inherent characteristics of DAGs make them ideal for constructing data pipelines:
- Directed: When there are several tasks, each task should have a designated upstream or downstream connection.
- Acyclic: Tasks should not depend on themselves, ensuring there are no never-ending loops.
- Graph: All tasks can be graphically represented, with the interconnections between them indicated by nodes and edges.
Creating the operators
An operator is a conceptual wrapper around Python code crafted to execute a designated action, represented as a function with parameters. Every operator in your DAG code equates to a single Airflow task.
Operators can be categorized into three types:
Operators can be categorized into three types:
- Action operators: These initiate or set off specific actions, such as BashOperator, PythonOperator, or TriggerDagRunOperator.
- Transfer operators: These are stateful and handle data migration from one spot to another.
- Sensors: Derived from BaseSensorOperator, these operators pause execution until a certain condition or operation concludes.
The first two operators (type BatchOperator), loaded and employees, wraps the batch command line execution python data_source generate and python data_source load.
The actual data processing is performed through the Airflow Spark submit operator (SparkSubmitOperator). In this scenario, you need to setup the development environment in order to execute the operator as follow:
- Build a Spark jar file.
- Install apache-airflow-providers-apache-spark package.
- Implement the SparkSubmitOperator [See below]
- Configure the Spark connection in Airflow. UI/Admin/Connections, id=spark_default, type: Spark, host/port
The operator, spark_job, defines the minimum set of configuration parameters to execute a Spark submit implemented in Scala [ref 4]:
- application: Path to the Jar file
- java_class: Name of the class (Scala in our case) invoked by Spark submit
- conf: Spark dynamically loaded properties defined in SparkConf or provided as part of the Spark submit command line arguments.
- application_args: List of application specific arguments to the Spark submit command line.
Specifying the workflow
The final stage is to define the dependencies between the various tasks. This is accomplished very simply by using the self-explainable overloaded >> operator.
Tasks implementation
The Python functions triggered by the operators are provided to showcase how the Airflow operators work. However, they aren't necessary for grasping the Airflow framework itself.
Data generator
It relies on a random generator to generate the fields of each employee record then save the 'records' onto a file.
class DataOperator(object):
def __init__(self, input_file: AnyStr):
self.input_file = input_file
@staticmethod
def generate_and_save(this_file_name: AnyStr, num_entries: int) -> bool: """
Generate employee records with random attributes (score, tenure,...) :param this_file_name HDFS file name, the records are stored :param num_entries Number of records to generate :return True if the records are correctly generated and save, False otherwise
""" try:
records = [f'emp_{i},{valid_employee_role[i % len(valid_employee_role)]},{int(random.uniform(1, 10))},{random.uniform(0, 25):.1f}'
for i in range(num_entries)]
header = 'name,role,score,tenure\n'
content = header + '\n'.join(records)
# State the records with open(this_file_name, 'w') as f:
f.write(content)
return True
except Exception as e:
task_logger.error(f'generate and save failed {str(e)}')
return False
Data loader
This Python method loads the records from the CSV file then generate the list employees. It returns an empty list in case of exception.
def load(self) -> List[Employee]:
try: # Load the records from a CSV file with open(self.input_file, 'rt') as f:
csv_reader = csv.reader(f, delimiter=',', quotechar='\'')
# Build the list of employee records to be cleansed. employee_list = []
for idx, attributes in enumerate(csv_reader):
if idx > 0:
employee_list.append(DataOperator.__get_employee(attributes))
return employee_list
except Exception as e:
task_logger.error(f'Loader failed: {str(e)}') return []
@staticmethod
def __get_employee(fields: List[AnyStr]) -> Employee:
return Employee(
fields[0], # Name of employee
fields[1], # Role of employee in the organization
int(fields[2]), # Score of the employee from performance review
float(fields[3]) # Tenure or seniority of the employee
)
Data cleanser
The outlier elimination is carried out by the Spark cluster, which is written in Scala. Its primary and straightforward role is to apply the filter outlined in the Use case section. While the Scala code for this task isn't crucial for grasping the Airflow orchestration aspects, it's provided in the Appendix for reference.
Airflow best practices
To ensure Airflow doesn't overconsume computational resources, adhere to the following guidelines:
- View Airflow as a task orchestrator, not an execution platform. Delegate heavy processing to execution frameworks and maximize the use of ETL processes.
- Refrain from importing large datasets directly into a task.
- Segment intricate tasks (or elaborate Python functions encapsulated by the PythonOperator) into more manageable units to simplify debugging.
- Utilize template variables using the Jinja template format {{...}}.
- Maintain a one-to-one ratio of DAGs to Python files.
- Steer clear of top-level code in your DAG files as it triggers execution during each DAG parse.
Note: DAG code undergoes parsing/analysis every 'min_file_process_interval' (set to 30 seconds by default).
Conclusion
The article's example understates Airflow's capabilities. In practical scenarios, more complex orchestration is necessary, involving Spark, Kafka, NoSQL databases, and deep learning models developed in different programming languages.
Apache Airflow stands out as the perfect instrument for orchestrating Spark pipelines, whether they're written in Python, Scala, or Java. The prowess of Airflow operators in handling diverse services can spare software and MLOps engineers numerous development hours.
Several other data orchestration platforms are available, including:
- BMC's Control-M: This is a workflow orchestration solution that places a strong focus on adhering to Service Level Agreements [ref 5].
- Flyte: Originally created at Lyft, this is a scalable platform for automating workflows [ref 6].
- Y42: This platform offers a comprehensive solution for data orchestration, featuring an SQL design interface [ref 7].
- Shipyard App: A data orchestration tool emphasizing code reusability, package management, version control, and observability [ref 8].
Thank you for reading this article. For more information ...
References
[1] Apache Airflow
[3] Apache Spark
[5] BMC Control-M
[6] flyte.org
[7] y42.com
[8] Shipyard App
Appendix
A basic outlier removal function has been implemented in Spark using Scala. The validation function, termed 'validate', acts as a parameter for the 'OutlierRemoval' class. The primary method, named 'apply', retrieves the raw employee records from a CSV file named 'srcFilename', applies the specified filter, and then saves the results to another CSV file, called 'destFileName'.
final class OutlierRemoval(validate: Employee => Boolean)(implicit sparkSession: SparkSession){
def apply(srcFilename: String, destFilename: String): Unit = try {
import sparkSession.implicits._
// Load data from HDFS
val employeeDS = sparkSession.read.option("delimiter", ",").csv(srcFilename).map(Employee(_))
employeeDS.show(5) // Filter out the outlier
val filteredEmployeeDS = employeeDS.filter(validate(_))
filteredEmployeeDS.write.csv(destFilename)
}
catch {
case e: Exception => logger.error(s"ERROR: ${e.getMessage}")
}
}object OutlierRemoval { // Default constructor
def apply()(implicit sparkSession: SparkSession): OutlierRemoval = new OutlierRemoval(default_validate)
private val valid_employee_role = Set[String](
"intern", "engineer", "manager", "director", "VP"
)
private val default_validate = (employee: Employee) => {
valid_employee_role.contains(employee.role) && employee.score <= 10 &&
employee.score > 0 && employee.tenure > 0.0 && employee.tenure < 50.0
}
}
object Cleanser extends App{
// Initialize the context implicit val sparkSession: SparkSession = { val conf = new SparkConf().setMaster("local[8]") SparkSession.builder() .appName("sparkjob") .config(conf) .getOrCreate() } val srcFilename = args(0) private val outputFilename = args(1) private val outlierRemoval = OutlierRemoval()
// Apply the filter to remove outliers
outlierRemoval(srcFilename, outputFilename)
// Cleanup sparkSession.close()
}
---------------------------
Patrick Nicolas has over 25 years of experience in software and data engineering, architecture design and end-to-end deployment and support with extensive knowledge in machine learning.
He has been director of data engineering at Aideo Technologies since 2017 and he is the author of "Scala for Machine Learning" Packt Publishing ISBN 978-1-78712-238-3
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