4 ways to process super-large-scale data in Python

Hey, dear Python programming enthusiasts! Imagine that the data in your hands is no longer the few hundred megabytes of small fuss, but it directly soars to the TB level. At this time, isn’t the ordinary Python data processing method like a horse pulling a big cart, and you’re a little overwhelmed? Don’t worry, today the editor will introduce to you the four "superheroes" that deal with super-large data in Python - Mars, Dask, CuPy and Vaex. They are all powerful characters that can easily grasp TB of data!

In the wonderful world of data, the amount of data is like a snowball, getting bigger and bigger. From the initial small GB-level data pile, it gradually evolved into a TB-level data mountain. For those of us who want to show our skills in the data field, mastering the skills to handle large-scale data becomes crucial! It's like you want to conquer a towering mountain, and it won't work without handy equipment. And these four tools are the "magic tools" we climb on the data peak.

1. Mars: "Transformers" in the data processing world

Mars is an open source large-scale distributed data computing framework, which is like a super warrior with magical deformation capabilities. Mars can cleverly distribute data processing tasks on multiple computing nodes, whether it is a stand-alone or a cluster environment, it can handle it with ease. What does this mean? When you have massive data to process, Mars can assign these tasks to different "little helpers" (computing resources), letting them work together to greatly improve processing efficiency.

Mars supports a variety of data structures and algorithms, which are seamlessly connected with the familiar NumPy and Pandas. Look at the table below and compare the speed of Mars and Pandas when processing data of different sizes. Can you clearly see the advantages of Mars at a glance!

From the table we can clearly see that as the data scale increases, Mars' processing speed advantage becomes more and more obvious. This is because Mars adopts intelligent task scheduling and data parallel processing mechanisms. It will analyze your data processing tasks, then break the tasks into small tasks, and send them to the most appropriate computing resources for processing. It’s like you’re going to prepare a big party, where a person is busy, and Mars is like an experienced party planner, which will assign tasks such as preparing food, setting up venues, and arranging entertainment to different staff, allowing the entire party preparation process to be carried out efficiently and orderly.

To learn more about Mars, you can access its official documentation:Mars official documentation link. In this document, you can find detailed usage tutorials, API instructions and various interesting cases, which will definitely give you a deeper understanding of Mars.

2. Dask: The "conductor" of distributed computing

Dask is another master of large-scale data, and can be regarded as the "conductor" in the field of distributed computing. Dask is built on the existing Python ecosystem, such as NumPy, Pandas, and Scikit - learn, providing distributed processing power for these commonly used tools.

Dask's data structures and operations are very similar to the Python data structures we are familiar with, which greatly reduces learning costs. You are used to using Pandas' DataFrame. There are similar Dask DataFrames in Dask, and there are almost no obstacles to using them. Moreover, Dask DataFrame supports many similar operations as Pandas DataFrame, such as data filtering, aggregation, merging, etc. Let's take a look below

Some functional comparisons between Dask and Pandas:

From this comparison table, we can see that Dask has unique advantages in processing large-scale data. Its distributed parallel processing capability makes it many times faster than Pandas when performing aggregation operations on terabytes of data. Dask is like an orchestra conductor, coordinating numerous performers (computing resources) to work at a unified rhythm (task schedule), thus playing a wonderful data processing "movement".

Dask's official website:Dask official website link, there are rich resources here, including tutorials, documents, community forums, etc., which can help you get started with Dask quickly and start a journey of large-scale data processing.

3. CuPy: GPU-accelerated “rocket booster”

CuPy, which I want to introduce next, is the "speed responsibility" of the data processing industry. It is like adding a powerful rocket booster to data processing. CuPy is a Python library based on NVIDIA CUDA, which allows you to perform NumPy - like operations on the GPU.

We know that GPUs have powerful parallel computing power, which is a super weapon for processing large-scale data. CuPy makes full use of this advantage of GPU, greatly improving data processing speed. For example, when performing matrix operations, traditional NumPy runs on the CPU, while CuPy can run on the GPU. Let's take a simple comparison to see their speed differences:

From this comparison, we can clearly see that with the support of GPU, CuPy's computing speed is one order of magnitude faster than NumPy. This is because the GPU has a large number of compute cores that can process multiple data elements at the same time, just like there are a group of workers working at the same time, while the CPU may only have a few workers doing it slowly.

CuPy's official document address:CuPy official documentation link, here you can learn in-depth how to use CuPy to speed up your data processing tasks and give full play to the powerful performance of your GPU.

4. Vaex: "Magic" who visualizes and analyzes large-scale table data

The last one to appear is Vaex, a magical tool for visualizing and analyzing large-scale table data, like a magician that allows you to easily see through the secrets behind large-scale data. Vaex supports efficient statistical analysis and visualization of data sets, and it does not require the entire data set to be loaded into memory, which is simply too friendly to process terabytes of data!

Vaex provides rich functions, such as data filtering, histogram drawing, scatter plotting, etc. It allows you to quickly conduct exploratory analysis of large-scale data and discover patterns and trends in the data. For example, you have a user behavior dataset with billions of records. Using Vaex, you can easily filter out user behavior data for a specific time period and in a specific region and map their behavior trends.

Vaex's official website:Vaex official website link, On this website, you can find detailed tutorials, sample codes, and various usage experiences shared by the community to help you quickly master the powerful tool Vaex.

Code combat

Mars Code Practical

Suppose we have a very large CSV file that records the user's behavior data, and the file size may be several GB or even larger. Now we need to use Mars to read this file and perform a simple analysis of the data.

First, make sure you have Mars installed. If not installed, you can use the following command to install:

pip install mars

Next is the code section:

import  as md
# Read large-scale CSV files, the chunksize parameter specifies the data block size for each readdf = md.read_csv('large_user_behavior.csv', chunksize=1024 * 1024)  # Here is a setting to read 1MB data blocks each time# View the first 5 lines of dataprint(())
# Calculate the number of behaviors per useruser_behavior_count = ('user_id').size()
print(user_behavior_count)

In this code, we first import Mars' dataframe module. Then use the md.read_csv method to read the large file and specify a 1MB data block read each time through the chunksize parameter. Doing this can avoid reading the entire large file into memory at once, thereby improving memory usage efficiency. Next, we use the head method to view the first 5 rows of the data. This step is like looking at the head in the vast ocean of data to see what the data inside looks like. Finally, group by user_id through the groupby method, and calculate the number of behaviors of each user using the size method, so that you can quickly understand the activity level of each user.

Dask code practice

The CSV file of the user behavior data just now is also processed, and this time we use Dask to operate it.

Install Dask:

pip install dask

The code is as follows:

from  import Client, LocalCluster
from dask import dataframe as dd
# Start the local cluster, use LocalCluster here, and you can also connect to the remote clustercluster = LocalCluster()
client = Client(cluster)
# Read the CSV file, the blocksize parameter specifies the size of each data blockdf = dd.read_csv('large_user_behavior.csv', blocksize='100MB')
# View the first 5 lines of dataprint(())
# Calculate the average behavior duration for each userdf['behavior_duration'] = df['end_time'] - df['start_time']
user_avg_duration = ('user_id')['behavior_duration'].mean()
print(user_avg_duration)

In this code, we first import the Client and LocalCluster from the module, and create a local cluster to connect to this cluster through the Client. This is like forming a small data processing team, and each member (computing resources) is ready to work at any time. Then use dd.read_csv to read the file, and the blocksize is set to 100MB, which means Dask will divide the file into multiple 100MB data blocks for processing. Next, check the first 5 rows of data. Similar to the operations in Mars, you will first have a preliminary understanding of the data. Later, we created a new behavior_duration column, subtracting the start time to obtain the duration of each behavior, and then grouped by user_id to calculate the average duration of each user behavior, so that we can understand the average time-consuming situation of different user behaviors.

CuPy code practice

Suppose we want to perform calculations on a very large matrix and use CuPy to leverage the powerful computing power of the GPU to speed up.

Install CuPy (need to make sure that NVIDIA's GPU driver and CUDA toolkit are installed on your machine):

pip install cupy

The code is as follows:

import cupy as cp
# Create a large matrix of 10000x10000, and the data is on the GPUlarge_matrix = (10000, 10000)
# Compute the transposition of the matrixtransposed_matrix = (large_matrix)
# Calculate the dot product of two matricesresult_matrix = (large_matrix, transposed_matrix)
# Copy the data from the result matrix from the GPU back to the CPU (if further processing is required on the CPU)result_on_cpu = (result_matrix)
print(result_on_cpu)

In this code, we imported the CuPy library. First, create a large matrix of 10000x10000, and the data of this matrix is ​​stored on the GPU, making full use of the parallel computing power of the GPU. Then transpose the matrix and use the method. Then calculate the dot product of the original matrix and the transposed matrix and pass the method. Finally, if we need to process the results further on the CPU, use the method to copy the data from the result matrix from the GPU back to the CPU. The whole process is like letting the GPU, a supercar, run rapidly on the data track, greatly improving the speed of matrix computing.

Vaex code practice

Suppose we have an astronomical data file (stored in HDF5 format) containing billions of records, using Vaex for data exploration and visualization.

Install Vaex:

pip install vaex

The code is as follows:

import vaex
# Open astronomical data file in HDF5 formatdf = ('astronomy_data.hdf5')
# View basic data informationprint(())
# Draw a histogram of a numeric column(df['magnitude'], bins=100)
# Filter out data for specific conditions, such as celestial bodies with brightness greater than a certain valuebright_objects = df[df['brightness'] > 100]
print(bright_objects)

Notes on using

Resource configuration: When using Mars for distributed computing, the resources of the computing node should be reasonably configured. If the resource allocation is unreasonable, a certain node may be loaded too high and other nodes are idle, which will reduce the overall processing efficiency. For example, to run a Mars cluster on a machine with multiple CPU cores and a certain memory, the number of CPU cores and memory size of each node should be reasonably allocated according to the amount of data and task type.

Data consistency: Since Mars processes data in a distributed manner, you should pay attention to data consistency issues when updating and synchronizing data. Especially when multiple tasks operate on the same data at the same time, data conflicts may occur. For example, if two tasks try to modify the same user behavior record at the same time, a suitable synchronization mechanism is required to ensure the accuracy of the data.

Task scheduling optimization: Dask's task scheduling strategy has a great impact on performance. Complex task dependencies can lead to inefficient scheduling. In practical applications, we should try to simplify the dependence between tasks so that Dask can allocate tasks to various computing resources more efficiently. For example, splitting a large data analysis task into multiple relatively independent subtasks reduces unnecessary waiting and dependency between tasks.

Network overhead: When using Dask for distributed computing, data transmission between different nodes will incur network overhead. We must minimize unnecessary data transmission and reasonably plan the location of data storage and computing nodes. For example, if the data is concentrated on a server in a certain region, the computing nodes should also be deployed in a similar network environment as much as possible to reduce network latency.

GPU Compatibility: CuPy relies on NVIDIA's GPU and CUDA toolkits to ensure that your GPU model is compatible with the CUDA version. Different GPU models have different requirements for CUDA versions, and if they do not match, it may cause CuPy to not work properly. Before installing and using CuPy, be sure to carefully review the official NVIDIA documentation to confirm the compatibility of GPU and CUDA.

Memory Management: Although GPU has strong computing power, GPU memory is also limited. When processing large-scale data, be careful to avoid GPU memory overflow. For example, when creating a large matrix, the size of the matrix should be reasonably planned based on the memory size of the GPU, or block calculations should be used to reduce memory usage.

File format support: Vaex has better support for certain file formats, such as HDF5. When choosing a data storage format, consider the characteristics of Vaex. If you use an incompatible format, you may not be able to fully utilize the performance advantages of Vaex. For example, for a project with a large amount of tabular data, the HDF5 format is preferred to store data so that Vaex can read and analyze efficiently.

Visualization performance: Vaex's visualization operations may become slow when the data volume is very large. When performing visualization, pay attention to setting appropriate parameters, such as reducing the number of data points displayed, optimizing the graph drawing algorithm, etc., to improve the visualization performance. For example, when drawing a scatter plot, you can use sampling to display only some data points, which can not only display the approximate distribution of the data, but also improve the drawing speed.

Frequently Asked Questions and Solutions

Node connection failed: It may be due to network configuration problems or insufficient node resources. The solution is to check the network connection to ensure that each node can communicate normally, and at the same time check the resource usage of the nodes, such as CPU, memory, etc., and increase resources or adjust task allocation if necessary.

Data reading error: It may be that the file format is not supported or the file is corrupted. You can try to use other tools to check whether the file is normal and to see if Mars supports the file format. If the file format is not supported, consider converting the file format, such as converting some uncommon formats to CSV or Parquet formats.

Slow task execution: It may be due to unreasonable task scheduling or insufficient computing resources. You can optimize task scheduling strategies, such as reducing task dependencies, reasonably allocating task priorities, etc., and at the same time increase computing resources, such as adding more computing nodes or upgrading the hardware configuration of nodes.

Data loss: During distributed computing, data loss may be caused by node failure or data transmission errors. The solution is to use data backup and recovery mechanisms, such as regularly backing up data, and using checksum and other technologies during data transmission to ensure the integrity of the data.

CUDA driver error: It may be that the CUDA version is incompatible or the driver is not installed correctly. The correct version of the CUDA driver needs to be uninstalled and reinstalled while ensuring that the CUDA version matches CuPy's requirements. You can find relevant version matching information on the official NVIDIA website and CuPy's documentation.

Computation result error: It may be due to data type mismatch or algorithm implementation problems. Carefully check the data type to ensure consistency of the data type during GPU calculations. At the same time, check whether the algorithm implementation is correct and compare the CPU calculation results to verify the accuracy of GPU calculations.

Slow file reading: It may be because the file is too large or the file format problem. For files that are too large, you can consider chunking the files or optimize the file storage structure. If it is a file format problem, try converting the file to a more efficiently supported format by Vaex, such as HDF5.

The visual interface is stuck: it can be alleviated by reducing the resolution of the visualization, reducing the amount of data displayed, etc. For example, when drawing a histogram, reduce the number of bins in the histogram, or sample the data when drawing a line chart.

Common interview questions

Please briefly describe the main differences between Mars and Dask in distributed computing.

Mars focuses more on data parallelism. By dividing the data into multiple blocks and processing it in parallel on different nodes, the support for data structures and algorithms is similar to that of NumPy and Pandas, and the learning cost is relatively low. Dask is built on the existing Python ecosystem and not only supports data parallelism, but also task parallelism. Its data structure and operations are very similar to Python's native data structures and can be seamlessly integrated with other Python libraries such as Scikit - learn.

How to optimize memory usage when using CuPy for GPU computing?

Blocked calculations can be used to avoid loading large amounts of data into GPU memory at one time. At the same time, release GPU memory that is no longer used in time. For example, after completing a matrix operation, use other functions to delete the matrix objects that are no longer needed. In addition, plan the data type reasonably and select data types that occupy less memory. For example, using float16 instead of float32 can reduce memory usage if the accuracy allows.

What are the advantages of Vaex when processing large-scale tabular data compared to traditional data processing tools such as Pandas?

Vaex does not need to load the entire dataset into memory and can be easily processed for terabytes of data, while Pandas is susceptible to memory limitations when processing large-scale data. Vaex also provides powerful visualization capabilities that enable direct visualization of large-scale data, while Pandas can become very slow when the data volume is too large. In addition, Vaex supports efficient statistical analysis and performs better than Pandas when processing operations such as aggregation and screening of large-scale data.

Conclusion

You have mastered how these tools are used and can flexibly use them in real work and learning to deal with various large-scale data problems. The data world is like a vast ocean, with endless treasures waiting for us to dig. And these four killing weapons are the solid ships we navigate in this ocean. During the use process, you will definitely encounter various problems, but don’t be afraid, each problem is an opportunity for us to grow.

The above is a detailed explanation of the 4 major methods of Python processing of super-large-scale data. For more information about Python processing of super-large-scale data, please pay attention to my other related articles!