Author: admin yep

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The Pandas DataFrame has several methods concerning Computations and Descriptive Stats. When applied to a DataFrame, these methods evaluate the elements and return the results.

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().

Getting Started

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing those libraries here:

• How to Install Pandas on PyCharm?
• How to Install NumPy on PyCharm?

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame pct_change()

The pct_change() method calculates and returns the percentage change between the current and prior element(s) in a DataFrame. The return value is the caller.

To fully understand this method and other methods in this tutorial from a mathematical point of view, feel free to watch this short tutorial:

The syntax for this method is as follows:

DataFrame.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

This example calculates and returns the percentage change of four (4) fictitious stocks over three (3) months.

df = pd.DataFrame({'ASL':  [18.93, 17.03, 14.87],
                   'DBL':   [39.91, 41.46, 40.99],
                   'UXL':   [44.01, 43.67, 41.98]},
                   index= ['2021-10-01', '2021-11-01', '2021-12-01'])

result = df.pct_change(axis='rows', periods=1)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df.
• Line [2] uses the pc_change() method with a selected axis and period to calculate the change. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

Note: The first line contains NaN values as there is no previous row.

DataFrame quantile()

The quantile() method returns the values from a DataFrame/Series at the specified quantile and axis.

The syntax for this method is as follows:

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

To fully understand the interpolation parameter from a mathematical point of view, feel free to check out this tutorial:

This example uses the same stock DataFrame as noted above to determine the quantile(s).

df = pd.DataFrame({'ASL':  [18.93, 17.03, 14.87],
                   'DBL':   [39.91, 41.46, 40.99],
                   'UXL':   [44.01, 43.67, 41.98]})

result = df.quantile(0.15)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df.
• Line [2] uses the quantile() method to calculate by setting the q (quantile) parameter to 0.15. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

DataFrame rank()

The rank() method returns a DataFrame/Series with the values ranked in order. The return value is the same as the caller.

The syntax for this method is as follows:

DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

For this example, a CSV file is read in and is ranked on Population and sorted. Click here to download and move this file to the current working directory.

df = pd.read_csv("countries.csv")
df["Rank"] = df["Population"].rank()
df.sort_values("Population", inplace=True)
print(df)

• Line [1] reads in the countries.csv file and saves it to df.
• Line [2] appends a column to the end of the DataFrame (df). 
• Line [3] sorts the CSV file in ascending order.
• Line [4] outputs the result to the terminal.

Output:

DataFrame round()

The round() method rounds the DataFrame output to a specified number of decimal places.

The syntax for this method is as follows:

DataFrame.round(decimals=0, *args, **kwargs)

For this example, the Bank of Canada’s mortgage rates over three (3) months display and round to three (3) decimal places.

Code Example 1:

df = pd.DataFrame([(2.3455, 1.7487, 2.198)], columns=['Month 1', 'Month 2', 'Month 3']) 
result = df.round(3)
print(result)

• Line [1] creates a DataFrame complete with column names and saves to df.
• Line [2] rounds the mortgage rates to three (3) decimal places. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

Another way to perform the same task is with a Lambda!

Code Example 2:

df = pd.DataFrame([(2.3455, 1.7487, 2.198)], 
                  columns=['Month 1', 'Month 2', 'Month 3']) 
result = df.apply(lambda x: round(x, 3))
print(result)

• Line [1] creates a DataFrame complete with column names and saves to df.
• Line [2] rounds the mortgage rates to three (3) decimal places using a Lambda. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Note: The output is identical to that of the above.

DataFrame Prod and Product

The prod() and product() methods are identical.  Both return the product of the values of a requested axis.

The syntax for these methods is as follows:

DataFrame.prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

DataFrame.product(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Parameters:

Axis:                      If zero (0) or index, apply the function to each column. Default is None.

                              If one (1) or column, apply the function to each row.

Skip_na:               If set to True, this parameter excludes NaN/NULL values when calculating the result.

Level:                   Set the appropriate parameter if the DataFrame/Series is multi-level.

                              If no value, then None is assumed.

Numeric_only: Only include columns that contain integers, floats, or boolean values.

Min_count:         The number of values on which to perform the calculation.

**kwargs:           Additional keywords passed into a DataFrame/Series.

For this example, random numbers generate and the product on the selected axis returns.

Code:

df = pd.DataFrame({‘A’:   [2, 4, 6],

                                    ‘B’:   [7, 3, 5],

                                   ‘C’:   [6, 3, 1]})

index_ = [‘A’, ‘B’, ‘C’]

df.index = index_

result = df.prod(axis=0)

print(result)

Line [1] creates a DataFrame complete with random numbers and saves to df.

Line [2-3] creates and sets the DataFrame index.

Line [3] calculates the product along axis 0. This output saves to the result variable.

Line [4] outputs the result to the terminal.

Output:

Formula Example: 2*4*6=48

Finxter

http://img.youtube.com/vi/ytUj53rYCME/0.jpg

The Pandas DataFrame has several methods concerning Computations and Descriptive Stats. When applied to a DataFrame, these methods evaluate the elements and return the results.

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().

Getting Started

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing those libraries here:

• How to Install Pandas on PyCharm?
• How to Install NumPy on PyCharm?

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame mad()

The mad() method (Mean Absolute Deviation) is the average distance of all DataFrame elements from the mean.

To fully understand MAD from a mathematical point of view, feel free to watch this short tutorial:

The syntax for this method is as follows:

DataFrame.mad(axis=None, skipna=None, level=None)

This example retrieves the MAD of four (4) Hockey Teams.

df_teams = pd.DataFrame({'Bruins':   [4, 5, 9],
                         'Oilers':   [3, 6, 10],
                         'Leafs':    [2, 7, 11],
                         'Flames': [1, 8, 12]})

result = df_teams.mad(axis=0).apply(lambda x:round(x,3))
print(result)

• Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
• Line [2] uses the mad() method with the axis parameter set to columns to calculate MAD from the DataFrame. The lambda function formats the output to three (3) decimal places. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

DataFrame min()

The min() method returns the smallest value(s) from a DataFrame/Series. The following methods can accomplish this task:

• The DataFrame.min() method, or
• The numpy.minimum() method

The syntax for this method is as follows:

DataFrame.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

For this example, we will determine which Team(s) have the smallest amounts of wins, losses, or ties.

Code Example 1:

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.min(axis=0)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df_teams.
• Line [2] uses the min() method with the axis parameter set to columns to retrieve the minimum value(s) from the DataFrame. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

This example uses two (2) arrays and retrieves the minimum value(s) of the Series.

Code Example 2:

c11_grades = [63, 78, 83, 93]
c12_grades = [73, 84, 79, 83]

result = np.minimum(c11_grades, c12_grades)
print(result)

• Line [1-2] create lists of random grades and assigns them to the appropriate variable.
• Line [3] uses NumPy minimum to compare the two (2) arrays. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

[63 78 79 83]

DataFrame max()

The max() method returns the largest value(s) from a DataFrame/Series. The following methods can accomplish this task:

• The DataFrame.max() method, or
•  The np.maximum() method

The syntax for this method is as follows:

DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

For this example, we will determine which Team(s) have the largest amounts of wins, losses, or ties.

Code Example 1:

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.max(axis=0)
print(result)

• Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
• Line [2] uses max() with the axis parameter set to columns to retrieve the maximum value(s) from the DataFrame. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

This example uses two (2) arrays and retrieves the maximum value(s) of the Series.

Code Example 2:

c11_grades = [63, 78, 83, 93]
c12_grades = [73, 84, 79, 83]

result = np.maximum(c11_grades, c12_grades)
print(result)

• Line [1-2] create lists of random grades and assigns them to the appropriate variable.
• Line [3] uses the NumPy library maximum function to compare the two (2) arrays. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

[73 84 83 93]

DataFrame mean()

The mean() method returns the average of the DataFrame/Series across a requested axis. If a DataFrame is used, the results will return a Series. If a Series is used, the result will return a single number (float).

The following methods can accomplish this task:

• The DataFrame.mean() method, or
•  The Series.mean() method

The syntax for this method is as follows:

DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

For this example, we will determine average wins, losses and ties for our Hockey Teams.

Code Example 1:

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.mean(axis=0).apply(lambda x:round(x,2))
print(result)

• Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
• Line [2] uses the mean() method with the axis parameter set to columns to calculate means (averages) from the DataFrame. The lambda function formats the output to two (2) decimal places. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

For this example, Alice Accord, an employee of Rivers Clothing has logged her hours for the week. Let’s calculate the mean (average) hours worked per day.

Code Example 2:

hours  = pd.Series([40.5, 37.5, 40, 55])
result = hours.mean()
print(result)

• Line [1] creates a Series of hours worked for the week and saves to hours.
• Line [2] uses the mean() method to calculate the mean (average). This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output:

42.25

DataFrame median()

The median() method calculates and returns the median of DataFrame/Series elements across a requested axis. In other words, the median determines the middle number(s) of the dataset.

To fully understand median from a mathematical point of view, watch this short tutorial:

The syntax for this method is as follows:

DataFrame.median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Parameters

Axis:                      If zero (0) or index, apply the function to each column. Default is None.

                              If one (1) or column, apply the function to each row.

Skipna:                 If this parameter is True, any NaN/NULL value(s) ignored.

                              If False, all value(s) included: valid or empty.                    

If no value, then None is assumed.

Level:                   Set the appropriate parameter if the DataFrame/Series is multi-level.

                              If no value, then None is assumed.

Numeric_only: Only include columns that contain integers, floats, or boolean values.

**kwargs:           This is where you can add additional keywords.

For this example, we will determine the median value(2) for our Hockey Teams.

df_teams = pd.DataFrame({‘Bruins’:   [4, 5,  9],

                                                  ‘Oilers’:    [3, 6, 14],

                                                  ‘Leafs’:     [2, 7, 11],

                                                  ‘Flames’:  [21, 8, 7]})

result = df_teams.median(axis=0)

print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.

Line [2] uses the median() method to calculate the median of the Teams. This output saves to the result variable.

Line [3] outputs the result to the terminal.

Output

DataFrame Mode

The mode() method determines the most commonly used numbers in a DataFrame/Series.

The syntax for this method is as follows:

DataFrame.mode(axis=0, numeric_only=False, dropna=True)

Parameters

Axis:                      If zero (0) or index, apply the function to each column. Default is None.

                              If one (1) or column, apply the function to each row.

Numeric_only: Only include columns that contain integers, floats, or boolean values.

Dropna:               If set to True, this parameter ignores all NaN and NaT values. By default, this value is True.

For this example, we determine the numbers that appear more than once.

Code

df_teams = pd.DataFrame({‘Bruins’:   [4, 5,  9],

                                                  ‘Oilers’:    [3, 9, 13],

                                                  ‘Leafs’:     [2, 7, 4],

                                                  ‘Flames’:  [13, 9, 7]})

result = df_teams.mode(axis=0)

print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.

Line [2] uses the mode() method across the column axis. This output saves to the result variable.

Line [3] outputs the result to the terminal.

Output

Finxter

https://www.futurity.org/wp/wp-content/uploads/2022/01/feet-covid-19-working-from-home-slippers-1600.jpg

If you’ve been spending more time at home during the pandemic, you may not realize how important proper footwear is for keeping certain injuries at bay.

Sean Peden, an orthopaedic foot and ankle specialist from Yale University Medicine, says not wearing supportive footwear on a regular basis can lead to foot pain and other problems.

“Many people are continuing to work at home part- or full-time, which for some can mean wearing slippers or walking around barefoot,” Peden says. “And because of that, many patients are coming to us with foot problems.”

Taking good care of your feet will not only help you avoid common injuries like tendonitis and plantar fasciitis, but it can also prevent other issues with your hips, knees, and back from developing, he adds.

Here, Peden shares some of the most common foot problems he sees—and simple treatments to get relief:

Think of shoes as shock absorbers

Just as you would pick out an appropriate shoe for your commute into the office, it’s important to put the same level of thought into selecting an at-home shoe.

Walking barefoot at home is not recommended for the same reason walking barefoot outside is ill-advised, Peden says.

“All kinds of footwear protect your feet. Over the course of weeks or months, the strain of walking barefoot can add significant stress to your arches, tendons, plantar fascia, and joints,” he says. “This can lead to a range of complications, from minor conditions such as calluses to major issues such as arch collapse.”

It may help to think of footwear as shock absorbers and, based on body type and gait, some of us need more shock absorption than others, Peden says.

“If you have sore feet—or have had foot problems in the past—wearing a pair of what I call ‘house shoes,’ or ‘house slippers,’ is a good idea.”

By that, Peden means a hard-soled, slip-on shoe or slipper that is worn exclusively inside the home (ideally) to avoid bringing in dirt or bacteria.

“To be practical, I suggest a slip-on clog or slipper without laces. That way, you don’t have to tie and untie your shoes 10 times a day,” Peden says. “A hard sole is important because the harder the sole, the less stress the joints and tendons in your foot experience with each step. The hard sole transfers that stress to the shoe rather than to the foot.”

In general, avoid fluffy, formless slippers, he advises. “If you are at home, you might go up and down stairs dozens of times a day—or do chores around the house. And those are not activities to do with footwear that doesn’t have any support,” Peden says. “A good rule of thumb is if it isn’t something you could walk in for a few blocks comfortably, you shouldn’t wear it around the house all day, either.”

Painful tendonitis

One of the most common foot problems Peden has seen in patients since the pandemic started is Achilles tendonitis, or inflammation of a tendon (a thick cord of tissue that connects muscles to bones). The Achilles tendon runs from the back of your calf to your heel bone. Achilles tendonitis can cause pain and swelling in the foot and ankle.

An injury, overuse, and flat fleet are all causes for Achilles tendonitis, Peden says. “It can be an issue especially if people with flat feet spend six months to a year not wearing supportive shoes on a regular basis,” he says. “The tendon in the arch of the foot becomes inflamed as the foot gets flatter. It is quite painful and can be debilitating.”

Peden says he is also seeing more patients with posterior tibial tendonitis, which causes a collapsed arch or flat foot.

The Fix: For acute pain, the first things to try are rest, ice, and staying off your feet as much as possible. Finding footwear with good arch support is another must, Peden says.

“Some people might need an ankle brace or additional inserts for their shoes, but for the vast majority, proper footwear is the answer. These tendon flares generally last a few months, but patients usually see improvement within a week or two.”

People with tendon issues should get proper treatment, Peden says. “You want to avoid developing a chronic tendon issue, because those are harder to cure.”

Plantar fasciitis: ‘stabbing’ heal pain

Many patients have developed plantar fasciitis, inflammation of the band of tissue on the bottom of your foot.

A common symptom is a stabbing pain in the heel that can be the most intense when you first step out of bed in the morning. That’s because the plantar fascia, which runs from the heel to the base of your toes, tightens overnight.

The plantar fascia supports the arch of the foot and absorbs stress. Too much stress—from standing on your feet on a hard surface for a long time, improper shoes, or running—can cause irritation and tiny tears in the band of tissue.

“The pain is usually on the bottom part of the heel,” Peden says. “It’s associated with tight Achilles tendons and calf muscles. If people spend a lot of their day sitting, for example, the muscles can tighten up, and wearing improper footwear can exacerbate the issue.

“For people who work outside the home and are on their feet all day, including nurses, they should wear a supportive shoe—and not something too soft or flexible. This can include sneakers, a hard clog, or a work shoe, depending on personal preference.”

The Fix: Besides supportive footwear and avoiding walking around barefoot, treatment should include a home stretching program to address the tightness in the calf muscles and Achilles tendons, Peden says.

Another effective treatment is to wear a soft, flexible splint that holds your foot at a 90-degree angle while you are sleeping; this keeps the plantar fascia stretched out. You can also wear a splint while lying on the couch watching TV.

As painful as plantar fasciitis can be, Peden says it is not a progressive condition. “People often worry it’s the start of something like arthritis, which continues to get worse,” he says. “It might take a few months of conservative, noninvasive, nonsurgical treatments, but patients with plantar fasciitis typically get better.”

Physical therapy and lifestyle

Exercise, physical therapy, and weight loss can all make a difference in addressing foot pain, too.

“One pound of additional weight on your body leads to six pounds of additional pressure on your foot. So, if you lose 10 pounds, that is really taking 60 pounds of pressure off your foot,” Peden says.

With the pandemic, many people have gained weight, which compounds the problem. But the key is not to do too much too quickly to try to reverse it, Peden says.

“If you try to lose weight by suddenly walking too much, that’s hard on your feet, too, and may lead to other foot problems. So, I often recommend cross-training, including low-impact cardio activities like biking or swimming. You can walk, but try to take it easy and, as always, wear good, supportive shoes.”

Hiking shoes are often a good option, particularly if you walk on uneven surfaces, including trails. “They are a little safer than sneakers, and protect your foot and ankle better,” he says.

In certain cases, physical therapy is recommended for lingering foot issues. “Physical therapists have many techniques that can speed up the recovery process,” Peden says.

Surgery is rarely needed for chronic conditions like tendonitis or plantar fasciitis. “We always treat our patients first with nonsurgical options to hopefully manage the condition before we ever talk about surgery,” Peden says.

But if you are feeling foot pain, don’t be afraid to seek medical help, Peden advises.

“I know people have different comfort levels right now about seeking medical care during the pandemic, but if you have a foot issue and it’s been hurting for a while, you should go see your doctor. There are likely easy solutions.”

Source: Yale University

The post How to avoid achy feet while working at home appeared first on Futurity.

Futurity

https://www.percona.com/blog/wp-content/uploads/2021/12/MySQL-Backup-Performance-Comparison.png

In this blog post, we will compare the performance of performing a backup from a MySQL database using mysqldump, MySQL Shell feature called Instance Dump, mysqlpump, mydumper, and Percona XtraBackup. All these available options are open source and free to use for the entire community.

To start, let’s see the results of the test.

Benchmark Results

The benchmark was run on an m5dn.8xlarge instance, with 128GB RAM, 32 vCPU, and 2xNVMe disks of 600GB (one for backup and the other one for MySQL data). The MySQL version was 8.0.26 and configured with 89Gb of buffer pool, 20Gb of redo log, and a sample database of 177 GB (more details below).

We can observe the results in the chart below:

And if we analyze the chart only for the multi-threaded options:

As we can see, for each software, I’ve run each command three times in order to experiment using 16, 32, and 64 threads. The exception for this is mysqldump, which does not have a parallel option and only runs in a single-threaded mode.

We can observe interesting outcomes:

1. When using zstd compression, mydumper really shines in terms of performance. This option was added not long ago (MyDumper 0.11.3).
2. When mydumper is using gzip, MySQL Shell is the fastest backup option.
3. In 3rd we have Percona XtraBackup.
4. mysqlpump is the 4th fastest followed closer by mydumper when using gzip.
5. mysqldump is the classic old-school style to perform dumps and is the slowest of the four tools.
6. In a server with more CPUs, the potential parallelism increases, giving even more advantage to the tools that can benefit from multiple threads.

Hardware and Software Specs

These are the specs of the benchmark:

• 32 CPUs
• 128GB Memory
• 2x NVMe disks 600 GB
• Centos 7.9
• MySQL 8.0.26
• MySQL shell 8.0.26
• mydumper 0.11.5 – gzip
• mydumper 0.11.5 – zstd
• Xtrabackup 8.0.26

The my.cnf configuration:

[mysqld]
innodb_buffer_pool_size = 89G
innodb_log_file_size = 10G

Performance Test

For the test, I used sysbench to populate MySQL. To load the data, I choose the tpcc method:

$ ./tpcc.lua  --mysql-user=sysbench --mysql-password='sysbench' --mysql-db=percona --time=300 --threads=64 --report-interval=1 --tables=10 --scale=100 --db-driver=mysql prepare

Before starting the comparison, I ran mysqldump once and discarded the results to warm up the cache, otherwise our test would be biased because the first backup would have to fetch data from the disk and not the cache.

With everything set, I started the mysqldump with the following options:

$ time mysqldump --all-databases --max-allowed-packet=4294967295 --single-transaction -R --master-data=2 --flush-logs | gzip > /backup/dump.dmp.gz

For the Shell utility:

$ mysqlsh
MySQL JS > shell.connect('root@localhost:3306');
MySQL localhost:3306 ssl test JS > util.dumpInstance("/backup", {ocimds: true, compatibility: ["strip_restricted_grants","ignore_missing_pks"],threads: 16})

For mydumper:

$ time mydumper  --threads=16  --trx-consistency-only --events --routines --triggers --compress --outputdir /backup/ --logfile /backup/log.out --verbose=2

PS: To use zstd, there are no changes in the command line, but you need to download the zstd binaries.

For mysqlpump:

$ time mysqlpump --default-parallelism=16 --all-databases > backup.out

For xtrabackup:

$ time xtrabackup --backup --parallel=16 --compress --compress-threads=16 --datadir=/mysql_data/ --target-dir=/backup/

Analyzing the Results

And what do the results tell us?

Parallel methods have similar performance throughput. The mydumper tool cut the execution time by 50% when using zstd instead of gzip, so the compression method makes a big difference when using mydumper.

For the util.dumpInstance utility, one advantage is that the tool stores data in both binary and text format and uses zstd compression by default. Like mydumper, it uses multiple files to store the data and has a good compression ratio. 

XtraBackup got third place with a few seconds of difference from MySQL shell. The main advantage of XtraBackup is its flexibility, providing PITR and encryption for example. 

Next, mysqlpump is more efficient than mydumper with gzip, but only by a small margin. Both are logical backup methods and works in the same way. I tested mysqlpump with zstd compression, but the results were the same, hence the reason I didn’t add it to the chart. One possibility is because mysqlpump streams the data to a single file.

Lastly, for mysqldump, we can say that it has the most predictable behavior and has similar execution times with different runs. The lack of parallelism and compression is a disadvantage for mysqldump; however, since it was present in the earliest MySQL versions, based on Percona cases, it is still used as a logical backup method.

Please leave in the comments below what you thought about this blog post, if I missed something, or if it helped you. I will be glad to discuss it!

Useful Resources

Finally, you can reach us through the social networks, our forum, or access our material using the links presented below:

• • Blog 
  • Solution Briefs
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  • Ebooks
  • Technical Presentations archive
  • Videos/Recorded Webinars
  • Forum
  • Knowledge Base (Percona Subscriber exclusive content)

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Take a look at our top 10 Laravel Tutorials from 2021.

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Andre Antunes improves another viral video by adding a heavy metal soundtrack to it. This time, he amped up the already formidable footage of New Zealand’s All Blacks rugby team performing a traditional Maori war dance known as the Haka. Original video here.

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The path to becoming a proficient and successful programmer is difficult, but one that is certainly achievable. Data structures are a core component that every programming student must master, and chances are you may have already learned or worked with some basic data structures such as arrays or lists.

Interviewers tend to prefer asking questions related to data structures, so if you’re preparing for a job interview, you’re going to need to brush up on your data structures knowledge. Read on as we list the most important data structures for programmers and job interviews.

1. Linked List

Linked lists are one of the most basic data structures and often the starting point for students in most data structures courses. Linked lists are linear data structures that allow sequential data access.

Elements within the linked list are stored in individual nodes that are connected (linked) using pointers. You can think of a linked list as a chain of nodes connected to each other via different pointers.

Related: An Introduction to Using Linked Lists in Java

Before we get into the specifics of the different types of linked lists, it is crucial to understand the structure and implementation of the individual node. Each node in a linked list has at least one pointer (doubly linked list nodes have two pointers) that connects it to the next node in the list and the data item itself.

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Each linked list has a head and tail node. Single-linked list nodes only have one pointer that points to the next node in the chain. In addition to the next pointer, doubly linked list nodes have another pointer that points to the previous node in the chain.

Interview questions related to linked lists usually revolve around the insertion, searching, or deletion of a specific element. Insertion in a linked list takes O(1) time, but deletion and searching can take O(n) time in the worst case. So linked lists are not ideal.

2. Binary Tree

Binary trees are the most popular subset of the tree family data structure; elements in a binary tree are arranged in a hierarchy. Other types of trees include AVL, red-black, B trees, etc. Nodes of the binary tree contain the data element and two pointers to each child node.

Each parent node in a binary tree can have a maximum of two child nodes, and each child node, in turn, can be a parent to two nodes.

Related: A Beginner’s Guide to Binary Trees

A binary search tree (BST) stores data in a sorted order, where elements with a key-value less than the parent node are stored on the left, and elements with a key-value greater than the parent node are stored on the right.

Binary trees are commonly asked in interviews, so if you’re getting ready for an interview, you should know how to flatten a binary tree, look up a specific element, and more.

3. Hash Table

Hash tables or hash maps are a highly efficient data structure that stores data in an array format. Each data element is assigned a unique index value in a hash table, which allows for efficient searching and deletion.

The process of assigning or mapping keys in a hash map is called hashing. The more efficient the hash function, the better the efficiency of the hash table itself.

Each hash table stores data elements in a value-index pair.

Where value is the data to be stored, and index is the unique integer used for mapping the element into the table. Hash functions can be very complex or very simple, depending on the required efficiency of the hash table and how you will resolve collisions.

Collisions often arise when a hash function produces the same mapping for different elements; hash map collisions can be resolved in different ways, using open addressing or chaining.

Hash tables or hash maps have a variety of different applications, including cryptography. They are the first choice data structure when insertion or searching in constant O(1) time is required.

4. Stacks

Stacks are one of the simpler data structures and are pretty easy to master. A stack data structure is essentially any real-life stack (think of a stack of boxes or plates) and operates in a LIFO (Last In First Out) manner.

Stacks’ LIFO property means the element you inserted last will be accessed first. You cannot access elements below the top element in a stack without popping the elements above it.

Stacks have two primary operations—push and pop. Push is used to insert an element into the stack, and pop removes the topmost element from the stack.

They also have plenty of useful applications, so it is very common for interviewers to ask questions related to stacks. Knowing how to reverse a stack and evaluate expressions is quite essential.

5. Queues

Queues are similar to stacks but operate in a FIFO (First In First Out) manner, meaning you can access the elements you inserted earlier. The queue data structure can be visualized as any real-life queue, where people are positioned based on their order of arrival.

Insertion operation of a queue is called enqueue, and deleting/removing an element from the beginning of the queue is referred to as dequeuing.

Related: A Beginner’s Guide to Understanding Queues and Priority Queues

Priority queues are an integral application of queues in many vital applications such as CPU scheduling. In a priority queue, elements are ordered according to their priority rather than the order of arrival.

6. Heaps

Heaps are a type of binary tree where nodes are arranged in ascending or descending order. In a Min Heap, the key value of the parent is equal to or less than that of its children, and the root node contains the minimum value of the entire heap.

Similarly, the root node of a Max Heap contains the maximum key value of the heap; you must retain the min/max heap property throughout the heap.

Related: Heaps vs. Stacks: What Sets Them Apart?

Heaps have plenty of applications due to their very efficient nature; primarily, priority queues are often implemented through heaps. They are also a core component in heapsort algorithms.

Learn Data Structures

Data structures can seem harrowing at first but devote enough time, and you’ll find them easy as pie.

They are a vital part of programming, and almost every project will require you to use them. Knowing what data structure is ideal for a given scenario is critical.

7 Algorithms Every Programmer Should Know

These algorithms are essential to every programmer’s workflow.

Read Next

About The Author

M. Fahad Khawaja
(84 Articles Published)

Fahad is a writer at MakeUseOf and is currently majoring in Computer Science. As an avid tech-writer he makes sure he stays updated with the latest technology. He finds himself particularly interested in football and technology.

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Anyone working with data is going to encounter date values at some point. Dates are necessary for many reasons. If you’re a PHP/MySQL Developer, you have date function options for both the database and your programming language of choice. In this post, I cover 5 PHP date functions and their (somewhat) MySQL equivalents with examples of each. Continue reading…

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PHP: date_format() | MySQL: DATE_FORMAT()

We always hope that the data comes to us in the format we need. In reality, we know better and that normally, data is formatted (if at all) in a way that we can’t use or don’t wish to use. With that being said, you can use either of these functions and put date data in pretty much any format you need.

Both functions have a myriad number of different formatting options and covering them all in one blog post would be convoluted at best. Instead, I’ll cover a couple of the common ones I am familiar with and leave the exploration up to you.

date_format()

Syntax PHP date_format():

date_format($object, $format)

• Returns a date as specified by the format.
• Both parameters are required.

Using the date_create() function, I make a practice date we can use for the examples:

$some_date = date_create('2021-12-04');
echo date_format($some_date, 'Y-d-m');

Echoing out in the browser returns:

2021-04-12

In order to format the date value to a full month name, 2-digit day (with suffix), and 4-digit year, we can use these format parameters:

echo date_format($some_date, 'F jS, Y');

Which returns:

December 4th, 2021

Oftentimes, you see dates in the 2-digit month 2-digit day, and 4-digit year format (MM/DD/YYYY). These format strings shown in the following example produce that format for you:

echo date_format($some_date, 'd/m/Y');
04/12/2021

(There are several format options for PHP’s date_format(). Visit the date_format() documentation to see them all.)

DATE_FORMAT()

Syntax MySQL DATE_FORMAT():

DATE_FORMAT(date, format)

• Returns a date as specified by the format.
• Both parameters are required.

For simplicity’s sake, I am using the ‘store’ table from the sakila practice database with this data:

mysql> SELECT last_update
    -> FROM store;
+---------------------+
| last_update         |
+---------------------+
| 2006-02-15 04:57:12 |
| 2006-02-15 04:57:12 |
+---------------------+
2 rows in set (0.00 sec)

For a 3-letter month abbreviation, a 2-digit day – with the suffix – and the full 4-digit year, we can use these format specifiers:

mysql> SELECT DATE_FORMAT(last_update, '%b %D, %Y') AS frmt
    -> FROM store;
+----------------+
| frmt           |
+----------------+
| Feb 15th, 2006 |
| Feb 15th, 2006 |
+----------------+
2 rows in set (0.00 sec

As you have seen in the 1st exploratory query on the ‘store’ table, the ‘last_update’ column date values are stored in a ‘YYYY-MM-DD’ format. If these values were not already stored as such, you can easily format them with the format specifiers shown in the next query:

mysql> SELECT DATE_FORMAT(last_update, '%Y-%m-%d') AS frmt
    -> FROM store;
+------------+
| frmt       |
+------------+
| 2006-02-15 |
| 2006-02-15 |
+------------+
2 rows in set (0.00 sec)

A common format you see is the ‘DD-MON-YY’ format (a lot of Oracle SQL dates are stored in this format). There are format specifiers for that pattern as well:

mysql> SELECT DATE_FORMAT(last_update, '%d-%b-%y') AS frmt 
    -> FROM store;
+-----------+
| frmt      |
+-----------+
| 15-Feb-06 |
| 15-Feb-06 |
+-----------+
2 rows in set (0.00 sec)

(Visit this MySQL DATE_FORMAT() resource for more information.)

PHP: date_diff() | MySQL: DATEDIFF()

PHP’s date_diff() function returns the difference between 2 DateTime objects. The difference can be a number of different values such as days, months, or even hours (and many more).

date_diff()

Syntax PHP date_diff()

date_diff($origin_date, $target_date)

I’ll create 2 dates and then call the format() method on the resulting object from the date_diff() function call:

$current_date = date_create('2021-12-04');
$xmas_date = date_create('2021-12-25');

$num_days = date_diff($current_date, $xmas_date);
echo $num_days->format('%R%a days');

The a format echoed in the browser returns:

+21 days

Using the m format, we can know the number of months difference between the date objects:

$current_date = date_create('2021-12-04');
$six_mnths_later = date_create('2021-06-25');

$num_mnths = date_diff($current_date, $six_mnths_later);
echo $num_mnths->format('%m months');

5 months

(Note: date_diff() is in the procedural style. Read more in the official date_diff() documentation.)

DATEDIFF()

MySQL’s DATEDIFF() function returns the number of days between 2 date values. This function is handy for calculating how many days an event happened in the past or will happen in the future (i.e., a birthday, holiday, anniversary, etc…).

Syntax MySQL DATEDIFF():

DATEDIFF(date_1, date_2)

At the time of writing this post, MySQL’s CURRENT_DATE() function returns this value in my local learning/development environment:

mysql> SELECT CURRENT_DATE();
+----------------+
| CURRENT_DATE() |
+----------------+
| 2021-12-04     |
+----------------+
1 row in set (0.00 sec)

Christmas is on December the 25th and we can calculate the remaining number of days until the holiday using DATEDIFF():

mysql> SELECT DATEDIFF('2021-12-25', CURRENT_DATE()) AS days_to_xmas;
+--------------+
| days_to_xmas |
+--------------+
|           21 |
+--------------+
1 row in set (0.00 sec)

Hope y’all were good this year so Santa visits!

(More information on DATEDIFF().)

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PHP: date() | MySQL: NOW()

PHP’s date() function returns the current time, formatted according to the $format_string argument, if no $timestamp argument value is provided in the function call.

date()

Syntax PHP date():

date(string $format_string, $timestamp = null)

As of the time of writing this segment of the post, the date() function returns this date in my local development environment:

$current_date = date('Y-m-d');
echo $current_date;

2021-12-10

Just as several other example PHP date functions, there are many formatting options for date().

$current_date = date('F jS, Y');
echo $current_date;

December 10th, 2021

(Visit the PHP date() function documentation for more information on this function.)

NOW()

The MySQL NOW() function returns the current date and time. NOW() accepts no arguments.

Syntax MySQL NOW():

NOW()

Let’s see an example of NOW() in my local development environment:

mysql> SELECT NOW();
+---------------------+
| NOW()               |
+---------------------+
| 2021-12-04 19:36:44 |
+---------------------+
1 row in set (0.00 sec)

The NOW() function is easy to use and provides a handy date/time value you can use to calculate information based off on well, now.

(Learn more about the NOW() function here.)

PHP: date_add() | MySQL: DATE_ADD()

If you have a date value that you want to add another unit (DAY, MONTH, YEAR, etc) to, both PHP’s date_add() and MySQL’s DATE_ADD() functions return this type of calculation.

date_add()

Syntax PHP date_add():

date_add($date_object, $interval_amount)

(Note: date_add() is in the procedural style.)

There are several date intervals you can pass to date_add() as the 2nd parameter. In this example, I specify ’21 days’ in the date_interval_create_from__date_string() function (which is the 2nd parameter for date_add()):

$some_date = date_create('2021-12-04');
date_add($some_date, date_interval_create_from_date_string('21 days'));
echo date_format($some_date,'Y-m-d');

Returns:

2021-12-25

Months are a perfectly valid date interval and in the following example, we calculate a date 3 months in the future:

$some_date = date_create('2021-12-04');
date_add($some_date, date_interval_create_from_date_string('3 months'));
echo date_format($some_date,'F jS, Y');

And in the browser we see:

March 4th, 2022

(See the online date_add() documentation for more information.)

DATE_ADD()

Syntax MySQL DATE_ADD():

DATE_ADD(date_value, INTERVAL value add_unit_value)

DATE_ADD() accepts a date or time INTERVAL value, adds that INTERVAL value to the specified date, and returns a date based on the calculation.

Adding a 6 DAY INTERVAL to the date ‘2021-12-25’ using DATE_ADD() returns the date ‘2021-12-31’ (New Year’s Eve):

mysql> SELECT DATE_ADD('2021-12-25', INTERVAL 6 DAY) AS new_yr_eve;
+------------+
| new_yr_eve |
+------------+
| 2021-12-31 |
+------------+
1 row in set (0.00 sec)

The INTERVAL value must be singular. A plural form returns an error:

mysql> SELECT DATE_ADD('2021-12-25', INTERVAL 6 DAYS) AS new_yr_eve;
ERROR 1064 (42000): You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'DAYS) AS new_yr_eve' at line 1

You can add whole months as the INTERVAL value as well:

mysql> SELECT DATE_ADD('2021-12-25', INTERVAL 6 MONTH) AS half_way;
+------------+
| half_way   |
+------------+
| 2022-06-25 |
+------------+
1 row in set (0.01 sec)

(Learn more about DATE_ADD() by visiting this site.)

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PHP: date_sub() | MySQL: DATE_SUB()

With PHP’s date_sub() function, you have all the same capabilities you do with date_add() as far as calculations are concerned. Except with date_sub(), you are subtracting the date intervals.

date_sub()

Syntax PHP date_sub():

date_sub($date_object, $interval_amount)

$some_date = date_create('2021-12-04');
date_sub($some_date, date_interval_create_from_date_string('3 months'));
echo date_format($some_date,'F jS, Y');

This echo of date_format() on the $some_date object is now:

September 4th, 2021

And if we subtract 3 days using date_sub(), we get:

$some_date = date_create('2021-12-04');
date_sub($some_date, date_interval_create_from_date_string('3 days'));
echo date_format($some_date,'F jS, Y');

December 1st, 2021

DATE_SUB()

Syntax MySQL DATE_SUB():

DATE_SUB(date_value, INTERVAL value subtract_unit_value)

We can add an INTERVAL to a date/time value using DATE_ADD() and are returned a calculated date. On the same token, we can subtract an INTERVAL using DATE_SUB(). Just as with DATE_ADD(), the specified INTERVAL unit must be in the singular.

Again, I won’t cover every available INTERVAL unit. Visit any of the links throughout the post for more information.

mysql> SELECT DATE_SUB('2021-12-25', INTERVAL 1 WEEK) AS wk_bf_xmas;
+------------+
| wk_bf_xmas |
+------------+
| 2021-12-18 |
+------------+
1 row in set (0.02 sec)

For example, an INTERVAL value of ‘2 MONTH’ returns a date 2 months prior to the target date parameter:

mysql> SELECT DATE_SUB('2021-12-25', INTERVAL 2 MONTH) AS 2mnths_bf_xmas;
+----------------+
| 2mnths_bf_xmas |
+----------------+
| 2021-10-25     |
+----------------+
1 row in set (0.00 sec)

(Learn more about DATE_SUB() and the many INTERVAL values you can use in the function call.)

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I hope you have learned something new about working with date values in PHP and MYSQL because I know I have. If you have any questions or see any mistakes in the code, please leave a comment below. Constructive comments help me better provide accurate information and are much appreciated.

Like what you have read? See anything incorrect? Please comment below and thank you for reading!!!

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Be sure and visit the “Best Of” page for a collection of my best blog posts.

Josh Otwell has a passion to study and grow as a SQL Developer and blogger. Other favorite activities find him with his nose buried in a good book, article, or the Linux command line. Among those, he shares a love of tabletop RPG games, reading fantasy novels, and spending time with his wife and two daughters.

Disclaimer: The examples presented in this post are hypothetical ideas of how to achieve similar types of results. They are not the utmost best solution(s). The majority, if not all, of the examples provided, are performed on a personal development/learning workstation environment and should not be considered production quality or ready. Your particular goals and needs may vary. Use those practices that best benefit your needs and goals. Opinions are my own.

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The post 5 PHP Date Functions and Their MySQL Equivalents appeared first on Digital Owl’s Prose.

Planet MySQL

    $webhooks = $this->configuration->newQuery()->where('enabled', 1)->get();

    foreach ($webhooks as $webhook) {
        $this->runEventThroughWebhook($event, $webhook);
    }

    private function runEventThroughWebhook(EloquentModel $event, WebhookConfiguration $webhook)
    {
        $delivery = $this->delivery->newQuery()->create([
            'webhook_id' => $webhook->id,
            'status' => 'processing',
            'request_method' => $webhook->method,
            'request_url' => $webhook->url,
        ]);

        $bag = $this->executeWebhook($webhook, $event);

        $delivery->update([
            'status' => $bag->status,
            'request_body' => $bag->body,
            'response_status' => $bag->responseStatus,
            'response_body' => $bag->responseBody,
        ]);
    }

        if ($webhook->relationLoaded('oauth2') && $webhook->oauth2) {
            try {
                $client = $this->httpFactory->oauth2($webhook->oauth2);
            } catch (RequestException $exception) {
                return WebhookExecutionBag::auth($exception->response);
            }
        } else {
            $client = $this->httpFactory->client();
        }

if ($webhook->secret) {
    $headers['X-ACME-SIGNATURE'] = hash_hmac('sha256', $body, $webhook->secret);
}

{
  "email": "",
  "country": "",
  "login_at": "",
  "organization": ""
}

    private function body(string $template, EloquentModel $event): string
    {
        $loader = new \Twig\Loader\ArrayLoader(['template' => $template]);

        $twig = new \Twig\Environment($loader);

        $policy = $this->twigSecurityPolicy();

        $twig->addExtension(new \Twig\Extension\SandboxExtension($policy));

        return $twig->render('template', ['event' => $event]);
    }

    private function twigSecurityPolicy(): TwigSecurityPolicyForWebhooks
    {
        $tags = [];

        $filters = [];

        $methods = [];

        $properties = []; 

        $functions = [];

        $policy = new \Twig\Sandbox\SecurityPolicy($tags, $filters, $methods, $properties, $functions);

        return new TwigSecurityPolicyForWebhooks($policy);
    }

public function getTeamNameAttribute() {
    return $this->team->name;
}

final class TwigSecurityPolicyForWebhooks implements SecurityPolicyInterface
{
    public function __construct(private SecurityPolicy $policy) {}

    public function checkSecurity($tags, $filters, $functions): void
    {
        $this->policy->checkSecurity($this, $filters, $functions);
    }

    public function checkMethodAllowed($obj, $method): void
    {
        $this->policy->checkMethodAllowed($obj, $method);
    }

    public function checkPropertyAllowed($obj, $method): void
    {
        
        if ($obj instanceof \Illuminate\Database\Eloquent\Model) {
            return;
        }

        $this->policy->checkPropertyAllowed($obj, $method);
    }
}

try {
    $response = $client->withHeaders($headers)->send($webhook->method, $webhook->url, ['body' => $body]);
} catch (HttpClientException|GuzzleException $t) {
    return WebhookExecutionBag::exception($body, $t);
}

return WebhookExecutionBag::processed($body, $response);

Laravel News Links

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Link

macOS X and its successors have been around since 2001, but those of us who used Apple’s computers in the 20th century remember even earlier versions of the operating system. Designer Michael Feeney imagines what it might have been like if today’s apps ran on the more primitive user interface of macOS 9.

The Awesomer