4 ms·

Ultimately the problem we care about is very simple, and there is no way to solve it, despite your claims. Say we have a database of customers, with two replic

Ultimately the problem we care about is very simple, and there is no way to solve it, despite your claims. Say we have a database of customers, with two replicas - one in the USA, one in Europe. Say a customer in Europe wants to update their shipping address. We ship products every month to this customer from the USA to their current shipping address, on the 10th of that month. The customer is updating their address on the 9th at 10 AM PST. However, we are in the midst of a massive network partition that started on the 9th at 02 AM PST and is expected to last until the 11th. Do we perform the update in the European replica and tell the customer it succeeded (giving up consistency)? If we do, the US side of the business will ship the products to the old address, even though the update happened a full day earlier. Alternatively, do we tell the customer the update failed (giving up availability)? If we do, then the customer can't even let the European side of the business know of their new address. This is the CAP theorem in a nutshell, and it is obviously inescapable. It doesn't require appeals to immediacy that are anywhere close to the bounds of relativity. And while 3-day long network partitions are quite rare, partitions that last for many hours are not.

andras_gerlits 3y ago
Any modification to existing data must be "haggled for" somewhere, you're right about that. When you say "partition event", what is being partitioned here? A specific communication-link between two nodes. It's entirely possible (no, extremely likely) that your EU node would have access to a different US node, but not the one that's having the partition-event right now. That's exactly the point of this section in the essay: https://medium.com/p/5e397cb12e63#7df1 https://medium.com/p/5e397cb12e63#7df1 Networks will have latency-spikes, but if you can stream time-information the same way you can stream others, you can use redundancies to mitigate the disruption of any single channel.
- mrkeen 3y ago
  > It's entirely possible (no, extremely likely) that your EU node would have access to a different US node CAP theorem beaten by declaring P to be unlikely.
  - andras_gerlits 3y ago
    Well no, by making the probability of latency-events extremely unlikely by establishing redundant channels. https://medium.com/p/5e397cb12e63#7df1 https://medium.com/p/5e397cb12e63#7df1 Considering that this is maybe the 12th time I'm linking the explanation, I now think you're not looking for a discussion, you're here for a fight. Please let me know if you find any problems with the article in its reasoning. It was vetted by a lot of people before, so I really need to notify them too if you do.
    - sausagefeet 3y ago
      > So how can we fix this problem? The same way we solve all our failure issues in IT: redundancy. Redundancy doesn't fix any problems, it just makes them less likely to occur. Again this not only does nothing to address the CAP theorem but is done for a wide array of problems already, there is nothing new here.
      - andras_gerlits 3y ago
        Okay, so we can move beyond CAP. So we don't talk about implementation in either the science-paper or in the intro for it (which is what this thread is about). I mostly write about implementation, Mark mostly writes about the science. So yes, redundancy will only make latency-spikes less likely. Notice however, that the way we establish strong consistency is based on communication also. There's a part in the essay I keep linking where I talk about what happens to nodes with flaky connections, it's called "Stability. https://medium.com/p/5e397cb12e63#373c https://medium.com/p/5e397cb12e63#373c There are three separate mitigation-solutions that go into how total-order strong consistency can keep marching on even if a specific child-group is intermittently isolated. The first one is redundancy by deterministic replication, so there will always be many replicas which aren't just shards, but full copies of the _consistency ledger_. It's not a database, not a cache, just the thing that establishes the consistency between nodes. These instances all "race each other" to provide the first value of the outputs to other nodes. The second one is the latency-mitigation we talked about earlier, I don't think we need to waste more breath on that. The third one is that since the consistency-mechanism requires an explicit association-instruction to interleave the children's versions into its parent's (so that these versions can be exposed to nodes observing it from afar). If the child goes AWOL, it won't be able to associate its versions to its parent, so it won't keep up everyone else either. In this case the total-order won't be affected by the child-group's local-order, which is still allowed to make progress, as long as it's not trying to update any record that is distant to it.
- tsimionescu 3y ago
  No, by definition, a partition event is one in which some amount of the nodes are entirely non accessible by any other node in the cluster. Even if there were two different US nodes, and only one node lost connectivity to the others, the problem wouldn't change at all - the branch which only has access to the disconnected node would still either send shipments to the wrong address or stop being able to read the status at all.
  - andras_gerlits 3y ago
    We need to clarify first if we're talking about CAP or real life. CAP requirements are absurd. To quote Dominick Tornow from here: https://blog.dtornow.com/the-cap-theorem.-the-bad-the-bad-the-ugly/ https://blog.dtornow.com/the-cap-theorem.-the-bad-the-bad-th... "Note that Gilbert and Lynch’s definition requires any non-failed nodenot just some non-failed nodeto be able to generate valid responses, even if that node is isolated from other nodes. That is an unnecessary and unrealistic restriction. In essence, that restriction renders consensus protocols not highly available, because only some nodes, the nodes in the majority, are able to respond." Partition with a capital P doesn't really move me as our experiences contradict its assertions, as the model it bases its statements on is fundamentally broken. So let's talk about partition in the real world. Practically speaking, partitions mean that some nodes experience high latency when communicating. If you need to update some record in a specific data-centre and you can only to talk to that place via a single channel and that channel is disrupted; yes, you're going to experience a slowdown or even halt. Nowadays however, even widely used consensus-algorithms can mitigate that, and the delinquent node will eventually be dropped from the group if it causes enough problems. We don't do anything very different in this regard. Since our deterministic ledger can be replicated across multiple data-centres (as no nodes in it create original information), and since observers will only rely on records the ledger already sent out and since the only reason you would need to talk to this ledger is if you need to modify a shared record with other observers, you can always pick a different ledger-instance, there are no "master copies" anywhere. Remember that we can stream time-information with the data, so the client can always calculate its "point in time" and reconstruct a (even globally) consistent view of the data it has. Sure, if you choose to centralise all your data behind a single flaky connection, you're gonna have a bad time. The point is that the setup we built allows you to not need to do that and it does that transparently, behind SQL semantics.

andras_gerlits 3y ago

Any modification to existing data must be "haggled for" somewhere, you're right about that. When you say "partition event", what is being partitioned here? A specific communication-link between two nodes. It's entirely possible (no, extremely likely) that your EU node would have access to a different US node, but not the one that's having the partition-event right now. That's exactly the point of this section in the essay: https://medium.com/p/5e397cb12e63#7df1 https://medium.com/p/5e397cb12e63#7df1 Networks will have latency-spikes, but if you can stream time-information the same way you can stream others, you can use redundancies to mitigate the disruption of any single channel.

mrkeen 3y ago
> It's entirely possible (no, extremely likely) that your EU node would have access to a different US node CAP theorem beaten by declaring P to be unlikely.
- andras_gerlits 3y ago
  Well no, by making the probability of latency-events extremely unlikely by establishing redundant channels. https://medium.com/p/5e397cb12e63#7df1 https://medium.com/p/5e397cb12e63#7df1 Considering that this is maybe the 12th time I'm linking the explanation, I now think you're not looking for a discussion, you're here for a fight. Please let me know if you find any problems with the article in its reasoning. It was vetted by a lot of people before, so I really need to notify them too if you do.
  - sausagefeet 3y ago
    > So how can we fix this problem? The same way we solve all our failure issues in IT: redundancy. Redundancy doesn't fix any problems, it just makes them less likely to occur. Again this not only does nothing to address the CAP theorem but is done for a wide array of problems already, there is nothing new here.
    - andras_gerlits 3y ago
      Okay, so we can move beyond CAP. So we don't talk about implementation in either the science-paper or in the intro for it (which is what this thread is about). I mostly write about implementation, Mark mostly writes about the science. So yes, redundancy will only make latency-spikes less likely. Notice however, that the way we establish strong consistency is based on communication also. There's a part in the essay I keep linking where I talk about what happens to nodes with flaky connections, it's called "Stability. https://medium.com/p/5e397cb12e63#373c https://medium.com/p/5e397cb12e63#373c There are three separate mitigation-solutions that go into how total-order strong consistency can keep marching on even if a specific child-group is intermittently isolated. The first one is redundancy by deterministic replication, so there will always be many replicas which aren't just shards, but full copies of the _consistency ledger_. It's not a database, not a cache, just the thing that establishes the consistency between nodes. These instances all "race each other" to provide the first value of the outputs to other nodes. The second one is the latency-mitigation we talked about earlier, I don't think we need to waste more breath on that. The third one is that since the consistency-mechanism requires an explicit association-instruction to interleave the children's versions into its parent's (so that these versions can be exposed to nodes observing it from afar). If the child goes AWOL, it won't be able to associate its versions to its parent, so it won't keep up everyone else either. In this case the total-order won't be affected by the child-group's local-order, which is still allowed to make progress, as long as it's not trying to update any record that is distant to it.
tsimionescu 3y ago
No, by definition, a partition event is one in which some amount of the nodes are entirely non accessible by any other node in the cluster. Even if there were two different US nodes, and only one node lost connectivity to the others, the problem wouldn't change at all - the branch which only has access to the disconnected node would still either send shipments to the wrong address or stop being able to read the status at all.
- andras_gerlits 3y ago
  We need to clarify first if we're talking about CAP or real life. CAP requirements are absurd. To quote Dominick Tornow from here: https://blog.dtornow.com/the-cap-theorem.-the-bad-the-bad-the-ugly/ https://blog.dtornow.com/the-cap-theorem.-the-bad-the-bad-th... "Note that Gilbert and Lynch’s definition requires any non-failed nodenot just some non-failed nodeto be able to generate valid responses, even if that node is isolated from other nodes. That is an unnecessary and unrealistic restriction. In essence, that restriction renders consensus protocols not highly available, because only some nodes, the nodes in the majority, are able to respond." Partition with a capital P doesn't really move me as our experiences contradict its assertions, as the model it bases its statements on is fundamentally broken. So let's talk about partition in the real world. Practically speaking, partitions mean that some nodes experience high latency when communicating. If you need to update some record in a specific data-centre and you can only to talk to that place via a single channel and that channel is disrupted; yes, you're going to experience a slowdown or even halt. Nowadays however, even widely used consensus-algorithms can mitigate that, and the delinquent node will eventually be dropped from the group if it causes enough problems. We don't do anything very different in this regard. Since our deterministic ledger can be replicated across multiple data-centres (as no nodes in it create original information), and since observers will only rely on records the ledger already sent out and since the only reason you would need to talk to this ledger is if you need to modify a shared record with other observers, you can always pick a different ledger-instance, there are no "master copies" anywhere. Remember that we can stream time-information with the data, so the client can always calculate its "point in time" and reconstruct a (even globally) consistent view of the data it has. Sure, if you choose to centralise all your data behind a single flaky connection, you're gonna have a bad time. The point is that the setup we built allows you to not need to do that and it does that transparently, behind SQL semantics.