Hi Xiaoxiang, ShaoFeng, Thank you for your answers!
Regarding the segment overlap between batch and streaming, my point was that it seems to be different to how I understand segment overlap to work in streaming OLAP. That is, assuming I build a "batch" segment from 2019-06-25 00:00:00.0 to 2019-06-26 00:00:00.0 (1 day). Then if a late event comes in for the same period (e.g. event timestamp field contains 2019-06-25 12:34:56), but after this batch segment has already been built, it will not show up in the query result unless I set up a mechanism to detect the late event and trigger the rebuilding of the batch segment. This is because the results from the batch segments overwrite the results from the streaming segments. On the other hand, for segments built by the streaming engine, my understanding is that they can have overlapping time periods and the query engine will merge the results. I understand this behaviour is actually useful in optimizing the query path in case there were many overlapping segments created by the streaming cube build, since with the batch-built segment, the results can be served from a single segment and don't need to be merged from multiple overlapping segments. I guess the solution here is to ensure that the batch segment is always built for a time period from which we practically don't expect late events anymore. Best regards, Andras On Wed, Jun 26, 2019 at 11:40 AM Xiaoxiang Yu <[email protected]> wrote: > Hi Andras, Shaofeng, > I will update this information asap. > About segment overlaping problem, I have a test in my env, looks like > everything works well. Since the segment range created by kylin’s streaming > coordinator is something like "201906290000_201906290100" , if you want to > build a segment, I think you should use the exact match segment range (such > as "201906290000_201906290100"), or merge multi exist segments range (such > as "201906290100_201906290300") . > > > *-----------------* > *-----------------* > *Best wishes to you ! * > *From ：**Xiaoxiang Yu* > > At 2019-06-26 12:00:38, "ShaoFeng Shi" <[email protected]> wrote: > > Hi Xiaoxiang, > > Thank you for the detailed information. Could you please record these > limitations as JIRA issues (if not yet)? Thanks. > > Best regards, > > Shaofeng Shi 史少锋 > Apache Kylin PMC > Email: [email protected] > > Apache Kylin FAQ: https://kylin.apache.org/docs/gettingstarted/faq.html > Join Kylin user mail group: [email protected] > Join Kylin dev mail group: [email protected] > > > > > Xiaoxiang Yu <[email protected]> 于2019年6月25日周二 下午11:42写道： > >> >> Hi, Andras >> I am glad to see that you have have a strong understanding with >> Kylin's Realtime OLAP. Most of them are correct, the following is my >> understanding: >> 1) Currently, there is no such documentation which talk about how to >> use lambda mode, we will publish one after 3.0.0-beta release (maybe this >> wekend or after a week?). >> 2) Hive table must have the same name as the streaming table , and >> should be locate at "default" namespace of hive. The column name should >> match exactly and data type should be compatible. >> 3) If you want to build segment which data from hive, you have to >> built by rest api. >> 4) Cube build engine must be mapreduce, spark is not supported at >> the moment. >> >> >> *-----------------* >> *-----------------* >> *Best wishes to you ! * >> *From ：**Xiaoxiang Yu* >> >> At 2019-06-25 17:20:55, "Andras Nagy" <[email protected]> >> wrote: >> >> Hi ShaoFeng, >> >> Thanks a lot for the pointer on the lambda mode, yes, that's exactly what >> I need :) >> >> Is there perhaps documentation on this? For now, I was trying to get this >> working 'empirically' and finally succeeded, but some of my conclusions may >> be wrong. This is what I concluded: >> >> - hive table must have the same name as the streaming table (name given >> to the data source) >> - cube can't be built from UI (to build the historic segments from the >> data in hive), but it can be built using the REST API >> - cube build engine must be mapreduce. For Spark as build engine I got >> exception "Cannot adapt to interface >> org.apache.kylin.engine.spark.ISparkOutput" >> - endTime must be non-overlapping with the streaming data. When I had >> overlap, the streaming data coming from kafka did not show up in the >> output, I guess this is what you meant by "the segments from Hive will >> overwrite the segments from Kafka". >> >> Are these correct conclusions? Is there anything else I should be aware >> of? >> >> Many thanks, >> Andras >> >> On Tue, Jun 25, 2019 at 9:19 AM ShaoFeng Shi <[email protected]> >> wrote: >> >>> Hello Andras, >>> >>> Kylin's realtime-OLAP feature supports a "Lambda" mode (mentioned in >>> https://kylin.apache.org/blog/2019/04/12/rt-streaming-design/), which >>> means, you can define a fact table whose data can be from both Kafka and >>> Hive. The only requirement is that all the cube columns appear in both >>> Kafka data and Hive data. I think maybe that can fit your need. The cube >>> can be built from Kafka, in the meanwhile, it can also be built from Hive, >>> the segments from Hive will overwrite the segments from Kafka (as usually >>> Hive data is more accurate). When querying the cube, Kylin will firstly >>> query historical segments, and then real-time segments (adding the max-time >>> of historical segments as the condition). >>> >>> >>> Best regards, >>> >>> Shaofeng Shi 史少锋 >>> Apache Kylin PMC >>> Email: [email protected] >>> >>> Apache Kylin FAQ: https://kylin.apache.org/docs/gettingstarted/faq.html >>> Join Kylin user mail group: [email protected] >>> Join Kylin dev mail group: [email protected] >>> >>> >>> >>> >>> Andras Nagy <[email protected]> 于2019年6月24日周一 下午11:29写道： >>> >>>> Dear Ma, >>>> >>>> Thanks for your reply. >>>> >>>> Slightly related to my original question on the hybrid model, I was >>>> wondering if it's possible to combine a batch and a streaming cube. I >>>> realized this is not possible, as a hybrid model can only be created from >>>> cubes of the same model (and a model points to either a batch or a >>>> streaming datasource). >>>> >>>> The usecase would be this: >>>> - we have a large amount of streaming data in Kafka that we would like >>>> to process with Kylin streaming >>>> - Kafka retention is only a few days, so if we need to change anything >>>> in the cubes (e.g. introduce a new metric or dimension which has been >>>> present in the events, but not in the cube definition), we can only >>>> reprocess a few days worth of data in the streaming model >>>> - the raw events are also written to a data lake for long-term storage >>>> - the data written to the data lake could be used to feed the historic >>>> data into a batch kylin model (and cubes) >>>> - I'm looking for a way to combine these, so if we want to change >>>> anything in the cubes, we can recalculate them for the historic data as >>>> well >>>> >>>> Is there a way to achieve this with current Kylin? (Without >>>> implementing a custom query layer that combines the two cubes.) >>>> >>>> Best regards, >>>> Andras >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Fri, Jun 14, 2019 at 6:43 AM Ma Gang <[email protected]> wrote: >>>> >>>>> Hi Andras, >>>>> >>>>> Currently it doesn't support consume from specified offsets, only >>>>> support consume from startOffset or latestOffset, if you want to consume >>>>> from startOffset, you need to set the >>>>> configuration: kylin.stream.consume.offsets.latest to false in the cube's >>>>> overrides page. >>>>> >>>>> If you do need to start from specified offsets, please create a jira >>>>> request, but I think it is hard for user to know what's the offsets should >>>>> be set for all partitions. >>>>> >>>>> At 2019-06-13 22:34:59, "Andras Nagy" <[email protected]> >>>>> wrote: >>>>> >>>>> Dear Ma, >>>>> >>>>> Thank you very much! >>>>> >>>>> >1)yes, you can specify a configuration in the new cube, to consume >>>>> data from start offset >>>>> That is, an offset value for each partition of the topic? That would >>>>> be good - could you please point me where to do this in practice, or point >>>>> me to what I should read? (I haven't found it on the cube designer UI - >>>>> perhaps this is something that's only available on the API?) >>>>> >>>>> Many thanks, >>>>> Andras >>>>> >>>>> >>>>> >>>>> On Thu, Jun 13, 2019 at 1:14 PM Ma Gang <[email protected]> wrote: >>>>> >>>>>> Hi Andras, >>>>>> 1)yes, you can specify a configuration in the new cube, to consume >>>>>> data from start offset >>>>>> >>>>>> 2)It should work, but I haven't tested it yet >>>>>> >>>>>> 3)as I remember, currently we use Kafka 1.0 client library, so it is >>>>>> better to use the version later, I'm sure that the version before 0.9.0 >>>>>> cannot work, but not sure 0.9.x can work or not >>>>>> >>>>>> >>>>>> >>>>>> Ma Gang >>>>>> 邮箱：[email protected] >>>>>> >>>>>> <https://maas.mail.163.com/dashi-web-extend/html/proSignature.html?ftlId=1&name=Ma+Gang&uid=mg4work%40163.com&iconUrl=https%3A%2F%2Fmail-online.nosdn.127.net%2Fqiyelogo%2FdefaultAvatar.png&items=%5B%22%E9%82%AE%E7%AE%B1%EF%BC%9Amg4work%40163.com%22%5D> >>>>>> >>>>>> 签名由 网易邮箱大师 <https://mail.163.com/dashi/dlpro.html?from=mail88> 定制 >>>>>> >>>>>> On 06/13/2019 18:01, Andras Nagy <[email protected]> >>>>>> wrote: >>>>>> Greetings, >>>>>> >>>>>> I have a few questions related to the new streaming (real-time OLAP) >>>>>> implementation. >>>>>> >>>>>> 1) Is there a way to have data reprocessed from kafka? E.g. I change >>>>>> a cube definition and drop the cube (or add a new cube definition) and >>>>>> want >>>>>> to have data that is still available on kafka to be reprocessed to build >>>>>> the changed cube (or new cube)? Is this possible? >>>>>> >>>>>> 2) Does the hybrid model work with streaming cubes (to combine two >>>>>> cubes)? >>>>>> >>>>>> 3) What is minimum kafka version required? The tutorial asks to >>>>>> install Kafka 1.0, is this the minimum required version? >>>>>> >>>>>> Thank you very much, >>>>>> Andras >>>>>> >>>>>> >>>>> >>>>> >>>>> >>>>
