First of all, I'm very new to Informatica PowerCenter and PowerExchange.
We are using Informatica PowerCenter and PowerExchange to receive CDC data from our source DB2 to a PostgreSQL DB. Therefore we have one workflow where 7 tables are mapped and we get the result in our PostgreSQL. It works fine so far, but it's lacking performance. Not that the size of data is the problem, it's more the delay I see results in the target DB.
When I insert or delete some data on the DB2 (just like 10 rows in one db), I see the results in our PostgreSQL mostly in about ~10-30 seconds (very rare in less than 5 seconds).
My goal would be to speed up this delay. Is this possible? What would I need for that?
I played a little bit with commit interval, and DTM Buffer size, but nothing helped pretty much.
Also I have the feeling that when I configure the workflow to run continuously, it's even slower, compared to when I execute the workflow, after I made the Inserts/Deletes.
Thanks in advance
Related
We’re experiencing slow query performance on AWS Redshift. Frequently we see that queries can take ±12 seconds to run, but only very little time (<500ms) is spent actually executing the query (according to the AWS Redshift console for an individual query).
Querying from svl_compile we can confirm that the query compilation plan is already compiled.
In svl_query_report we see a long time delay between the start times of 2 segments accounting for the majority of the run time, although the segments themselves all execute very quickly (milliseconds)
There are a number of things that could be going on but I suspect network distribution is involved. Check STL_DIST.
Another possibility is that Redshift broke the query up and a subquery is running during that window. This can happen with very complex queries. Review the plan and see if there are any references to computer generated table names (I think they begin with't' but this is just from memory).
Spilling to disk could be happening but this seems unlikely given what you have said so far. Also queuing delays doesn't seem like a match. Both are possible but not likely.
If you post more info about how the query is running things will narrow down. Actual execution report, explain plan, and/or logging table info would help hone in on what is happening during this time window.
I am looking for a way to save the result set of an ANALYZE Compression to a table / Joining it to another table in order to automate compression scripts.
Is it Possible and how?
You can always run analyze compression from an external program (bash script is my go to), read the results and store them back up to Redshift with inserts. This is usually the easiest and fastest way when I run into these type of "no route from leader to compute node" issues on Redshift. These are often one-off scripts that don't need automation or support.
If I need something programatic I'll usually write a Lambda function (or possibly a python program on an ec2). Fairly easy and execution speed is high but does require an external tool and some users are not happy running things outside of the database.
If it needs to be completely Redshift internal then I make a procedure that keeps the results of the leader only query in cursor and then loops on the cursor inserting the data into a table. Basically the same as reading it out and then inserting back in but the data never leaves Redshift. This isn't too difficult but is slow to execute. Looping on a cursor and inserting 1 row at a time is not efficient. Last one of these I did took 25 sec for 1000 rows. It was fast enough for the application but if you need to do this on 100,000 rows you will be waiting a while. I've never done this with analyze compression before so there could be some issue but definitely worth a shot if this needs to be SQL initiated.
I am reading this paper: "Need for Speed - Boost Performance in Data Processing with SAS/Access® Interface to Oracle". And I would like to know how to clear the cache / buffer in SAS, so my repeated query / test will be reflective of the changes accurately?
I noticed the same query running the first time takes 10 seconds, and (without) changes running it immediately after will take shorter time (say 1-2 seconds). Is there a command / instruction to clear the cache / buffer. So I can have a clean test for my new changes.
I am using SAS Enterprise Guide with data hosted on an Oracle server. Thanks!
In order to flush caches on the Oracle side, you need both DBA privileges (to run alter system flush buffer_cache; in Oracle) and OS-level access (to flush the OS' buffer cache - echo 3 > /proc/sys/vm/drop_caches on common filesystems under Linux).
If you're running against a production database, you probably don't have those permissions -- you wouldn't want to run those commands on a production database anyways, since it would degrade the performance for all users of the database, and other queries would affect the time it takes to run yours.
Instead of trying to accurately measure the time it takes to run your query, I would suggest paying attention to how the query is executed:
what part of it is 'pushed down' to the DB and how much data flows between SAS and Oracle
what is Oracle's explain plan for the query -- does it have obvious inefficiencies
When a query is executed in a clearly suboptimal way, you will find (more often than not) that the fixed version will run faster both with cold and hot caches.
To apply this to the case you mention (10 seconds vs 2 seconds) - before thinking how to measure this accurately, start by looking
if your query gets correctly pushed down to Oracle (it probably does),
and whether it requires a full table (partition) scan of a sufficiently large table (depending on how slow the IO in your DB is - on the order of 1-10 GB).
If you find that the query needs to read 1 GB of data and your typical (in-database) read speed is 100MB/s, then 10s with cold cache is the expected time to run it.
I'm no Oracle expert but I doubt there's any way you can 'clear' the oracle cache (and if there were you would probably need to be a DBA to do so).
Typically what I do is I change the parameters of the query slightly so that the exact query no longer matches anything in the cache. For example, you could change the date range you are querying against.
It won't give you an exact performance comparison (because you're pulling different results) but it will give you a pretty good idea if one query performs significantly better than the other.
I am working on production environment. Last day accidentally I made changes to Master dataset permanently while trying to get the sample out of it in work directory. Unfortunately they don't have any backup for this data.
I wanted to execute this:
Data work.facttable;
Set Master.facttable(obs=10);
run;
instead of this, accidentally I executed the following:
data Master.facttable;
set Master.facttable(obs=10);
run;
You can clearly see what sort of blunder it was!
Facttable has been building up nearly from 2 long years and it is of 250GB and has millions of rows. Now it has 10 rows and is of 128kb :(
I am very much worried how to recover the data back. It is crucial for the business teams. I have no idea how to proceed to get it back.
I know that SAS doesn't support any rollback options or recovery process. We don't use Audit trail method also.
I am just wondering if there is any way that still we can get the data back in spite of all these.
Details: Dataset is assigned on SPDE Engine. I checked the data files(.dpf) but all were disappeared except yesterday's data file which is of 128kb
You appear to have exhausted most of the simple options already:
Restore from external/OS-level backup
Restore from previous generation via the gennum= data set option (only available if the genmax option was set to 1+ when creating the dataset).
Restore from SAS audit trail
I think that leaves you with just 2 options:
Rebuild the dataset from the underlying source(s), if you still have them.
Engage the services of a professional data recovery company, who might be able to recover some or all of the deleted files, depending on the complexity of your storage environment, and how much of the original 250GB has since been overwritten.
Either way, it sounds as though this may prove to have been an expensive mistake.
I'm trialing FluentMigrator as a way of keeping my database schema up to date with minimum effort.
For the release I'm currently building, I need to run a database script to make a simple change to a large number of rows of existing data (around 2% of 21,000,000 rows need to be updated).
There's too much data for to be updated in a single transaction (the transaction log gets full and the script aborts), so I use a WHILE loop to iterate through the table, updating 10,000 rows at a time, each batch in a separate transacticon. This works, and takes around 15 minutes to run to completion.
Now I have the script complete, I'm trying to integrate it into FluentMigrator.
FluentMigrator seems to run all the migrations for a single batch in one transaction.
How do I get FM to run each migration in a separate transaction?
Can I tell FM to not use a transaction for a specific migration?
This is not possible as of now.
There are ongoing discussions and some work already in progress.
Check it out here : https://github.com/schambers/fluentmigrator/pull/178
But your use case will surely help in pushing the things in the right direction.
You are welcome to take part to the discussion!
Maybe someone will find a temporary workaround?