Hey.. i wanna know which time is a good accesstime, because i'm searching for a good sql database and hsqldb says their accesstime is 12ms... <-- good?
I think it would depend on your needs. Is it for a web server or a desktop application? The amount of data is also important, because reading lots of small records will perform differently than reading a few large records. Access time is also based upon your hardware, software and maybe even some other factors.
For example, you can use a database with lightning-fast access, but if your users need to connect to it over a 5 megabit VPN connection, passing through three different proxies and with trafic world-wide, your database would then just be a waste of power.
Basically, it's a marketing thing that they're claiming. It's a good product but don't just focus on access time. Make sure you also look at your other needs. Another system might just perform better, even if it has a slower acess time, because it is more optimized in reading it's indices and stuff.
So, what do you want, exactly?
I don't think access time tells you anything, really. If you have slow or incorrectly configured storage, then this access time metric will be dwarfed by how much time is spent on waits and split I/Os. Network latency is also a factor, since I'm guessing you probably won't want to have your code on the same machine as your database, and you will most likely have a few network devices you'll need to traverse in your production environment.
In my experience, all the database platforms these days will all perform adequately if configured correctly and paired with a complementary application. Pick the DBMS that best fits your requirements, follow the best practices for configuration of the DBMS on your hardware, and you should be please with the outcome.
Related
I have been hoping to find out what different server setups equate to in theory for concurrent page requests, and the answer always seems to be soaked in voodoo and sorcery. What is the approximation of max concurrent page requests for the following setups?
apache+php+mysql(1 server)
apache+php+mysql+caching(like memcached or similiar (still one server))
apache+php+mysql+caching+dedicated Database Server (2 servers)
apache+php+mysql+caching+dedicatedDB+loadbalancing(multi webserver/single dbserver)
apache+php+mysql+caching+dedicatedDB+loadbalancing(multi webserver/multi dbserver)
+distributed (amazon cloud elastic) -- I know this one is "as much as you can afford" but it would be nice to know when to move to it.
I appreciate any constructive criticism, I am just trying to figure out when its time to move from one implementation to the next, because they each come with their own implementation feat either programming wise or setup wise.
In your question you talk about caching and this is probably one of the most important factors in a web architecture r.e performance and capacity.
Memcache is useful, but actually, before that, you should be ensuring proper HTTP cache directives on your server responses. This does 2 things; it reduces the number of requests and speeds up server response times (if you have Apache configured correctly). This can also be improved by using an HTTP accelerator like Varnish and a CDN.
Another factor to consider is whether your system is stateless. By stateless, it usually means that it doesn't store sessions on the server and reference them with every request. A good systems architecture relies on state as little as possible. The less state the more horizontally scalable a system. Most people introduce state when confronted with issues of personalisation - i.e serving up different content for different users. In such cases you should first investigate using the HTML5 session storage (i.e store the complete user data in javascript on the client, obviously over https) or if the data set is smaller, secure javascript cookies. That way you can still serve up cached resources and then personalise with javascript on the client.
Finally, your stack includes a database tier, another potential bottleneck for performance and capacity. If you are only reading data from the system then again it should be quite easy to horizontally scale. If there are reads and writes, its typically better to separate the read write datasets into a separate database and have the read only in another. You can then use more relevant methods to scale.
These setups do not spit out a single answer that you can then compare to each other. The answer will vary on way more factors than you have listed.
Even if they did spit out a single answer, then it is just one metric out of dozens. What makes this the most important metric?
Even worse, each of these alternatives is not free. There is engineering effort and maintenance overhead in each of these. Which could not be analysed without understanding your organisation, your app and your cost/revenue structures.
Options like AWS not only involve development effort but may "lock you in" to a solution so you also need to be aware of that.
I know this response is not complete, but I am pointing out that this question touches on a large complicated area that cannot be reduced to a single metric.
I suspect you are approaching this from exactly the wrong end. Do not go looking for technologies and then figure out how to use them. Instead profile your app (measure, measure, measure), figure out the actual problem you are having, and then solve that problem and that problem only.
If you understand the problem and you understand the technology options then you should have an answer.
If you have already done this and the problem is concurrent page requests then I apologise in advance, but I suspect not.
I inhereted a high volume OLTP DB which I have free reign to improve as much as I find reasonably possible. The improvements already were very helpful but I want to take it to the next level. The data access patterns I found made it a good candidate IMO to cache the data on other servers and I would love to hear anyone's experience or recommendations with this type of setup.
We have a DB that gets about 3GB of data added to a table every day and reporting on it used to be very slow. The data does not change once it's put in, and no data gets inserted that is over a week old. Rows inputted within the last 3 days tend to see thousands of inserts between tens of millions of rows.
I was thinking of having data over 2 weeks old get pushed out to MongoDB. I could then have the 2 week sliding window data that is not pushed out to Mongo be, be cached by some kind of caching software so those get queried and displayed instead of the data being read out of the DB the whole time. I figure this way we still get full A.C.I.D compliance by having the DB engine validate all the data, have high read performance as it is not hitting the DB, then Mongo can take it when it is no longer a 'transaction'.
Anyone have any recommended solutions? I was looking at MemCached, but not quite sure if that's a good or even plausible solution. Thanks!
Another thing you could consider is using the new In-Memory OLTP feature in SQL Server 2014. That feature improves efficiency and scaling for OLTP workloads. You will potentially be able to get a lot more out of your existing server, without the need to consider specific caching mechanisms.
I don't have specific experience with SQL Server, but what you are describing does seem like a valid use case for MongoDB.
Note that while MongoDB can't directly handle transactions, it is capable of handling certain operations in an atomic fashion (see findAndModify, for instance). Additionally, with journaling enabled, you shouldn't have any reason to worry about durability. MongoDB is a reliable data store and will not lose or corrupt your data.
MongoDB itself can also act as a performant cache if you run a second deployment with journaling disabled. In this instance, writes will take place in memory and only be persisted to the disk every 60 seconds (unless otherwise configured). This will provide comparable performance to memcache which is solely in-memory while allowing you to keep your stack a bit simpler.
Hope this helps!
I have this scenario:
You have a factory process line which runs 24/7. Downtime is extremely expensive.
The software controlling all different parts must use a shared form of database storage
The main reason for this is to know in which state the factory is in. For example some products can be mixed when using the same set of equipement and others DEFINITELY not.
requirements:
I want to the software be able to detect that an error in one part of
the plant must result in some machine shutdown more then 1 km away. so stoing data in the plc's is not an option.
Updates and upgrades to the factory environment are frequent
load (in computer terms) will be really low.
The systems handles a few hunderd assignments a day for which calculations / checks are done followed by instructions send for the factory machines. Systems will be bored most of the time. Most important requirement is the central computer system must be correct and always working.
I was thinking to use a dynamo based database (riak or cassandra) where data gets written to multiple machines with each machine having the whole database
When one system goes down it will go down unoticed. A Traditional sql databse might be more of a pain to upgrade when tables changes and this master slave is harder to configure.
What would be your solution?
Network has been made redundant and most other single points of failure to. The database system is critical because downtime of the db means downtime for the entire plant not just one of the machines which is acceptable.
How to solve shared state problem.
complexity in the database will not be a problem. I will be more like a simple key value store to get the most current and correct data.
I don't think this is a sql/nosql question. All of Postgres, MySQL and MS SQL Server have some kind of cluster or hot standby option.
Configuration is a one-time thing, but any NoSQL option is going to give you headaches from top-to-bottom of code, if you are trying to do something fundamentally relational on a platform that has given up relational for the purposes of running things like Amazon or Facebook. The configuration is once, the coding is forever.
So I would say stick with a tried and true solution and get that hot replication going.
This also provides a solution for upgrades. The typical sequence is to "fail over" to the standby, upgrade the master, flip back to the master, upgrade the standby, and resume. With details specific to the situation of course.
Use an established RDBMS that supports such things natively
Do you really want to run a 24/7 mission critical system on something that may be consistent at any point in time?
You need to avoid single points of failure.
All the major players in our dbms world offer at least one way to avoid making the database itself a single point of failure. I might question whether they can propagate changes fast enough for your manufacturing processes. (Or is data update not really an issue? Can't really tell from your question.) My db work in manufacturing is limited to the car and the chemical industry. Microseconds didn't matter to them.
But the dbms isn't the only thing that can fail. "Always working" means that the clients have to always be working, too. Client hardware, connections to the network, the network and network servers themselves all probably have single points of failure. Failure-tolerant servers have multiple power supplies, multiple NICs, etc.
"Always working" is really expensive. I have a feeling that the database isn't going to be the biggest problem for your company.
I was recently given the task of rebuilding an existing RIA. The new RIA that I've designed is based on Silverlight, with a WCF service to connect to MS SQL Server. This is my first time doing something like this, so I'm not sure how to design the entire thing.
Basically, the client can look through graphs of "stocks" (allowing the client to choose different time periods, settings, etc). I've written the whole application essentially, but I'm not sure how to put it together.
The graphs are supposed to be directly based on the database, and to create the datapoints on the graph, some calculations need to be done (not very expensive ones).
The problem I'm having is to decide where to put the calculations (client or serverside? Or half and half?)
What factors should I look for to help me decide where the calculations should be done? And how can I go about optimizing this (caching, etc)?
Obviously this is a very broad subject, so I'm not expecting an immediate answer, but any help/pointing in the right direction/resources would be appreciated.
A few tips for this kind of app.
Put as much logic as possible on the client.
Make the client responsible for session data, making all your server code stateless.
Try to minimize traffic to and from the server (Bigger requests are more efficient than multiple smaller ones) so consolidate requests when possible.
If this project is likely to grow beyond it's current feature set I think it's probably a good idea to perform the calculations client side. This can avoid scaling issues, because you're using all the client side CPUs ratther than you're single, precious server CPU. This does however rely on being able to transfer the required data to the client in an efficient way, otherwise you replace a processor bottleneck with a network bottleneck.
As for caching it depends on your inputs, what variables can users of the client affect? If any of the variables they can alter are discrete (ie they can be a fixed set of values) then they're candidates for caching. For example if a user can select a date range of stock variations to view then that's probably not so useful, if however they can only select a year then you could cache your data sets by year (download each data set to the client and perform your calculation). I'd not worry about caching too much unless you find it's a real performance problem, it'll only make your code more complex, so don't add it until you have proven you need it.
One other thing, if this project is unlikely to be a long term concern then implement the calculations wherever is easiest and fastest, you can revisit if the project becomes more important later on.
Be REALLY REALLY careful about implementing client-side caching. Caching is INSANELY hard to do right while maintaining performance, security and correctness. Note that your DB Server's caching mechanism is already likely to be way better than any local caching mechanism you're likely to implement in less than 2 weeks' effort!
I would urge you to do as much work on the back-end as possible and to limit your client to render the data in a manner that is appropriate for your users. While many may balk at this suggestion, it's based on a number of observations from building many such systems in the past:
If you're going to filter some of the data returned by your service, you've just wasted thousands of clock cycles shipping data that need never have left your server
If you're going to sort your data, your DB could have done the sorting for you (often using otherwise idle CPU ticks) while waiting for the data to be read from its disks.
Your server most likely has more CPU and RAM available than your clients and has a surprising amount of "free time" to use for sorting, filtering, running inline calculations, etc., while its waiting for disks to read sectors etc.
As Roman suggested: Minimize your round-trips between your client and your server as much as possible.
But perhaps most importantly:
BEFORE YOU START DESIGNING YOUR SYSTEM, state your performance goals
Design what you think will achieve those goals. Try to find bottlenecks in your design, particularly areas where you make blocking calls. Re-design those areas to use async patterns wherever you can.
Build your intended solution
Measure your actual perforamnce under actual real-world load
If you're within your expected performance goals, then you're done.
If not, work out where you're spending too long and tune the design of that portion of the system. Goto 3.
Don't try to build the perfect system in one try - chances are that you won't manage it, no matter how hard you try, for a variety of reasons including user expectations, your servers ability to process the required load, your clients' ability to handle the returned data, your network's ability to carry the traffic, etc.
They're a little old now, but I suggest you read through some of the earlier posts at http://blogs.msdn.com/richardt for more thoughts around designing and constructing Service Oriented and distributed systems.
I'm sure it repeats everywhere. You can 'feel' network is slow, or machine or slow or something. But the server/chassis logs are not showing anything, so IT doesn't believe you. What do you do?
Your regressions are taking twice the time ... but that's not enough
Okay you transfer 100 GB using dd etc, but ... that's not enough.
Okay you get server placed in different chassis for 2 week, it works fine ... but .. that's not enough...
so HOW do you get IT to replace the chassis ?
More specifically:
Is there any suite which I can run on two setups ( supposed to be identical ), which can show up difference in network/cpu/disk access .. which IT will believe ?
Computers don't age and slow down the same way we do. If your server is getting slower -- actually slower, not just feels slower because every other computer you use is getting faster -- then there is a reason and it is possible that you may be able to fix it. I'd try cleaning up some disk space, de-fragmenting the disk, and checking what other processes are running (perhaps someone's added more apps to the system and you're just not getting as many cycles).
If your app uses a database, you may want to analyze your query performance and see if some indices are in order. Queries that perform well when you have little data can start taking a long time as the amount of data grows if they have to use table scans. As a former "IT" guy, I'd also be reluctant to throw hardware at a problem because someone tells me the system is slowing down. I'd want to know what has changed and see if I could get the system running the way it should be. If the app has simply out grown the hardware -- after you've made suitable optimizations -- then upgrading is a reasonable choice.
Run a standard benchmark suite. See if it pinpoints memory, cpu, bus or disk, when compared to a "working" similar computer.
See http://en.wikipedia.org/wiki/Benchmark_(computing)#Common_benchmarks for some tips.
The only way to prove something is to do a stringent audit.
Now traditionally, we should keep the system constant between two different sets while altering the variable we are interested. In this case the variable is the hardware that your code is running on. So in simple terms, you should audit the running of your software on two different sets of hardware, one being the hardware you are unhappy about. And see the difference.
Now if you are to do this properly, which I am sure you are, you will first need to come up with a null hypothesis, something like:
"The slowness of the application is
unrelated to the specific hardware we
are using"
And now you set about disproving that hypothesis in favour of an alternative hypothesis. Once you have collected enough results, you can apply statistical analyses on them, to decide whether any differences are statistically significant. There are analyses to find out how much data you need, and then compare the two sets to decide if the differences are random, or not random (which would disprove your null hypothesis). The type of tests you do will mostly depend on your data, but clever people have made checklists to help us decide.
It sounds like your main problem is being listened to by IT, but raw technical data may not be persuasive to the right people. Getting backup from the business may help you and that means talking about money.
Luckily, both platforms already contain a common piece of software - the application itself - designed to make or save money for someone. Why not measure how quickly it can do that e.g. how long does it take to process an order?
By measuring how long your application spends dealing with each sub task or data source you can get a rough idea of the underlying hardware which is under performing. Writing to a local database, or handling a data structure larger than RAM will impact the disk, making network calls will impact the network hardware, CPU bound calculations will impact there.
This data will never be as precise as a benchmark, and it may require expensive coding, but its easier to translate what it finds into money terms. Log4j's NDC and MDC features, and Springs AOP might be good enabling tools for you.
Run perfmon.msc from Start / Run in Windows 2000 through to Vista. Then just add counters for CPU, disk etc..
For SQL queries you should capture the actual queries then run them manually to see if they are slow.
For instance if using SQL Server, run the profiler from Tools, SQL Server Profiler. Then perform some operations in your program and look at the capture for any suspicous database calls. Copy and paste one of the queries into a new query window in management studio and run it.
For networking you should try artificially limiting your network speed to see how it affects your code (e.g. Traffic Shaper XP is a simple freeware limiter).