I have created a vb program for a small local business. They have 2 locations. The Access database resides on a computer at one of the locations, and data is transmitted across a VPN through the local internet provider (a major carrier). This VPN is not in my control, and the transmission method can’t be changed.
Everything works well, as long as the provider’s network doesn’t fail. Unfortunately, it sometimes does. The program requires frequent database updates, for instance: a new customer is entered into the database, and after an update, the customer number (auto-generated) is retrieved. This customer number is then used as part of the order table row, which is then updated and its auto-number is retrieved. The order number is then used as part of the order-item table rows, which lists the items in the order. You get the picture… lots of updates, which must occur to get the auto numbers for the next step. Inventory, payment info, etc.
If the network fails at a time when say, after an order has been updated, but before the order-items have been updated, the database becomes a mess, and is out of sync. Because the network is down, a database correction or rollback is not possible. I have try/catch blocks in place to find out whether an update was successful or not. My question is, how to
Possibly try again, the network might have just hiccupped – I know I can loop here, are there better methods?
If that fails and I must consider the update a no-go and the network truly down, how to clean up the mess. Unluckily for me, a major 2-day failure occurred an hour after my program went live for the first time, so this is not an unlikely possibility.
Thank you for any suggestions
Related
I have a web service sitting on IIS that has been quite happy for months but now I'm getting timeouts and I don't know how to diagnose what the problem is.
The client sends up basic information in a 'heartbeat' message to IIS which then updates this in a SQL database (on a different server). There are 250 clients in the wild, all sending up their heartbeat every 5 minutes ... so there's only 250 rows in the table, with appropriate indexing on the column being used for the update.
Ordinarily it only takes 50-100ms to do the update, but since last week you can see that the response time in the IIS log has increased and I'm also getting timeouts too.
Nothing has changed with the setup so I don't know what I'm looking for to determine the reason. The error I get back is:
System.ServiceModel.FaultException: An error occurred while updating
the entries. See the inner exception for details.An error occurred
while updating the entries. See the inner exception for
details.Execution Timeout Expired. The timeout period elapsed prior to
completion of the operation or the server is not responding. The
statement has been terminated.The wait operation timed out
Any advice on where to start looking? I did enable the failed request log trace in IIS but I don't know what it all means if I'm perfectly honest. The difference between a successful requiest and a failed one is that the request log stops after the 'AspNetStart' entry.
Thanks!
Mark
There are lots of reasons a service can gradually or suddenly become slow. Poor code structure can lead to things like memory leaks on the server, small enough they don't really show up or cause problems during testing, but when run over weeks/months start to stack up. Unauthorized requests could be targeting your server if this is a public-facing service, or has a link to public-facing services.
Things to look at:
Does this happen at certain times of the day or throughout the day?
Is this a load issue that starts occurring when multiple users are sending updates concurrently? 250 users isn't a lot. Has the # of users grown over the last few months or has it been relatively stable since the start?
What is the memory and CPU usage looking like on the Web server(s) and DB server?
This is the first clue to check to see if either server is under considerable load. From there you can investigate why it might be under load or if it possibly needs a bit more grunt to deal with the load. Look at the running processes. If these servers are managed by an IT department or such some culprits can include things like Virus Scanners hogging resources. (I.e. policy changes in the last few months have lead to additional load on the servers)
What recovery model is your database set up for?
What is the size of your Tx Log (.mdx file)
Do you have a regular scheduled database backup and index maintenance?
This is one that new projects tend to forget. An empty database is small and has no Tx Log history being recorded, but as it runs over time that Tx Log grows silently in the background, especially with Full recovery. Larger Tx Logs can lead to slower performance over time especially if the log file needs to be enlarged. A good thing to check is whether the log file is set to grow by a # of bytes or percentage. Percentage is I believe the default but this can cause exponential "grow" time/space issues so it's better to set it to a fixed size per grow. You'll want regular backups being done that allow the Tx Log to reset. Ideally don't shrink the file if the Log size between backups stays consistent.
How many records across all tables are being inserted or updated in a given day?
This is important to build a picture of how much the database will be tracking through the day between backups. You may have 250 clients, but every heartbeat is potentially updating a row and inserting others.
What are you using for PKs for inserted records? (Ints vs. UUIDs) If using UUIDs are you using NEWSEQUENTIALID() or NEWID()/Guid.New()?
GUIDs can be a time bomb for indexing if done poorly. A GUID combined with NEWID() or Guid.New() will lead to considerable index fragmentation when inserting rows. Provided the GUIDs are not visible to clients you should use NEWSEQUENTIALID(). If IDs are set via code then there are implementations you can find to generate sequential GUIDs. (It's a matter of re-arranging the parts that make up the GUID) Regular index maintenance is a requirement for using UUID columns in indexed fields.
Are you using Dependency Injection in your web service?
What is the lifetime scope of the DbContexts performing the updates?
This is a potential time bomb for web servers if the lifetime scope for a DbContext is set up incorrectly. You want a DbContext to be alive for no longer than it is needed. At a maximum the lifetime scope should be set to PerRequest. A DbContext set up for Singleton for instance would be tracking entities across requests. The more entities a DbContext is tracking, the slower read and update operations become. This would be a possible culprit if the web server memory usage is climbing.
Are you running an SQL Profiler?
In a test environment with nothing else touching the database, running scenarios through the application with an SQL Profiler can reveal potential issues such as unexpected queries being kicked off due to things like lazy loading. For one operation you might expect one or a small number of queries to be run, only to find dozens or even hundreds. Multiply this across concurrent requests and you have a recipe for the database server to say "Just sit down and wait, dammit!" :) Any queries you don't expect based on the code that is running should be investigated for either eager loading relationships or implementing projection. (Recommended for best performance)
Do the web servers get restarted periodically?
For some tricky to debug issues and memory leaks, sometimes the easiest "fix" is to schedule regular restarts of the web server. It's a hack, but compared to the considerable cost of trying to track down memory leaks or fix up inefficient code that slows down over time, it is a cheap and effective fix. (At least while you do research options to address the issues and optimize the code)
That should give you a start into things to check with the service & database.
Should I log requests info (client ip, request status code, execution time etc.) in my web app into the database to analyse users behavoir and arised errors? And what info log for better experience?
Its often tempting to log lots of information, however I usually find that when I come to use it to answer a question it's often the case that the wrong piece of information has been recorded or only partially. Or it has been recorded but has not been stored in a usable way and takes further programming to turn the log into meaningful information.
So I would start with the question of what you want to see/find and log accordingly. Generally then logging capability can be expanded in the future as new issues/insights are required.
remember every time you log something you are slowing your application down. You are also using more disk space, no one is going to thank you for buying more disk / longer backups just because you have logged everything on every action.
I guess I would follow a train of thought a bit like:
1) What are you trying to find, if its an error you can predict then why not cater for it in your code to start with. If its usability what format does the data need to be in at at what points should it be recorded.
2) How long do you need it for, be sure to purge the logs after a period to conserve disk space.
3) Every element stored is a performance hit, might be small but for high number of transactions it adds up.
4) Be wary of privacy rules, an IP address may be considered as identifiable data, in which case you need to publish a data privacy policy (see point 2).
5) Consider using a flag to control logging on or off. Then you can use it at times of a known issue but not record everything always when not needed.
For learning purposes, I want to write my own database, that is able to replicate itself. I have made some progress, but now I am facing a problem that I can not solve. Supposed I have a database (let's call this source) that I would like to replicate to another database (let's call this target).
The basic principle is easy: In the source you don't store actual tables, but instead a log of transactions. It's easy to send over the transaction log to the target, where the database then rebuilds itself. If you want to update the target, you simply request the part of the transaction log that has changed ever since. Basically this is what almost every database does.
While this works, it has one major drawback: If a table already exists for a long time, the transaction log is very long, and hence replicating the table requires lots of time…
To avoid this you can store the current state as well. This means you have an up-to-date snapshot that you can copy fast. Additionally, the target has to subscribe to the transaction log of the source. Once it contains additional entries, the target applies them to its copied table. This works well, too, and it's way better in terms of performance and transferred volume.
But now I am facing a problem: Supposed the snapshot is large, then it may happen that changes are made to it while it is being delivered. That means that the copied snapshot contains some old and some new data. Now, how do I get the target database in a consistent state? Even if I know from where to start the transaction log, I either have to apply a change that was already applied to some of the records, or I have to leave it out, but then a change is not applied at all to some other records.
Of course I could use the isolation level sequential, but then performance drops. Of course I could do what e.g. CouchDB does and remember the current table revision in every record, and keep a copy of every record for every revision. But then the required space grows enormously.
So, what shall I do?
Everything that I was able to find on the web always either relies on the idea of replaying the entire transaction log, or by using a process as in CouchDB which takes up huge amounts of space.
Any ideas?
Your snapshot needs to be consistent and you need to know at what time (in regards to the tx log) it is consistent. You then apply any transactions that have been committed since this point.
Obtaining a consistent snapshot can be done with exclusive locking, which may delay other transactions from committing, or using row versions (MVCC).
Good luck with your project.
I am developing a web-app right now, where clients will frequently (every few seconds), send read/write requests on certain data. As of right now, I have my server immediately write to the database when a user changes something, and immediately read from the database when they want to view something. This is working fine for me, but I am guessing that it would be quite slow if there were thousands of users online.
Would it be more efficient to save write requests in an object on the server side, then do a bulk update at a certain time interval? This would help in situations where the same data is edited multiple times, since it would now only require one db insert. It would also mean that I would read from the object for any data that hasn't yet been synced, which could mean increased efficiency by avoiding db reads. At the same time though, I feel like this would be a liability for two reasons: 1. A server crash would erase all data that hasn't yet been synced. 2. A bulk insert has the possibility of creating sudden spikes of lag due to mass database calls.
How should I approach this? Is my current approach ok, or should I queue inserts for a later time?
If a user makes a change to data and takes an action that (s)he expects will save the data, you should do everything you can to ensure the data is actually saved. Example: Let's say you delay the write for a while. The user is in a hurry, makes a change then closes the browser. If you don't save right when they take an action that they expect saves the data, there would be a data loss.
Web stacks generally scale horizontally. Don't start to optimize this kind of thing unless there's evidence that you really have to.
I have an asp.net-mvc website and people manage a list of projects. Based on some algorithm, I can tell if a project is out of date. When a user logs in, i want it to show the number of stale projects (similar to when i see a number of updates in the inbox).
The algorithm to calculate stale projects is kind of slow so if everytime a user logs in, i have to:
Run a query for all project where they are the owner
Run the IsStale() algorithm
Display the count where IsStale = true
My guess is that will be real slow. Also, on everything project write, i would have to recalculate the above to see if changed.
Another idea i had was to create a table and run a job everything minutes to calculate stale projects and store the latest count in this metrics table. Then just query that when users log in. The issue there is I still have to keep that table in sync and if it only recalcs once every minute, if people update projects, it won't change the value until after a minute.
Any idea for a fast, scalable way to support this inbox concept to alert users of number of items to review ??
The first step is always proper requirement analysis. Let's assume I'm a Project Manager. I log in to the system and it displays my only project as on time. A developer comes to my office an tells me there is a delay in his activity. I select the developer's activity and change its duration. The system still displays my project as on time, so I happily leave work.
How do you think I would feel if I receive a phone call at 3:00 AM from the client asking me for an explanation of why the project is no longer on time? Obviously, quite surprised, because the system didn't warn me in any way. Why did that happen? Because I had to wait 30 seconds (why not only 1 second?) for the next run of a scheduled job to update the project status.
That just can't be a solution. A warning must be sent immediately to the user, even if it takes 30 seconds to run the IsStale() process. Show the user a loading... image or anything else, but make sure the user has accurate data.
Now, regarding the implementation, nothing can be done to run away from the previous issue: you will have to run that process when something that affects some due date changes. However, what you can do is not unnecessarily run that process. For example, you mentioned that you could run it whenever the user logs in. What if 2 or more users log in and see the same project and don't change anything? It would be unnecessary to run the process twice.
Whatsmore, if you make sure the process is run when the user updates the project, you won't need to run the process at any other time. In conclusion, this schema has the following advantages and disadvantages compared to the "polling" solution:
Advantages
No scheduled job
No unneeded process runs (this is arguable because you could set a dirty flag on the project and only run it if it is true)
No unneeded queries of the dirty value
The user will always be informed of the current and real state of the project (which is by far, the most important item to address in any solution provided)
Disadvantages
If a user updates a project and then upates it again in a matter of seconds the process would be run twice (in the polling schema the process might not even be run once in that period, depending on the frequency it has been scheduled)
The user who updates the project will have to wait for the process to finish
Changing to how you implement the notification system in a similar way to StackOverflow, that's quite a different question. I guess you have a many-to-many relationship with users and projects. The simplest solution would be adding a single attribute to the relationship between those entities (the middle table):
Cardinalities: A user has many projects. A project has many users
That way when you run the process you should update each user's Has_pending_notifications with the new result. For example, if a user updates a project and it is no longer on time then you should set to true all users Has_pending_notifications field so that they're aware of the situation. Similarly, set it to false when the project is on time (I understand you just want to make sure the notifications are displayed when the project is no longer on time).
Taking StackOverflow's example, when a user reads a notification you should set the flag to false. Make sure you don't use timestamps to guess if a user has read a notification: logging in doesn't mean reading notifications.
Finally, if the notification itself is complex enough, you can move it away from the relationship between users and projects and go for something like this:
Cardinalities: A user has many projects. A project has many users. A user has many notifications. A notifications has one user. A project has many notifications. A notification has one project.
I hope something I've said has made sense, or give you some other better idea :)
You can do as follows:
To each user record add a datetime field sayng the last time the slow computation was done. Call it LastDate.
To each project add a boolean to say if it has to be listed. Call it: Selected
When you run the Slow procedure set you update the Selected fileds
Now when the user logs if LastDate is enough close to now you use the results of the last slow computation and just take all project with Selected true. Otherwise yourun again the slow computation.
The above procedure is optimal, becuase it re-compute the slow procedure ONLY IF ACTUALLY NEEDED, while running a procedure at fixed intervals of time...has the risk of wasting time because maybe the user will neber use the result of a computation.
Make a field "stale".
Run a SQL statement that updates stale=1 with all records where stale=0 AND (that algorithm returns true).
Then run a SQL statement that selects all records where stale=1.
The reason this will work fast is because SQL parsers, like PHP, shouldn't do the second half of the AND statement if the first half returns true, making it a very fast run through the whole list, checking all the records, trying to make them stale IF NOT already stale. If it's already stale, the algorithm won't be executed, saving you time. If it's not, the algorithm will be run to see if it's become stale, and then stale will be set to 1.
The second query then just returns all the stale records where stale=1.
You can do this:
In the database change the timestamp every time a project is accessed by the user.
When the user logs in, pull all their projects. Check the timestamp and compare it with with today's date, if it's older than n-days, add it to the stale list. I don't believe that comparing dates will result in any slow logic.
I think the fundamental questions need to be resolved before you think about databases and code. The primary of these is: "Why is IsStale() slow?"
From comments elsewhere it is clear that the concept that this is slow is non-negotiable. Is this computation out of your hands? Are the results resistant to caching? What level of change triggers the re-computation.
Having written scheduling systems in the past, there are two types of changes: those that can happen within the slack and those that cause cascading schedule changes. Likewise, there are two types of rebuilds: total and local. Total rebuilds are obvious; local rebuilds try to minimize "damage" to other scheduled resources.
Here is the crux of the matter: if you have total rebuild on every update, you could be looking at 30 minute lags from the time of the change to the time that the schedule is stable. (I'm basing this on my experience with an ERP system's rebuild time with a very complex workload).
If the reality of your system is that such tasks take 30 minutes, having a design goal of instant gratification for your users is contrary to the ground truth of the matter. However, you may be able to detect schedule inconsistency far faster than the rebuild. In that case you could show the user "schedule has been overrun, recomputing new end times" or something similar... but I suspect that if you have a lot of schedule changes being entered by different users at the same time the system would degrade into one continuous display of that notice. However, you at least gain the advantage that you could batch changes happening over a period of time for the next rebuild.
It is for this reason that most of the scheduling problems I have seen don't actually do real time re-computations. In the context of the ERP situation there is a schedule master who is responsible for the scheduling of the shop floor and any changes get funneled through them. The "master" schedule was regenerated prior to each shift (shifts were 12 hours, so twice a day) and during the shift delays were worked in via "local" modifications that did not shuffle the master schedule until the next 12 hour block.
In a much simpler situation (software design) the schedule was updated once a day in response to the day's progress reporting. Bad news was delivered during the next morning's scrum, along with the updated schedule.
Making a long story short, I'm thinking that perhaps this is an "unask the question" moment, where the assumption needs to be challenged. If the re-computation is large enough that continuous updates are impractical, then aligning expectations with reality is in order. Either the algorithm needs work (optimizing for local changes), the hardware farm needs expansion or the timing of expectations of "truth" needs to be recalibrated.
A more refined answer would frankly require more details than "just assume an expensive process" because the proper points of attack on that process are impossible to know.