How to read .sql file into pandas without a database? - sql

I was given a .sql file, which is snapshot of a postgres db. I have only the file itself, which is quite large, and want to read the tables into pandas or some other container. There is no external database to make a connection to, apparently it's all in the file.
How can I read a file like this using a Python script?
I searched on this and found references to cases where the .sql file contains queries into an existing database but this is not my case.

Related

How to store files in sqlite3 database

is there a way to store .txt or .pdf files within a table of my sqlite3 database?
yes is possible to store file in sqlite you can see this link to find how to store file
but I suggest you to don't do that because if you store some file in database, it's become too Heavy and slow to get query
the manageable situation is your save file in file manager and store file location address in database
Yes, you can store file in database in 2 ways
Store as binary or blobs
Store file in physical and path only store in database.
But both got some disadvantages.
If binary or blobs , database get heavy and make slow performance in query.
if file path only in database, when u backup and restore database in another place, then need to move physical file also and also anybody delete file from physical folder directly.
Is your requirements is small , then got with binary.let choose yourself.

Loading a csv file on local system to redshift database

I want to load a csv file which is present on my local system to my redshift database. What are the options I can use to upload it?
I am currently using Dbeaver for the db connection and tried importing the data directly. However, that doesn't seem to work.
You can't, Redshift can't see your local machine. Transfer it to S3, then Redshift can read it. Or write A Lot of insert statements.
https://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html

Quickest way to import a large (50gb) csv file into azure database

I've just consolidated 100 csv.files into a single monster file with a total size of about 50gb.
I now need to load this into my azure database. Given that I have already created my table in the database what would be the quickest method for me to get this single file into the table?
The methods I've read about include: Import Flat File, blob storage/data factory, BCP.
I'm looking for the quickest method that someone can recommend please?
Azure data factory should be a good fit for this scenario as it is built to process and transform data without worrying about the scale.
Assuming that you have the large csv file stored somewhere on the disk you do not want to move it to any external storage (to save time and cost) - it would be better if you simply create a self integration runtime pointing to your machine hosting your csv file and create linked service in ADF to read the file. Once that is done, simply ingest the file and point it to the sink which is your SQL Azure database.
https://learn.microsoft.com/en-us/azure/data-factory/connector-file-system

Import large table to azure sql database

I want to transfer one table from my SQL Server instance database to newly created database on Azure. The problem is that insert script is 60 GB large.
I know that the one approach is to create backup file and then load it into storage and then run import on azure. But the problem is that when I try to do so than while importing on azure IO have an error:
Could not load package.
File contains corrupted data.
File contains corrupted data.
Second problem is that using this approach I cant copy only one table, the whole database has to be in the backup file.
So is there any other way to perform such an operation? What is the best solution. And if the backup is the best then why I get this error?
You can use tools out there that make this very easy (point and click). If it's a one time thing, you can use virtually any tool (Red Gate, BlueSyntax...). You always have BCP as well. Most of these approaches will allow you to backup or restore a single table.
If you need something more repeatable, you should consider using a backup API or code this yourself using the SQLBulkCopy class.
I don't know that I'd ever try to execute a 60gb script. Scripts generally do single inserts which aren't very optimized. Have you explored using various bulk import/export options?
http://msdn.microsoft.com/en-us/library/ms175937.aspx/css
http://msdn.microsoft.com/en-us/library/ms188609.aspx/css
If this is a one-time load, using a IaaS VM to do the import into the SQL Azure database might be a good alternative. The data file, once exported could be compressed/zipped and uploaded to blob storage. Then pull that file back out of storage into your VM so you can operate on it.
Have you tried using BCP in the command prompt?
As explained here: Bulk Insert Azure SQL.
You basically create a text file with all your table data in it and bulk copy it your azure sql database by using the BCP command in the command prompt.

Has HSQLDB some mechanism to save in-memory data to file?

Has HSQLDB some mechanism for saving in-memory data to file?
As I know after the server is shutted down, all in-memory data become unaccessible. So I want to save all in-memory data to file.
Unfortunately I can't use BACKUP mechanism, because it can't be applied for in-memory data.
HSQLDB databases are of different types. The all-in-memory databases do not store the data to disk. These databases have URLs in the form jdbc:hsqldb:mem:<name>.
If your database URL is in the form jdbc:hsqldb:file:<file path> and your tables are the default MEMORY tables, the data is all in memory but the changes are written to a set of disk files.
With all types of database, including all_in_memory, you can use the SQL statement SCRIPT <file path> to save the full database to a file. If you save the data to a file with the .script extension, you can open the file as a file database.
When you run a server, the URL's are used without the jdbc:hsqldb prefix, for example server.database.0=file:C:/myfile
See the guide here http://hsqldb.org/doc/2.0/guide/running-chapt.html#running_db-sect
There is an SQL command for that. Try this:
SCRIPT '/tmp/data.sql'
See here for details.