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linkIt's interesting to hear that has worked well, obviously this wasn't a small project. Your point about knowing how to use your tool definitely rings true. Also interesting that you had a use case where data loss and integrity actually mattered and in real time, unlike a social network or most start ups operating today. Going with a heavy oracle system instead of trying to roll your own creative distributed architecture definitely seems to make sense in that scenario. Just out curiosity, was this Java/Hibernate? On one system we used Java/Oracle/Hibernate and went with the big single cluster. We also worked with Microsoft on integrating their latest (at the time beta) caching servers. We did indeed have to roll our own distributed architecture in that case, but it's not like we had to drop ORM to do it. fendale 1212 days ago linkAs another 'Oracle guy' this is an interesting post. I have said before on here, if you pay for Oracle, and also pay for decent storage arrays, Oracle can shift a serious amount of data before it reaches its limit. At some point the bottleneck will likely be disk however, so buying a high end storage system would probably become priority. jacques_chester 1212 days ago linkDo those massive systems on Oracle etc scale out, or simply scale up with expensive hardware?Both. If you still need ACID guarantees and want hundreds of thousands (or even millions, if necessary) of TPM, you will need to pay the piper.

sucked, but we did scale (that's why friendster was friendster and we were myspace :). In an environment with 450+ million users, we had extensive caching systems and still had to use every sql trick in the book to get our systems to scale well. I know because my job was working with the DBAs to bridge the sql and front end worlds together. I can say with great certainty that front end developers who did not know sql and were simply following a logical object model would not have produced code that scaled in our environment, there were way too many things that were done that were extremely non obvious. Since myspace i've been working at a python/postgres start up where we've been applying the same principles pretty successfully, at a much different scale of course. So I think the ratio of hardware to scale at other sites is comparable, and so I think the same lessons apply. Our systems were more like online banking systems; very much an even split of fast writes and reads, transactionally bound to third party systems (in game payment, in game "real time" use of consumables, etc), real time analytics for fraud detection, etc. I ensured that the stress and load testing was done so that we could directly simulate the load of our expected user base, with realistic profiles, in order to better engineer our databases and disk IO. It also allowed us to measure the impacts of feature additions, etc. Cache Fusion, high speed and low latency interconnects, and shared block access provides incredible scaling without having to do anything special in the middle tier. n_are_q 1212 days ago Nike Jackets Womens

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person or two to help write the data access logic. But hey, I can't argue with results if it worked for you that's great. But as a general statement I think that sort development methodology is highly conducive to errors and systematic problems that would not become evident until later, and at that point take a great deal of effort to fix. The two big systems I architected where I made the decision to go with ORM's were the online EA Sports system (all EA Sports games on all platforms, currently running in a Nike Coats For Boys 7 node Oracle cluster), and most recently, the Need For Speed World Online system. We launched the EA Sports system with Madden, and went from 50 to 11 million users hitting the DB in less than an hour. Then we rolled out the other EA Sports games. As a matter of fact, in both cases, I was the only DBA on the project, and it was a predominately part time role. We'd meet, ensure we were all on the same page with the object/data model, and then they'd go and build it. The developers were able to immediately build and run and test and integrate something that was functional and operational, when they needed it. This was HUGE, and something that most people don't properly appreciate. Timelines were already insane enough as it was, the last thing we needed to do was artificially constrain ourselves by waiting for other (db) devs before work could go on. We intentionally set up our testing to be able to monitor and test the effectiveness of the ORM, and to point it out when it didn't work efficiently. The devs would do the majority of the heavy lifting with the initial data model, and the results would be tested, reviewed, and then modified if required. The performance modifications were not a lot of effort to fix, either. Usually it was a very slight data model change, or using a named query to take advantage of a database specific features. And CLOBS. Anyway, the user facing product might have Nike Sb Hoodie Grey

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Wrapping both caching logic and database access in an ORM like system is no doubt the right thing to do. Letting front end developers write queries to be converted by an orm and reviewed by a DBA later in my opinion that's not the most efficient method of development. I probably would have invested in an extra DB Nike Hoodie Blue

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