Building Dependency Trees and Parallel Processing in EMR Clusters: A Deeper Dive into HQL Files

Today, I want to discuss a performance-enhancing method for Hive Query Language (HQL) file execution on Amazon's Elastic MapReduce (EMR) clusters -

HQL allows us to interact with large datasets in a manner similar to SQL. As our data operations grow in complexity, handling multiple HQL files and their interdependencies can pose significant challenges. Imagine a situation where you have multiple HQL queries within a single HQL file, each with a varying level of dependency on the other - managing them can get tricky.

This blog post will discuss a Python-based approach to solve this problem: Splitting HQL files into individual queries, building a dependency tree, and parallelizing the query executions based on the dependencies. Let's get started!

Creating a Dependency Tree for Parallel Processing.

So, how can we optimize this? By creating a dependency tree for HQL queries and parallelly executing them based on these dependencies! We will cover two major steps:

1. Splitting HQL files into individual queries and building a dependency tree.
2. Parallelizing the query executions based on dependencies.

Step 1: Splitting HQL Files into Individual Queries

We need to first extract individual queries from HQL files. We'll use Python to open and read the HQL file, then split the content into separate queries. Note: comments and blank lines are filtered out.

def extract_queries(hql_file)
? ? with open(hql_file, 'r') as file:
? ? ? ? content = file.read()
? ? # Split by semicolon and filter out blank lines and comments
? ? queries = [query for query in content.split(';') if query and not query.startswith("--")]
? ? return queries        

Step 2: Build a Dependency Tree

Next, we build a dependency tree. The dependencies are defined by the tables a query operates on. For instance, if a query creates table 'c' from tables 'a' and 'b', it's dependent on the queries creating 'a' and 'b'. To create a Directed Acyclic Graph (DAG) representing these dependencies, we'll leverage the networkx library.

import networkx as nx


def build_dependency_tree(queries):
? ? G = nx.DiGraph()
? ? for query in queries:
? ? ? ? query_tables = extract_tables(query) # Function to extract tables from a query
? ? ? ? table_created = query_tables.pop(0) # The first table is the one being created
? ? ? ? for table in query_tables:
? ? ? ? ? ? G.add_edge(table, table_created) # The other tables are dependencies
? ? return G        

Step 3: Parallelize Query Executions Based on Dependencies

Now comes the fun part: parallelizing the query executions based on their dependencies. For this, we first sort the queries in the order they should be executed using a topological sort. Then, we'll execute them in parallel on an EMR cluster using the boto3 library (Amazon's AWS SDK for Python).

from concurrent.futures import ThreadPoolExecuto
import boto3


def execute_query(query, cluster_id, steps):
? ? client = boto3.client('emr')
? ? action = client.add_job_flow_steps(JobFlowId=cluster_id, Steps=steps)


def execute_queries_in_emr(G, cluster_id):
? ? order = list(nx.topological_sort(G))
? ? with ThreadPoolExecutor() as executor:
? ? ? ? for table in order:
? ? ? ? ? ? query = queries[table] # Fetch the corresponding query
? ? ? ? ? ? # Define step
? ? ? ? ? ? step = {
? ? ? ? ? ? ? ? 'Name': f'Executing {table}',
? ? ? ? ? ? ? ? 'ActionOnFailure': 'CONTINUE',
? ? ? ? ? ? ? ? 'HadoopJarStep': {
? ? ? ? ? ? ? ? ? ? 'Jar': 'command-runner.jar',
? ? ? ? ? ? ? ? ? ? 'Args': ["hive", "-e", query]
? ? ? ? ? ? ? ? }
? ? ? ? ? ? }
? ? ? ? ? ? # Execute the query on the EMR cluster
? ? ? ? ? ? executor.submit(execute_query, query, cluster_id, [step])
        

And that's it! By implementing this approach, you can handle multiple HQL queries within an HQL file with varying dependencies effectively. The parallel processing aspect helps maximize the utility of EMR clusters leading to more efficient operations.

Remember, big data management involves handling complex dependencies and volumes. This is just one way to simplify those tasks and improve overall performance. Always stay curious and keep exploring!

Thanks for reading, and please feel free to share your thoughts and comments.

#bigdata #Hadoop #Hive #EMR #python #parallelprocessing