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Many thanks. Your points on the topology of the architecture of the servers is well taken. My idea for taking a session state server off line has been just (1

Many thanks. Your points on the topology of the architecture of the servers is well taken. My idea for taking a session state server off line has been just (1) to send it a message to decline more new pairs and (2) to wait until all its key-value pairs have timed out and then just shut it down. But there's a flaw here: An actual point in server farm system management uses a variable part of the Web site logic, that is, the time out intervals. That's not so good. But other ways of session state server shutdown would about have to be able to move key-value pairs from the server to be shutdown to another key value server. Maybe easier would be just to replace each session state server with two essentially identical. Then if don't have a proxy (itself a single point of failure?) have each client send everything to both of the session state servers. In a serious data base environment, there would have to be commit logic, etc. Hmm. Otherwise, when want to shutdown a session state server, then just shutdown one of a pair. To bring new pairs on-line, then just send to all the clients a table of all the session state servers they could use. Lock the table, change it, and unlock it? Or just have the table accessed via a pointer and in an atomic operation change the pointer to the new table? Then to delete the old table have to wait until it is no longer in use? Hmm ... concurrency issues which I'm trying to avoid! Hmm, put the new table on some server and then send all the clients a message to go to the server and get their copy of the new table -- single threaded solution! Moving key-value pairs from one key-value server to another sounds to me like Cadillac, filtered air, humidity controlled air conditioning in an open Jeep! Wow. My startup isn't for stock trading, banking, or health care! If a key-value store fails, then on some Web servers there will be some TCP/IP session time outs and some Web site users will get some messages with apologies and some cartoon of a mess up. Hashing could be faster than AVL trees. There is a tweak to hashing in Ronald Fagin, Jurg Nievergelt, Nicholas Pippenger, H. Raymond Strong, 'Extendible hashing—a fast access method for dynamic files', "ACM Transactions on Database Systems", ISSN 0362-5915, Volume 4, Issue 3, September 1979, Pages: 315 - 344. that has a graceful way around collisions. We used that in some work at IBM's Watson lab for their Resource Object Data Manager (RODM) shipped with NetView. For one of the collection classes I'm using, for detecting time outs, the key is a time-date stamp, and I want a fast way to read the pairs in ascending order on time-date stamp, right, starting with the oldest time-date stamp. So, mere hashing would not offer that. Ah, I fell in love with AVL trees when I first read about them in Knuth, The Art .... I'm just hoping that the .Net class is based on AVL trees or something equivalent. Somewhere between the user's fingers and the most distant parts of the server farm there have to be some flow controls of some kind. So, if the TCP/IP input queue to a session state server fills, then likely, right, the Web server making the request will block, that is, wait, maybe time out. So, that would be one source of flow control. Maybe I should think about implementing more. There's an old approach to flow control: Just don't respond to the user so fast. Instead, make the user wait a little. That approach, in effect, slows the rate of data input to the whole Web site. Maybe I should think, at least for the future, say, version 2.0, about actually designed means of flow control, rate limiting, etc. What do people do about that, say, at high end sites?

zemo 7y ago
> wait until all its key-value pairs have timed out and then just shut it down you can do it that way but it often winds up making the server's shutdown dependent on a client behavior. For example, the liveness of a key is determined by the client in most applications, not the server. Any sort of naive TTL system where a read on a key extends its lifetime means that the server cannot shut down without permission from the clients, because the client can just keep reading the same key over and over, extending its lifetime until the client shuts down. I have a system that works this way now, I'm working on removing that behavior. When I want to take a server offline, I have no idea if it will take ten minutes or four hours to drain. It's very annoying. > have each client send everything to both of the session state servers generally a client would write to just one server (server A), and that server would write to the other server (server B). If server A applies the change locally and then confirms it with the client, then syncs the change to server B independently of the client request, that's asynchronous replication. If server A waits until server B has applied the change locally before confirming the write with the client, that's synchronous replication. If the client can only send writes to one of the servers, but can send reads to either server, that's master-replica replication. The easiest thing to reason about is synchronous master-replica systems: client sends a request to A, A applies it to A's storage, A sends it to B, B applies it to B's storage, B confirms it with A, A confirms it with the client. If a client can send a write to either server, that's master-master replication. Master-master replication is harder to implement; what if two different clients write the same key on two different servers at the same time? A conflict arises. Concurrency issues are unavoidable in web systems. Uncoordinated users are using the system simultaneously; "everybody wait in line" is not a realistic option in practice. > What do people do about that, say, at high end sites? that varies pretty widely. Depends a lot on what you're building, how much money you have, how much time you have, how many users you have, how many engineers you have, how easy it is for you to hire, what skills your current engineering team has, what clients will be accessing the data, etc. The permutations of concerns are innumerable. Lots of people buy hosted redis from redislabs.com or AWS Elasticache because they are either unwilling or unable to run a redis server themselves. That's a great solution for most people most of the time; it works great for a lot of use cases, it's relatively inexpensive, and the providers give you useful tools that you would otherwise have to build yourself. > Maybe I should think about implementing more. Are you selling a key-value store? What is your startup? Maybe you should think about implementing less, and focusing on building the things that actually matter to users. Key-value stores are nice engineering problems, but they're implementation details: users don't know about and don't care about their existence, and their existence does not drive revenue. And that's why most people just buy hosted redis, download a redis client library, and call it a day. It's not technically the best solution, but it's often times the most economical solution.

zemo 7y ago

> wait until all its key-value pairs have timed out and then just shut it down you can do it that way but it often winds up making the server's shutdown dependent on a client behavior. For example, the liveness of a key is determined by the client in most applications, not the server. Any sort of naive TTL system where a read on a key extends its lifetime means that the server cannot shut down without permission from the clients, because the client can just keep reading the same key over and over, extending its lifetime until the client shuts down. I have a system that works this way now, I'm working on removing that behavior. When I want to take a server offline, I have no idea if it will take ten minutes or four hours to drain. It's very annoying. > have each client send everything to both of the session state servers generally a client would write to just one server (server A), and that server would write to the other server (server B). If server A applies the change locally and then confirms it with the client, then syncs the change to server B independently of the client request, that's asynchronous replication. If server A waits until server B has applied the change locally before confirming the write with the client, that's synchronous replication. If the client can only send writes to one of the servers, but can send reads to either server, that's master-replica replication. The easiest thing to reason about is synchronous master-replica systems: client sends a request to A, A applies it to A's storage, A sends it to B, B applies it to B's storage, B confirms it with A, A confirms it with the client. If a client can send a write to either server, that's master-master replication. Master-master replication is harder to implement; what if two different clients write the same key on two different servers at the same time? A conflict arises. Concurrency issues are unavoidable in web systems. Uncoordinated users are using the system simultaneously; "everybody wait in line" is not a realistic option in practice. > What do people do about that, say, at high end sites? that varies pretty widely. Depends a lot on what you're building, how much money you have, how much time you have, how many users you have, how many engineers you have, how easy it is for you to hire, what skills your current engineering team has, what clients will be accessing the data, etc. The permutations of concerns are innumerable. Lots of people buy hosted redis from redislabs.com or AWS Elasticache because they are either unwilling or unable to run a redis server themselves. That's a great solution for most people most of the time; it works great for a lot of use cases, it's relatively inexpensive, and the providers give you useful tools that you would otherwise have to build yourself. > Maybe I should think about implementing more. Are you selling a key-value store? What is your startup? Maybe you should think about implementing less, and focusing on building the things that actually matter to users. Key-value stores are nice engineering problems, but they're implementation details: users don't know about and don't care about their existence, and their existence does not drive revenue. And that's why most people just buy hosted redis, download a redis client library, and call it a day. It's not technically the best solution, but it's often times the most economical solution.