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Hmm. Okay. I open a transaction for three days (let's say indefinitely), maybe reading a few tuples here and there and updating a few, but never committing. 500KRead/Second on such small values is not really fantastic performance over 24 machines. I also don't understand the initial burst capacity on the read side either, although I can guess on writes it can make sense because some work is deferred, but I'm trying to understand how that can happen on reads as well. For large databases, this is a very, very long snapshot to maintain, if MVCC, or a Nike Hoodies Colors

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not see any initial burst (and might see a ramp up time from cold cache effects).

aphyr 762 days ago link

It's not architecturally impossible for us to support long running read snapshots, but it is expensive for our storage servers to keep snapshots alive for a long time. So our backup solution instead works by backing up transaction logs while doing a non isolated read of the database. But it sounds like instead your solution to the snapshot read is more like how the online binary backups work, whereby you combine transaction logs with inconsistent reads. That system works very well. On the other hand, "logical" backups have been a major problem for me; sidestepping this by using lower level transaction log mechanics and dirty reads I think is a much better idea. The reason that we see higher burst than steady state performance has nothing to do with transactions. We have to do reads from SSD as the application requests them, and we have to make writes durable by logging them right away, but as you surmise we can defer the hard work of doing random writes to our btrees for a while. Even a workload that is 90% reads benefits a lot from deferring writes because writes have to be 3x replicated and because consumer SSDs are comparatively slow at mixed read/write workloads. It's not read only, it's 90/10, I Nike Dri Fit Hoodie misread. That makes more sense. Cached reads (from the same dataset, with a different distribution of read keys) you can see on our website at 3.2 M/sec. Starting a transaction requires selecting a version number for the read snapshot, and that incurs some latency to deal with the concerns you mention (but scales fine).

Gotcha. a node is unreachable by a peer, but reachable by a coordinator, or vice versa?

voidmain 762 days ago link

We require a quorum over coordination servers to stay up. The commit process is the same, but we can notify the client of success earlier if it is willing to take the risk that a WAN partition or meteor strike violates its 'D' guarantee. ACI are guaranteed either way.

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This is a beautiful site with tons of information, graphs, lists, examples, etc. Cross machine transactions mean that this version check has to happen on multiple machines. ACID means that both machines have to either commit (if all the version checks succeed) or roll back. This isn't a complete explanation of how our system works!Most people trying to solve this problem have approached it by trying to take multiple machines, each capable of processing transactions, and do distributed transactions between them. Say you have conflicting transactions T1 and T2 that both update data on two machines M1 and M2. If these transactions race to commit, how do you guarantee consistency? Even if you have a perfect, zero latency oracle that can tell you that T1 and T2 conflict, you still need M1 and M2 to form consensus about which transaction commits first, and to make sure that both machines either commit or roll back (I am assuming that both machines have their own transaction log). Transactions that fail don't do any writes. (The key/value store where we locally store data on each node happens to also use write ahead logging, but this is not an architectural requirement). Ideally transactions just have to be made durable in some N places to be considered committed. It proactively fetches writes for newer versions. If it gets a request for a version it hasn't managed to get the writes for yet, the read has to wait while that storage server catches up. This means that when a datacenter is disconnected or poorly connected to the internet, the database is not available for writes in that datacenter. (For example, a grocery list for a mobile phone, or an application that must be available everywhere if the entire Internet partitions.)

fdr 763 days ago link

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fdr 762 days ago link

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A new generation of NoSQL database

So you require quorum over data centers to stay up? How do you provide transactional consistency between dcs? Multipaxos over blocks of transactions with delayed failure?

lot of locks to acquire, if 2PL. So our backup solution instead works by backing up transaction logs while doing a non isolated read of the database. We have to do reads from SSD as the application requests them, and we have to make writes durable by logging them right away, but as you surmise we can Nike Coat Girls

defer the hard work of doing random writes to our btrees for a while. Even a workload that is 90% reads benefits a lot from deferring writes because writes have to be 3x replicated and because consumer SSDs are comparatively slow at mixed read/write workloads. Read only workloads will Nike Tech Fleece Long Sleeve Crew - Men's

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aphyr 762 days ago link

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