Hi Alberto, After try to apply your suggestion, our queríe is improved so much. Thanks a lot. However, we have problem with ORDER BY function. When we use ORDER BY with a large data set (for example: with long date-range filter), performance is very slow. Result: *User: ADMIN* *Success: true* *Duration: 23.311* *Project: metrixa_global_database_new* *Realization Names: [account_global_convtrack_summary_daily_by_location]* *Cuboid Ids: [135]* *Total scan count: 2595584* *Result row count: 250* *Accept Partial: true* *Is Partial Result: false* *Hit Exception Cache: false* *Storage cache used: false* *Message: null*
ORDER BY performance goes down when Total Scan Count is big. So how can i improve this problem? Thanks 2017-01-16 18:45 GMT+07:00 Alberto Ramón <[email protected]>: > Hi Phon, I'm not expert but I have some suggestions: > > - All Dim en are using Dict: you can change a lot to Integer (Fix length) > - Re-Order row key its a good idea. I always try to first fields of key > have Fix Length. Put mandatory the First its a good Idea > - See hierarchy optimizations, will be very interesting for you: Country, > Region, City, site . Perhaps Company and Account also can be included (I > don't know your data) > - If you use Left join, the first step of building cube (flat table) will > be more slow > - Check if your ORC input table is compressed > - Try to use derived DIm with very low cardinality columns, perhaps: > TypeID, NetworkID, LanguajeID, IsMovileDevice. > I understand that Affiliated, Account, Company, ... will growth in the > future, because you are working with test data ? > > Check this references: > http://kylin.apache.org/docs/howto/howto_optimize_cubes.html > http://mail-archives.apache.org/mod_mbox/kylin-user/201611.mbox > /%3Ctencent_F5A1E061EFFB778CC5BF9909%40qq.com%3E > http://mail-archives.apache.org/mod_mbox/kylin-user/201607.mbox > /%3C004201d1d4ef%240151b7e0%2403f527a0%24%40fishbowl.com%3E > http://mail-archives.apache.org/mod_mbox/kylin-user/201612.mbox/% > 3CCAEcyM171RGhk0QoXJUjjZJeSxXwgUGu0vO%2B_T71KXMU1k00L%2Bg%40mail.gmail.com > %3E > Check this tunning example: https://github.com/albertoRamon/Kylin > /tree/master/KylinPerformance > > BR, Alb > > > 2017-01-16 3:47 GMT+01:00 Phong Pham <[email protected]>: > >> Hi all, >> Hi all, >> * We still meet problems with query performance. Here is the cube >> info of one cube*: >> { >> "uuid": "6b2f4643-72a3-4a51-b9f2-47aa8e1322a5", >> "last_modified": 1484533219336, >> "version": "1.6.0", >> "name": "account_global_convtrack_summary_daily_test", >> "owner": "ADMIN", >> "descriptor": "account_global_convtrack_summary_daily_test", >> "cost": 50, >> "status": "READY", >> "segments": [ >> { >> "uuid": "85fa970e-6808-47c8-ae35-45d1975bb3bc", >> "name": "20160101000000_20161226000000", >> "storage_location_identifier": "KYLIN_7E4KIJ3YGX", >> "date_range_start": 1451606400000, >> "date_range_end": 1482710400000, >> "source_offset_start": 0, >> "source_offset_end": 0, >> "status": "READY", >> "size_kb": 9758001, >> "input_records": 8109122, >> "input_records_size": 102078756, >> "last_build_time": 1484533219335, >> "last_build_job_id": "a4f67403-17cb-4474-84d1-21ad64ed17a8", >> "create_time_utc": 1484527504660, >> "cuboid_shard_nums": {}, >> "total_shards": 4, >> "blackout_cuboids": [], >> "binary_signature": null, >> "dictionaries": { >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/CITYID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/CITYID/0015e15c-9336-4040-b8ad-b7afba71d51c.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/TYPE": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/TYPE/56cc3576-3c19-40fb-8704-29dba88e3511.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/NETWORKID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/NETWORKID/edc1b900-8b8a-4834-a8ab-4d23e0087d61.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/WEEKGROUP": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/WEEKGROUP/3c3ae7e2-05a0-49a3-b396-ded7b1faaebd.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/DATESTATSBIGINT": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/DATESTATSBIGINT/b2003335-f10c-48b5-ac98- >> 6d2ddd25854b.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/COUNTRYID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/COUNTRYID/233a3b35-9e0f-46e3-bb01-3330c907ab33.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/ACCOUNTID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/ACCOUNTID/612d8a57-8ed8-4fdd-bf99-c64fb2a583fe.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/DEVICEID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/DEVICEID/8813544c-aac3-4f26-849b-3e3d1b71d9e2.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/LANGUAGEID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/LANGUAGEID/02dea027-86cf-44e6-9bcf-9dbd4c33e54b.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/COMPANYID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/COMPANYID/75a5566e-b419-4fc8-9184-757b207a35d2.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/REGIONID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/REGIONID/81d5b463-8639-4633-83b9-9ac9e43e32cb.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/AFFILIATEID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/AFFILIATEID/0a35d5ce-dabb-4e32-ad5f-b87ef4c18ee3.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/SITEID": >> "/dict/MTX_SYSTEM.TBL_CONVTRACK_SITES_ORC/SITEID/07e4f091- >> f6aa-4520-9069-416ee4c904de.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/MONTHGROUP": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/MONTHGROUP/e3bf45aa-3ff3-477b-aafd-d2c38a70caea.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/DATESTATS": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/DATESTATS/5a3d3dc6-90eb-493b-84d0-b1b8ca8b70ec.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/ISMOBILEDEVICE": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/ISMOBILEDEVICE/eba9f8db-c5f0-4283-8a77- >> 5f72d75c5867.dict", >> "METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_SUMMARY_DAILY_ORC/SOURCEURLID": >> "/dict/METRIXA_GLOBAL_DATABASE.ACCOUNT_GLOBAL_CONVTRACK_ >> SUMMARY_DAILY_ORC/SOURCEURLID/3f90d0de-6d04-4bc6-af20-0030a91326f0.dict" >> }, >> "snapshots": { >> "MTX_SYSTEM.TBL_MCM_COUNTRY_CITY_ORC": "/table_snapshot/MTX_SYSTEM.TB >> L_MCM_COUNTRY_CITY_ORC/f32ec683-f83f-423a-820e-1bfd4b65426f.snapshot", >> "METRIXA_GLOBAL_DATABASE.GLOBAL_SOURCEURL_ORC": >> "/table_snapshot/METRIXA_GLOBAL_DATABASE.GLOBAL_SOURCEURL_ >> ORC/32e8df3f-7188-4646-9eff-6c96792897f4.snapshot", >> "MTX_SYSTEM.TBL_MCM_COUNTRY_REGION_ORC": "/table_snapshot/MTX_SYSTEM.TB >> L_MCM_COUNTRY_REGION_ORC/e4378b9c-ff08-4207-92fa-3f0cf37f00d5.snapshot", >> "MTX_SYSTEM.TBL_MCM_COUNTRY_ORC": "/table_snapshot/MTX_SYSTEM.TB >> L_MCM_COUNTRY_ORC/2f2ffb19-d675-43a2-bb08-66a83801f875.snapshot", >> "MTX_SYSTEM.GLOBAL_ACCOUNT_SEARCH_ENGINE_ORC": "/table_snapshot/ >> MTX_SYSTEM.GLOBAL_ACCOUNT_SEARCH_ENGINE_ORC/53ef6022-7249-4ef8-8518-b7d84 >> c65fdfa.snapshot", >> "MTX_SYSTEM.TBL_CONVTRACK_SITES_ORC": "/table_snapshot/MTX_SYSTEM.TB >> L_CONVTRACK_SITES_ORC/0cbb0323-d434-44de-8891-85b024589743.snapshot", >> "MTX_SYSTEM.TBL_MCM_LANGUAGE_ORC": "/table_snapshot/MTX_SYSTEM.TB >> L_MCM_LANGUAGE_ORC/957e6a54-c618-4e5c-bc8d-c89952cafe1e.snapshot", >> "MTX_SYSTEM.TBL_CONVTRACK_AFFILIATES_ORC": "/table_snapshot/MTX_SYSTEM.TB >> L_CONVTRACK_AFFILIATES_ORC/f794bce2-dcb1-41b0-b9bf-fe3c9e1ad661.snapshot" >> }, >> "index_path": "/kylin/kylin_metadata/kylin-a >> 4f67403-17cb-4474-84d1-21ad64ed17a8/account_global_convtrack >> _summary_daily_clone/secondary_index/", >> "rowkey_stats": [ >> [ >> "DATESTATS", >> 360, >> 2 >> ], >> [ >> "CITYID", >> 60804, >> 2 >> ], >> [ >> "SOURCEURLID", >> 38212, >> 2 >> ], >> [ >> "REGIONID", >> 2822, >> 2 >> ], >> [ >> "COUNTRYID", >> 238, >> 1 >> ], >> [ >> "LANGUAGEID", >> 173, >> 1 >> ], >> [ >> "AFFILIATEID", >> 36, >> 1 >> ], >> [ >> "ACCOUNTID", >> 62, >> 1 >> ], >> [ >> "COMPANYID", >> 19, >> 1 >> ], >> [ >> "SITEID", >> 103, >> 1 >> ], >> [ >> "WEEKGROUP", >> 52, >> 1 >> ], >> [ >> "MONTHGROUP", >> 12, >> 1 >> ], >> [ >> "TYPE", >> 2, >> 1 >> ], >> [ >> "ISMOBILEDEVICE", >> 2, >> 1 >> ], >> [ >> "DEVICEID", >> 338, >> 2 >> ], >> [ >> "NETWORKID", >> 161, >> 1 >> ], >> [ >> "DATESTATSBIGINT", >> 360, >> 2 >> ] >> ] >> } >> ], >> "create_time_utc": 1484286587541, >> "size_kb": 9758001, >> "input_records_count": 8109122, >> "input_records_size": 102078756 >> } >> *+ We have 2 colums that is high cardinality*: [ >> "CITYID", >> 60804, >> 2 >> ], >> [ >> "SOURCEURLID", >> 38212, >> 2 >> ], >> *+ We define left-join from model for all relations* >> *+ With new aggregation:* >> Includes >> ["SITEID","COMPANYID","SOURCEURLID","DATESTATS","WEEKGROUP", >> "MONTHGROUP","COUNTRYID","REGIONID","TYPE","ISMOBILEDEVI >> CE","LANGUAGEID","DEVICEID","NETWORKID","ACCOUNTID"," >> AFFILIATEID","CITYID"] >> >> Mandatory Dimensions >> ["DATESTATS"]: Because we always use datestats as a filter >> >> Hierarchy Dimensions: None < Maybe wee will put CountryId, RegionId, and >> CityId >> Joint Dimensions >> ["LANGUAGEID","ACCOUNTID","AFFILIATEID","SITEID","CITYID"," >> REGIONID","COUNTRYID","SOURCEURLID"]: Please explain to me more about >> join dimensions? I don't understand fully about this theory. >> *+ Rowkeys:* >> We defined all rows is dict, because all of them are not ultra high >> cardinality >> >> The query that is very slow is that: >> + We get all dims and metrics, left join all dim tables and group by all >> dims >> + We set datetstats condition for 1 year >> >> And query often take a long time to executed: >10s >> >> So are there problems with our cube design? I would like to hear your >> reply soon. >> Thanks so much for your help. >> >> 2017-01-12 21:28 GMT+07:00 ShaoFeng Shi <[email protected]>: >> >>> Obviously there are too many segments (24*3=72), try to merge them as >>> Billy suggested. >>> >>> Secondly if possible try to review and optimize the cube design >>> (especially the rowkey sequence, put high-cardinality filter column to the >>> begin position to minimal the scan range), see >>> http://www.slideshare.net/YangLi43/design-cube-in-apache-kylin >>> >>> Thirdly try to give more power to the cluster, e.g use physical >>> machines; and also use multiple kylin query nodes to balance the concurrent >>> work load. >>> >>> Just some cents, hope it can help. >>> >>> 2017-01-12 22:16 GMT+08:00 Billy Liu <[email protected]>: >>> >>>> I have concerns with so many segments. Please try query only one cube >>>> with one segment first. >>>> >>>> 2017-01-12 13:36 GMT+08:00 Phong Pham <[email protected]>: >>>> >>>>> Hi, >>>>> Thank you so much for your help. I really appreciate it. Im really >>>>> impressed with your project and trying to apply it to our product. Our >>>>> live >>>>> product is still working on Mysql and MongoDb, but data is growing fast. >>>>> That's why we need your product for the database engine replacement. >>>>> About our problem with many queries on same time on Apache Kylin, I'm >>>>> trying to monitor some elements on our system and review cubes. So are >>>>> there some tutorials about concurrency of Kylin or HBase? >>>>> I will give you more details abour our system: >>>>> Hardware: >>>>> 2 physical machines -> 7 vitural machines >>>>> Each vitural machine: >>>>> CPU: 8cores >>>>> RAM: 24GB >>>>> We are setup hadoop env with hortonwork 2.5 and setup HBase with 5 >>>>> RegionServer, 2 Hbase masters >>>>> Apahce Kylin we setup on 2 machines: >>>>> + Node 1: using for build cubes >>>>> + Node 2: using for only queries (this node also contain RegionServer) >>>>> Cube and Queries: >>>>> + Size of Cubes: >>>>> - Cube 1: 20GB/14M rows - 24 segments (maybe we need to meger them >>>>> into 2-3 segments) >>>>> - Cube 2: 460MB/3M rows - 24 segments >>>>> - Cube 3: 1.3GB/1.4M rows - 24 segments >>>>> + We use one query to read data from 3 cubes and union all into 1 >>>>> result >>>>> Test case: >>>>> + On single request: 3s >>>>> + On 5 requests on same times: (submit multi-requests from client): >>>>> 20s/request >>>>> And that is not acceptable when we go live. >>>>> So hope you all review our struture and give us some best pratices >>>>> with Kylin And Hbase. >>>>> Thanks >>>>> >>>>> 2017-01-12 8:24 GMT+07:00 ShaoFeng Shi <[email protected]>: >>>>> >>>>>> In this case you need do some profiling to see what's the bottleneck: >>>>>> Kylin or HBase or other factors like CPU, memory or network; maybe it is >>>>>> related with the cube design, try to optimize the cube design with the >>>>>> executed query is also a way; It is hard to give you good answer with a >>>>>> couple words. >>>>>> >>>>>> 2017-01-11 19:50 GMT+08:00 Phong Pham <[email protected]>: >>>>>> >>>>>>> Heres about detail on our system: >>>>>>> >>>>>>> Hbase: 5 nodes >>>>>>> Data size: 24M rows >>>>>>> >>>>>>> Query result: >>>>>>> *Success: true* >>>>>>> *Duration: 20s* >>>>>>> *Project: metrixa_global_database* >>>>>>> *Realization Names: [xxx, xxx, xxx]* >>>>>>> *Cuboid Ids: [45971, 24]* >>>>>>> >>>>>>> >>>>>>> 2017-01-11 18:34 GMT+07:00 Phong Pham <[email protected]>: >>>>>>> >>>>>>>> Hi all, >>>>>>>> I have a problem with concurrency on Apache Kylin. Execute >>>>>>>> single query, it takes about 3s. Howerver,when i run multiple queries >>>>>>>> on >>>>>>>> the same time, each query take about 13-15s. So how can i solve >>>>>>>> problems? >>>>>>>> My Kylin Version is 1.6.1 >>>>>>>> Thanks >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Best regards, >>>>>> >>>>>> Shaofeng Shi 史少锋 >>>>>> >>>>>> >>>>> >>>> >>> >>> >>> -- >>> Best regards, >>> >>> Shaofeng Shi 史少锋 >>> >>> >> >
