What are some methods for optimizing Oracle databases for large data inserts?

For large data inserts I can suggest you few things like:

Use trigger(PL/SQL)
Use APPEND hint
Remove indexes on tables

Firstly, while you use triggers in the table it could leave data to be logically corrupt. And it will then perform insert in a very conventional way. Which is a time consuming process and won’t helps us!

Secondly, using APPEND hint will help us to an extend.

So, APPEND hint tells the optimizer to perform a direct-insert into the table, which improves the performance.

Now there is a way which we could achieve this by minimizing the Redo generation.

What Redo do is; it basically ensures the recoverability of data to the database. It writes down every transaction to the archive log.

Let’s take a scenario, where if the database is running on the NOARCHIVELOG mode, using APPEND hint will reduce the redo generation i.e; it won’t write into the archive log anymore and thus increases the speed.

But then it won’t be able to recover at any point in time if your data is ambiguous.

Furthermore, in the ARCHIVELOG mode, using APPEND hint won’t help us and will not reduce the redo generation until and unless your table is in NOLOGGING mode.

But also, in NOLOGGING mode our data is no longer protected. In fact, in OLTP it is very rare to see your table to NOLOGGING mode for obvious reasons.

Lastly, yes we can drop the indexes, constraints when bulk inserting into tables. Coz writing down index for every row formed will depreciate the performance of our db.

So, Ya! These are the methods for optimizing Oracle database for large data inserts.

I hope you got this!

Cheers! 👍

Link to my space: Click here

Comments

Stock Market using Python

"The stock market is a device for transferring money from the impatient to the patient." - Warren Buffett Today we'll look into few ways for accessing the stock market. And we'll do this using Python ! Now, as we know that there are 2 stock exchange in India; BSE and NSE So we'll get the data from both! To begin with let's access the data from BSE first. (P.S: I certainly like the 2nd and the 3rd method to access stock market!) * So, to import the BSE data we need to " pip install bsedata ". => And then import the module, => Create an object to store the Driver Class => Then we need to do " getQuote('script_code')" where we need to provide a script code of a company which we need to access. Just like here we have given; => And from here we can see that the script code was for the company named "V-MART". But we can't remember all the script code hence we need to download this script file from the BSE websi...

Predicting whether the bank will give loan to its customers based on their credit score (Logistic Regression)

"The best thing about data is that it tells a story." - Naveen Jain Today let's have a look at another machine learning model know as Logistic Regression. Logistic Regression is a statistical model where the outcome is predicted as binary such as YES or NO, based on the previous/train_dataset. ( Please open the images in a new tab or try to zoom-in ) import required models read your dataframe visualize your dataset create Logistic Regression model split your data into train and test dataset fit your train dataset into the regression model predict future values visualize your predicted values Github link for pdf: click here

Machine Learning model to predict price_of_house

Today let's look at one of the machine learning model which can predict the prices of house based on the described data. We'll look at the linear regression model for which we'll follow a few steps such as: import required modules read the csv file plot the graph building the linear regression model to fit/train the data predict value with a parameter have a new data file for value prediction ( to view the images either open it in a new tab or zoom-in please! )

code feat

Search This Blog