thanks Cliff, this is really good info. I am tempted to do the benchmarks myself but need to find a sponsor :) Snowflake gets A LOT of traction lately, and based on a few conversations in slack, looks like Kudu team is not tracking competition that much and I think there are awesome features in Snowflake that would be great to have in Kudu.
I think they do support upserts now and back in-time queries - while they do not upsert/delete records, they just create new micro-partitions and it is a metadata operation - that's who they can see data in a given time. The idea of virtual warehouse is also very appealing especially in healthcare as we often need to share data with other departments or partners. do not get me wrong, I decided to use Kudu for the same reasons and I really did not have a choice on CDH other than HBase but I get questioned lately why we should not just give up CDH and go all in on Cloud with Cloud native tech like Redshift and Snowflake. It is getting harder to explain why :) My biggest gripe with Kudu besides known limitations is the management of partitions and compaction process. For our largest tables, we just max out the number of tablets as Kudu allows per cluster. Smaller tables though is a challenge and more like an art while I feel there should be an automated process with good defaults like in other data storage systems. No one expects to assign partitions manually in Snowflake, BigQuery or Redshift. No one is expected to tweak compaction parameters and deal with fast degraded performance over time on tables with a high number of deletes/updates. We are happy with performance but it is not all about performance. We also struggle on Impala side as it feels it is limiting what we can do with Kudu and it feels like Impala team is always behind. pyspark is another problem - Kudu pyspark client is lagging behind java client and very difficult to install/deploy. Some api is not even available in pyspark. And honestly, I do not like that Impala is the only viable option with Kudu. We are forced a lot of time to do heavy ETL in Hive which is like a tank but we cannot do that anymore with Kudu tables and struggle with disk spilling issues, Impala choosing bad explain plans and forcing us to use straight_join hint many times etc. Sorry if this post came a bit on a negative side, I really like Kudu and the dev team behind it rocks. But I do not like their industry is going and why Kudu is not getting the traction it deserves. On Tue, Mar 10, 2020 at 9:03 PM Cliff Resnick <[email protected]> wrote: > This is a good conversation but I don't think the comparison with > Snowflake is a fair one, at least from an older version of Snowflake (In my > last job, about 5 years ago, I pretty much single-handedly scale tested > Snowflake in exchange for a sweetheart pricing deal) . Though Snowflake is > closed source, it seems pretty clear the architectures are quite different. > Snowflake has no primary key index, no UPSERT capability, features that > make Kudu valuable for some use cases. > > It also seems to me that their intended workloads are quite different. > Snowflake is great for intensive analytics on demand, and can handle deeply > nested data very well, where Kudu can't handle that at all. Snowflake is > not designed for heavy concurrency, but complex query plans for a small > group of users. If you select an x-large Snowflake cluster it's probably > because you have a large amount of data to churn through, not because you > have a large number of users. Or, at least that's how we used it. > > At my current workplace we use Kudu/Impala to handle about 30-60 > concurrent queries. I agree that getting very fussy about partitioning can > be a pain, but for the large fact tables we generally use a simple strategy > of twelves: 12 hash x a12 (month) ranges in two 12-node clusters fronted > by a load balancer. We're in AWS, and thanks to Kudu's replication we can > use "for free" instance-store NVMe. We also have associated > compute-oriented stateless Impala Spot Fleet clusters for HLL and other > compute oriented queries. > > The net result blows away what we had with RedShift at less than 1/3 the > cost, with performance improvements mostly from better concurrency > handling. This is despite the fact that RedShift has built-in cache. We > also use streaming ingestion which, aside from being impossible with > RedShift, removes the added cost of staging. > > Getting back to Snowflake, there's no way we could use it the same way we > use Kudu, and even if we could, the cost would would probably put us out of > business! > > On Tue, Mar 10, 2020, 10:59 AM Boris Tyukin <[email protected]> wrote: > >> thanks Andrew for taking your time responding to me. It seems that there >> are no exact recommendations. >> >> I did look at scaling recommendations but that math is extremely >> complicated and I do not think anyone will know all the answers to plug >> into that calculation. We have no control really what users are doing, how >> many queries they run, how many are hot vs cold etc. It is not realistic >> IMHO to expect that knowledge of user query patterns. >> >> I do like the Snowflake approach than the engine takes care of defaults >> and can estimate the number of micro-partitions and even repartition tables >> as they grow. I feel Kudu has the same capabilities as the design is very >> similar. I really do not like to pick random number of buckets. Also we >> manager 100s of tables, I cannot look at them each one by one to make these >> decisions. Does it make sense? >> >> >> On Mon, Mar 9, 2020 at 4:42 PM Andrew Wong <[email protected]> wrote: >> >>> Hey Boris, >>> >>> Sorry you didn't have much luck on Slack. I know partitioning in general >>> can be tricky; thanks for the question. Left some thoughts below: >>> >>> Maybe I was not asking a clear question. If my cluster is large enough >>>> in my example above, should I go with 3, 9 or 18 tablets? or should I pick >>>> tablets to be closer to 1Gb? >>>> And a follow-up question, if I have tons of smaller tables under 5 >>>> million rows, should I just use 1 partition or still break them on smaller >>>> tablets for concurrency? >>> >>> >>> Per your numbers, this confirms that the partitions are the units of >>> concurrency here, and that therefore having more and having smaller >>> partitions yields a concurrency bump. That said, extending a scheme of >>> smaller partitions across all tables may not scale when thinking about the >>> total number of partitions cluster-wide. >>> >>> There are some trade offs with the replica count per tablet server here >>> -- generally, each tablet replica has a resource cost on tablet servers: >>> WALs and tablet-related metadata use a shared disk (if you can put this on >>> an SSD, I would recommend doing so), each tablet introduces some >>> Raft-related RPC traffic, each tablet replica introduces some maintenance >>> operations in the pool of background operations to be run, etc. >>> >>> Your point about scan concurrency is certainly a valid one -- there have >>> been patches for other integrations that have tackled this to decouple >>> partitioning from scan concurrency (KUDU-2437 >>> <https://issues.apache.org/jira/browse/KUDU-2437> and KUDU-2670 >>> <https://issues.apache.org/jira/browse/KUDU-2670> are an example, where >>> Kudu's Spark integration will split range scans into smaller-scoped scan >>> tokens to be run concurrently, though this optimization hasn't made its way >>> into Impala yet). I filed KUDU-3071 >>> <https://issues.apache.org/jira/browse/KUDU-3071> to track what I think >>> is left on the Kudu-side to get this up and running, so that it can be >>> worked into Impala. >>> >>> For now, I would try to take into account the total sum of resources you >>> have available to Kudu (including number of tablet servers, amount of >>> storage per node, number of disks per tablet server, type of disk for the >>> WAL/metadata disks), to settle on roughly how many tablet replicas your >>> system can handle (the scaling guide >>> <https://kudu.apache.org/docs/scaling_guide.html> may be helpful here), >>> and hopefully that, along with your own SLAs per table, can help guide how >>> you partition your tables. >>> >>> confused why they say "at least" not "at most" - does it mean I should >>>> design it so a tablet takes 2Gb or 3Gb in this example? >>> >>> >>> Aiming for 1GB seems a bit low; Kudu should be able to handle in the low >>> tens of GB per tablet replica, though exact perf obviously depends on your >>> workload. As you show and as pointed out in documentation, larger and fewer >>> tablets can limit the amount of concurrency for writes and reads, though >>> we've seen multiple GBs works relatively well for many use cases while >>> weighing the above mentioned tradeoffs with replica count. >>> >>> It is recommended that new tables which are expected to have heavy read >>>> and write workloads have at least as many tablets as tablet servers. >>> >>> if I have 20 tablet servers and I have two tables - one with 1MM rows >>>> and another one with 100MM rows, do I pick 20 / 3 partitions for both >>>> (divide by 3 because of replication)? >>> >>> >>> The recommendation here is to have at least 20 logical partitions per >>> table. That way, a scan were to touch a table's entire keyspace, the table >>> scan would be broken up into 20 tablet scans, and each of those might land >>> on a different tablet server running on isolated hardware. For a >>> significantly larger table into which you expect highly concurrent >>> workloads, the recommendation serves as a lower bound -- I'd recommend >>> having more partitions, and if your data is naturally time-oriented, >>> consider range-partitioning on timestamp. >>> >>> On Sat, Mar 7, 2020 at 7:13 AM Boris Tyukin <[email protected]> >>> wrote: >>> >>>> just saw this in the docs but it is still confusing statement >>>> No Default Partitioning >>>> Kudu does not provide a default partitioning strategy when creating >>>> tables. It is recommended that new tables which are expected to have heavy >>>> read and write workloads have at least as many tablets as tablet servers. >>>> >>>> >>>> if I have 20 tablet servers and I have two tables - one with 1MM rows >>>> and another one with 100MM rows, do I pick 20 / 3 partitions for both >>>> (divide by 3 because of replication)? >>>> >>>> >>>> >>>> >>>> On Sat, Mar 7, 2020 at 9:52 AM Boris Tyukin <[email protected]> >>>> wrote: >>>> >>>>> hey guys, >>>>> >>>>> I asked the same question on Slack on and got no responses. I just >>>>> went through the docs and design doc and FAQ and still did not find an >>>>> answer. >>>>> >>>>> Can someone comment? >>>>> >>>>> Maybe I was not asking a clear question. If my cluster is large enough >>>>> in my example above, should I go with 3, 9 or 18 tablets? or should I pick >>>>> tablets to be closer to 1Gb? >>>>> >>>>> And a follow-up question, if I have tons of smaller tables under 5 >>>>> million rows, should I just use 1 partition or still break them on smaller >>>>> tablets for concurrency? >>>>> >>>>> We cannot pick the partitioning strategy for each table as we need to >>>>> stream 100s of tables and we use PK from RBDMS and need to come with an >>>>> automated way to pick number of partitions/tablets. So far I was using 1Gb >>>>> rule but rethinking this now for another project. >>>>> >>>>> On Tue, Sep 24, 2019 at 4:29 PM Boris Tyukin <[email protected]> >>>>> wrote: >>>>> >>>>>> forgot to post results of my quick test: >>>>>> >>>>>> Kudu 1.5 >>>>>> >>>>>> Table takes 18Gb of disk space after 3x replication >>>>>> >>>>>> Tablets Tablet Size Query run time, sec >>>>>> 3 2Gb 65 >>>>>> 9 700Mb 27 >>>>>> 18 350Mb 17 >>>>>> [image: image.png] >>>>>> >>>>>> >>>>>> On Tue, Sep 24, 2019 at 3:58 PM Boris Tyukin <[email protected]> >>>>>> wrote: >>>>>> >>>>>>> Hi guys, >>>>>>> >>>>>>> just want to clarify recommendations from the doc. It says: >>>>>>> >>>>>>> https://kudu.apache.org/docs/kudu_impala_integration.html#partitioning_rules_of_thumb >>>>>>> >>>>>>> Partitioning Rules of Thumb >>>>>>> <https://kudu.apache.org/docs/kudu_impala_integration.html#partitioning_rules_of_thumb> >>>>>>> <https://kudu.apache.org/docs/kudu_impala_integration.html#partitioning_rules_of_thumb> >>>>>>> >>>>>>> - >>>>>>> >>>>>>> For large tables, such as fact tables, aim for as many tablets >>>>>>> as you have cores in the cluster. >>>>>>> - >>>>>>> >>>>>>> For small tables, such as dimension tables, ensure that each >>>>>>> tablet is at least 1 GB in size. >>>>>>> >>>>>>> In general, be mindful the number of tablets limits the parallelism >>>>>>> of reads, in the current implementation. Increasing the number of >>>>>>> tablets >>>>>>> significantly beyond the number of cores is likely to have diminishing >>>>>>> returns. >>>>>>> >>>>>>> >>>>>>> I've read this a few times but I am not sure I understand it >>>>>>> correctly. Let me use concrete example. >>>>>>> >>>>>>> If a table ends up taking 18Gb after replication (so with 3x >>>>>>> replication it is ~9Gb per tablet if I do not partition), should I aim >>>>>>> for >>>>>>> 1Gb tablets (6 tablets before replication) or should I aim for 500Mb >>>>>>> tablets if my cluster capacity allows so (12 tablets before >>>>>>> replication)? >>>>>>> confused why they say "at least" not "at most" - does it mean I should >>>>>>> design it so a tablet takes 2Gb or 3Gb in this example? >>>>>>> >>>>>>> Assume that I have tons of CPU cores on a cluster... >>>>>>> Based on my quick test, it seems that queries are faster if I have >>>>>>> more tablets/partitions...In this example, 18 tablets gave me the best >>>>>>> timing but tablet size was around 300-400Mb. But the doc says "at least >>>>>>> 1Gb". >>>>>>> >>>>>>> Really confused what the doc is saying, please help >>>>>>> >>>>>>> Boris >>>>>>> >>>>>>> >>> >>> -- >>> Andrew Wong >>> >>
