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I am looking at designing a system that will be recieving very many records.

Is there any fixed limit on how many inserts you can do in a database per second?

We normally use MS SQL server, is Oracle any better? Is it possible to get better performance on a No-SQL cloud solution?

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I don't know of any database system that has an artificial limit on the number of operations per second, and if I found one that did I would be livid. Your only limiting factor should be the practical restrictions imposed by your OS and hardware, particularly disk throughput.

The rest of your question (which database is "better") is subject to your implementation and requirements. If you're just dumping data into a bucket a NoSQL solution like MongoDB may be appropriate, and their performance can be quite impressive. If your data is highly relational SQL-based RDBMS systems are the better choice.

With any SQL-based RDBMS you should expect to spend some time tuning the system for optimum performance -- Your database vendor will probably have a small mountain of documentation on the subject, and the difference between an optimally-tuned system and one that was just thrown on the hardware can be dramatic.

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I stumbled across this post when I was looking for why I am seeing some interesting results in some of my own performance testing.

I did testing on postgres to see how performance could be increased by grouping my inserts together. As in instead of doing 1 insert at I time, I stick several into a large sql string and then run that sql.

I knew going into the testing that the more I stuck together the better performance I could expect since the overhead starts to get minimized compared to the actual data. I also figured the performance would start to decrease at some point due to the overhead associated with having to retransmit when there is a single bit error in such a long string.

What I did not expect was my experimental results. I am not sure how to explain this data, so maybe if there is anyone who knows more about this they can try to explain it.

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