We're doing a monthly data dump of our databases using Toad for Oracle's Export function. We've got some SQL queries to create statistics about the data. I'd like to compare the results of the current state with the last few dumps.
I can open the files with the Export File Browser in Toad (v11) and sort/filter the data using the GUI, but that's not powerful enough. Is there a way to query the dump files with SQL without having to take extra steps like creating a new schema and importing it?
By far the best way would be to reimport the data.
Related
What should be the approach to export data from clickhouse database. I want to export it into any file which supports MBs of data and create a new database locally(MySQL/PostgreSQL) for learning and project purposes.
I am using DBeaver to query and export, to .csv but process terminates itself when it reaches max rows limit.
Please suggest.
Problem:
I need to get data sets from CSV files into SQL Server Express (SSMS v17.6) as efficiently as possible. The data sets update daily into the same CSV files on my local hard drive. Currently using MS Access 2010 (v14.0) as a middleman to aggregate the CSV files into linked tables.
Using the solutions below, the data transfers perfectly into SQL Server and does exactly what I want. But I cannot figure out how to refresh/update/sync the data at the end of each day with the newly added CSV data without having to re-import the entire data set each time.
Solutions:
Upsizing Wizard in MS Access - This works best in transferring all the tables perfectly to SQL Server databases. I cannot figure out how to update the tables though without deleting and repeating the same steps each day. None of the solutions or links that I have tried have panned out.
SQL Server Import/Export Wizard - This works fine also in getting the data over to SSMS one time. But I also cannot figure out how to update/sync this data with the new tables. Another issue is that choosing Microsoft Access as the data source through this method requires a .mdb file. The latest MS Access file formats are .accdb files so I have to save the database in an older .mdb version in order to export it to SQL Server.
Constraints:
I have no loyalty towards MS Access. I really am just looking for the most efficient way to get these CSV files consistently into a format where I can perform SQL queries on them. From all I have read, MS Access seems like the best way to do that.
I also have limited coding knowledge so more advanced VBA/C++ solutions will probably go over my head.
TLDR:
Trying to get several different daily updating local CSV files into a program where I can run SQL queries on them without having to do a full delete and re-import each day. Currently using MS Access 2010 to SQL Server Express (SSMS v17.6) which fulfills my needs, but does not update daily with the new data without re-importing everything.
Thank you!
You can use a staging table strategy to solve this problem.
When it's time to perform the daily update, import all of the data into one or more staging tables. Execute SQL statement to insert rows that exist in the imported data but not in the base data into the base data; similarly, delete rows from the base data that don't exist in the imported data; similarly, update base data rows that have changed values in the imported data.
Use your data dependencies to determine in which order tables should be modified.
I would run all deletes first, then inserts, and finally all updates.
This should be a fun challenge!
EDIT
You said:
I need to get data sets from CSV files into SQL Server Express (SSMS
v17.6) as efficiently as possible.
The most efficient way to put data into SQL Server tables is using SQL Bulk Copy. This can be implemented from the command line, an SSIS job, or through ADO.Net via any .Net language.
You state:
But I cannot figure out how to refresh/update/sync the data at the end
of each day with the newly added CSV data without having to re-import
the entire data set each time.
It seems you have two choices:
Toss the old data and replace it with the new data
Modify the old data so that it comes into alignment with the new data
In order to do number 1 above, you'd simply replace all the existing data with the new data, which you've already said you don't want to do, or at least you don't think you can do this efficiently. In order to do number 2 above, you have to compare the old data with the new data. In order to compare two sets of data, both sets of data have to be accessible wherever the comparison is to take place. So, you could perform the comparison in SQL Server, but the new data will need to be loaded into the database for comparison purposes. You can then purge the staging table after the process completes.
In thinking further about your issue, it seems the underlying issue is:
I really am just looking for the most efficient way to get these CSV
files consistently into a format where I can perform SQL queries on
them.
There exist applications built specifically to allow you to query this type of data.
You may want to have a look at Log Parser Lizard or Splunk. These are great tools for querying and digging into data hidden inside flat data files.
An Append Query is able to incrementally add additional new records to an existing table. However the question is whether your starting point data set (CSV) is just new records or whether that data set includes records already in the table.
This is a classic dilemma that needs to be managed in the Append Query set up.
If the CSV includes prior records - then you have to establish the 'new records' data sub set inside the CSV and append just those. For instance if you have a sequencing field then you can use a > logic from the existing table max. If that is not there then one would need to do a NOT compare of the table data with the csv data to identify which csv records are not already in the table.
You state you seek something 'more efficient' - but in truth there is nothing more efficient than a wholesale delete of all records and write of all records. Most of the time one can't do that - but if you can I would just stick with it.
I want to transfer SQL query results to a new csv file. This is because I have placed my SQL query inside a loop which will generate export query results to csv file each time. I'm using MS SQL Server 2012. I don't want to take GUI option.
Sql Server is not really designed to import and export files. You can use bulk copy program but I dont think it works in tsql code (looping). You can use openrowset but you need to set a special flag that opens up your surface area of attack which some do not want to do.
The answer is SSIS (or a tool like Talend). It comes with Sql and is designed by MS as the go to tool for import and export from Sql. If you were to right click on the data base, choose tasks and then export the wizard eventually creates and executes an SSIS package.
I recommend you reconsider a GUI option.
ps - Another answer was to use save results as. I have heard of problems using this method including problems with delimiters or text qualified fields.
There are multiple ways to attain this. Either you can export the resultset using BCP or using IMPORT/ EXPORT or using CTRL+SHIFT+S (this will change the resultset to SAVE AS. Hope this may help.
That's my first post in stackoverflow, and i've to say that i really like that website !
For a project i need to export then re-import some huge Oracle tables from one DB to another (around 100 million lines and 30 rows).
My idea is to export the table in a flat file and then reimport into another empty table considering that the schema already exists.
I'm using PL/SQL Developer and/or SQL*Plus to make my operations.
I've tested SQL*Loader which seems to do a good job but it's really slow in my opinion : about 30 seconds to make an import of a CSV file with 1 million lines/30 rows.
Which solution could you bring? Is SQL*Loader is the best tool? Some better tools already exists?
Is CSV the better format talking about size and processing time?
Thanks a lot in advance.
Use Oracle DataPump aka expdp and impdp Overview of Oracle Data Pump See Examples of Using Data Pump Export and Examples of Using Data Pump Import
There really is no need to program this on your own, there is no way that you can outperform expdp/impdp. Don't forget there is also an impdp option to use a network_link. In that case, you just skip the dmp file and import directly into the target database. This can be done using impdp from the commandline but also using dbms_datapump using pl/sql. See PL/SQL Packages and Types Reference for docu.
You can use one of the following option:
SQL Loader (which you already might be trying out).
Traditional data export and Import (exp / imp commands).
Oracle data pump (expdp /impdp).
Also if you need to do this regularly then you can schedule this using oracle scheduler or shell script.
I am a C# developer, I am not really good with SQL. I have a simple questions here. I need to move more than 50 millions records from a database to other database. I tried to use the import function in ms SQL, however it got stuck because the log was full (I got an error message The transaction log for database 'mydatabase' is full due to 'LOG_BACKUP'). The database recovery model was set to simple. My friend said that importing millions records using task->import data will cause the log to be massive and told me to use loop instead to transfer the data, does anyone know how and why? thanks in advance
If you are moving the entire database, use backup and restore, it will be the quickest and easiest.
http://technet.microsoft.com/en-us/library/ms187048.aspx
If you are just moving a single table read about and use the BCP command line tools for this many records:
The bcp utility bulk copies data between an instance of Microsoft SQL Server and a data file in a user-specified format. The bcp utility can be used to import large numbers of new rows into SQL Server tables or to export data out of tables into data files. Except when used with the queryout option, the utility requires no knowledge of Transact-SQL. To import data into a table, you must either use a format file created for that table or understand the structure of the table and the types of data that are valid for its columns.
http://technet.microsoft.com/en-us/library/ms162802.aspx
The fastest and probably most reliable way is to bulk copy the data out via SQL Server's bcp.exe utility. If the schema on the destination database is exactly identical to that on the source database, including nullability of columns, export it in "native format":
http://technet.microsoft.com/en-us/library/ms191232.aspx
http://technet.microsoft.com/en-us/library/ms189941.aspx
If the schema differs between source and target, you will encounter...interesting (yes, interesting is a good word for it) problems.
If the schemas differ or you need to perform any transforms on the data, consider using text format. Or another format (BCP lets you create and use a format file to specify the format of the data for export/import).
You might consider exporting data in chunks: if you encounter problems it gives you an easier time of restarting without losing all the work done so far.
You might also consider zipping the exported data files up to minimize time on the wire.
Then FTP the files over to the destination server.
bcp them in. You can use the bcp utility on the destination server for the BULK IMPORT statement in SQL Server to do the work. Makes no real difference.
The nice thing about using BCP to load the data is that the load is what is described as a 'non-logged' transaction, though it's really more like a 'minimally logged' transaction.
If the tables on the destination server have IDENTITY columns, you'll need to use SET IDENTITY statement to disable the identity column on the the table(s) involved for the nonce (don't forget to reenable it). After your data is imported, you'll need to run DBCC CHECKIDENT to get things back in synch.
And depending on what your doing, it can sometimes be helpful to put the database in single-user mode or dbo-only mode for the duration of the surgery: http://msdn.microsoft.com/en-us/library/bb522682.aspx
Another approach I've used to great effect is to use Perl's DBI/DBD modules (which provide access to the bulk copy interface) and write a perl script to suck out the data from the source server, transform it and bulk load it directly into the destination server, without having to save it to disk and move it. Also means you can trap errors and design things for recovery and restart right at the point of failure.
Use BCP to migrate data.
Another approach i have used in the past is to take a backup of the transaction log and shrink the log Prior to the migration. Split the migration script in parts and run the log backup- shrink - migrate iteration a few times.