Generating programming tips for dealing with large datasets

The tricks that work in a general programming context still apply for data science. Several might be worded slightly differently, but the principles are essentially the same for all programmers. This section recapitulates those tricks that are important in a data science context.
You can divide the general tricks into three parts, as shown in the figure mind map:

1. Don’t reinvent the wheel. Use tools and libraries developed by others.
2. Get the most out of your hardware. Your machine is never used to its full potential; with simple adaptions you can make it work harder.
3. Reduce the computing need. Slim down your memory and processing needs as much as possible.

 

 

1. Avoid duplicating existing efforts / Don’t reinvent the wheel

“Avoid repetition” is likely superior to “avoid repeating yourself.” Act in a way that adds significance and worth. Revisiting an issue that has previously been resolved is inefficient. As a data scientist, there are two fundamental principles that can enhance your productivity while working with enormous datasets:

• Harness the potential of databases. Most data scientists first choose to create their analytical base tables within a database when dealing with huge data sets. This strategy is effective for preparing straightforward features. Determine if user-defined functions and procedures may be utilized while using advanced modeling in this preparation. The last example in this chapter demonstrates how to include a database into your workflow.
• Utilize optimized libraries. Developing libraries such as Mahout, Weka, and other machine learning algorithms demands effort and expertise. The products are highly optimized and utilize best practices and cutting-edge technologies. Focus your attention on accomplishing tasks rather than duplicating or reiterating the labor of others, unless it is for the purpose of comprehending processes.

Then you must take into account your hardware constraints.

 

2. Get the most out of your hardware

Over-utilization of resources can slow down programs and cause them to fail.

Shifting workload from overtaxed to underutilized resources can be achieved using techniques.

1. Feeding CPU compressed data: Shift more work from hard disk to CPU to avoid CPU starvation.
2. Utilizing GPU: Switch to GPU for parallelize computations due to its higher throughput.
3. Using CUDA Packages: Use CUDA packages like PyCUDA for parallelization.
4. Using Multiple Threads: Parallelize computations on CPU using normal Python threads

3. Reduce the computing need

• Utilize a profiler to identify and remediate slow code parts.
• Use compiled code, especially when loops are involved, and functions from packages optimized for numerical computations.
• If a package is not available, compile the code yourself.
• Use computational libraries like LAPACK, BLAST, Intel MKL, and ATLAS for high performance.
• Avoid pulling data into memory when working with data that doesn't fit in memory.
• Use generators to avoid intermediate data storage by returning data per observation instead of in batches.
• Use as little data as possible if no large-scale algorithm is available.
• Use math skills to simplify calculations

General Techniques for handling large volumes of data

 The main obstacles encountered while dealing with enormous data include perpetual algorithms, memory overflow faults, and performance deficiencies.The solutions can be divided into three categories: using the correct algorithms, choosing the right data structure, and using the right tools.

 

 1. Selecting the appropriate algorithm

• Opting for the appropriate algorithm can resolve a greater number of issues than simply upgrading technology. 
• An algorithm optimized for processing extensive data can provide predictions without requiring the complete dataset to be loaded into memory. 
• The method should ideally allow parallelized computations. 
• Here I will explore three types of algorithms: online algorithms, block algorithms, and MapReduce algorithms.

a) Online Algorithms: 

Definition: These algorithms make decisions based on a limited and sequential stream of data, without knowledge of future inputs.

Applications: They are commonly used in scenarios where data arrives continuously, and decisions need to be made in real-time. Examples include: 

• Online scheduling algorithms for resource allocation in computer systems
• Spam filtering algorithms that classify incoming emails as spam or not spam as they arrive
• Online game playing algorithms that make decisions based on the current state of the game

b) Block Algorithms: 

Definition: These algorithms operate on fixed-size chunks of data, also known as blocks. Each block is processed independently, allowing for a degree of parallelization and improved efficiency when dealing with large datasets.

Applications: They are often used in scenarios where data is too large to be processed as a whole, but it can be efficiently divided into smaller, manageable parts. Examples include: 

• Sorting algorithms like the merge sort or quicksort that divide the data into sub-arrays for sorting
• Image processing tasks where image data can be divided into smaller blocks for individual filtering or manipulation
• Scientific computing problems where large datasets are processed in chunks to utilize parallel computing resource

c) MapReduce Algorithms: 

Definition: This is a programming framework specifically designed for processing large datasets in a distributed manner across multiple computers. It involves two key phases: 

Map: This phase takes individual data elements as input and processes them independently, generating intermediate key-value pairs.

Reduce: This phase aggregates the intermediate key-value pairs from the "Map" phase based on the key, performing a specific operation on the values for each unique key. 

Applications: MapReduce is widely used in big data analytics tasks, where massive datasets need to be processed and analyzed. Examples include:

• Log analysis: analyzing large log files from web servers to identify trends and patterns
• Sentiment analysis: analyzing large amounts of text data to understand the overall sentiment 
• Scientific data processing: analyzing large datasets from scientific experiments

2. Choosing the right data structure

Algorithms can make or break your program, but the way you store your data is of equal importance. Data structures have different storage requirements, but also influence the performance of CRUD (create, read, update, and delete) and other operations on the data set.

Below figure shows you have many different data structures to choose from, three of which we’ll discuss here: sparse data, tree data, and hash data. Let’s first have a look at sparse data sets.

These three terms represent different approaches to storing and organizing data, each with its own strengths and weaknesses:

1. Sparse Data:

• Definition: Sparse data refers to datasets where most of the values are empty or zero. This often occurs when dealing with high-dimensional data where most data points have values for only a few features out of many.
• Examples:
  • Customer purchase history: Most customers might not buy every available product, resulting in many zeros in the purchase matrix.
  • Text documents: Most words don't appear in every document, leading to sparse word-document matrices.
• Challenges:
  • Storing and processing sparse data using conventional methods can be inefficient due to wasted space for empty values.
  • Specialized techniques like sparse matrices or compressed representations are needed to optimize storage and processing.
• Applications:
  • Recommender systems: Analyzing sparse user-item interactions to recommend relevant products or content.
  • Natural language processing: Analyzing sparse word-document relationships for tasks like topic modeling or text classification.

2. Tree Data:

• Definition: Tree data structures represent data in a hierarchical manner, resembling an upside-down tree. Each node in the tree can have child nodes, forming parent-child relationships.
• Examples:
  • File systems: Files and folders are organized in hierarchical structures using tree data structures.
  • Biological taxonomies: Classification of species into kingdoms, phylum, class, etc., can be represented as a tree.
• Advantages:
  • Efficient for representing hierarchical relationships and performing search operations based on specific criteria.
  • Can be traversed in various ways (preorder, inorder, postorder) to access data in different orders.
• Disadvantages:
  • May not be suitable for all types of data, particularly non-hierarchical relationships.
  • Inserting and deleting nodes can be expensive operations in certain tree structures.

3. Hash Data:

• Definition: Hash data uses hash functions to map data elements (keys) to unique fixed-size values (hashes). These hashes are used for quick retrieval and identification of data within a larger dataset.
• Examples:
  • Hash tables: Used in dictionaries and associative arrays to quickly access data based on key-value pairs.
  • Username and password storage: Passwords are typically stored as hashed values for security reasons.
• Advantages:
  • Extremely fast for data lookup operations using the hash key.
  • Efficient for storing and retrieving data when quick access by a unique identifier is necessary.
• Disadvantages:
  • Hash collisions can occur when different keys map to the same hash value, requiring additional techniques to resolve conflicts.
  • Not suitable for maintaining order or performing comparisons between data elements.

3. Selecting the right tools

With the right class of algorithms and data structures in place, it’s time to choose the right tool for the job.

Essential Python Libraries for Big Data:

NumPy:

• Purpose: The foundation for scientific computing in Python, offering a powerful multidimensional array object (ndarray) for efficient numerical operations.
• Strengths:
  • Fast and efficient array operations (vectorized computations).
  • Linear algebra capabilities (matrix operations, eigenvalue decomposition, etc.).
  • Integration with other libraries like Pandas and SciPy.

2. Pandas:

• Purpose: A high-performance, easy-to-use data analysis and manipulation library built on top of NumPy.
• Strengths:
  • DataFrames (tabular data structures) for flexible and efficient data handling.
  • Time series functionality (date/time data manipulation).
  • Grouping and aggregation operations.
  • Data cleaning and wrangling capabilities.

3. Dask:

• Purpose: A parallel processing framework built on NumPy and Pandas, allowing you to scale computations across multiple cores or machines.
• Strengths:
  • Scalable parallel execution of NumPy and Pandas operations on large datasets.
  • Fault tolerance and efficient handling of data distribution.
  • Ability to use existing NumPy and Pandas code with minor modifications.

4.  SciPy:

• Purpose: A collection of algorithms and functions for scientific computing and technical computing, built on top of NumPy and often relying on NumPy arrays.
• Strengths:
  • Wide range of scientific functions (optimization, integration, interpolation, etc.).
  • Statistical analysis and modeling tools.
  • Signal and image processing capabilities.

5. Scikit-learn:

• Purpose: A comprehensive and user-friendly machine learning library offering a variety of algorithms and tools for classification, regression, clustering, dimensionality reduction, and more.
• Strengths:
  • Extensive collection of well-tested machine learning algorithms.
  • Easy-to-use API for building and evaluating models.
  • Scalability and efficiency for working with large datasets.

The problems we face when handling large data

 A large volume of data poses new challenges, such as overloaded memory and algorithms that never stops running. It forces you to adapt and expand your repertoire of techniques. But even when you can perform your analysis, you should take care of issues. such as I/O (input/output) and CPU hunger, as these might lead to performance problems.

Improving your code and using effective data structures can help reduce these issues. Moreover, exploring parallel processing or distributed computing might enhance performance when working with extensive datasets. 

Below figure shows a mind map that will gradually unfold as we go through the steps:

The “Problems” section outlines three issues that arise when dealing with large datasets:

1. Not Enough Memory: When a dataset surpasses the available RAM, the computer might not be able to handle all the data at once, causing errors .
2. Processes that Never End: Large datasets can lead to extremely long processing times, making it seem like the processes never terminate.
3. Bottlenecks: Processing large datasets can strain the computer’s resources. Certain components, like the CPU, might become overloaded while others remain idle. This is referred to as a bottleneck.

Now, I will provide a more details discussion on above problems

1. Not Enough Memory (RAM): 

• Random Access Memory (RAM) acts as the computer's short-term memory. When you work with a dataset, a portion of it is loaded into RAM for faster processing.
• If the dataset surpasses the available RAM, the computer might resort to using slower storage devices like hard disk drives (HDDs) to swap data in and out of memory as needed. This process, known as paging, significantly slows down operations because HDDs have much slower read/write speeds compared to RAM.
• In severe cases, exceeding RAM capacity can lead to program crashes or errors if the computer cannot allocate enough memory to handle the data.

 2. Processes that Never End (Long Processing Times): 

• Large datasets naturally take longer to process because the computer needs to perform operations on each data point. 
• This can include calculations, filtering, sorting, or any other manipulation required for your task.The processing time can become impractical for very large datasets, making it seem like the computer is stuck in an infinite loop. This can be frustrating and impede your workflow.

3. Bottlenecks (Resource Overload)

• When processing large datasets, the computer's central processing unit (CPU) is typically the most stressed component. The CPU is responsible for executing all the instructions required for data manipulation.
• If the CPU becomes overloaded, it can create a bottleneck, where other components like the graphics processing unit (GPU) or storage might be underutilized while waiting for the CPU to complete its tasks. This imbalance in resource usage hinders the overall processing speed.

These limitations can significantly impact the efficiency and feasibility of working with large datasets on a single computer. In extreme cases, it might become impossible to handle the data altogether due to memory constraints or excessively long processing times.

Bottlenecks (Resource Overload): When processing large datasets, the computer's central processing unit (CPU) is typically the most stressed component. The CPU is responsible for executing all the instructions required for data manipulation.

 How to overcome problems we face when handling large data?

Even though working with massive datasets on a single computer can be challenging, there are several strategies and techniques you can employ to overcome the limitations mentioned earlier:

1. Optimizing Memory Usage:

• Data Partitioning: Divide your large dataset into smaller, manageable chunks. Work on each chunk independently, reducing the overall memory footprint at any given time. Libraries like Pandas in Python offer functionalities for efficient data partitioning.
• Data Sampling: Instead of processing the entire dataset, consider selecting a representative subset (sample) that captures the essential characteristics of the whole data. This can be helpful for initial analysis or testing purposes without overloading the system.
• Data Type Optimization: Analyze your data and convert variables to appropriate data types that require less memory. For instance, storing integers as 16-bit values instead of 32-bit can significantly reduce memory usage.

2. Reducing Processing Time:

• Parallelization: Utilize multi-core processors available in most modern computers. Break down large tasks into smaller subtasks and distribute them across multiple cores for simultaneous execution, speeding up the overall process. Libraries like Dask in Python or NumPy can facilitate parallel processing.
• Code Optimization: Review and optimize your code to improve its efficiency. Look for redundant operations or areas where algorithms can be streamlined. Even small code improvements can lead to significant performance gains when dealing with large datasets.
• Utilize Specialized Libraries: Take advantage of libraries and frameworks designed for handling big data. These tools often employ efficient data structures and algorithms optimized for large-scale processing, significantly improving performance compared to generic programming languages.

3. Addressing Bottlenecks:

• Upgrade Hardware: If feasible, consider upgrading your computer's hardware, particularly RAM and CPU. Adding more RAM directly increases the available memory for data processing, while a more powerful CPU can handle large datasets with greater efficiency.
• Cloud Computing: For extremely large datasets that exceed the capabilities of a single computer, consider utilizing cloud computing platforms like Google Cloud Platform or Amazon Web Services. These platforms offer virtual machines with significantly larger memory and processing power, allowing you to tackle tasks that wouldn't be possible on your local machine.