I have a question about how to handle errors with holistic SQL queries. We are using Oracle PL/SQL. Most of our codebase is row-by-row processing which ends up in extremely poor performance. As far as I understand the biggest problem with that is the context switches between the PL/SQL and SQL engine.
Problem with that is that the user don't know what went wrong. Old style would be like:
Cursor above some data
Fire SELECT (count) in another table if data exists, if not show errormsg
SELECT that data
Fire second SELECT (count) in another table if data exists, if not show errormsg
SELECT that data
modify some other table
And that could go on for 10-20 tables. It's basically pretty much like a C program. It's possible to remodel that to something like:
UPDATE (
SELECT TAB1.Status,
10 AS New_Status
FROM TAB1
INNER JOIN TAB2 ON TAB1.FieldX = TAB2.FieldX
INNER ..
INNER ..
INNER ..
INNER ..
LEFT ..
LEFT ..
WHERE TAB1.FieldY = 2
AND TAB3.FieldA = 'ABC'
AND ..
AND ..
AND ..
AND ..
) TAB
SET TAB.Status = New_Status
WHERE TAB.Status = 5;
A holistic SELECT like that would speed up a lot of things extremely. I changed some queries like that and that stuff went down from 5 hours to 3 minutes but that was kinda easy because it was a service without human interaction.
Question is how would you handle stuff like that were someone fills some form and waits for a response. So if something went wrong they need an errormsg. Only solution that came to my mind was checking if rows were updated and if not jump into another code section that still does all the single selects to determinate that error. But after every change we would have to update the holistic select and all single selects. Guess after some time they would differ and lead to more problems.
Another solution would be a generic errormsg which would lead to hundred calls a day and us replacing 50 variables into the query, kill some of the where conditions/joins to find out what condition filtered away the needed rows.
So what is the right approach here to get performance and still be kinda user friendly. At the moment our system feels unusable slow. If you press a button you often have to wait a long time (typically 3-10 seconds, on some more complex tasks 5 minutes).
Set-based operations are faster than row-based operations for large amounts of data. But set-based operations mostly apply to batch tasks. UI tasks usually deal with small amounts of data in a row by row fashion.
So it seems your real aim should be understanding why your individual statements take so long.
" If you press a button you often have to wait a long time (typically 3-10 seconds on some complexer tasks 5 minutes"
That's clearly unacceptable. Equally clearly it's not possible for us to explain it: we don't have the access or the domain knowledge to diagnose systemic performance issues. Probably you need to persuade your boss to spring for a couple of days of on-site consultancy.
But here is one avenue to explore: locking.
"many other people working with the same data, so state is important"
Maybe your problems aren't due to slow queries, but to update statements waiting on shared resources? If so, a better (i.e. pessimistic) locking strategy could help.
"That's why I say people don't need to know more"
Data structures determine algorithms. The particular nature of your business domain and the way its data is stored is key to writing performative code. Why are there twenty tables involved in a search? Why does it take so long to run queries on these tables? Is STORAGE_BIN_ID not a primary key on all those tables?
Alternatively, why are users scanning barcodes on individual bins until they find one they want? It seems like it would be more efficient for them to specify criteria for a bin, then a set-based query could allocate the match nearest to their location.
Or perhaps you are trying to write one query to solve multiple use cases?
Related
i try to tune an existing View. I'm sorry for not posting an example but i failed to replicate the Problem. And i can't understand the behavior.
My View (View A) based on another view (View b) and a Table (Table C). In the select list some fields of these and some Package calls are used.
The runtime of a specific Select is nearly 32 seconds.
I analyzed the Statement and startet to optimize View b. I droped all columns i don't need in View a and reduced the overhead of the View b.
After this the select on View a was 5 seconds faster. I executed the select multiple times to get a valid average execute time, to be sure.
A Few minutes later i axecuted the Statement again and i got 32 Seconds. I executed this multiple times but it won't become faster.
There is no trafic on this database, the amount of data didn't change. And this is, until now, the first statement i have problems with getting reasonable results will analyzing the statment.
The Explainplan, I watched first, looks fine. No Full Table Scan (I know FTS is not overall bad). I have no idea why the executetime is so unstable, this made it hard to optimize the View and compare the results.
I think this sounds like a dump question, but i can't see the problem or have an idea what to do next.
Thanks and sorry for my bad english.
Unstable execute time
1) Did you clean up buffer cache between select statements?
2) In some situation you can improve your function and views with result_cacheing. But you have to figure out if it is applicable for your problem? (long story about resultchahe)[http://www.oracle.com/technetwork/articles/datawarehouse/vallath-resultcache-rac-284280.html]
I have a product table which as many columns. Primary key is productid. There 50,000 rows but when I issue a select statement like select * from products then it is taking 10 minutes to get the full data. So advise me what to do as a result I can run my query faster.
Is your primary key also the clustering key on that table?
If you do a SELECT * .... you'll basically always get a full table scan. There's really nothing that can speed that query up - you want all rows, all columns - so you get it all and it takes the time it takes.
If you do more "focused" queries like
SELECT col1, col2 FROM dbo.Products WHERE SomeColumn = 42
then you have a chance of speeding this up by using the appropriate indices.
Buy a better computer.
Seriously.
SQL Server 2000 has been retired years ago, so this is an OLD install. 50.000 products is a joke - any table below 1 million is nothing.
But when i issue a select statement like select * from products then it is taking 10 minute
to get the full data.
Assuming this is over LAN, not over a slow internet connection, there can be 2 reasons for that:
System is TERRIBLY OVERLOADED. Like SERIOUS overloaded. Not like I have not seen that on old setups. Been there, seen that - hard discs so overloaded (hey, they are SCSI, they are fast) that they took more than 2 seconds to answer to a request.
System is programmed by incompetents. Could be bad transaction level handling leading to terrible locks for long duration which block you. This is possible, but then you are in for a LOT of rework to get the ridiculous code out of the programming.
A select * from table should not take more than a couple of seconds to transfer all the data over LAN. Point. Unless the bale has tons of binary data (i.e. HUGH amounts of data in some fields).
As your local database specialist to make an analysis. Start with hardware load then move to locking behavior. Consider upgrading to a technology that is more modern, By now you are a LOT of generations behind.
Because there's no criterium (WHERE), the time your query takes is not due to the selection (determining which rows to select) but most likely due to the sheer size of the data.
The only solution is:
Do not use SELECT *, but select only the columns you need.
I have a table where I need to constrain by category and then find all overlapping dates against some date range. This takes about 2 seconds, which is unacceptable to do on every transaction which occurs at roughly 50/s. The alternative is to create some tally table -- then again, I don't know how great of an idea this is because things can get out of sync.
Date on Rent # Rented Category
9/5/2011 5 CATEGORY1
In Oracle (PL/SQL, if it matters), how can I maintain this performance, but ensure that concurrent transactions don't screw up the increment / decrement by making it one less or one more than it really is?
I have two types of transactions, kind of like a search and a rent. Only rents will be updating this tally table (and searches just reading from it). I don't mind if rents slow down, but do not want search performance impacted. Rents can occur as frequently as 5-10 / second.
Oracle uses locks to ensure data consistency during a query. The details of how they work can get complex, but the effect is that it guarantees that an update/insert to your tally table will only use the data in the main table as it was when the query began. If there is another update or insert to your main table while you are doing an update/insert to the tally table, it won't affect it.
You'll have to experiment with your data to see if keeping a summary/tally table helps or hurts you. It really depends on how quickly the main table is getting updated, how much time you spend updating your tally table vs how much time you save by being able to select on it, and how up-to-date you need selects to be.
I'm about to have to rewrite some rather old code using SQL Server's BULK INSERT command because the schema has changed, and it occurred to me that maybe I should think about switching to a stored procedure with a TVP instead, but I'm wondering what effect it might have on performance.
Some background information that might help explain why I'm asking this question:
The data actually comes in via a web service. The web service writes a text file to a shared folder on the database server which in turn performs a BULK INSERT. This process was originally implemented on SQL Server 2000, and at the time there was really no alternative other than chucking a few hundred INSERT statements at the server, which actually was the original process and was a performance disaster.
The data is bulk inserted into a permanent staging table and then merged into a much larger table (after which it is deleted from the staging table).
The amount of data to insert is "large", but not "huge" - usually a few hundred rows, maybe 5-10k rows tops in rare instances. Therefore my gut feeling is that BULK INSERT being a non-logged operation won't make that big a difference (but of course I'm not sure, hence the question).
The insertion is actually part of a much larger pipelined batch process and needs to happen many times in succession; therefore performance is critical.
The reasons I would like to replace the BULK INSERT with a TVP are:
Writing the text file over NetBIOS is probably already costing some time, and it's pretty gruesome from an architectural perspective.
I believe that the staging table can (and should) be eliminated. The main reason it's there is that the inserted data needs to be used for a couple of other updates at the same time of insertion, and it's far costlier to attempt the update from the massive production table than it is to use an almost-empty staging table. With a TVP, the parameter basically is the staging table, I can do anything I want with it before/after the main insert.
I could pretty much do away with dupe-checking, cleanup code, and all of the overhead associated with bulk inserts.
No need to worry about lock contention on the staging table or tempdb if the server gets a few of these transactions at once (we try to avoid it, but it happens).
I'm obviously going to profile this before putting anything into production, but I thought it might be a good idea to ask around first before I spend all that time, see if anybody has any stern warnings to issue about using TVPs for this purpose.
So - for anyone who's cozy enough with SQL Server 2008 to have tried or at least investigated this, what's the verdict? For inserts of, let's say, a few hundred to a few thousand rows, happening on a fairly frequent basis, do TVPs cut the mustard? Is there a significant difference in performance compared to bulk inserts?
Update: Now with 92% fewer question marks!
(AKA: Test Results)
The end result is now in production after what feels like a 36-stage deployment process. Both solutions were extensively tested:
Ripping out the shared-folder code and using the SqlBulkCopy class directly;
Switching to a Stored Procedure with TVPs.
Just so readers can get an idea of what exactly was tested, to allay any doubts as to the reliability of this data, here is a more detailed explanation of what this import process actually does:
Start with a temporal data sequence that is ordinarily about 20-50 data points (although it can sometimes be up a few hundred);
Do a whole bunch of crazy processing on it that's mostly independent of the database. This process is parallelized, so about 8-10 of the sequences in (1) are being processed at the same time. Each parallel process generates 3 additional sequences.
Take all 3 sequences and the original sequence and combine them into a batch.
Combine the batches from all 8-10 now-finished processing tasks into one big super-batch.
Import it using either the BULK INSERT strategy (see next step), or TVP strategy (skip to step 8).
Use the SqlBulkCopy class to dump the entire super-batch into 4 permanent staging tables.
Run a Stored Procedure that (a) performs a bunch of aggregation steps on 2 of the tables, including several JOIN conditions, and then (b) performs a MERGE on 6 production tables using both the aggregated and non-aggregated data. (Finished)
OR
Generate 4 DataTable objects containing the data to be merged; 3 of them contain CLR types which unfortunately aren't properly supported by ADO.NET TVPs, so they have to be shoved in as string representations, which hurts performance a bit.
Feed the TVPs to a Stored Procedure, which does essentially the same processing as (7), but directly with the received tables. (Finished)
The results were reasonably close, but the TVP approach ultimately performed better on average, even when the data exceeded 1000 rows by a small amount.
Note that this import process is run many thousands of times in succession, so it was very easy to get an average time simply by counting how many hours (yes, hours) it took to finish all of the merges.
Originally, an average merge took almost exactly 8 seconds to complete (under normal load). Removing the NetBIOS kludge and switching to SqlBulkCopy reduced the time to almost exactly 7 seconds. Switching to TVPs further reduced the time to 5.2 seconds per batch. That's a 35% improvement in throughput for a process whose running time is measured in hours - so not bad at all. It's also a ~25% improvement over SqlBulkCopy.
I am actually fairly confident that the true improvement was significantly more than this. During testing it became apparent that the final merge was no longer the critical path; instead, the Web Service that was doing all of the data processing was starting to buckle under the number of requests coming in. Neither the CPU nor the database I/O were really maxed out, and there was no significant locking activity. In some cases we were seeing a gap of a few idle seconds between successive merges. There was a slight gap, but much smaller (half a second or so) when using SqlBulkCopy. But I suppose that will become a tale for another day.
Conclusion: Table-Valued Parameters really do perform better than BULK INSERT operations for complex import+transform processes operating on mid-sized data sets.
I'd like to add one other point, just to assuage any apprehension on part of the folks who are pro-staging-tables. In a way, this entire service is one giant staging process. Every step of the process is heavily audited, so we don't need a staging table to determine why some particular merge failed (although in practice it almost never happens). All we have to do is set a debug flag in the service and it will break to the debugger or dump its data to a file instead of the database.
In other words, we already have more than enough insight into the process and don't need the safety of a staging table; the only reason we had the staging table in the first place was to avoid thrashing on all of the INSERT and UPDATE statements that we would have had to use otherwise. In the original process, the staging data only lived in the staging table for fractions of a second anyway, so it added no value in maintenance/maintainability terms.
Also note that we have not replaced every single BULK INSERT operation with TVPs. Several operations that deal with larger amounts of data and/or don't need to do anything special with the data other than throw it at the DB still use SqlBulkCopy. I am not suggesting that TVPs are a performance panacea, only that they succeeded over SqlBulkCopy in this specific instance involving several transforms between the initial staging and the final merge.
So there you have it. Point goes to TToni for finding the most relevant link, but I appreciate the other responses as well. Thanks again!
I don't really have experience with TVP yet, however there is an nice performance comparison chart vs. BULK INSERT in MSDN here.
They say that BULK INSERT has higher startup cost, but is faster thereafter. In a remote client scenario they draw the line at around 1000 rows (for "simple" server logic). Judging from their description I would say you should be fine with using TVP's. The performance hit - if any - is probably negligible and the architectural benefits seem very good.
Edit: On a side note you can avoid the server-local file and still use bulk copy by using the SqlBulkCopy object. Just populate a DataTable, and feed it into the "WriteToServer"-Method of an SqlBulkCopy instance. Easy to use, and very fast.
The chart mentioned with regards to the link provided in #TToni's answer needs to be taken in context. I am not sure how much actual research went into those recommendations (also note that the chart seems to only be available in the 2008 and 2008 R2 versions of that documentation).
On the other hand there is this whitepaper from the SQL Server Customer Advisory Team: Maximizing Throughput with TVP
I have been using TVPs since 2009 and have found, at least in my experience, that for anything other than simple insert into a destination table with no additional logic needs (which is rarely ever the case), then TVPs are typically the better option.
I tend to avoid staging tables as data validation should be done at the app layer. By using TVPs, that is easily accommodated and the TVP Table Variable in the stored procedure is, by its very nature, a localized staging table (hence no conflict with other processes running at the same time like you get when using a real table for staging).
Regarding the testing done in the Question, I think it could be shown to be even faster than what was originally found:
You should not be using a DataTable, unless your application has use for it outside of sending the values to the TVP. Using the IEnumerable<SqlDataRecord> interface is faster and uses less memory as you are not duplicating the collection in memory only to send it to the DB. I have this documented in the following places:
How can I insert 10 million records in the shortest time possible? (lots of extra info and links here as well)
Pass Dictionary<string,int> to Stored Procedure T-SQL
Streaming Data Into SQL Server 2008 From an Application (on SQLServerCentral.com ; free registration required)
TVPs are Table Variables and as such do not maintain statistics. Meaning, they report only having 1 row to the Query Optimizer. So, in your proc, either:
Use statement-level recompile on any queries using the TVP for anything other than a simple SELECT: OPTION (RECOMPILE)
Create a local temporary table (i.e. single #) and copy the contents of the TVP into the temp table
I think I'd still stick with a bulk insert approach. You may find that tempdb still gets hit using a TVP with a reasonable number of rows. This is my gut feeling, I can't say I've tested the performance of using TVP (I am interested in hearing others input too though)
You don't mention if you use .NET, but the approach that I've taken to optimise previous solutions was to do a bulk load of data using the SqlBulkCopy class - you don't need to write the data to a file first before loading, just give the SqlBulkCopy class (e.g.) a DataTable - that's the fastest way to insert data into the DB. 5-10K rows isn't much, I've used this for up to 750K rows. I suspect that in general, with a few hundred rows it wouldn't make a vast difference using a TVP. But scaling up would be limited IMHO.
Perhaps the new MERGE functionality in SQL 2008 would benefit you?
Also, if your existing staging table is a single table that is used for each instance of this process and you're worried about contention etc, have you considered creating a new "temporary" but physical staging table each time, then dropping it when it's finished with?
Note you can optimize the loading into this staging table, by populating it without any indexes. Then once populated, add any required indexes on at that point (FILLFACTOR=100 for optimal read performance, as at this point it will not be updated).
Staging tables are good! Really I wouldn't want to do it any other way. Why? Because data imports can change unexpectedly (And often in ways you can't foresee, like the time the columns were still called first name and last name but had the first name data in the last name column, for instance, to pick an example not at random.) Easy to research the problem with a staging table so you can see exactly what data was in the columns the import handled. Harder to find I think when you use an in memory table. I know a lot of people who do imports for a living as I do and all of them recommend using staging tables. I suspect there is a reason for this.
Further fixing a small schema change to a working process is easier and less time consuming than redesigning the process. If it is working and no one is willing to pay for hours to change it, then only fix what needs to be fixed due to the schema change. By changing the whole process, you introduce far more potential new bugs than by making a small change to an existing, tested working process.
And just how are you going to do away with all the data cleanup tasks? You may be doing them differently, but they still need to be done. Again, changing the process the way you describe is very risky.
Personally it sounds to me like you are just offended by using older techniques rather than getting the chance to play with new toys. You seem to have no real basis for wanting to change other than bulk insert is so 2000.
Are there general ABAP-specific tips related to performance of big SELECT queries?
In particular, is it possible to close once and for all the question of FOR ALL ENTRIES IN vs JOIN?
A few (more or less) ABAP-specific hints:
Avoid SELECT * where it's not needed, try to select only the fields that are required. Reason: Every value might be mapped several times during the process (DB Disk --> DB Memory --> Network --> DB Driver --> ABAP internal). It's easy to save the CPU cycles if you don't need the fields anyway. Be very careful if you SELECT * a table that contains BLOB fields like STRING, this can totally kill your DB performance because the blob contents are usually stored on different pages.
Don't SELECT ... ENDSELECT for small to medium result sets, use SELECT ... INTO TABLE instead.
Reason: SELECT ... INTO TABLE performs a single fetch and doesn't keep the cursor open while SELECT ... ENDSELECT will typically fetch a single row for every loop iteration.
This was a kind of urban myth - there is no performance degradation for using SELECT as a loop statement. However, this will keep an open cursor during the loop which can lead to unwanted (but not strictly performance-related) effects.
For large result sets, use a cursor and an internal table.
Reason: Same as above, and you'll avoid eating up too much heap space.
Don't ORDER BY, use SORT instead.
Reason: Better scalability of the application server.
Be careful with nested SELECT statements.
While they can be very handy for small 'inner result sets', they are a huge performance hog if the nested query returns a large result set.
Measure, Measure, Measure
Never assume anything if you're worried about performance. Create a representative set of test data and run tests for different implementations. Learn how to use ST05 and SAT.
There won't be a way to close your second question "once and for all". First of all, FOR ALL ENTRIES IN 'joins' a database table and an internal (memory) table while JOIN only operates on database tables. Since the database knows nothing about the internal ABAP memory, the FOR ALL ENTRIES IN statement will be transformed to a set of WHERE statements - just try and use the ST05 to trace this. Second, you can't add values from the second table when using FOR ALL ENTRIES IN. Third, be aware that FOR ALL ENTRIES IN always implies DISTINCT. There are a few other pitfalls - be sure to consult the on-line ABAP reference, they are all listed there.
If the number of records in the second table is small, both statements should be more or less equal in performance - the database optimizer should just preselect all values from the second table and use a smart joining algorithm to filter through the first table. My recommendation: Use whatever feels good, don't try to tweak your code to illegibility.
If the number of records in the second table exceeds a certain value, Bad Things [TM] happen with FOR ALL ENTRIES IN - the contents of the table are split into multiple sets, then the query is transformed (see above) and re-run for each set.
Another note: The "Avoid SELECT *" statement is true in general, but I can tell you where it is false.
When you are going to take most of the fields anyway, and where you have several queries (in the same program, or different programs that are likely to be run around the same time) which take most of the fields, especially if they are different fields that are missing.
This is because the App Server Data buffers are based on the select query signature. If you make sure to use the same query, then you can ensure that the buffer can be used instead of hitting the database again. In this case, SELECT * is better than selecting 90% of the fields, because you make it much more likely that the buffer will be used.
Also note that as of the last version I tested, the ABAP DB layer wasn't smart enough to recognize SELECT A, B as being the same as SELECT B, A, which means you should always put the fields you take in the same order (preferable the table order) in order to make sure again that the data buffer on the application is being well used.
I usually follow the rules stated in this pdf from SAP: "Efficient Database Programming with ABAP"
It shows a lot of tips in optimizing queries.
This question will never be completely answered.
ABAP statement for accessing database is interpreted several times by different components of whole system (SAP and DB). Behavior of each component depends from component itself, its version and settings. Main part of interpretation is done in DB adapter on SAP side.
The only viable approach for reaching maximum performance is measurement on particular system (SAP version and DB vendor and version).
There are also quite extensive hints and tips in transaction SE30. It even allows you (depending on authorisations) to write code snippets of your own & measure it.
Unfortunately we can't close the "for all entries" vs join debate as it is very dependent on how your landscape is set up, wich database server you are using, the efficiency of your table indexes etc.
The simplistic answer is let the DB server do as much as possible. For the "for all entries" vs join question this means join. Except every experienced ABAP programmer knows that it's never that simple. You have to try different scenarios and measure like vwegert said. Also remember to measure in your live system as well, as sometimes the hardware configuration or dataset is significantly different to have entirely different results in your live system than test.
I usually follow the following conventions:
Never do a select *, Select only the required fields.
Never use 'into corresponding table of' instead create local structures which has all the required fields.
In the where clause, try to use as many primary keys as possible.
If select is made to fetch a single record and all primary keys are included in where clause use Select single, or else use SELECT UP TO TO 1 ROWS, ENDSELECT.
Try to use Join statements to connect tables instead of using FOR ALL ENTRIES.
If for all entries cannot be avoided ensure that the internal table is not empty and a delete the duplicate entries to increase performance.
Two more points in addition to the other answers:
usually you use JOIN for two or more tables in the database and you use FOR ALL ENTRIES IN to join database tables with a table you have in memory. If you can, JOIN.
usually the IN operator is more convinient than FOR ALL ENTRIES IN. But the kernel translates IN into a long select statement. The length of such a statement is limited and you get a dump when it gets too long. In this case you are forced to use FOR ALL ENTRIES IN despite the performance implications.
With in-memory database technologies, it's best if you can finish all data and calculations on the database side with JOINs and database aggregation functions like SUM.
But if you can't, at least try to avoid accessing database in LOOPs. Also avoid reading the database without using indexes, of course.