I'm trying to figure out how I can parallelize some procedural code to create records in a table.
Here's the situation (sorry I can't provide much in the way of actual code):
I have to predict when a vehicle service will be needed, based upon the previous service date, the current mileage, the planned daily mileage and the difference in mileage between each service.
All in all - it's very procedural, for each vehicle I need to take into account it's history, it's current servicing state, the daily mileage (which can change based on ranges defined in the mileage plan), and the sequence of servicing.
Currently I'm calculating all of this in PHP, and it takes about 20 seconds for 100 vehicles. Since this may in future be expanded to several thousand, 20 seconds is far too long.
So I decided to try and do it in a CLR stored procedure. At first I thought I'd try multithreading it, however I quickly found out it's not easy to do in the TSQL host. I was recommended to allow TSQL to work out the parallelization itself. Yet I have no idea how. If it wasn't for the fact the code needs to create records I could define it as a function and do:
SELECT dbo.PredictServices([FleetID]) FROM Vehicles
And TSQL should figure out it can parallelize that, but I know of no alternative for procedures.
Is there anything I can do to parallelize this?
The recommendation you received is a correct one. You simply don't have .NET framework facilities for parallelism available in your CLR stored procedure. Also please keep in mind that the niche for CLR Stored Procedures is rather narrow and they adversely impact SQL Server's performance and scalability.
If I understand the task correctly you need to compute a function PredictServices for some records and store the results back to database. In this case CLR Stored procedures could be your option provided PredictServices is just data access/straightforward transformation of data. Best practice is to create WWF (Windows Workflow Foundation) service to perform computations and call it from PHP. In Workflow Service you can implement any solution including one involving parallelism.
Related
I have a couple of millions entries in a table which start and end timestamps. I want to implement an analysis tool which determines unique entries for a specific interval. Let's say between yesterday and 2 month before yesterday.
Depending on the interval the queries take between a couple of seconds and 30 minutes. How would I implement an analysis tool for a web front-end which would allow to quite quickly query this data, similar to Google Analytics.
I was thinking of moving the data into Redis and do something clever with interval and sorted sets etc. but I was wondering if there's something in PostgreSQL which would allow to execute aggregated queries, re-use old queries, so that for instance, after querying the first couple of days it does not start from scratch again when looking at different interval.
If not, what should I do? Export the data to something like Apache Spark or Dynamo DB and analysis in there to fill Redis for retrieving it quicker?
Either will do.
Aggregation is a basic task they all can do, and your data is smll enough to fit into main memory. So you don't even need a database (but the aggregation functions of a database may still be better implemented than if you rewrite them; and SQL is quite convenient to use.
Jusr do it. Give it a try.
P.S. make sure to enable data indexing, and choose the right data types. Maybe check query plans, too.
I'm trying to optimise ADO .NET (.Net 4.5) data access with Task parallel library (.Net 4.5), For an example when selecting 1000,000,000 records from a database how can we use the machine multicore processor effectively with Task parallel library. If anyone has found use full sources to get some idea please post :)
The following applies to all DB access technologies, not just ADO.NET.
Client-side processing is usually the wrong place to solve data access problems. You can achieve several orders of magnitude improvement in performance by optimizing your schema, create proper indexes and writing proper SQL queries.
Why transfer 1M records to a client for processing, over a limited network connection with significant latency, when a proper query could return the 2-3 records that matter?
RDBMS systems are designed to take advantage of available processors, RAM and disk arrays to perform queries as fast as possible. DB servers typically have far larger amounts of RAM and faster disk arrays than client machines.
What type of processing are you trying to do? Are you perhaps trying to analyze transactional data? In this case you should first extract the data to a reporting, or better yet, an OLAP database. A star schema with proper indexes and precalculated analytics can be 1000x times faster than an OLTP schema for analysis.
Improved SQL coding can also result in 10x-50x times improvement or more. A typical mistake by programmers not accustomed to SQL is to use cursors instead of set operations to process data. This usually leads to horrendous performance degradation, in the order of 50x times and worse.
Pulling all data to the client to process them row-by-row is even worse. This is essentially the same as using cursors, only the data has to travel over the wire and processing will have to use the client's often limited memory.
The only place where asynchronous processing offers any advantage, is when you want to fire off a long operation and execute code when processing finishes. ADO.NET already provides asynchronous operations using the APM model (BeginExecute/EndExecute). You can use TPL to wrap this in a task to simplify programming but you won't get any performance improvements.
It could be that your problem is not suited to database processing at all. If your algorithm requires that you scan the entire dataset multiple times, it would be better to extract all the data to a suitable file format in one go, and transfer it to another machine for processing.
I'm setting up a new application using Entity Framework Code Fist and I'm looking at ways to try to reduce the number of round trips to the SQL Server as much as possible.
When I first read about the .Local property here I got excited about the possibility of bringing down entire object graphs early in my processing pipeline and then using .Local later without ever having to worry about incurring the cost of extra round trips.
Now that I'm playing around with it I'm wondering if there is any way to take down all the data I need for a single request in one round trip. If for example I have a web page that has a few lists on it, news and events and discussions. Is there a way that I can take down the records of their 3 unrelated source tables into the DbContext in one single round trip? Do you all out there on the interweb think it's perfectly fine when a single page makes 20 round trips to the db server? I suppose with a proper caching mechanism in place this issue could be mitigated against.
I did run across a couple of cracks at returning multiple results from EF queries in one round trip but I'm not sure the complexity and maturity of these kinds of solutions is worth the payoff.
In general in terms of composing datasets to be passed to MVC controllers do you think that it's best to simply make a separate query for each set of records you need and then worry about much of the performance later in the caching layer using either the EF Caching Provider or asp.net caching?
It is completely ok to make several DB calls if you need them. If you are affraid of multiple roundtrips you can either write stored procedure and return multiple result sets (doesn't work with default EF features) or execute your queries asynchronously (run multiple disjunct queries in the same time). Loading unrealted data with single linq query is not possible.
Just one more notice. If you decide to use asynchronous approach make sure that you use separate context instance in each asynchronous execution. Asynchronous execution uses separate thread and context is not thread safe.
I think you are doing a lot of work for little gain if you don't already have a performance problem. Yes, pay attention to what you are doing and don't make unnecessary calls. The actual connection and across the wire overhead for each query is usually really low so don't worry about it.
Remember "Premature optimization is the root of all evil".
My rule of thumb is that executing a call for each collection of objects you want to retrieve is ok. Executing a call for each row you want to retrieve is bad. If your web page requires 20 collections then 20 calls is ok.
That being said, reducing this to one call would not be difficult if you use the Translate method. Code something like this would work
var reader = GetADataReader(sql);
var firstCollection = context.Translate<whatever1>(reader);
reader.NextResult();
var secondCollection = context.Translate<whateve2r>(reader);
etc
The big down side to doing this is that if you place your sql into a stored proc then your stored procs become very specific to your web pages instead of being more general purpose. This isn't the end of the world as long as you have good access to your database. Otherwise you could just define your sql in code.
I have a ADO.NET/TSQL performance question. We have two options in our application:
1) One big database call with multiple result sets, then in code step through each result set and populate my objects. This results in one round trip to the database.
2) Multiple small database calls.
There is much more code reuse with Option 2 which is an advantage of that option. But I would like to get some input on what the performance cost is. Are two small round trips twice as slow as one big round trip to the database, or is it just a small, say 10% performance loss? We are using C# 3.5 and Sql Server 2008 with stored procedures and ADO.NET.
I would think it in part would depend on when you need the data. For instance if you return ten datasets in one large process, and see all ten on the screen at once, then go for it. But if you return ten datasets and the user may only click through the pages to see three of them then sending the others was a waste of server and network resources. If you return ten datasets but the user really needs to see sets seven and eight only after making changes to sets 5 and 6, then the user would see the wrong info if you returned it too soon.
If you use separate stored procs for each data set called in one master stored proc, there is no reason at all why you can't reuse the code elsewhere, so code reuse is not really an issue in my mind.
It sounds a wee bit obvious, but only send what you need in one call.
For example, we have a "getStuff" stored proc for presentation. The "updateStuff" proc calls "getStuff" proc and the client wrapper method for "updateStuff" expects type "Thing". So one round trip.
Chatty servers are one thing you prevent up front with minimal effort. Then, you can tune the DB or client code as needed... but it's hard to factor out the roundtrips later no matter how fast your code runs. In the extreme, what if your web server is in a different country to your DB server...?
Edit: it's interesting to note the SQL guys (HLGEM, astander, me) saying "one trip" and the client guys saying "multiple, code reuse"...
I am struggling with this problem myself. And I don't have an answer yet, but I do have some thoughts.
Having reviewed the answers given by others to this point, there is still a third option.
In my appllication, around ten or twelve calls are made to the server to get the data I need. Some of the datafields are varchar max and varbinary max fields (pictures, large documents, videos and sound files). All of my calls are synchronous - i.e., while the data is being requested, the user (and the client side program) has no choice but to wait. He may only want to read or view the data which only makes total sense when it is ALL there, not just partially there. The process, I believe, is slower this way and I am in the process of developing an alternative approach which is based on asynchronous calls to the server from a DLL libaray which raises events to the client to announce the progress to the client. The client is programmed to handle the DLL events and set a variable on the client side indicating chich calls have been completed. The client program can then do what it must do to prepare the data received in call #1 while the DLL is proceeding asynchronously to get the data of call #2. When the client is ready to process the data of call #2, it must check the status and wait to proceed if necessary (I am hoping this will be a short or no wait at all). In this manner, both server and client side software are getting the job done in a more efficient manner.
If you're that concerned with performance, try a test of both and see which performs better.
Personally, I prefer the second method. It makes life easier for the developers, makes code more re-usable, and modularizes things so changes down the road are easier.
I personally like option two for the reason you stated: code reuse
But consider this: for small requests the latency might be longer than what you do with the request. You have to find that right balance.
As the ADO.Net developer, your job is to make the code as correct, clear, and maintainable as possible. This means that you must separate your concerns.
It's the job of the SQL Server connection technology to make it fast.
If you implement a correct, clear, maintainable application that solves the business problems, and it turns out that the database access is the major bottleneck that prevents the system from operating within acceptable limits, then, and only then, should you start persuing ways to fix the problem. This may or may not include consolidating database queries.
Don't optimize for performance until a need arisess to do so. This means that you should analyze your anticipated use patterns and determine what the typical frequency of use for this process will be, and what user interface latency will result from the present design. If the user will receive feedback from the app is less than a few (2-3) seconds, and the application load from this process is not an inordinate load on server capacity, then don't worry about it. If otoh the user is waiting an unacceptable amount of time for a response (subjectve but definitiely measurable) or if the server is being overloaded, then it's time to begin optimization. And then, which optimization techniques will make the most sense, or be the most cost effective, depend on what your analysis of the issue tells you.
So, in the meantime, focus on maintainability. That means, in your case, code reuse
Personally I would go with 1 larger round trip.
This will definately be influenced by the exact reusability of the calling code, and how it might be refactored.
But as mentioned, this will depend on your exact situation, where maintainability vs performance could be a factor.
I am very eager to know the real cause though earned some knowledge from googling.
Thanks in adavnce
Because SQL is a really poor language for writing procedural code, and because the SQL engine, storage, and optimizer are designed to make it efficient to assemble and join sets of records.
(Note that this isn't just applicable to SQL Server, but I'll leave your tags as they are)
Because, in general, the hundreds of man-years of development time that have gone into the database engine and optimizer, and the fact that it has access to real-time statistics about the data, have resulted in it being better than the user in working out the best way to process the data, for a given request.
Therefore by saying what we want to achieve (with a set-based approach), and letting it decide how to do it, we generally achieve better results than by spelling out exactly how to provess the data, line by line.
For example, suppose we have a simple inner join from table A to table B. At design time, we generally don't know 'which way round' will be most efficient to process: keep a list of all the values on the A side, and go through B matching them, or vice versa. But the query optimizer will know at runtime both the numbers of rows in the tables, and also the most recent statistics may provide more information about the values themselves. So this decision is obviously better made at runtime, by the optimizer.
Finally, note that I have put a number of 'generally's in this post - there will always be times when we know better than the optimizer will, and for such times we can provide hints (NOLOCK etc).
Set based approaches are declarative, so you don't describe the way the work will be done, only what you want the result to look like. The server can decide between several strategies how to complay with your request, and hopefully choose one that is efficient.
If you write procedural code, that code will at best be less then optimal in some situation.
Because using a set-based approach to SQL development conforms to the design of the data model. SQL is a very set-based language, used to build sets, subsets, unions, etc, from data. Keeping that in mind while developing in TSQL will generally lead to more natural algorithms. TSQL makes many procedural commands available that don't exist in plain SQL, but don't let that switch you to a procedural methodology.
This makes me think of one of my favorite quotes from Rob Pike in Notes on Programming C:
Data dominates. If you have chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
SQL databases and the way we query them are largely set-based. Thus, so should our algorithms be.
From an even more tangible standpoint, SQL servers are optimized with set-based approaches in mind. Indexing, storage systems, query optimizers, and other optimizations made by various SQL database implmentations will do a much better job if you simply tell them the data you need, through a set-based approach, rather than dictating how you want to get it procedurally. Let the SQL engine worry about the best way to get you the data, you just worry about telling it what data you want.
As each one has explained, let the SQL engine help you, believe, it is very smart.
If you do not use to write set based solution and use to develop procedural code, you will have to spend some time until write well formed set based solutions. This is a barrier for most people. A tip if you wish to start coding set base solutions is, stop thinking what you can do with rows, and start thinking what you can do with collumns, and do practice functional languages.