Big Data Programming Models

Introduction to Big Data Programming Models

• A Big Data programming model is a style of programming designed for parallel, distributed applications.
• The primary focus of Big Data programming models is high-performance parallel processing of large datasets.
• Characteristics of Big Data frameworks:
  • Fault tolerance
  • Scalability
  • Parallelism
• High latency is NOT a characteristic of Big Data frameworks.
• MapReduce serves as an example of a Big Data programming model.
• Fault tolerance ensures computations continue, even if a node fails.
• Distributed computing increases processing power using multiple nodes.
• Big Data programming models solve large-scale, data-intensive computations.
• Big Data frameworks handle parallelism by distributing computations across multiple nodes.
• Data locality is important to reduce data transfer latency.
• Handling structured and unstructured data is a significant challenge for Big Data programming models.
• Big Data programming emphasizes distributed and parallel processing, differentiating it from traditional programming.
• Low-latency execution makes Big Data programming models ideal for real-time data processing.
• Apache Hadoop is the standard framework for distributed computing.
• Parallel processing distributes tasks across multiple cores or machines.
• Load balancing prevents system crashes by evenly distributing workloads.

MapReduce Programming Model

• The two main functions are "Map" and "Reduce".
• The shuffle phase groups and sorts mapped results before reducing.
• Hadoop is an implementation of MapReduce.
• The primary data structure used is key-value pairs.
• Linear scaling with additional nodes is the main advantage of MapReduce.
• Java is the primary programming language used.
• The Map phase reads the input data.
• The Map step occurs first in a job execution.
• Fault tolerance is achieved via automatic re-execution of failed tasks.
• If a node fails, the task is re-executed on another node.
• Real-time processing is NOT a key characteristic.
• Large-scale log analysis is a real-world example.
• MapReduce is not suitable for iterative and real-time applications, which is a main limitation.
• The Reduce phase processes key-value pairs and combines results.
• Amazon EMR offers a solution based on MapReduce.

Functional Programming for Big Data

• Avoiding side effects and using immutable variables are the main principles.
• Spark follows the functional programming paradigm.
• A major benefit is enabling parallel execution with minimal side effects.
• A Map function is an example of a functional transformation in Spark.
• Referential transparency means function calls produce the same result with the same inputs.
• Higher-order functions take functions as input or return functions as output.
• Spark uses Resilient Distributed Datasets (RDDs) for functional transformations.
• The reduce() function aggregates elements in a dataset.
• Tail recursion is when the recursive call is the last operation.
• Scala is widely used for functional programming.
• Data modifications involve creating new data copies instead of modifying existing data.
• SparkRDD allows users to apply functional programming principles.
• Immutability prevents race conditions and side effects.
• The flatMap function maps elements and flattens nested structures.
• Functional programming avoids side effects by using pure functions.

SQL-Like Querying for Big Data

• The four basic SQL primitives are Create, Insert, Update, and Delete.
• SQL is a declarative language for querying structured data.
• SQL clauses specify conditions and structure statements.
• SQL is declarative and self-interpretable.
• Execution plans optimize query performance.
• JSON-SQL is NOT a variation of SQL.
• HiveQL uses Hadoop MapReduce as its execution backend.
• HiveQL lacks support for transactions and materialized views.
• Cassandra Query Language (CQL) is primarily used for querying and manipulating data in Apache Cassandra.
• Apache Impala is designed for high-performance analytics in Hadoop.
• Apache Drill executes schema-free SQL queries across multiple data sources.
• Spark SQL is a relational query engine for Apache Spark.
• Spark SQL supports SQL execution on streaming data.
• Presto supports federated queries across multiple data sources.
• Asterix Query Language (AQL) is based on a NoSQL-style data model.

Actor Model for Big Data

• The Actor Model is a programming model for concurrent computation.
• The universal primitive unit of computation is called an "Actor".
• Actors communicate by exchanging messages asynchronously.
• Key features are stateless and isolated actors.
• Failures are handled using a hierarchical supervision model.
• It enables high concurrency without shared state.
• Akka is based on the Actor Model.
• "Tell" (!)` sends a message asynchronously and doesn't expect a response.
• "Ask" (?) sends a message and waits for a response.
• Akka's supervision model prevents system crashes by isolating failures.
• When an actor receives a message, it processes the message and may create more actors.
• Actors operate independently and process messages asynchronously, making the Actor Model inherently concurrent.
• Storm uses the Actor Model for real-time data processing.
• A Spout in Apache Storm is a source that continuously generates or collects data.
• A Bolt in Apache Storm is a processing unit within a streaming flow.

Dataflow Programming for Big Data

• Data processing is modeled as a directed graph of operations.
• A key advantage is inherent trackable states during execution.
• Dataflow programming emphasizes modularization and task connections, differing it from traditional programming models.
• Apache Oozie exemplifies a Dataflow-based system.
• It provides control-logic-based modularization.
• Apache Oozie schedules and manages workflow execution in Hadoop.
• Dependencies are managed through data-driven execution flow.
• Tasks execute asynchronously based on data availability.
• Graph-based representations are commonly used.
• It efficiently handles dependencies and task execution, which makes this suitable for Big Data applications.
• Apache Oozie uses a directed acyclic graph (DAG) workflow.
• Higher programming complexity compared to SQL is a challenge.
• It benefits modularization by structuring applications as connected components.
• Workflow automation in Hadoop ecosystems is a real-world application of Dataflow models.
• It is harder to integrate compared to functional programming models is a key limitation.

Explore Big Data programming models designed for parallel, distributed applications. These models focus on high-performance parallel processing and offer fault tolerance and scalability. Data locality is important to reduce data transfer latency.