We have a severe issue with threads hanging in operations to an Oracle DB (v19, connected to via JDBC connections).
The situation frequently happens while our application runs a big transaction within which it does a lot of major (i.e. quite complicated, lots of joins, etc.) queries and then updates a bunch of rows. These transactions can take several minutes.
As we were able to analyze so far the transaction processing blocks other concurrent tasks when they try to delete individual entries from tables that are involved in said transaction. Concurrent selects and also updates to these same tables work fine! It's only deletes that have issues! And, as we were able to "proof", this happens even for deletes of individual entries that for sure do not interfere with or touch on any entry involved in the ongoing transaction.
While we first suspected Hibernate to interfere and do funny things for deletions we had to learn that even deletes executed via SQLDeveloper (i.e. triggered "manually" by a completely unrelated DB session and client) do hang during such periods.
To us it almost seems as if an ongoing transaction does not only lock specific rows from manipulation but locks entire tables.
But can that really be that a transaction block entire tables from concurrent delete operations for extended periods?
We think that would be absurd but - as we had to learn and can easily reproduce - deleting entries from tables touched by our long-running transaction invariably hang. Several times we also witnessed that - as soon as the transaction finishes - those deletes that haven't timed out, yet, continue and run to completion.
We are not aware of doing anything weird or unusual in our Hibernate-based application. We certainly don't fiddle with any locking mechanism or such. Any idea or hint what could cause these hangs and/or in which direction to investigate further to resolve this?
Later addition:
We are currently considering the following work-around: we add a column to these tables where we mark entries as being "to-be-deleted" (instead of actually deleting them as we do now). We then run a regular job during times (e.g. nightly) which actually deletes these entries. We "only" need to make sure that no transaction is ever executed on these tables while that delete-job runs.
I really hate that approach, esp. since it will require to add another condition to many queries to exclude those "virtually deleted" entries but we have no better idea so far.
I have a job which picks a record from a cursor and then it calls a stored procedure which processes the record picked up from the cursor.
The stored procedure has multiple queries to process the records. In all, procedure takes about 0.3 seconds to process a single record picked up by the cursor but since cursor contains more than 100k records it takes hours to complete the job.
The queries in the stored procedure are all optimized
I was thinking of making the procedure run in multi threaded way as in java and other programming language.
Can it be done in oracle? or is there any other way I can reduce the run time of my job.
I agree with the comments regarding processing cursors in a loop. As Tom Kyte often said "Row at a time [processing] is slow at a time"; Oracle performs best with set based operations and row-at-a-time operations usually have scalability issues (i.e. very susceptible to poor performance when things change on the DB such as CPU capacity, workload, number of records that need processing, changes in size of underlying tables, ...).
You probably already know that Oracle since 8i has a Java VM built in to the DB engine, so you might be able to have java code wrappered as PL/SQL, but this is not for the faint of heart [not saying that you are, just sayin'].
Before going to the trouble of re-writing your application, I would recommend the following tuning approach as it may yield some actionable tunings [assumes diagnostics and tuning pack licenses; won't remove the scalability issues but may lessen the impact of them]:
In versions of oracle 11g and above:
Find the the top level sql id recorded in gv$active_session_history and dba_hist_active_sess_history for the call to the PL/SQL procedure.
Examine the wait events for the sql_id's under that top_level_sql_id. (they tell you what the SQL is waiting on).
Run the tuning advisor on those sql_id's and check for any tuning recommendations. Sometimes if SQL is already sub-second getting it from hundredths of a second to thousandths of a second can have a big impact when call many times.
Run the ADDM report for the period when the procedure is running. Often you will find that heavy PL/SQL processes require increase in PGA. Further, ADDM may advise other relevant actions (e.g. increase SGA, session cached cursors, db writer processes, log buffer, run segment tuning advisor, ...)
I have an application that do like:
delete from tableA where columnA='somevalue' and rownum<1000
In cycle like:
while(deletedRows>0) {
begin tran
deletedRows = session ... "delete from tableA where columnA='somevalue'
and rownum<1000"....
commit tran
}
It runs few times (each deleting takes near 20 seconds) and after hungs for long time
Why? Does it possible to fix?
Thanks.
The reason why the deletes are run in a loop rather than as a single SQL statement is lack of rollback space. See this question for more info.
Every time the query scans the table from the beginning. So, it scans the zones where there are no rows to delete(columnA='somevalue'). They are more and more far away from the first block of the table.
If the table is big and there would be no columnA='somevalue' the query will take the time to verify all the row for your condition.
What you can do is to make an index on columnA. In this case the engine will know faster where are the rows with that condition(search on index is exponential time faster).
Another possibility, if you are in a concurent system, is that someone updated a row that you ar trying to delete, but doesn't commited the transaction, so the row is locked.
You probably run into many different issues. As you are saying that database hungs the main reason is that your database is hitting ORA-00257 Archiver error.
Every delete produces a redo vector, all redos are then downloaded into an archive log. When archivelog space is exahausted your session hang and remain stuck until someone frees the space.
Usually your DBA has a job that run an archivelog backup every hour (this might be any couple of hours, or every 5 mins, depending by the database workload, etc...) and after the backup has done all sessions go ahead correctly.
Depending by the database configuration, from the client point of view, you might not see the error but just have the behaviour described where you session waits until the space is freed.
In term of design, I agree with other users that a DELETE in a loop is not a good idea. It could be interesting to know why you are trying to do this loop instead a single DELETE statement.
I'm trying to create a Ruby script that spawns several concurrent child processes, each of which needs to access the same data store (a queue of some type) and do something with the data. The problem is that each row of data should be processed only once, and a child process has no way of knowing whether another child process might be operating on the same data at the same instant.
I haven't picked a data store yet, but I'm leaning toward PostgreSQL simply because it's what I'm used to. I've seen the following SQL fragment suggested as a way to avoid race conditions, because the UPDATE clause supposedly locks the table row before the SELECT takes place:
UPDATE jobs
SET status = 'processed'
WHERE id = (
SELECT id FROM jobs WHERE status = 'pending' LIMIT 1
) RETURNING id, data_to_process;
But will this really work? It doesn't seem intuitive the Postgres (or any other database) could lock the table row before performing the SELECT, since the SELECT has to be executed to determine which table row needs to be locked for updating. In other words, I'm concerned that this SQL fragment won't really prevent two separate processes from select and operating on the same table row.
Am I being paranoid? And are there better options than traditional RDBMSs to handle concurrency situations like this?
As you said, use a queue. The standard solution for this in PostgreSQL is PgQ. It has all these concurrency problems worked out for you.
Do you really want many concurrent child processes that must operate serially on a single data store? I suggest that you create one writer process who has sole access to the database (whatever you use) and accepts requests from the other processes to do the database operations you want. Then do the appropriate queue management in that thread rather than making your database do it, and you are assured that only one process accesses the database at any time.
The situation you are describing is called "Non-repeatable read". There are two ways to solve this.
The preferred way would be to set the transaction isolation level to at least REPEATABLE READ. This will mean that any row that concurrent updates of the nature you described will fail. if two processes update the same rows in overlapping transactions one of them will be canceled, its changes ignored, and will return an error. That transaction will have to be retried. This is achieved by calling
SET TRANSACTION ISOLATION LEVEL REPEATABLE READ
At the start of the transaction. I can't seem to find documentation that explains an idiomatic way of doing this for ruby; you may have to emit that sql explicitly.
The other option is to manage the locking of tables explicitly, which can cause a transaction to block (and possibly deadlock) until the table is free. Transactions won't fail in the same way as they do above, but contention will be much higher, and so I won't describe the details.
That's pretty close to the approach I took when I wrote pg_message_queue, which is a simple queue implementation for PostgreSQL. Unlike PgQ, it requires no components outside of PostgreSQL to use.
It will work just fine. MVCC will come to the rescue.
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.