I am experiencing problems with a MSSQL instance, where deadlocks occur from time to time. I have a Table A, which holds temperature measurements. My application contains 1-10 worker threads, which collect measurements via TCP from remote locations and then want to store them inside the database. Of course these workers use transactions to conduct their tasks. The IsolationLevel of the transactions is set to ReadCommitted. Still deadlocks occur and the CPU load of the database server is up at 100%. Can anyone tell me, what I have to consider to get this working? I thought the database system will do the multi-user-synchronization for me. At least this is, what I learned at university.
My suggestion is to create another thread that will handle your updates into the database. So add the information into a collection from the threads in a thread safe manner, and let 1 worker thread do the updates/inserts into the table. You can even concatenate 10-30 of these statements and execute them together.
This is what we have done on a SMS Sender where we used up to 50 threads each sending SMS a SMS every 100ms. It worked brilliantly for us.
Related
I've got a pretty simple update statement in my application in one of the APIs.
UPDATE "table" SET "column" = "column" + 1 WHERE "id" = $1
The problem is when this API incurs a sudden load it starts to fail. There is no increase in DB CPU only the number of active sessions shoot up. When checking the RDS performance insights this is what I see.
The number of updates to this table doesn't shoot up more than 300. I don't think postgres should be behaving this way with such low number of updates (time spent in waits). It'll be helpful if I could have suggestions what might be going wrong here? I understand what does wait in tuple and transactionid mean but how can I reduce these waits?
Thanks
The problem is obvious: all these sessions are waiting for a row lock. If all these sessions are running the statement you quote with the same id (or only a low number of different ids), then they are blocking each other.
Since the sessions don't seem to do any work (if I read the graph right, they are all waiting), I'd say that the problem is that your transactions are taking too long. PostgreSQL holds the row lock, like all other user-facing locks, until the transaction ends, so having that UPDATE in your workload effectively serializes processing.
You could improve the situation by performing the UPDATE at the very end of the transaction, so that the lock is not held for a long time. In addition, you should reduce your connection pool size considerably. I don't think that your database can deal with 1000 active connections.
Reduce the maximum number of parallel HTTP requests your web server accepts (assuming HTTP(S)) so you don't overload the database. This will cause the API requests to your service to queue waiting for a TCP connection. This will result in improved performance rather than worse at time of peak loads.
I want to implement a mutual exclusion system in PostgreSQL where multiple worker processes will temporarily lock resources (rows) from a table (queue) while they work on them. If the worker processes crash, I want the lock to be cleanly released and not have to rely on another process to clean up the leaked locks.
What I have come up with so far is to use a SELECT ... FOR UPDATE SKIP LOCKED query within a transaction, which locks the row it finds and skips any other locked row.
It works well but one of the issues is that the worker might take a while to do its task and I need to keep the transaction open for the entire duration of its task.
Another problem is that the workers work incrementally and persist their state to the database so that if they're stopped or crash, they can resume quickly where they were. The row being locked makes it impossible to persist their state in the same table (though I think I can get away from that by using another table to persist the state).
I've searched on the Web on how to implement a semaphore or a resource borrowing system in SQL/PostgreSQL but I haven't found something that fits my needs. Is there a simple way of achieving this with PostgreSQL?
During load testing of our module we found that bigquery insert calls are taking time (3-4 s). I am not sure if this is ok. We are using java biguqery client libarary and on an average we push 500 records per api call. We are expecting a million records per second traffic to our module so bigquery inserts are bottleneck to handle this traffic. Currently it is taking hours to push data.
Let me know if we need more info regarding code or scenario or anything.
Thanks
Pankaj
Since streaming has a limited payload size, see Quota policy it's easier to talk about times, as the payload is limited in the same way to both of us, but I will mention other side effects too.
We measure between 1200-2500 ms for each streaming request, and this was consistent over the last month as you can see in the chart.
We seen several side effects although:
the request randomly fails with type 'Backend error'
the request randomly fails with type 'Connection error'
the request randomly fails with type 'timeout' (watch out here, as only some rows are failing and not the whole payload)
some other error messages are non descriptive, and they are so vague that they don't help you, just retry.
we see hundreds of such failures each day, so they are pretty much constant, and not related to Cloud health.
For all these we opened cases in paid Google Enterprise Support, but unfortunately they didn't resolved it. It seams the recommended option to take for these is an exponential-backoff with retry, even the support told to do so. Which personally doesn't make me happy.
The approach you've chosen if takes hours that means it does not scale, and won't scale. You need to rethink the approach with async processes. In order to finish sooner, you need to run in parallel multiple workers, the streaming performance will be the same. Just having 10 workers in parallel it means time will be 10 times less.
Processing in background IO bound or cpu bound tasks is now a common practice in most web applications. There's plenty of software to help build background jobs, some based on a messaging system like Beanstalkd.
Basically, you needed to distribute insert jobs across a closed network, to prioritize them, and consume(run) them. Well, that's exactly what Beanstalkd provides.
Beanstalkd gives the possibility to organize jobs in tubes, each tube corresponding to a job type.
You need an API/producer which can put jobs on a tube, let's say a json representation of the row. This was a killer feature for our use case. So we have an API which gets the rows, and places them on tube, this takes just a few milliseconds, so you could achieve fast response time.
On the other part, you have now a bunch of jobs on some tubes. You need an agent. An agent/consumer can reserve a job.
It helps you also with job management and retries: When a job is successfully processed, a consumer can delete the job from the tube. In the case of failure, the consumer can bury the job. This job will not be pushed back to the tube, but will be available for further inspection.
A consumer can release a job, Beanstalkd will push this job back in the tube, and make it available for another client.
Beanstalkd clients can be found in most common languages, a web interface can be useful for debugging.
On our production server, for some reason at specific amount of time, thread count goes over and over to the certain point that though CPU Utilization is normal(30-50%), but the query starting to run slow we so lot more blocking statements.
I am not sure where to look at it, basically when our site runs normally, the thread count is around 150 threads, but during the specific time in a day(during 1:30 to 2:30) it come up to 270 threads.there are no extra sql transaction goes on, everything as normal as it was before but thread count grows and sql start behaving very very slow.
After restarting the SQL service immediately thread count comes to normal, and our site function fine for another 24 hours.
We are using SQL Server 2005, it is 24 core machine.
any idea?
Blocking statements steal workers (sys.dm_os_workers) so the server will spawn more workers to handle the incoming tasks. At 24 cores you'll have some 700 max worker threads out-of-the-box by default. So seeing 270 'threads' is not an issue, is well within the normal functioning parameters. You real problem must be the blocking, and you have to investigate it accordingly: who is blocking who and why. My bet is that you have a job running between 1:30 and 2:30 that is locking large portions of the database (a delete job perhaps?) and your queries block on locked rows. You'll have to investigate, find the root cause, and act accordingly. Reboot is not a solution, nor is blaming unrelated components (thread count). Use Activity Monitor, use Who Is Active, follow the methodical approach of Waits and Queues methodology. There are plenty of ways to identify the real problem. SQL Server will never appear slow due to thread count. It simply doesn't work like that.
You can control the degree of parallalism using the MAXDOP query hint. For more details please check this article:
http://blog.sqlauthority.com/2010/03/15/sql-server-maxdop-settings-to-limit-query-to-run-on-specific-cpu/
Thanks for your valuable feedback, yes it is true it is nothing which sql is behaving weiredly, it is something our Site which is based on Ektron CMS is responsible for, one of the Functionality of Ektron CMS (which is PageBuilder), while doing operation on this piece of Content was holding of the table badly, we have around 10 million users to our site, and probably since this was blocking the tables SQL Server goes nuts and does not respond very well.
We have finally eliminated the issue.
Our database architecture consists of two Sql Server 2005 servers each with an instance of the same database structure: one for all reads, and one for all writes. We use transactional replication to keep the read database up-to-date.
The two servers are very high-spec indeed (the write server has 32GB of RAM), and are connected via a fibre network.
When deciding upon this architecture we were led to believe that the latency for data to be replicated to the read server would be in the order of a few milliseconds (depending on load, obviously). In practice we are seeing latency of around 2-5 seconds in even the simplest of cases, which is unsatisfactory. By a simplest case, I mean updating a single value in a single row in a single table on the write db and seeing how long it takes to observe the new value in the read database.
What factors should we be looking at to achieve latency below 1 second? Is this even achievable?
Alternatively, is there a different mode of replication we should consider? What is the best practice for the locations of the data and log files?
Edit
Thanks to all for the advice and insight - I believe that the latency periods we are experiencing are normal; we were mis-led by our db hosting company as to what latency times to expect!
We're using the technique described near the bottom of this MSDN article (under the heading "scaling databases"), and we'd failed to deal properly with this warning:
The consequence of creating such specialized databases is latency: a write is now going to take time to be distributed to the reader databases. But if you can deal with the latency, the scaling potential is huge.
We're now looking at implementing a change to our caching mechanism that enforces reads from the write database when an item of data is considered to be "volatile".
No. It's highly unlikely you could achieve sub-1s latency times with SQL Server transactional replication even with fast hardware.
If you can get 1 - 5 seconds latency then you are doing well.
From here:
Using transactional replication, it is
possible for a Subscriber to be a few
seconds behind the Publisher. With a
latency of only a few seconds, the
Subscriber can easily be used as a
reporting server, offloading expensive
user queries and reporting from the
Publisher to the Subscriber.
In the following scenario (using the
Customer table shown later in this
section) the Subscriber was only four
seconds behind the Publisher. Even
more impressive, 60 percent of the
time it had a latency of two seconds
or less. The time is measured from
when the record was inserted or
updated at the Publisher until it was
actually written to the subscribing
database.
I would say it's definately possible.
I would look at:
Your network
Run ping commands between the two servers and see if there are any issues
If the servers are next to each other you should have < 1 ms.
Bottlenecks on the server
This could be network traffic (volume)
Like network cards not being configured for 1GB/sec
Anti-virus or other things
Do some analysis on some queries and see if you can identify indexes or locking which might be a problem
See if any of the selects on the read database might be blocking the writes.
Add with (nolock), and see if this makes a difference on one or two queries you're analyzing.
Essentially you have a complicated system which you have a problem with, you need to determine which component is the problem and fix it.
Transactional replication is probably best if the reports / selects you need to run need to be up to date. If they don't you could look at log shipping, although that would add some down time with each import.
For data/log files, make sure they're on seperate drives so the performance is maximized.
Something to remember about transaction replication is that a single update now requires several operations to happen for that change to occur.
First you update the source table.
Next the log readers sees the change and writes the change to the distribution database.
Next the distribution agent sees the new entry in the distribution database and reads that change, then runs the correct stored procedure on the subscriber to update the row.
If you monitor the statement run times on the two servers you'll probably see that they are running in just a few milliseconds. However it is the lag time while waiting for the log reader and distribution agent to see that they need to do something which is going to kill you.
If you truly need sub second processing time then you will want to look into writing your own processing engine to handle data moving from one server to another. I would recommend using SQL Service Broker to handle this as this way everything is native to SQL Server and no third party code has to be written.