> I think your hardware situation hurts not only for the number of tablets but also for Kudu + Impala. Impala afaik will only use one core per host per query, so is a poor fit for large complex queries on vertical hardware.
This is basically true as of current releases of Impala but I'm working on addressing this. It's been possible to set mt_dop per-query on queries without joins or table sinks for a long time now (the limitations make it of limited use). Rough join and table sink support is behind a hidden flag in 3.3 and 3.4. I've been working on making it performant with joins (add doing all the requisite testing), which should land in master very soon and if things go remotely to plan, be a fully supported option in the next release after 3.4. We saw huge speedups on a lot of queries (like 10x or more). Some queries didn't benefit much, if they were limited by the scan perf (including if the runtime filters pushed into the scans were filtering most data before joins). On Mon, Mar 16, 2020 at 2:58 PM Boris Tyukin <bo...@boristyukin.com> wrote: > appreciate your thoughts, Cliff > > On Mon, Mar 16, 2020 at 11:18 AM Cliff Resnick <cre...@gmail.com> wrote: > >> Boris, >> >> I think the crux of the problem is that "real-time analytics" over deeply >> nested storage does not really exist. I'll qualify that statement with >> "real-time" meaning streaming per-record ingestion, and "analytics" as >> columnar query access. The closest thing I know of today is Google BigQuery >> or Snowflake, but those are actually micro-batch ingestion done well, not >> per-record like Kudu. The only real-time analytics solution I know of that >> has a modicum of nesting is Druid, with a single level of "GroupBy" >> dimensions that get flattened into virtual rows as part of its non-SQL >> rollup API. Columnar storage and real-time are probably aways going to be a >> difficult pairing and in fact Kudu's "real-time" storage format is >> row-based, and Druid requires a whole lambda architecture batch compaction. >> As we all know, nothing is for free, whether the trade-off for nested data >> be column-shredding into something like Kudu as Adar suggested, or >> micro-batching to Parquet or some combination of both. I won't even go into >> how nested analytics are handled on the SQL/query side because it gets >> weird there, too. >> >> I think your hardware situation hurts not only for the number of tablets >> but also for Kudu + Impala. Impala afaik will only use one core per host >> per query, so is a poor fit for large complex queries on vertical hardware. >> >> In conclusion, I don't know how far you've gone into your investigations >> but I can say that if your needs are to support "power users" at a premium >> then something like Snowflake is great for your problem space. But if >> analytics are also more centrally integrated in pipelines then parquet is >> hard to beat for the price and flexibility, as is Kudu for dashboards or >> other intelligence that leverages upsert/key semantics. Ultimately, like >> many of us, you may find that you'll need all of the above. >> >> Cliff >> >> >> >> On Sun, Mar 15, 2020 at 1:11 PM Boris Tyukin <bo...@boristyukin.com> >> wrote: >> >>> thanks Adar for your comments and thinking this through! I really think >>> Kudu has tons of potential and very happy we've made a decision to use it >>> for our real-time pipeline. >>> >>> you asked about use cases for support of nested and large binary/text >>> objects. I work for a large healthcare organization and there is a lot of >>> going with newer HL7 FHIR standard. FHIR documents are highly nested json >>> objects that might get as big as a few Gbs in size. We could not use Kudu >>> for storing FHIR bundles/documents due to the size and inability to store >>> FHIR objects natively. We ended up using Hive/Spark for that, but that >>> solution is not real-time. And it is not only about storing, but also about >>> fast search in those FHIR documents. I know we could use Hbase for that but >>> Hbase is really bad when it comes to analytics type of workloads. >>> >>> As for large text/binaries, the good and common example is >>> narratives notes (physician notes, progress notes, etc.) They can be in >>> form of RTF or PDF documents or images. >>> >>> 300 column limit was not a big deal so far but we have high-density >>> nodes with 2 44-core cpus and 12 12-Tb drives and we cannot use the full >>> power of them due to limitations of Kudu in terms of number of tablets per >>> node. This is more concerning than 300 column limit. >>> >>> >>> >>> On Sat, Mar 14, 2020 at 3:45 AM Adar Lieber-Dembo <a...@cloudera.com> >>> wrote: >>> >>>> Snowflake's micro-partitions sound an awful lot like Kudu's rowsets: >>>> 1. Both are created transparently and automatically as data is inserted. >>>> 2. Both may overlap with one another based on the sort column >>>> (Snowflake lets you choose the sort column; in Kudu this is always the PK). >>>> 3. Both are pruned at scan time if the scan's predicates allow it. >>>> 4. In Kudu, a tablet with a large clustering depth (known as "average >>>> rowset height") will be a likely compaction candidate. It's not clear to me >>>> if Snowflake has a process to automatically compact their micro-partitions. >>>> >>>> I don't understand how this solves the partitioning problem though: the >>>> doc doesn't explain how micro-partitions are mapped to nodes, or if/when >>>> micro-partitions are load balanced across the cluster. I'd be curious to >>>> learn more about this. >>>> >>>> Below you'll find some more info on the other issues you raised: >>>> - Nested data type support is tracked in KUDU-1261. We've had an >>>> on-again/off-again relationship with it: it's a significant amount of work >>>> to implement, so we're hesitant to commit unless there's also significant >>>> need. Many users have been successful in "shredding" their nested types >>>> across many Kudu columns, which is one of the reasons we're actively >>>> working on increasing the number of supported columns above 300. >>>> - We're also working on auto-rebalancing right now; see KUDU-2780 for >>>> more details. >>>> - Large cell support is tracked in KUDU-2874, but there doesn't seem to >>>> have been as much interest there. >>>> - The compaction issue you're describing sounds like KUDU-1400, which >>>> was indeed fixed in 1.9. This was our bad; for quite some time we were >>>> focused on optimizing for heavy workloads and didn't pay attention to Kudu >>>> performance in "trickling" scenarios, especially not over a long period of >>>> time. That's also why we didn't think to advertise --flush_threshold_secs, >>>> or even to change its default value (which is still 2 minutes: far too >>>> short if we hadn't fixed KUDU-1400); we just weren't very aware of how >>>> widespread of a problem it was. >>>> >>>> >>>> On Fri, Mar 13, 2020 at 6:22 AM Boris Tyukin <bo...@boristyukin.com> >>>> wrote: >>>> >>>>> thanks Adar, I am a big fan of Kudu and I see a lot of potential in >>>>> Kudu, and I do not think snowflake deserves that much credit and noise. >>>>> But >>>>> they do have great ideas that take their engine to the next level. I do >>>>> not >>>>> know architecture-wise how it works but here is the best doc I found >>>>> regarding micro partitions: >>>>> >>>>> https://docs.snowflake.net/manuals/user-guide/tables-clustering-micropartitions.html >>>>> >>>>> >>>>> It is also appealing to me that they support nested data types with >>>>> ease (and they are still very efficient to query/filter by), they scale >>>>> workload easily (Kudu requires manual rebalancing and it is VERY slow as >>>>> I've learned last weekend by doing it on our dev cluster). Also, they >>>>> support very large blobs and text fields that Kudu does not. >>>>> >>>>> Actually, I have a much longer wish list on the Impala side than Kudu >>>>> - it feels that Kudu itself is very close to Snowflake but lacks these >>>>> self-management features. While Impala is certainly one of the fastest SQL >>>>> engines I've seen, we struggle with mutli-tenancy and complex queries, >>>>> that >>>>> Hive would run like a champ. >>>>> >>>>> As for Kudu's compaction process - it was largely an issue with 1.5 >>>>> and I believed was addressed in 1.9. We had a terrible time for a few >>>>> weeks >>>>> when frequent updates/delete almost froze all our queries to crawl. A lot >>>>> of smaller tables had a huge number of tiny rowsets causing massive scans >>>>> and freezing the entire Kudu cluster. We had a good discussion on slack >>>>> and >>>>> Todd Lipcon suggested a good workaround using flush_threshold_secs till we >>>>> move to 1.9 and it worked fine. Nowhere in the documentation, it was >>>>> suggested to set this flag and actually it was one of these "use it at >>>>> your >>>>> own risk" flags. >>>>> >>>>> >>>>> >>>>> On Thu, Mar 12, 2020 at 8:49 PM Adar Lieber-Dembo <a...@cloudera.com> >>>>> wrote: >>>>> >>>>>> This has been an excellent discussion to follow, with very useful >>>>>> feedback. Thank you for that. >>>>>> >>>>>> Boris, if I can try to summarize your position, it's that manual >>>>>> partitioning doesn't scale when dealing with hundreds of (small) tables >>>>>> and >>>>>> when you don't control the PK of each table. The Kudu schema design guide >>>>>> would advise you to hash partition such tables, and, following Andrew's >>>>>> recommendation, have at least one partition per tserver. Except that >>>>>> given >>>>>> enough tables, you'll eventually overwhelm any cluster based on the sheer >>>>>> number of partitions per tserver. And if you reduce the number of >>>>>> partitions per table, you open yourself up to potential hotspotting if >>>>>> the >>>>>> per-table load isn't even. Is that correct? >>>>>> >>>>>> I can think of several improvements we could make here: >>>>>> 1. Support splitting/merging of range partitions, first manually and >>>>>> later automatically based on load. This is tracked in KUDU-437 and >>>>>> KUDU-441. Both are complicated, and since we've gotten a lot of mileage >>>>>> out >>>>>> of static range/hash partitioning, there hasn't been much traction on >>>>>> those >>>>>> JIRAs. >>>>>> 2. Improve server efficiency when hosting large numbers of replicas, >>>>>> so that you can blanket your cluster with maximally hash-partitioned >>>>>> tables. We did some work here a couple years ago (see KUDU-1967) but >>>>>> there's clearly much more that we can do. Of note, we previously >>>>>> discussed >>>>>> implementing Multi-Raft ( >>>>>> https://issues.apache.org/jira/browse/KUDU-1913?focusedCommentId=15967031&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-15967031 >>>>>> ). >>>>>> 3. Add support for consistent hashing. I'm not super familiar with >>>>>> the details but YugabyteDB (based on Kudu) has seen success here. Their >>>>>> recent blog post on sharding is a good (and related) read: >>>>>> https://blog.yugabyte.com/four-data-sharding-strategies-we-analyzed-in-building-a-distributed-sql-database/ >>>>>> >>>>>> Since you're clearly more familiar with Snowflake than I, can you >>>>>> comment more on how their partitioning system works? Sounds like it's >>>>>> quite >>>>>> automated; is that ever problematic when it decides to repartition >>>>>> during a >>>>>> workload? The YugabyteDB blog post talks about heuristics "reacting" to >>>>>> hotspotting by load balancing, and concludes that hotspots can move >>>>>> around >>>>>> too quickly for a load balancer to keep up. >>>>>> >>>>>> Separately, you mentioned having to manage Kudu's compaction process. >>>>>> Could you go into more detail here? >>>>>> >>>>>> On Wed, Mar 11, 2020 at 6:49 AM Boris Tyukin <bo...@boristyukin.com> >>>>>> wrote: >>>>>> >>>>>>> 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 <cre...@gmail.com> >>>>>>> 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 <bo...@boristyukin.com> >>>>>>>> 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 <aw...@cloudera.com> >>>>>>>>> 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 < >>>>>>>>>> bo...@boristyukin.com> 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 < >>>>>>>>>>> bo...@boristyukin.com> 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 < >>>>>>>>>>>> bo...@boristyukin.com> 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 < >>>>>>>>>>>>> bo...@boristyukin.com> 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 >>>>>>>>>> >>>>>>>>>
