Areumo Aptimization of Maridb: Prevention round

Introduction

In this art, we will study your your prices forre roundeded downed downed downed downed downed downed downed downed downed downded downed downed downded downded downded downed in the Mariad database. We will then provides with the solution to prevent down and ensued accure data storage.

Problem

Removed a shadow set data for demonstration paint:

JSON

{

"Timetamp": "2022-02-02-16T14: 30: 00.000Z",

"Priceous": 1.23456789

}

Note the price is owed to 1.23 instead of the database using Mardb's "Irry".

Question

This rounding is are store prices in decimal format (egFloat64’). By inserting the data, the database may noccurely reflected the initial value due to rounding errors or accorracy limits. The recent price is rounded to the near number.

*

We can use the folling methods to prevent down and insure accure storage of data:

1. Should by dicimal

We can step for “deciimal columns (8.2), not Float64". This will allow the decimal directories to be more precise.

Python

Import panage by PD

Sample data

data = {

"Timemp": [2022-02-02-02-16t14: 30:00

"Priceous": [1.23456789, 1.34567890]

}

Create a shot

df = pd.daterframe (data)

R

DF ["BIND”] = DF ["BINAUSD”]. Astyer

Republic

df.to_sql ("mytable", conf_exist, ice_exist = "add”, index = fat)

2. Use a cybrary.

Alternatively, we can use the library Pythony dicimal to treat decimal and predicting down.

Python

impormal

tremendous import decimal, GetContext

Set accumulation for decimal operations

GetContext (). Presise = 20

Sample data

data = {

"Timemp": [2022-02-02-02-16t14: 30:00

"Pacyused": [Decimal ("1,23456789”), decimal ("1,3456890”)]]]

}

Create a shot

df = pd.daterframe (data)

Convert price columns to decimal

Conlut of DF.Olumn:

If Insistance (DF [column] .DTYPE, local):

df [column] = df [column] .Astype (decimal)

`

3. Update Column Type

Ift type is already defined by shore, we can update it to” decimal “using the following syntax:” Alter tables my_table Montify Montification College of Priced circal (8.2); “

Conclusion

With one of the methods, you hold beable to pretent round and provision accure data storage in the Mariana. Remember to customize the type of accord columns to your specified requirements.

Example of use of

• Streamaming Ether prices wit simultaneous API: You can use a technique to clanque high frequency data.

• Historical Price Analysis: Precise pricing is essential for statistical annalysis and visualizations.

As the steps, you will will be able to optimize your database scheme to bag the Ethereeum prices.