I really like Daniel's writing and exposition of the area. There are, however, a number of assumptions here which don't hold for what he calls 'partitioned consensus' systems. The whole article is written with Calvin and Spanner in mind.
We, however, build our platform on a 'partitioned consensus' system that is built on a fully 2-phase commit DB, NDB, with a transaction coordinator at every node. It doesn't fit in his model - cross-partition transactions across availability zones on google cloud take just a couple of milliseconds - nowhere near the "10ms for single-region deployments" that he claims. He assumes partitions have a leader, which again is not true for NDB. But, he knows this - as Spanner is the competition, not NDB.
NDB's concurrency model is "lock-aware programming". You, as a programmer, decide whether you need to lock a row for writing or reading or whether you don't need a lock at all. Calvin serializes transactions for you, which is great, but you pay the price in terms of scalability (nowhere near NDB) and latency (nowhere near NDB). Spanner is a global OLTP DB, which is not comparable to NDB or Calvin.
I have not worked with NDB, nor read any research papers or documentation about it. So that's why I didn't have it in mind when I wrote that post.
But I'm a little confused by your comment: How is it possible to partition consensus without having more than leader? To me, the definition of "partitioned consensus" is that there is more than one consensus group, which means more than one leader.
Also, FYI, Calvin does not serialize transactions. It processes transactions in parallel. But it guarantees equivalence to a predetermined serial order. That distinction is important. As far as scalability, I discussed that in my previous post. Calvin doesn't have any scalability constraints that can be reached by known real-world workloads.
I should have been more clear - Calvin serializes cross-partition transactions. NDB does not. There is a Transaction Coordinator (TC) on every node. TCs can execute cross-partition transactions in parallel, but programmers need to write "lock-aware" programs (more late). TCs can fail-over if one fails - so, it blocks for just a few seconds (Transaction inactive timeouts are typically just a couple of seconds). There are no leaders in each partition, because every node is a potential TC. There are primary TCs for each partition, but failure does not require a leader election.
NDB has "lock-aware" programming - you don't get "global consensus". You decide, as a programmer, that this row could be accessed concurrently by another process, so you lock it, with either a read of write lock. Linearizability is easily implemented by acquiring a lock on a well-known row, but, of course, kills scalability.
In our Usenix FAST paper on HopsFS on Spotify's Hadoop workload, we had 1m ops/sec on HDFS, which was about 10m ops/sec on NDB. We ran out of hardware. There are workloads that big.
[edited for clarity]
Please, please read the Calvin paper http://www.cs.umd.edu/~abadi/papers/calvin-sigmod12.pdf. The assumption that Calvin serializes cross-partition transactions is a common misunderstanding is 100% inaccurate. The paper shows how Calvin gets better parallelism on cross-partition transactions than traditional systems.
Ok, sorry about that if it wasn't correct. But are you still not globally ordering every cross-partition transaction - "every scheduler to piece together its own view of a global transaction order by interleaving (in a deterministic, round-robin manner) all sequencers’ batches for that epoch".
Even if the sequencers are distributed and execute the transactions in parallel, they need to agree on a total order. This contrast with the lock-aware programming model in NDB, where programmers can allow cross-partition transactions to proceed immediately in parallel if they are sure that they don't conflict.
Yes, that's what I refer to as "unified consensus". Batching helps to overcome the scalability challenges (i.e. order batches rather than each individual xact). I talk about this briefly at: http://dbmsmusings.blogspot.com/2018/09/newsql-database-syst...
The last time his blog post came up I noted his claim that single consensus quorums were, in theory, as fast as multiple. I get why he has to make that claim, the soundness of Calvin as a practical system rests on that. But as a matter of practical world, it just isn’t true.
I appreciate the distinction that Daniel is trying to make between what he calls "partitioned consensus" databases and "unified consensus" databases, but I don't know if the points that he makes about partitioned consensus truly generalize to all such systems.
In the context of this blog post, he specifically calls out partitioned consensus databases for requiring two wide-area round trips in order to run 2PC. However, we've seen multiple examples of partitioned databases (i.e. MDCC, TAPIR, Janus, and others) since the Spanner paper that can commit multi-partition transactions in a single wide-area round trip . Just as in the "unified consensus" approach, failures or concurrency may cause these systems to infrequently take multiple wide-area round trips to commit.
The blog post does a great job explaining the differences between "Calvin-like" systems and "Spanner-like" systems, but it falls short in convincing me that the "Calvin-like" architecture is fundamentally better, or makes better tradeoffs, than any partitioned architecture.
My take away is that it depends on your requirements. If most of your transactions are multi-entity, then Calvin-like is better. If most of your transactions are single-entity, then Spanner-like is better.
I implemented projects in GAE Datastore, though it's classic Paxos inside, it's clearly multi-partition database. For my workflow almost all of the frequqnt transactions were single-entity, so multi-partition Datastore worked fine with it.
I think that's a fair takeaway specifically because you are careful to limit the scope of your comparison to Calvin and Spanner. If you were to replace "Spanner-like" with "partitioned", I'd push back that it's not as clear Calvin is better than non-Spanner partitioned systems, even when you have mostly multi-entity transactions.
EDIT: To be clear, when I say better I mean higher throughput and lower latency.
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[ 2.0 ms ] story [ 33.3 ms ] threadNDB's concurrency model is "lock-aware programming". You, as a programmer, decide whether you need to lock a row for writing or reading or whether you don't need a lock at all. Calvin serializes transactions for you, which is great, but you pay the price in terms of scalability (nowhere near NDB) and latency (nowhere near NDB). Spanner is a global OLTP DB, which is not comparable to NDB or Calvin.
But I'm a little confused by your comment: How is it possible to partition consensus without having more than leader? To me, the definition of "partitioned consensus" is that there is more than one consensus group, which means more than one leader.
Also, FYI, Calvin does not serialize transactions. It processes transactions in parallel. But it guarantees equivalence to a predetermined serial order. That distinction is important. As far as scalability, I discussed that in my previous post. Calvin doesn't have any scalability constraints that can be reached by known real-world workloads.
NDB has "lock-aware" programming - you don't get "global consensus". You decide, as a programmer, that this row could be accessed concurrently by another process, so you lock it, with either a read of write lock. Linearizability is easily implemented by acquiring a lock on a well-known row, but, of course, kills scalability.
In our Usenix FAST paper on HopsFS on Spotify's Hadoop workload, we had 1m ops/sec on HDFS, which was about 10m ops/sec on NDB. We ran out of hardware. There are workloads that big. [edited for clarity]
masterful understatement imo
In the context of this blog post, he specifically calls out partitioned consensus databases for requiring two wide-area round trips in order to run 2PC. However, we've seen multiple examples of partitioned databases (i.e. MDCC, TAPIR, Janus, and others) since the Spanner paper that can commit multi-partition transactions in a single wide-area round trip . Just as in the "unified consensus" approach, failures or concurrency may cause these systems to infrequently take multiple wide-area round trips to commit.
The blog post does a great job explaining the differences between "Calvin-like" systems and "Spanner-like" systems, but it falls short in convincing me that the "Calvin-like" architecture is fundamentally better, or makes better tradeoffs, than any partitioned architecture.
I implemented projects in GAE Datastore, though it's classic Paxos inside, it's clearly multi-partition database. For my workflow almost all of the frequqnt transactions were single-entity, so multi-partition Datastore worked fine with it.
EDIT: To be clear, when I say better I mean higher throughput and lower latency.