I have written a VBScript to extract data from Active Directory into a record set. I'm now wondering what the most efficient way is to transfer the data into a SQL database.
I'm torn between;
Writing it to an excel file then firing an SSIS package to import it or...
Within the VBScript, iterating through the dataset in memory and submitting 3000+ INSERT commands to the SQL database
Would the latter option result in 3000+ round trips communicating with the database and therefore be the slower of the two options?
Sending an insert row by row is always the slowest option. This is what is known as Row by Agonizing Row or RBAR. You should avoid that if possible and take advantage of set based operations.
Your other option, writing to an intermediate file is a good option, I agree with #Remou in the comments that you should probably pick CSV rather than Excel if you are going to choose this option.
I would propose a third option. You already have the design in VB contained in your VBscript. You should be able to convert this easily to a script component in SSIS. Create an SSIS package, add a DataFlow task, add a Script Component (as a datasource {example here}) to the flow, write your fields out to the output buffer, and then add a sql destination and save yourself the step of writing to an intermediate file. This is also more secure, as you don't have your AD data on disk in plaintext anywhere during the process.
You don't mention how often this will run or if you have to run it within a certain time window, so it isn't clear that performance is even an issue here. "Slow" doesn't mean anything by itself: a process that runs for 30 minutes can be perfectly acceptable if the time window is one hour.
Just write the simplest, most maintainable code you can to get the job done and go from there. If it runs in an acceptable amount of time then you're done. If it doesn't, then at least you have a clean, functioning solution that you can profile and optimize.
If you already have it in a dataset and if it's SQL Server 2008+ create a user defined table type and send the whole dataset in as an atomic unit.
And if you go the SSIS route, I have a post covering Active Directory as an SSIS Data Source
Related
I often have to execute complex sql scripts in a single transaction on a large PostgreSQL database and I would like to verify everything that was changed during the transaction.
Verifying each single entry on each table "by hand" would take ages.
Dumping the database before and after the script to plain sql and using diff on the dumps isn't really an option since each dump would be about 50G of data.
Is there a way to show all the data that was added, deleted or modified during a single transaction?
Dude, What are you looking for is the most searchable thing on the internet when it comes to capturing Database changes. It is a kind of version control we can say.
But as long as I know, sadly there are no in-built approaches are available in PostgreSQL or MySql. But you can overcome it by setting/adding some triggers for your most usable operations.
You can create some backup schemas, and tables to capture your changes that are changed(updated), created, or deleted.
In this way you can achieve what you want. I know this process is fully manual, But really effective.
If you need to analyze the script's behaviour only sporadically, then the easiest approach would be to change server configuration parameter log_min_duration_statement to 0 and then back to any value it had before the analysis. Then all of the script activity will be written to the instance log.
This approach is not suitable if your storage is not prepared to accommodate this amount of data, or for systems in which you don't want sensitive client data to be written to a plain-text log file.
I have a scenario where I have a lot of files in a CSV file i need to do operations on. The script needs to be able to handle if script is stopped or failed, then it should continue where i stopped from. In a database scenario this would be fairly simple. I would have an updated column and update that when operation for the line has completed. I have looked if I somehow could update the CSV on the fly, but I dont think that is possible. I could start having multiple files, but not that elegant. Can anyone recommend some kind of simple file based DB like framework? Where I from PowerShell could create a new database file (maybe json) and read from it and update on the fly.
If your problem is really so complex, that you actually need somewhat of a local database solution, then consider to go with SQLite which was built for such scenarios.
In your case, since you process an CSV row-by-row, I assume storing the info for the current row only will be enough. (Line number, status etc.)
I am new to SSIS and am after some assistance in creating an SSIS package to do a specific task. My data is stored remotely within a MySQL Database and this is downloaded to a SQL Server 2014 Database. What I want to do is the following, create a package where I can enter 2 dates that can be compared against the create date/date modified per record on a number of tables to give me a snap shot and compare the MySQL Data to the SQL Data so that I can see if there are any rows that are missing from my local SQL Database or if any need to be updated. Some tables have no dates so I just want to see a record count on what is missing if anything between the 2. If this is better achieved through TSQL I am happy to hear about other suggestions or sites to look at where things have been done similar.
In relation to your query Tab :
"Hi Tab, What happens at the moment is our master data is stored in a MySQL Database, the data was then downloaded to a SQL Server Database as a one off. What happens at the moment is I have a SSIS package that uses the MAX ID which can be found on most of the tables to work out which records are new and just downloads them or updates them. What I want to do is run separate checks on the tables to make sure that during the download nothing has been missed and everything is within sync. In an ideal world I would like to pass in to a SSIS package or tsql stored procedure a date range, shall we say calender week, this would then check for any differences between the remote MySQL database tables and the local SQL tables. It does not currently have to do anything but identify issues, correcting them may come later or changes would need to be made to the existing sync package. Hope his makes more sense."
Thanks P
To do this, you need to implement a Type 1 Slowly Changing Dimension type data flow in SSIS. There are a number of ways to do this, including a built in transformation aptly called the Slowly Changing Dimension transformation. Whilst this is easy to set up, it is a pain to maintain and it runs horrendously slowly.
There are numerous ways to set this up using other transformations or even SQL merge statements which are detailed here: https://bennyaustin.wordpress.com/2010/05/29/alternatives-to-ssis-scd-wizard-component/
I would recommend that you use Lookup transformations as they perform better than the Slowly Changing Dimension transformation but offer better diagnostics and error handling than the better performing SQL merge statement.
Before you do this you will need to add a Checksum or Hashbytes column to your SQL data for ease of comparison with the incoming MySQL data.
In short, calculate some sort of repeatable checksum as the data is downloaded into your SQL Server, then use this in an SSIS Lookup, matching on the row key, to check for changes. Where the checksum value is different for the same row it needs updating and where there is no matching row key in your SQL Data you need to insert the new row.
I have been saving files as .csv for over a year now and connecting those files to Tableau Desktop for visualization for some end-users (who use Tableau Reader to view the data).
I think I settled on migrating to postgreSQL and I will be using the pandas library to_sql to fill it up.
I get 9 different files each day and I process each of them (I currently consolidate them into monthly files in .csv.bz2 format) by adding columns, calculations, replacing information, etc.
I create two massive csv files using pd.concat and pd.merge out of those
processed files which Tableau is connected to. These files are literally overwritten every day when new data is added which is time consuming
Is it okay to still do my file joins and concatenation with pandas and export the output data to postgres? This will be my first time using a real database and I am more comfortable with pandas compared to learning SQL syntax and creating views or tables. I just want to avoid overwriting the same csv files over and over (and some other csv problems I run into).
Don't worry too much about normalization. A properly normalized database will usually be more efficient and easier to handle than an non-normalized. On the other hand, if you have non-normalized csv data you dump into a database, your import functions will be a lot more complicated if you do a proper normalization. I think I would recommend you to make one step at the time. Start up with just loading the processed csv-files into postgres. I am pretty sure all processing following that will be a lot easier and quicker than doing it using csv-files (just make sure you set up the right indexes). When you start to get used to using the database, you can start to do more processing there.
Just remember, one thing a database is really good at is to pick out the subset of data you want to work on. Try as much as possible to avoid pulling out huge amount of data from the database when you only intend to work on a subset of it.
We're considering using SSIS to maintain a PostgreSql data warehouse. I've used it before between SQL Servers with no problems, but am having a lot of difficulty getting it to play nicely with Postgres. I’m using the evaluation version of the OLEDB PGNP data provider (http://www.postgresql.org/about/news.1004).
I wanted to start with something simple like UPSERT on the fact table (10k-15k rows are updated/inserted daily), but this is proving very difficult (not to mention I’ll want to use surrogate keys in the future).
I’ve attempted (Link) and (http://consultingblogs.emc.com/jamiethomson/archive/2006/09/12/SSIS_3A00_-Checking-if-a-row-exists-and-if-it-does_2C00_-has-it-changed.aspx) which are effectively the same (except I don’t really understand the union all at the end when I’m trying to upsert) But I run into the same problem with parameters when doing the update using a OLEDb command – which I tried to overcome using (http://technet.microsoft.com/en-us/library/ms141773.aspx) but that just doesn’t seem to work, I get a validation error –
The external columns for complent.... are out of sync with the datasource columns... external column “Param_2” needs to be removed from the external columns.
(this error is repeated for the first two parameters as well – never came across this using the sql connection as it supports named parameters)
Has anyone come across this?
AND:
The fact that this simple task is apparently so difficult to do in SSIS suggests I’m using the wrong tool for the job - is there a better (and still flexible) way of doing this? Or would another ETL package be better for use between two Postgres database? -Other options include any listed on (http://en.wikipedia.org/wiki/Extract,_transform,_load#Open-source_ETL_frameworks). I could just go and write a load of SQL to do this for me, but I wanted a neat and easily maintainable solution.
I have used the Slowly Changing Dimension wizard for this with good success. It may give you what you are looking for especially with the Wizard
http://msdn.microsoft.com/en-us/library/ms141715.aspx
The External Columns Out Of Sync: SSIS is Case Sensitive - I encountered this issue multiple times and it makes me want to pull my hair out.
This simple task is going to take some work either way. SSIS is by no means an enterprise class ETL product yet, but it does give you some quick and easy functionality, and is sufficient for most ETL work. I guess it is also about your level of comfort with it as well.
SCD is way too slow for what I want. I need to use set based sql.
It turned out that a lot of my problems were with bugs in the provider.
I opened a forum topic (http://www.pgoledb.com/forum/viewtopic.php?f=4&t=49) and had a useful discussion with the moderator/support/developer person.
Also Postgres doesn't let you do cross db querys, so I solved the problem this way:
Data Source from Production DB to a temp Archive DB table
Run set based query between temp table and archive table
Truncate temp table
Note that the temp table is not atchally a temp table, but a copy of the archive table schema to temporarily stored data in.
Took a while, but I got there in the end.
This simple task is going to take some work either way. SSIS is by no means an enterprise class ETL product yet, but it does give you some quick and easy functionality, and is sufficient for most ETL work. I guess it is also about your level of comfort with it as well.
What enterprise ETL solution would you suggest?