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How to perform efficient membership search on very large datasets in Python


How to perform efficient membership search on very large datasets in Python

By : Maureen Feeley
Date : November 21 2020, 04:03 PM
it fixes the issue BioPython has a trie implementation for exactly this purpose.
code :
from Bio import trie
tr = trie.trie()


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Efficient way to create large number of Membership via Membership.CreateUser?

Efficient way to create large number of Membership via Membership.CreateUser?


By : devgu
Date : March 29 2020, 07:55 AM
Does that help Solution1:
Get all data from user table into dataset ds in table "User" Get all data from member table into table "Member" in the same dataset ds Create a relationship between the two tables on field userid Run a loop on each row of table User For each user call Membership.CreateUser with UserName and Password as parameters Get all child rows for the current user DataRow.GetChildRows For each childrow returned, call Roles.AddUserToRole with username and rolename as parameters (add only if the role is active)
code :
protected void batchInsertButton_Click(object sender, EventArgs e)
{
string strConn = ConfigurationManager.ConnectionStrings["1ConnectionString"].ConnectionString;
string strSQL = "SELECT * FROM yourUsers$";

SqlConnection myConnection = new SqlConnection(strConn); 

myConnection.Open();

SqlCommand myCommand = new SqlCommand(strSQL,myConnection);

SqlDataReader myReader; 


myReader = myCommand.ExecuteReader(); 
    while (myReader.Read()) {

        ObjectDataSourceMembershipUser.InsertParameters["UserName"].DefaultValue = myReader["UserName"].ToString();//TextBoxUserName.Text; ;
        ObjectDataSourceMembershipUser.InsertParameters["password"].DefaultValue = myReader["password"].ToString();//TextBoxPassword.Text;
        ObjectDataSourceMembershipUser.InsertParameters["passwordQuestion"].DefaultValue ="your qestion";//TextBoxPasswordQuestion.Text;
        ObjectDataSourceMembershipUser.InsertParameters["passwordAnswer"].DefaultValue = "your answer";//TextBoxPasswordAnswer.Text;
        ObjectDataSourceMembershipUser.InsertParameters["email"].DefaultValue = myReader["email"].ToString();//TextBoxEmail.Text;
        ObjectDataSourceMembershipUser.InsertParameters["isApproved"].DefaultValue = "true";//CheckboxApproval.Checked == true ? "true" : "false";

ObjectDataSourceMembershipUser.Insert();


//hard code this user role
Roles.AddUserToRole(myReader["UserName"].ToString(), "NormalUser"); 

        } 
myConnection.Close();

GridViewMemberUser.DataBind();
GridViewRole.DataBind();
}  
how to perform efficient search on non-index field of a very large table in sql server

how to perform efficient search on non-index field of a very large table in sql server


By : sania dias
Date : March 29 2020, 07:55 AM
To fix the issue you can do Sounds like you'll need to use Full-Text Search:
Speeding up checking of IP address membership in CIDR ranges, for large datasets

Speeding up checking of IP address membership in CIDR ranges, for large datasets


By : SistemaMPIP
Date : March 29 2020, 07:55 AM
To fix the issue you can do In a Postgres DB, I need to filter a set of several hundred thousand rows in a table A by including only those rows for which an IP address column (of type inet) in the row matches any of several thousand IP address blocks (of type cidr) in another table B. I've tried various indexes on the inet addresses in the first table and the cidr ranges in the second, but no matter what I do, the planner does a nested sequential scan, applying the << operator to every pair of IP addresses and prefixes. , Case closed. To make things fast, do the following:
Creating custom iterators in Python to search for sublists within large datasets

Creating custom iterators in Python to search for sublists within large datasets


By : Dmitriy Shiryaev
Date : March 29 2020, 07:55 AM
hop of those help? As I understand your question, you need iteration in the dataset loading function. And here it depends, where does the data come from. For files and databases you will find easy solutions in the documentation. If you are using some web API, you may use pagination.
OverflowError: Python int too large to convert to C long torchtext.datasets.text_classification.DATASETS['AG_NEWS']()

OverflowError: Python int too large to convert to C long torchtext.datasets.text_classification.DATASETS['AG_NEWS']()


By : user3202185
Date : March 29 2020, 07:55 AM
This might help you I also encountered a similar problem. I changed a line of code in my torchtext\utils.py file and my error disappeared.
csv.field_size_limit(sys.maxsize) -- Changed this
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