http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/gettingstarted/concepts.md ---------------------------------------------------------------------- diff --git a/website/_docs/gettingstarted/concepts.md b/website/_docs/gettingstarted/concepts.md deleted file mode 100644 index 081ec54..0000000 --- a/website/_docs/gettingstarted/concepts.md +++ /dev/null @@ -1,65 +0,0 @@ ---- -layout: docs -title: "Technical Concepts" -categories: gettingstarted -permalink: /docs/gettingstarted/concepts.html -version: v1.2 -since: v1.2 ---- - -Here are some basic technical concepts used in Apache Kylin, please check them for your reference. -For terminology in domain, please refer to: [Terminology](terminology.md) - -## CUBE -* __Table__ - This is definition of hive tables as source of cubes, which must be synced before building cubes. - - -* __Data Model__ - This describes a [STAR SCHEMA](https://en.wikipedia.org/wiki/Star_schema) data model, which defines fact/lookup tables and filter condition. - - -* __Cube Descriptor__ - This describes definition and settings for a cube instance, defining which data model to use, what dimensions and measures to have, how to partition to segments and how to handle auto-merge etc. - - -* __Cube Instance__ - This is instance of cube, built from one cube descriptor, and consist of one or more cube segments according partition settings. - - -* __Partition__ - User can define a DATE/STRING column as partition column on cube descriptor, to separate one cube into several segments with different date periods. - - -* __Cube Segment__ - This is actual carrier of cube data, and maps to a HTable in HBase. One building job creates one new segment for the cube instance. Once data change on specified data period, we can refresh related segments to avoid rebuilding whole cube. - - -* __Aggregation Group__ - Each aggregation group is subset of dimensions, and build cuboid with combinations inside. It aims at pruning for optimization. - - -## DIMENSION & MEASURE -* __Mandotary__ - This dimension type is used for cuboid pruning, if a dimension is specified as âmandatoryâ, then those combinations without such dimension are pruned. -* __Hierarchy__ - This dimension type is used for cuboid pruning, if dimension A,B,C forms a âhierarchyâ relation, then only combinations with A, AB or ABC shall be remained. -* __Derived__ - On lookup tables, some dimensions could be generated from its PK, so there's specific mapping between them and FK from fact table. So those dimensions are DERIVED and don't participate in cuboid generation. - - -* __Count Distinct(HyperLogLog)__ - Immediate COUNT DISTINCT is hard to calculate, a approximate algorithm - [HyperLogLog](https://en.wikipedia.org/wiki/HyperLogLog) is introduced, and keep error rate in a lower level. -* __Count Distinct(Precise)__ - Precise COUNT DISTINCT will be pre-calculated basing on RoaringBitmap, currently only int or bigint are supported. -* __Top N__ - (Will release in 2.x) For example, with this measure type, user can easily get specified numbers of top sellers/buyers etc. - - -## CUBE ACTIONS -* __BUILD__ - Given an interval of partition column, this action is to build a new cube segment. -* __REFRESH__ - This action will rebuilt cube segment in some partition period, which is used in case of source table increasing. -* __MERGE__ - This action will merge multiple continuous cube segments into single one. This can be automated with auto-merge settings in cube descriptor. -* __PURGE__ - Clear segments under a cube instance. This will only update metadata, and won't delete cube data from HBase. - - -## JOB STATUS -* __NEW__ - This denotes one job has been just created. -* __PENDING__ - This denotes one job is paused by job scheduler and waiting for resources. -* __RUNNING__ - This denotes one job is running in progress. -* __FINISHED__ - This denotes one job is successfully finished. -* __ERROR__ - This denotes one job is aborted with errors. -* __DISCARDED__ - This denotes one job is cancelled by end users. - - -## JOB ACTION -* __RESUME__ - Once a job in ERROR status, this action will try to restore it from latest successful point. -* __DISCARD__ - No matter status of a job is, user can end it and release resources with DISCARD action. - \ No newline at end of file
http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/gettingstarted/events.md ---------------------------------------------------------------------- diff --git a/website/_docs/gettingstarted/events.md b/website/_docs/gettingstarted/events.md deleted file mode 100644 index 8605d4a..0000000 --- a/website/_docs/gettingstarted/events.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: docs -title: "Events and Conferences" -categories: gettingstarted -permalink: /docs/gettingstarted/events.html ---- - -__Coming Events__ - -* ApacheCon EU 2015 - -__Conferences__ - -* [Apache Kylin - Balance Between Space and Time](http://www.chinahadoop.com/2015/July/Shanghai/agenda.php) ([slides](http://www.slideshare.net/qhzhou/apache-kylin-china-hadoop-summit-2015-shanghai)) by [Qianhao Zhou](https://github.com/qhzhou), at Hadoop Summit 2015 in Shanghai, China, 2015-07-24 -* [Apache Kylin - Balance Between Space and Time](https://www.youtube.com/watch?v=jgvZSFaXPgI), [slides](http://www.slideshare.net/DebashisSaha/apache-kylin-balance-between-space-and-time-hadop-summit-2015) ([video](https://www.youtube.com/watch?v=jgvZSFaXPgI), [slides](http://www.slideshare.net/DebashisSaha/apache-kylin-balance-between-space-and-time-hadop-summit-2015)) by [Debashis Saha](https://twitter.com/debashis_saha) & [Luke Han](https://twitter.com/lukehq), at Hadoop Summit 2015 in San Jose, US, 2015-06-09 -* [HBaseCon 2015: Apache Kylin; Extreme OLAP Engine for Hadoop](https://vimeo.com/128152444) ([video](https://vimeo.com/128152444), [slides](http://www.slideshare.net/HBaseCon/ecosystem-session-3b)) by [Seshu Adunuthula](https://twitter.com/SeshuAd) at HBaseCon 2015 in San Francisco, US, 2015-05-07 -* [Apache Kylin - Extreme OLAP Engine for Hadoop](http://strataconf.com/big-data-conference-uk-2015/public/schedule/detail/40029) ([slides](http://www.slideshare.net/lukehan/apache-kylin-extreme-olap-engine-for-big-data)) by [Luke Han](https://twitter.com/lukehq) & [Yang Li](https://github.com/liyang-gmt8), at Strata+Hadoop World in London, UK, 2015-05-06 -* [Apache Kylin Open Source Journey](http://www.infoq.com/cn/presentations/open-source-journey-of-apache-kylin) ([slides](http://www.slideshare.net/lukehan/apache-kylin-open-source-journey-for-qcon2015-beijing)) by [Luke Han](https://twitter.com/lukehq), at QCon Beijing in Beijing, China, 2015-04-23 -* [Apache Kylin - OLAP on Hadoop](http://cio.it168.com/a2015/0418/1721/000001721404.shtml) by [Yang Li](https://github.com/liyang-gmt8), at Database Technology Conference China 2015 in Beijing, China, 2015-04-18 -* [Apache Kylin â Cubes on Hadoop](https://www.youtube.com/watch?v=U0SbrVzuOe4) ([video](https://www.youtube.com/watch?v=U0SbrVzuOe4), [slides](http://www.slideshare.net/Hadoop_Summit/apache-kylin-cubes-on-hadoop)) by [Ted Dunning](https://twitter.com/ted_dunning), at Hadoop Summit 2015 Europe in Brussels, Belgium, 2015-04-16 -* [Apache Kylin ï¼ Hadoop ä¸ç大è§æ¨¡èæºåæå¹³å°](http://bdtc2014.hadooper.cn/m/zone/bdtc_2014/schedule3) ([slides](http://www.slideshare.net/lukehan/apache-kylin-big-data-technology-conference-2014-beijing-v2)) by [Luke Han](https://twitter.com/lukehq), at Big Data Technology Conference China in Beijing, China, 2014-12-14 -* [Apache Kylin: OLAP Engine on Hadoop - Tech Deep Dive](http://v.csdn.hudong.com/s/article.html?arcid=15820707) ([video](http://v.csdn.hudong.com/s/article.html?arcid=15820707), [slides](http://www.slideshare.net/XuJiang2/kylin-hadoop-olap-engine)) by [Jiang Xu](https://www.linkedin.com/pub/xu-jiang/4/5a8/230), at Shanghai Big Data Summit 2014 in Shanghai, China , 2014-10-25 - -__Meetup__ - -* [Apache Kylin Meetup @Bay Area](http://www.meetup.com/Cloud-at-ebayinc/events/218914395/), in San Jose, US, 6:00PM - 7:30PM, Thursday, 2014-12-04 - http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/gettingstarted/faq.md ---------------------------------------------------------------------- diff --git a/website/_docs/gettingstarted/faq.md b/website/_docs/gettingstarted/faq.md deleted file mode 100644 index 24294ee..0000000 --- a/website/_docs/gettingstarted/faq.md +++ /dev/null @@ -1,90 +0,0 @@ ---- -layout: docs -title: "FAQ" -categories: gettingstarted -permalink: /docs/gettingstarted/faq.html -version: v0.7.2 -since: v0.6.x ---- - -### Some NPM error causes ERROR exit (ä¸å½å¤§éå°åºç¨æ·è¯·ç¹å«æ³¨ææ¤é®é¢)? -For people from China: - -* Please add proxy for your NPM (请为NPM设置代ç): -`npm config set proxy http://YOUR_PROXY_IP` - -* Please update your local NPM repository to using any mirror of npmjs.org, like Taobao NPM (请æ´æ°æ¨æ¬å°çNPMä»åºä»¥ä½¿ç¨å½å çNPMéåï¼ä¾å¦æ·å®NPMéå) : -[http://npm.taobao.org](http://npm.taobao.org) - -### Can't get master address from ZooKeeper" when installing Kylin on Hortonworks Sandbox -Check out [https://github.com/KylinOLAP/Kylin/issues/9](https://github.com/KylinOLAP/Kylin/issues/9). - -### Map Reduce Job information can't display on sandbox deployment -Check out [https://github.com/KylinOLAP/Kylin/issues/40](https://github.com/KylinOLAP/Kylin/issues/40) - -#### Install Kylin on CDH 5.2 or Hadoop 2.5.x -Check out discussion: [https://groups.google.com/forum/?utm_medium=email&utm_source=footer#!msg/kylin-olap/X0GZfsX1jLc/nzs6xAhNpLkJ](https://groups.google.com/forum/?utm_medium=email&utm_source=footer#!msg/kylin-olap/X0GZfsX1jLc/nzs6xAhNpLkJ) -{% highlight Groff markup %} -I was able to deploy Kylin with following option in POM. -<hadoop2.version>2.5.0</hadoop2.version> -<yarn.version>2.5.0</yarn.version> -<hbase-hadoop2.version>0.98.6-hadoop2</hbase-hadoop2.version> -<zookeeper.version>3.4.5</zookeeper.version> -<hive.version>0.13.1</hive.version> -My Cluster is running on Cloudera Distribution CDH 5.2.0. -{% endhighlight %} - -#### Unable to load a big cube as HTable, with java.lang.OutOfMemoryError: unable to create new native thread -HBase (as of writing) allocates one thread per region when bulk loading a HTable. Try reduce the number of regions of your cube by setting its "capacity" to "MEDIUM" or "LARGE". Also tweaks OS & JVM can allow more threads, for example see [this article](http://blog.egilh.com/2006/06/2811aspx.html). - -#### Failed to run BuildCubeWithEngineTest, saying failed to connect to hbase while hbase is active -User may get this error when first time run hbase client, please check the error trace to see whether there is an error saying couldn't access a folder like "/hadoop/hbase/local/jars"; If that folder doesn't exist, create it. - -#### SUM(field) returns a negtive result while all the numbers in this field are > 0 -If a column is declared as integer in Hive, the SQL engine (calcite) will use column's type (integer) as the data type for "SUM(field)", while the aggregated value on this field may exceed the scope of integer; in that case the cast will cause a negtive value be returned; The workround is, alter that column's type to BIGINT in hive, and then sync the table schema to Kylin (the cube doesn't need rebuild); Keep in mind that, always declare as BIGINT in hive for an integer column which would be used as a measure in Kylin; See hive number types: [https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types#LanguageManualTypes-NumericTypes](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types#LanguageManualTypes-NumericTypes) - -#### Why Kylin need extract the distinct columns from Fact Table before building cube? -Kylin uses dictionary to encode the values in each column, this greatly reduce the cube's storage size. To build the dictionary, Kylin need fetch the distinct values for each column. - -#### Why Kylin calculate the HIVE table cardinality? -The cardinality of dimensions is an important measure of cube complexity. The higher the cardinality, the bigger the cube, and thus the longer to build and the slower to query. Cardinality > 1,000 is worth attention and > 1,000,000 should be avoided at best effort. For optimal cube performance, try reduce high cardinality by categorize values or derive features. - -#### How to add new user or change the default password? -Kylin web's security is implemented with Spring security framework, where the kylinSecurity.xml is the main configuration file: -{% highlight Groff markup %} -${KYLIN_HOME}/tomcat/webapps/kylin/WEB-INF/classes/kylinSecurity.xml -{% endhighlight %} -The password hash for pre-defined test users can be found in the profile "sandbox,testing" part; To change the default password, you need generate a new hash and then update it here, please refer to the code snippet in: [https://stackoverflow.com/questions/25844419/spring-bcryptpasswordencoder-generate-different-password-for-same-input](https://stackoverflow.com/questions/25844419/spring-bcryptpasswordencoder-generate-different-password-for-same-input) -When you deploy Kylin for more users, switch to LDAP authentication is recommended; To enable LDAP authentication, update "kylin.sandbox" in conf/kylin.properties to false, and also configure the ldap.* properties in ${KYLIN_HOME}/conf/kylin.properties - -#### Using sub-query for un-supported SQL - -{% highlight Groff markup %} -Original SQL: -select fact.slr_sgmt, -sum(case when cal.RTL_WEEK_BEG_DT = '2015-09-06' then gmv else 0 end) as W36, -sum(case when cal.RTL_WEEK_BEG_DT = '2015-08-30' then gmv else 0 end) as W35 -from ih_daily_fact fact -inner join dw_cal_dt cal on fact.cal_dt = cal.cal_dt -group by fact.slr_sgmt -{% endhighlight %} - -{% highlight Groff markup %} -Using sub-query -select a.slr_sgmt, -sum(case when a.RTL_WEEK_BEG_DT = '2015-09-06' then gmv else 0 end) as W36, -sum(case when a.RTL_WEEK_BEG_DT = '2015-08-30' then gmv else 0 end) as W35 -from ( - select fact.slr_sgmt as slr_sgmt, - cal.RTL_WEEK_BEG_DT as RTL_WEEK_BEG_DT, - sum(gmv) as gmv36, - sum(gmv) as gmv35 - from ih_daily_fact fact - inner join dw_cal_dt cal on fact.cal_dt = cal.cal_dt - group by fact.slr_sgmt, cal.RTL_WEEK_BEG_DT -) a -group by a.slr_sgmt -{% endhighlight %} - - - http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/gettingstarted/terminology.md ---------------------------------------------------------------------- diff --git a/website/_docs/gettingstarted/terminology.md b/website/_docs/gettingstarted/terminology.md deleted file mode 100644 index 0f9e669..0000000 --- a/website/_docs/gettingstarted/terminology.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -layout: docs -title: "Terminology" -categories: gettingstarted -permalink: /docs/gettingstarted/terminology.html -version: v1.0 -since: v0.5.x ---- - - -Here are some domain terms we are using in Apache Kylin, please check them for your reference. -They are basic knowledge of Apache Kylin which also will help to well understand such concerpt, term, knowledge, theory and others about Data Warehouse, Business Intelligence for analycits. - -* __Data Warehouse__: a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, [wikipedia](https://en.wikipedia.org/wiki/Data_warehouse) -* __Business Intelligence__: Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes, [wikipedia](https://en.wikipedia.org/wiki/Business_intelligence) -* __OLAP__: OLAP is an acronym for [online analytical processing](https://en.wikipedia.org/wiki/Online_analytical_processing) -* __OLAP Cube__: an OLAP cube is an array of data understood in terms of its 0 or more dimensions, [wikipedia](http://en.wikipedia.org/wiki/OLAP_cube) -* __Star Schema__: the star schema consists of one or more fact tables referencing any number of dimension tables, [wikipedia](https://en.wikipedia.org/wiki/Star_schema) -* __Fact Table__: a Fact table consists of the measurements, metrics or facts of a business process, [wikipedia](https://en.wikipedia.org/wiki/Fact_table) -* __Lookup Table__: a lookup table is an array that replaces runtime computation with a simpler array indexing operation, [wikipedia](https://en.wikipedia.org/wiki/Lookup_table) -* __Dimension__: A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time, [wikipedia](https://en.wikipedia.org/wiki/Dimension_(data_warehouse)) -* __Measure__: a measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made, [wikipedia](https://en.wikipedia.org/wiki/Measure_(data_warehouse)) -* __Join__: a SQL join clause combines records from two or more tables in a relational database, [wikipedia](https://en.wikipedia.org/wiki/Join_(SQL)) - - - http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_backup_hbase.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_backup_hbase.md b/website/_docs/howto/howto_backup_hbase.md deleted file mode 100644 index 8714132..0000000 --- a/website/_docs/howto/howto_backup_hbase.md +++ /dev/null @@ -1,29 +0,0 @@ ---- -layout: docs -title: How to Clean/Backup HBase Tables -categories: howto -permalink: /docs/howto/howto_backup_hbase.html -version: v1.0 -since: v0.7.1 ---- - -Kylin persists all data (meta data and cube) in HBase; You may want to export the data sometimes for whatever purposes -(backup, migration, troubleshotting etc); This page describes the steps to do this and also there is a Java app for you to do this easily. - -Steps: - -1. Cleanup unused cubes to save storage space (be cautious on production!): run the following command in hbase CLI: -{% highlight Groff markup %} -hbase org.apache.hadoop.util.RunJar /${KYLIN_HOME}/lib/kylin-job-(version).jar org.apache.kylin.job.hadoop.cube.StorageCleanupJob --delete true -{% endhighlight %} -2. List all HBase tables, iterate and then export each Kylin table to HDFS; -See [https://hbase.apache.org/book/ops_mgt.html#export](https://hbase.apache.org/book/ops_mgt.html#export) - -3. Copy the export folder from HDFS to local file system, and then archive it; - -4. (optional) Download the archive from Hadoop CLI to local; - -5. Cleanup the export folder from CLI HDFS and local file system; - -Kylin provide the "ExportHBaseData.java" (currently only exist in "minicluster" branch) for you to do the -step 2-5 in one run; Please ensure the correct path of "kylin.properties" has been set in the sys env; This Java uses the sandbox config by default; http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_backup_metadata.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_backup_metadata.md b/website/_docs/howto/howto_backup_metadata.md deleted file mode 100644 index cf250b7..0000000 --- a/website/_docs/howto/howto_backup_metadata.md +++ /dev/null @@ -1,62 +0,0 @@ ---- -layout: docs -title: How to Backup Metadata -categories: howto -permalink: /docs/howto/howto_backup_metadata.html -version: v1.0 -since: v0.7.1 ---- - -Kylin organizes all of its metadata (including cube descriptions and instances, projects, inverted index description and instances, jobs, tables and dictionaries) as a hierarchy file system. However, Kylin uses hbase to store it, rather than normal file system. If you check your kylin configuration file(kylin.properties) you will find such a line: - -{% highlight Groff markup %} -## The metadata store in hbase -kylin.metadata.url=kylin_metadata@hbase -{% endhighlight %} - -This indicates that the metadata will be saved as a htable called `kylin_metadata`. You can scan the htable in hbase shell to check it out. - -## Backup Metadata Store with binary package - -Sometimes you need to backup the Kylin's Metadata Store from hbase to your disk file system. -In such cases, assuming you're on the hadoop CLI(or sandbox) where you deployed Kylin, you can go to KYLIN_HOME and run : - -{% highlight Groff markup %} -./bin/metastore.sh backup -{% endhighlight %} - -to dump your metadata to your local folder a folder under KYLIN_HOME/metadata_backps, the folder is named after current time with the syntax: KYLIN_HOME/meta_backups/meta_year_month_day_hour_minute_second - -## Restore Metadata Store with binary package - -In case you find your metadata store messed up, and you want to restore to a previous backup: - -Firstly, reset the metadata store (this will clean everything of the Kylin metadata store in hbase, make sure to backup): - -{% highlight Groff markup %} -./bin/metastore.sh reset -{% endhighlight %} - -Then upload the backup metadata to Kylin's metadata store: -{% highlight Groff markup %} -./bin/metastore.sh restore $KYLIN_HOME/meta_backups/meta_xxxx_xx_xx_xx_xx_xx -{% endhighlight %} - -## Backup/restore metadata in development env (available since 0.7.3) - -When developing/debugging Kylin, typically you have a dev machine with an IDE, and a backend sandbox. Usually you'll write code and run test cases at dev machine. It would be troublesome if you always have to put a binary package in the sandbox to check the metadata. There is a helper class called SandboxMetastoreCLI to help you download/upload metadata locally at your dev machine. Follow the Usage information and run it in your IDE. - -## Cleanup unused resources from Metadata Store (available since 0.7.3) -As time goes on, some resources like dictionary, table snapshots became useless (as the cube segment be dropped or merged), but they still take space there; You can run command to find and cleanup them from metadata store: - -Firstly, run a check, this is safe as it will not change anything: -{% highlight Groff markup %} -./bin/metastore.sh clean -{% endhighlight %} - -The resources that will be dropped will be listed; - -Next, add the "--delete true" parameter to cleanup those resources; before this, make sure you have made a backup of the metadata store; -{% highlight Groff markup %} -./bin/metastore.sh clean --delete true -{% endhighlight %} http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_build_cube_with_restapi.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_build_cube_with_restapi.md b/website/_docs/howto/howto_build_cube_with_restapi.md deleted file mode 100644 index 4113700..0000000 --- a/website/_docs/howto/howto_build_cube_with_restapi.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -layout: docs -title: How to Build Cube with Restful API -categories: howto -permalink: /docs/howto/howto_build_cube_with_restapi.html -version: v1.2 -since: v0.7.1 ---- - -### 1. Authentication -* Currently, Kylin uses [basic authentication](http://en.wikipedia.org/wiki/Basic_access_authentication). -* Add `Authorization` header to first request for authentication -* Or you can do a specific request by `POST http://localhost:7070/kylin/api/user/authentication` -* Once authenticated, client can go subsequent requests with cookies. -{% highlight Groff markup %} -POST http://localhost:7070/kylin/api/user/authentication - -Authorization:Basic xxxxJD124xxxGFxxxSDF -Content-Type: application/json;charset=UTF-8 -{% endhighlight %} - -### 2. Get details of cube. -* `GET http://localhost:7070/kylin/api/cubes?cubeName={cube_name}&limit=15&offset=0` -* Client can find cube segment date ranges in returned cube detail. -{% highlight Groff markup %} -GET http://localhost:7070/kylin/api/cubes?cubeName=test_kylin_cube_with_slr&limit=15&offset=0 - -Authorization:Basic xxxxJD124xxxGFxxxSDF -Content-Type: application/json;charset=UTF-8 -{% endhighlight %} -### 3. Then submit a build job of the cube. -* `PUT http://localhost:7070/kylin/api/cubes/{cube_name}/rebuild` -* For put request body detail please refer to [Build Cube API](howto_use_restapi.html#build-cube). - * `startTime` and `endTime` should be utc timestamp. - * `buildType` can be `BUILD` ,`MERGE` or `REFRESH`. `BUILD` is for building a new segment, `REFRESH` for refreshing an existing segment. `MERGE` is for merging multiple existing segments into one bigger segment. -* This method will return a new created job instance, whose uuid is the unique id of job to track job status. -{% highlight Groff markup %} -PUT http://localhost:7070/kylin/api/cubes/test_kylin_cube_with_slr/rebuild - -Authorization:Basic xxxxJD124xxxGFxxxSDF -Content-Type: application/json;charset=UTF-8 - -{ - "startTime": 0, - "endTime": 1388563200000, - "buildType": "BUILD" -} -{% endhighlight %} - -### 4. Track job status. -* `GET http://localhost:7070/kylin/api/jobs/{job_uuid}` -* Returned `job_status` represents current status of job. - -### 5. If the job got errors, you can resume it. -* `PUT http://localhost:7070/kylin/api/jobs/{job_uuid}/resume` http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_cleanup_storage.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_cleanup_storage.md b/website/_docs/howto/howto_cleanup_storage.md deleted file mode 100644 index ef35d68..0000000 --- a/website/_docs/howto/howto_cleanup_storage.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -layout: docs -title: How to Cleanup Storage (HDFS & HBase Tables) -categories: howto -permalink: /docs/howto/howto_cleanup_storage.html -version: v0.7.2 -since: v0.7.1 ---- - -Kylin will generate intermediate files in HDFS during the cube building; Besides, when purge/drop/merge cubes, some HBase tables may be left in HBase and will no longer be queried; Although Kylin has started to do some -automated garbage collection, it might not cover all cases; You can do an offline storage cleanup periodically: - -Steps: -1. Check which resources can be cleanup, this will not remove anything: -{% highlight Groff markup %} -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-(version).jar org.apache.kylin.job.hadoop.cube.StorageCleanupJob --delete false -{% endhighlight %} -Here please replace (version) with the specific Kylin jar version in your installation; -2. You can pickup 1 or 2 resources to check whether they're no longer be referred; Then add the "--delete true" option to start the cleanup: -{% highlight Groff markup %} -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-(version).jar org.apache.kylin.job.hadoop.cube.StorageCleanupJob --delete true -{% endhighlight %} -On finish, the intermediate HDFS location and HTables will be dropped; \ No newline at end of file http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_jdbc.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_jdbc.md b/website/_docs/howto/howto_jdbc.md deleted file mode 100644 index 3e088fe..0000000 --- a/website/_docs/howto/howto_jdbc.md +++ /dev/null @@ -1,94 +0,0 @@ ---- -layout: docs -title: How to Use kylin Remote JDBC Driver -categories: howto -permalink: /docs/howto/howto_jdbc.html -version: v1.2 -since: v0.7.1 ---- - -### Authentication - -###### Build on kylin authentication restful service. Supported parameters: -* user : username -* password : password -* ssl: true/false. Default be false; If true, all the services call will use https. - -### Connection URL format: -{% highlight Groff markup %} -jdbc:kylin://<hostname>:<port>/<kylin_project_name> -{% endhighlight %} -* If "ssl" = true, the "port" should be Kylin server's HTTPS port; -* If "port" is not specified, the driver will use default port: HTTP 80, HTTPS 443; -* The "kylin_project_name" must be specified and user need ensure it exists in Kylin server; - -### 1. Query with Statement -{% highlight Groff markup %} -Driver driver = (Driver) Class.forName("org.apache.kylin.jdbc.Driver").newInstance(); - -Properties info = new Properties(); -info.put("user", "ADMIN"); -info.put("password", "KYLIN"); -Connection conn = driver.connect("jdbc:kylin://localhost:7070/kylin_project_name", info); -Statement state = conn.createStatement(); -ResultSet resultSet = state.executeQuery("select * from test_table"); - -while (resultSet.next()) { - assertEquals("foo", resultSet.getString(1)); - assertEquals("bar", resultSet.getString(2)); - assertEquals("tool", resultSet.getString(3)); -} -{% endhighlight %} - -### 2. Query with PreparedStatement - -###### Supported prepared statement parameters: -* setString -* setInt -* setShort -* setLong -* setFloat -* setDouble -* setBoolean -* setByte -* setDate -* setTime -* setTimestamp - -{% highlight Groff markup %} -Driver driver = (Driver) Class.forName("org.apache.kylin.jdbc.Driver").newInstance(); -Properties info = new Properties(); -info.put("user", "ADMIN"); -info.put("password", "KYLIN"); -Connection conn = driver.connect("jdbc:kylin://localhost:7070/kylin_project_name", info); -PreparedStatement state = conn.prepareStatement("select * from test_table where id=?"); -state.setInt(1, 10); -ResultSet resultSet = state.executeQuery(); - -while (resultSet.next()) { - assertEquals("foo", resultSet.getString(1)); - assertEquals("bar", resultSet.getString(2)); - assertEquals("tool", resultSet.getString(3)); -} -{% endhighlight %} - -### 3. Get query result set metadata -Kylin jdbc driver supports metadata list methods: -List catalog, schema, table and column with sql pattern filters(such as %). - -{% highlight Groff markup %} -Driver driver = (Driver) Class.forName("org.apache.kylin.jdbc.Driver").newInstance(); -Properties info = new Properties(); -info.put("user", "ADMIN"); -info.put("password", "KYLIN"); -Connection conn = driver.connect("jdbc:kylin://localhost:7070/kylin_project_name", info); -Statement state = conn.createStatement(); -ResultSet resultSet = state.executeQuery("select * from test_table"); - -ResultSet tables = conn.getMetaData().getTables(null, null, "dummy", null); -while (tables.next()) { - for (int i = 0; i < 10; i++) { - assertEquals("dummy", tables.getString(i + 1)); - } -} -{% endhighlight %} http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_ldap_and_sso.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_ldap_and_sso.md b/website/_docs/howto/howto_ldap_and_sso.md deleted file mode 100644 index 1102559..0000000 --- a/website/_docs/howto/howto_ldap_and_sso.md +++ /dev/null @@ -1,124 +0,0 @@ ---- -layout: docs -title: How to Enable Security with LDAP and SSO -categories: howto -permalink: /docs/howto/howto_ldap_and_sso.html -version: v2.0 -since: v1.0 ---- - -## Enable LDAP authentication - -Kylin supports LDAP authentication for enterprise or production deployment; This is implemented with Spring Security framework; Before enable LDAP, please contact your LDAP administrator to get necessary information, like LDAP server URL, username/password, search patterns; - -#### Configure LDAP server info - -Firstly, provide LDAP URL, and username/password if the LDAP server is secured; The password in kylin.properties need be salted; You can Google "Generate a BCrypt Password" or run org.apache.kylin.rest.security.PasswordPlaceholderConfigurer to get a hash of your password. - -``` -ldap.server=ldap://<your_ldap_host>:<port> -ldap.username=<your_user_name> -ldap.password=<your_password_hash> -``` - -Secondly, provide the user search patterns, this is by LDAP design, here is just a sample: - -``` -ldap.user.searchBase=OU=UserAccounts,DC=mycompany,DC=com -ldap.user.searchPattern=(&(AccountName={0})(memberOf=CN=MYCOMPANY-USERS,DC=mycompany,DC=com)) -ldap.user.groupSearchBase=OU=Group,DC=mycompany,DC=com -``` - -If you have service accounts (e.g, for system integration) which also need be authenticated, configure them in ldap.service.*; Otherwise, leave them be empty; - -### Configure the administrator group and default role - -To map an LDAP group to the admin group in Kylin, need set the "acl.adminRole" to "ROLE_" + GROUP_NAME. For example, in LDAP the group "KYLIN-ADMIN-GROUP" is the list of administrators, here need set it as: - -``` -acl.adminRole=ROLE_KYLIN-ADMIN-GROUP -acl.defaultRole=ROLE_ANALYST,ROLE_MODELER -``` - -The "acl.defaultRole" is a list of the default roles that grant to everyone, keep it as-is. - -#### Enable LDAP - -For Kylin v0.x and v1.x: set "kylin.sandbox=false" in conf/kylin.properties, then restart Kylin server; -For Kylin since v2.0: set "kylin.security.profile=ldap" in conf/kylin.properties, then restart Kylin server; - -## Enable SSO authentication - -From v2.0, Kylin provides SSO with SAML. The implementation is based on Spring Security SAML Extension. You can read [this reference](http://docs.spring.io/autorepo/docs/spring-security-saml/1.0.x-SNAPSHOT/reference/htmlsingle/) to get an overall understand. - -Before trying this, you should have successfully enabled LDAP and managed users with it, as SSO server may only do authentication, Kylin need search LDAP to get the user's detail information. - -### Generate IDP metadata xml -Contact your IDP (ID provider), asking to generate the SSO metadata file; Usually you need provide three piece of info: - - 1. Partner entity ID, which is an unique ID of your app, e.g,: https://host-name/kylin/saml/metadata - 2. App callback endpoint, to which the SAML assertion be posted, it need be: https://host-name/kylin/saml/SSO - 3. Public certificate of Kylin server, the SSO server will encrypt the message with it. - -### Generate JKS keystore for Kylin -As Kylin need send encrypted message (signed with Kylin's private key) to SSO server, a keystore (JKS) need be provided. There are a couple ways to generate the keystore, below is a sample. - -Assume kylin.crt is the public certificate file, kylin.key is the private certificate file; firstly create a PKCS#12 file with openssl, then convert it to JKS with keytool: - -``` -$ openssl pkcs12 -export -in kylin.crt -inkey kylin.key -out kylin.p12 -Enter Export Password: <export_pwd> -Verifying - Enter Export Password: <export_pwd> - - -$ keytool -importkeystore -srckeystore kylin.p12 -srcstoretype PKCS12 -srcstorepass <export_pwd> -alias 1 -destkeystore samlKeystore.jks -destalias kylin -destkeypass changeit - -Enter destination keystore password: changeit -Re-enter new password: changeit -``` - -It will put the keys to "samlKeystore.jks" with alias "kylin"; - -### Enable Higher Ciphers - -Make sure your environment is ready to handle higher level crypto keys, you may need to download Java Cryptography Extension (JCE) Unlimited Strength Jurisdiction Policy Files, copy local_policy.jar and US_export_policy.jar to $JAVA_HOME/jre/lib/security . - -### Deploy IDP xml file and keystore to Kylin - -The IDP metadata and keystore file need be deployed in Kylin web app's classpath in $KYLIN_HOME/tomcat/webapps/kylin/WEB-INF/classes - - 1. Name the IDP file to sso_metadata.xml and then copy to Kylin's classpath; - 2. Name the keystore as "samlKeystore.jks" and then copy to Kylin's classpath; - 3. If you use another alias or password, remember to update that kylinSecurity.xml accordingly: - -``` -<!-- Central storage of cryptographic keys --> -<bean id="keyManager" class="org.springframework.security.saml.key.JKSKeyManager"> - <constructor-arg value="classpath:samlKeystore.jks"/> - <constructor-arg type="java.lang.String" value="changeit"/> - <constructor-arg> - <map> - <entry key="kylin" value="changeit"/> - </map> - </constructor-arg> - <constructor-arg type="java.lang.String" value="kylin"/> -</bean> - -``` - -### Other configurations -In conf/kylin.properties, add the following properties with your server information: - -``` -saml.metadata.entityBaseURL=https://host-name/kylin -saml.context.scheme=https -saml.context.serverName=host-name -saml.context.serverPort=443 -saml.context.contextPath=/kylin -``` - -Please note, Kylin assume in the SAML message there is a "email" attribute representing the login user, and the name before @ will be used to search LDAP. - -### Enable SSO -Set "kylin.security.profile=saml" in conf/kylin.properties, then restart Kylin server; After that, type a URL like "/kylin" or "/kylin/cubes" will redirect to SSO for login, and jump back after be authorized. While login with LDAP is still available, you can type "/kylin/login" to use original way. The Rest API (/kylin/api/*) still use LDAP + basic authentication, no impact. - http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_optimize_cubes.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_optimize_cubes.md b/website/_docs/howto/howto_optimize_cubes.md deleted file mode 100644 index 4ebf7be..0000000 --- a/website/_docs/howto/howto_optimize_cubes.md +++ /dev/null @@ -1,214 +0,0 @@ ---- -layout: docs -title: How to Optimize Cubes -categories: howto -permalink: /docs/howto/howto_optimize_cubes.html -version: v0.7.2 -since: v0.7.1 ---- - -## Hierarchies: - -Theoretically for N dimensions you'll end up with 2^N dimension combinations. However for some group of dimensions there are no need to create so many combinations. For example, if you have three dimensions: continent, country, city (In hierarchies, the "bigger" dimension comes first). You will only need the following three combinations of group by when you do drill down analysis: - -group by continent -group by continent, country -group by continent, country, city - -In such cases the combination count is reduced from 2^3=8 to 3, which is a great optimization. The same goes for the YEAR,QUATER,MONTH,DATE case. - -If we Donate the hierarchy dimension as H1,H2,H3, typical scenarios would be: - - -A. Hierarchies on lookup table - - -<table> - <tr> - <td align="center">Fact table</td> - <td align="center">(joins)</td> - <td align="center">Lookup Table</td> - </tr> - <tr> - <td>column1,column2,,,,,, FK</td> - <td></td> - <td>PK,,H1,H2,H3,,,,</td> - </tr> -</table> - ---- - -B. Hierarchies on fact table - - -<table> - <tr> - <td align="center">Fact table</td> - </tr> - <tr> - <td>column1,column2,,,H1,H2,H3,,,,,,, </td> - </tr> -</table> - ---- - - -There is a special case for scenario A, where PK on the lookup table is accidentally being part of the hierarchies. For example we have a calendar lookup table where cal_dt is the primary key: - -A*. Hierarchies on lookup table over its primary key - - -<table> - <tr> - <td align="center">Lookup Table(Calendar)</td> - </tr> - <tr> - <td>cal_dt(PK), week_beg_dt, month_beg_dt, quarter_beg_dt,,,</td> - </tr> -</table> - ---- - - -For cases like A* what you need is another optimization called "Derived Columns" - -## Derived Columns: - -Derived column is used when one or more dimensions (They must be dimension on lookup table, these columns are called "Derived") can be deduced from another(Usually it is the corresponding FK, this is called the "host column") - -For example, suppose we have a lookup table where we join fact table and it with "where DimA = DimX". Notice in Kylin, if you choose FK into a dimension, the corresponding PK will be automatically querable, without any extra cost. The secret is that since FK and PK are always identical, Kylin can apply filters/groupby on the FK first, and transparently replace them to PK. This indicates that if we want the DimA(FK), DimX(PK), DimB, DimC in our cube, we can safely choose DimA,DimB,DimC only. - -<table> - <tr> - <td align="center">Fact table</td> - <td align="center">(joins)</td> - <td align="center">Lookup Table</td> - </tr> - <tr> - <td>column1,column2,,,,,, DimA(FK) </td> - <td></td> - <td>DimX(PK),,DimB, DimC</td> - </tr> -</table> - ---- - - -Let's say that DimA(the dimension representing FK/PK) has a special mapping to DimB: - - -<table> - <tr> - <th>dimA</th> - <th>dimB</th> - <th>dimC</th> - </tr> - <tr> - <td>1</td> - <td>a</td> - <td>?</td> - </tr> - <tr> - <td>2</td> - <td>b</td> - <td>?</td> - </tr> - <tr> - <td>3</td> - <td>c</td> - <td>?</td> - </tr> - <tr> - <td>4</td> - <td>a</td> - <td>?</td> - </tr> -</table> - - -in this case, given a value in DimA, the value of DimB is determined, so we say dimB can be derived from DimA. When we build a cube that contains both DimA and DimB, we simple include DimA, and marking DimB as derived. Derived column(DimB) does not participant in cuboids generation: - -original combinations: -ABC,AB,AC,BC,A,B,C - -combinations when driving B from A: -AC,A,C - -at Runtime, in case queries like "select count(*) from fact_table inner join looup1 group by looup1 .dimB", it is expecting cuboid containing DimB to answer the query. However, DimB will appear in NONE of the cuboids due to derived optimization. In this case, we modify the execution plan to make it group by DimA(its host column) first, we'll get intermediate answer like: - - -<table> - <tr> - <th>DimA</th> - <th>count(*)</th> - </tr> - <tr> - <td>1</td> - <td>1</td> - </tr> - <tr> - <td>2</td> - <td>1</td> - </tr> - <tr> - <td>3</td> - <td>1</td> - </tr> - <tr> - <td>4</td> - <td>1</td> - </tr> -</table> - - -Afterwards, Kylin will replace DimA values with DimB values(since both of their values are in lookup table, Kylin can load the whole lookup table into memory and build a mapping for them), and the intermediate result becomes: - - -<table> - <tr> - <th>DimB</th> - <th>count(*)</th> - </tr> - <tr> - <td>a</td> - <td>1</td> - </tr> - <tr> - <td>b</td> - <td>1</td> - </tr> - <tr> - <td>c</td> - <td>1</td> - </tr> - <tr> - <td>a</td> - <td>1</td> - </tr> -</table> - - -After this, the runtime SQL engine(calcite) will further aggregate the intermediate result to: - - -<table> - <tr> - <th>DimB</th> - <th>count(*)</th> - </tr> - <tr> - <td>a</td> - <td>2</td> - </tr> - <tr> - <td>b</td> - <td>1</td> - </tr> - <tr> - <td>c</td> - <td>1</td> - </tr> -</table> - - -this step happens at query runtime, this is what it means "at the cost of extra runtime aggregation" http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_upgrade.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_upgrade.md b/website/_docs/howto/howto_upgrade.md deleted file mode 100644 index 87367ca..0000000 --- a/website/_docs/howto/howto_upgrade.md +++ /dev/null @@ -1,103 +0,0 @@ ---- -layout: docs -title: How to Upgrade -categories: howto -permalink: /docs/howto/howto_upgrade.html -version: v1.2 -since: v0.7.1 ---- - -## Upgrade among v0.7.x and v1.x - -From v0.7.1 to latest v1.2, Kylin's metadata is backward compatible, the upgrade can be finished in couple of minutes: - -#### 1. Backup metadata -Backup the Kylin metadata peridically is a good practice, and is highly suggested before upgrade; - -``` -cd $KYLIN_HOME -./bin/metastore.sh backup -``` -It will print the backup folder, take it down and make sure it will not be deleted before the upgrade finished. If there is no "metastore.sh", use HBase's snapshot command to do backup: - -``` -hbase shell -snapshot 'kylin_metadata', 'kylin_metadata_backup20150610' -``` -Here 'kylin_metadata' is the default kylin metadata table name, replace it with the right table name of your Kylin; - -#### 2. Install new Kylin and copy back "conf" -Download the new Kylin binary package from Kylin's download page; Extract it to a different folder other than current KYLIN_HOME; Before copy back the "conf" folder, do a compare and merge between the old and new kylin.properties to ensure newly introduced property will be kept. - -#### 3. Stop old and start new Kylin instance -``` -cd $KYLIN_HOME -./bin/kylin.sh stop -export KYLIN_HOME="<path_of_new_installation>" -cd $KYLIN_HOME -./bin/kylin.sh start -``` - -#### 4. Back-port if the upgrade is failed -If the new version couldn't startup and need back-port, shutdown it and then switch to the old KYLIN_HOME to start. Idealy that would return to the origin state. If the metadata is broken, restore it from the backup folder. - -``` -./bin/metastore.sh restore <path_of_metadata_backup> -``` - -## Upgrade from v0.6.x to v0.7.x - -In v0.7, Kylin refactored the metadata structure, for the new features like inverted-index and streaming; If you have cube created with v0.6 and want to keep in v0.7, a migration is needed; (Please skip v0.7.1 as -it has several compatible issues and the fix will be included in v0.7.2) Below is the steps; - -#### 1. Backup v0.6 metadata -To avoid data loss in the migration, a backup at the very beginning is always suggested; You can use HBase's backup or snapshot command to achieve this; Here is a sample with snapshot: - -``` -hbase shell -snapshot 'kylin_metadata', 'kylin_metadata_backup20150610' -``` - -'kylin_metadata' is the default kylin metadata table name, replace it with the right table name of your Kylin; - -#### 2. Dump v0.6 metadata to local file -This is also a backup method; As the migration tool is only tested with local file system, this step is must; All metadata need be downloaded, including snapshot, dictionary, etc; - -``` -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-x.x.x-job.jar org.apache.kylin.common.persistence.ResourceTool download ./meta_dump -``` - -(./meta_dump is the local folder that the metadata will be downloaded, change to name you preferred) - -#### 3. Run CubeMetadataUpgrade to migrate the metadata -This step is to run the migration tool to parse the v0.6 metadata and then convert to v0.7 format; A verification will be performed in the last, and report error if some cube couldn't be migrated; - -``` -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-x.x.x-job.jar org.apache.kylin.job.CubeMetadataUpgrade ./meta_dump -``` - -1. The tool will not overwrite v0.6 metadata; It will create a new folder with "_v2" suffix in the same folder, in this case the "./meta_dump_v2" will be created; -2. By default this tool will only migrate the job history in last 30 days; If you want to keep elder job history, please tweak upgradeJobInstance() method by your own; -3. If you see _No error or warning messages; The migration is success_ , that's good; Otherwise please check the error/warning messages carefully; -4. For some problem you may need manually update the JSON file, to check whether the problem is gone, you can run a verify against the new metadata: - -``` -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-x.x.x-job.jar org.apache.kylin.job.CubeMetadataUpgrade ./meta_dump2 verify -``` - -#### 4. Upload the new metadata to HBase -Now the new format of metadata will be upload to the HBase to replace the old format; Stop Kylin, and then: - -``` -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-x.x.x-job.jar org.apache.kylin.common.persistence.ResourceTool reset -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-x.x.x-job.jar org.apache.kylin.common.persistence.ResourceTool upload ./meta_dump_v2 -``` - -#### 5. Update HTables to use new coprocessor -Kylin uses HBase coprocessor to do server side aggregation; When Kylin instance upgrades to V0.7, the HTables that created in V0.6 should also be updated to use the new coprocessor: - -``` -hbase org.apache.hadoop.util.RunJar ${KYLIN_HOME}/lib/kylin-job-x.x.x-job.jar org.apache.kylin.job.tools.DeployCoprocessorCLI ${KYLIN_HOME}/lib/kylin-coprocessor-x.x.x.jar -``` - -Done; Update your v0.7 Kylin configure to point to the same metadata HBase table, then start Kylin server; Check whether all cubes and other information are kept; \ No newline at end of file http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_use_restapi.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_use_restapi.md b/website/_docs/howto/howto_use_restapi.md deleted file mode 100644 index a49a2db..0000000 --- a/website/_docs/howto/howto_use_restapi.md +++ /dev/null @@ -1,1006 +0,0 @@ ---- -layout: docs -title: How to Use Restful API -categories: howto -permalink: /docs/howto/howto_use_restapi.html -version: v1.2 -since: v0.7.1 ---- - -This page lists all the Rest APIs provided by Kylin; The base of the URL is `/kylin/api`, so don't forget to add it before a certain API's path. For example, to get all cube instances, send HTTP GET request to "/kylin/api/cubes". - -* Query - * [Authentication](#authentication) - * [Query](#query) - * [List queryable tables](#list-queryable-tables) -* CUBE - * [List cubes](#list-cubes) - * [Get cube](#get-cube) - * [Get cube descriptor (dimension, measure info, etc)](#get-cube-descriptor) - * [Get data model (fact and lookup table info)](#get-data-model) - * [Build cube](#build-cube) - * [Disable cube](#disable-cube) - * [Purge cube](#purge-cube) - * [Enable cube](#enable-cube) -* JOB - * [Resume job](#resume-job) - * [Discard job](#discard-job) - * [Get job step output](#get-job-step-output) -* Metadata - * [Get Hive Table](#get-hive-table) - * [Get Hive Table (Extend Info)](#get-hive-table-extend-info) - * [Get Hive Tables](#get-hive-tables) - * [Load Hive Tables](#load-hive-tables) -* Cache - * [Wipe cache](#wipe-cache) - -## Authentication -`POST /user/authentication` - -#### Request Header -Authorization data encoded by basic auth is needed in the header, such as: -Authorization:Basic {data} - -#### Response Body -* userDetails - Defined authorities and status of current user. - -#### Response Sample - -```sh -{ - "userDetails":{ - "password":null, - "username":"sample", - "authorities":[ - { - "authority":"ROLE_ANALYST" - }, - { - "authority":"ROLE_MODELER" - } - ], - "accountNonExpired":true, - "accountNonLocked":true, - "credentialsNonExpired":true, - "enabled":true - } -} -``` - -Example with `curl`: - -``` -curl -c /path/to/cookiefile.txt -X POST -H "Authorization: Basic XXXXXXXXX" -H 'Content-Type: application/json' http://<host>:<port>/kylin/api/user/authentication -``` - -If login successfully, the JSESSIONID will be saved into the cookie file; In the subsequent http requests, attach the cookie, for example: - -``` -curl -b /path/to/cookiefile.txt -X PUT -H 'Content-Type: application/json' -d '{"startTime":'1423526400000', "endTime":'1423526400', "buildType":"BUILD"}' http://<host>:<port>/kylin/api/cubes/your_cube/rebuild -``` - -*** - -## Query -`POST /query` - -#### Request Body -* sql - `required` `string` The text of sql statement. -* offset - `optional` `int` Query offset. If offset is set in sql, curIndex will be ignored. -* limit - `optional` `int` Query limit. If limit is set in sql, perPage will be ignored. -* acceptPartial - `optional` `bool` Whether accept a partial result or not, default be "false". Set to "false" for production use. -* project - `optional` `string` Project to perform query. Default value is 'DEFAULT'. - -#### Request Sample - -```sh -{ - "sql":"select * from TEST_KYLIN_FACT", - "offset":0, - "limit":50000, - "acceptPartial":false, - "project":"DEFAULT" -} -``` - -#### Response Body -* columnMetas - Column metadata information of result set. -* results - Data set of result. -* cube - Cube used for this query. -* affectedRowCount - Count of affected row by this sql statement. -* isException - Whether this response is an exception. -* ExceptionMessage - Message content of the exception. -* Duration - Time cost of this query -* Partial - Whether the response is a partial result or not. Decided by `acceptPartial` of request. - -#### Response Sample - -```sh -{ - "columnMetas":[ - { - "isNullable":1, - "displaySize":0, - "label":"CAL_DT", - "name":"CAL_DT", - "schemaName":null, - "catelogName":null, - "tableName":null, - "precision":0, - "scale":0, - "columnType":91, - "columnTypeName":"DATE", - "readOnly":true, - "writable":false, - "caseSensitive":true, - "searchable":false, - "currency":false, - "signed":true, - "autoIncrement":false, - "definitelyWritable":false - }, - { - "isNullable":1, - "displaySize":10, - "label":"LEAF_CATEG_ID", - "name":"LEAF_CATEG_ID", - "schemaName":null, - "catelogName":null, - "tableName":null, - "precision":10, - "scale":0, - "columnType":4, - "columnTypeName":"INTEGER", - "readOnly":true, - "writable":false, - "caseSensitive":true, - "searchable":false, - "currency":false, - "signed":true, - "autoIncrement":false, - "definitelyWritable":false - } - ], - "results":[ - [ - "2013-08-07", - "32996", - "15", - "15", - "Auction", - "10000000", - "49.048952730908745", - "49.048952730908745", - "49.048952730908745", - "1" - ], - [ - "2013-08-07", - "43398", - "0", - "14", - "ABIN", - "10000633", - "85.78317064220418", - "85.78317064220418", - "85.78317064220418", - "1" - ] - ], - "cube":"test_kylin_cube_with_slr_desc", - "affectedRowCount":0, - "isException":false, - "exceptionMessage":null, - "duration":3451, - "partial":false -} -``` - -## List queryable tables -`GET /tables_and_columns` - -#### Request Parameters -* project - `required` `string` The project to load tables - -#### Response Sample -```sh -[ - { - "columns":[ - { - "table_NAME":"TEST_CAL_DT", - "table_SCHEM":"EDW", - "column_NAME":"CAL_DT", - "data_TYPE":91, - "nullable":1, - "column_SIZE":-1, - "buffer_LENGTH":-1, - "decimal_DIGITS":0, - "num_PREC_RADIX":10, - "column_DEF":null, - "sql_DATA_TYPE":-1, - "sql_DATETIME_SUB":-1, - "char_OCTET_LENGTH":-1, - "ordinal_POSITION":1, - "is_NULLABLE":"YES", - "scope_CATLOG":null, - "scope_SCHEMA":null, - "scope_TABLE":null, - "source_DATA_TYPE":-1, - "iS_AUTOINCREMENT":null, - "table_CAT":"defaultCatalog", - "remarks":null, - "type_NAME":"DATE" - }, - { - "table_NAME":"TEST_CAL_DT", - "table_SCHEM":"EDW", - "column_NAME":"WEEK_BEG_DT", - "data_TYPE":91, - "nullable":1, - "column_SIZE":-1, - "buffer_LENGTH":-1, - "decimal_DIGITS":0, - "num_PREC_RADIX":10, - "column_DEF":null, - "sql_DATA_TYPE":-1, - "sql_DATETIME_SUB":-1, - "char_OCTET_LENGTH":-1, - "ordinal_POSITION":2, - "is_NULLABLE":"YES", - "scope_CATLOG":null, - "scope_SCHEMA":null, - "scope_TABLE":null, - "source_DATA_TYPE":-1, - "iS_AUTOINCREMENT":null, - "table_CAT":"defaultCatalog", - "remarks":null, - "type_NAME":"DATE" - } - ], - "table_NAME":"TEST_CAL_DT", - "table_SCHEM":"EDW", - "ref_GENERATION":null, - "self_REFERENCING_COL_NAME":null, - "type_SCHEM":null, - "table_TYPE":"TABLE", - "table_CAT":"defaultCatalog", - "remarks":null, - "type_CAT":null, - "type_NAME":null - } -] -``` - -*** - -## List cubes -`GET /cubes` - -#### Request Parameters -* offset - `required` `int` Offset used by pagination -* limit - `required` `int ` Cubes per page. -* cubeName - `optional` `string` Keyword for cube names. To find cubes whose name contains this keyword. -* projectName - `optional` `string` Project name. - -#### Response Sample -```sh -[ - { - "uuid":"1eaca32a-a33e-4b69-83dd-0bb8b1f8c53b", - "last_modified":1407831634847, - "name":"test_kylin_cube_with_slr_empty", - "owner":null, - "version":null, - "descriptor":"test_kylin_cube_with_slr_desc", - "cost":50, - "status":"DISABLED", - "segments":[ - ], - "create_time":null, - "source_records_count":0, - "source_records_size":0, - "size_kb":0 - } -] -``` - -## Get cube -`GET /cubes/{cubeName}` - -#### Path Variable -* cubeName - `required` `string` Cube name to find. - -## Get cube descriptor -`GET /cube_desc/{cubeName}` -Get descriptor for specified cube instance. - -#### Path Variable -* cubeName - `required` `string` Cube name. - -#### Response Sample -```sh -[ - { - "uuid": "a24ca905-1fc6-4f67-985c-38fa5aeafd92", - "name": "test_kylin_cube_with_slr_desc", - "description": null, - "dimensions": [ - { - "id": 0, - "name": "CAL_DT", - "table": "EDW.TEST_CAL_DT", - "column": null, - "derived": [ - "WEEK_BEG_DT" - ], - "hierarchy": false - }, - { - "id": 1, - "name": "CATEGORY", - "table": "DEFAULT.TEST_CATEGORY_GROUPINGS", - "column": null, - "derived": [ - "USER_DEFINED_FIELD1", - "USER_DEFINED_FIELD3", - "UPD_DATE", - "UPD_USER" - ], - "hierarchy": false - }, - { - "id": 2, - "name": "CATEGORY_HIERARCHY", - "table": "DEFAULT.TEST_CATEGORY_GROUPINGS", - "column": [ - "META_CATEG_NAME", - "CATEG_LVL2_NAME", - "CATEG_LVL3_NAME" - ], - "derived": null, - "hierarchy": true - }, - { - "id": 3, - "name": "LSTG_FORMAT_NAME", - "table": "DEFAULT.TEST_KYLIN_FACT", - "column": [ - "LSTG_FORMAT_NAME" - ], - "derived": null, - "hierarchy": false - }, - { - "id": 4, - "name": "SITE_ID", - "table": "EDW.TEST_SITES", - "column": null, - "derived": [ - "SITE_NAME", - "CRE_USER" - ], - "hierarchy": false - }, - { - "id": 5, - "name": "SELLER_TYPE_CD", - "table": "EDW.TEST_SELLER_TYPE_DIM", - "column": null, - "derived": [ - "SELLER_TYPE_DESC" - ], - "hierarchy": false - }, - { - "id": 6, - "name": "SELLER_ID", - "table": "DEFAULT.TEST_KYLIN_FACT", - "column": [ - "SELLER_ID" - ], - "derived": null, - "hierarchy": false - } - ], - "measures": [ - { - "id": 1, - "name": "GMV_SUM", - "function": { - "expression": "SUM", - "parameter": { - "type": "column", - "value": "PRICE", - "next_parameter": null - }, - "returntype": "decimal(19,4)" - }, - "dependent_measure_ref": null - }, - { - "id": 2, - "name": "GMV_MIN", - "function": { - "expression": "MIN", - "parameter": { - "type": "column", - "value": "PRICE", - "next_parameter": null - }, - "returntype": "decimal(19,4)" - }, - "dependent_measure_ref": null - }, - { - "id": 3, - "name": "GMV_MAX", - "function": { - "expression": "MAX", - "parameter": { - "type": "column", - "value": "PRICE", - "next_parameter": null - }, - "returntype": "decimal(19,4)" - }, - "dependent_measure_ref": null - }, - { - "id": 4, - "name": "TRANS_CNT", - "function": { - "expression": "COUNT", - "parameter": { - "type": "constant", - "value": "1", - "next_parameter": null - }, - "returntype": "bigint" - }, - "dependent_measure_ref": null - }, - { - "id": 5, - "name": "ITEM_COUNT_SUM", - "function": { - "expression": "SUM", - "parameter": { - "type": "column", - "value": "ITEM_COUNT", - "next_parameter": null - }, - "returntype": "bigint" - }, - "dependent_measure_ref": null - } - ], - "rowkey": { - "rowkey_columns": [ - { - "column": "SELLER_ID", - "length": 18, - "dictionary": null, - "mandatory": true - }, - { - "column": "CAL_DT", - "length": 0, - "dictionary": "true", - "mandatory": false - }, - { - "column": "LEAF_CATEG_ID", - "length": 0, - "dictionary": "true", - "mandatory": false - }, - { - "column": "META_CATEG_NAME", - "length": 0, - "dictionary": "true", - "mandatory": false - }, - { - "column": "CATEG_LVL2_NAME", - "length": 0, - "dictionary": "true", - "mandatory": false - }, - { - "column": "CATEG_LVL3_NAME", - "length": 0, - "dictionary": "true", - "mandatory": false - }, - { - "column": "LSTG_FORMAT_NAME", - "length": 12, - "dictionary": null, - "mandatory": false - }, - { - "column": "LSTG_SITE_ID", - "length": 0, - "dictionary": "true", - "mandatory": false - }, - { - "column": "SLR_SEGMENT_CD", - "length": 0, - "dictionary": "true", - "mandatory": false - } - ], - "aggregation_groups": [ - [ - "LEAF_CATEG_ID", - "META_CATEG_NAME", - "CATEG_LVL2_NAME", - "CATEG_LVL3_NAME", - "CAL_DT" - ] - ] - }, - "signature": "lsLAl2jL62ZApmOLZqWU3g==", - "last_modified": 1445850327000, - "model_name": "test_kylin_with_slr_model_desc", - "null_string": null, - "hbase_mapping": { - "column_family": [ - { - "name": "F1", - "columns": [ - { - "qualifier": "M", - "measure_refs": [ - "GMV_SUM", - "GMV_MIN", - "GMV_MAX", - "TRANS_CNT", - "ITEM_COUNT_SUM" - ] - } - ] - } - ] - }, - "notify_list": null, - "auto_merge_time_ranges": null, - "retention_range": 0 - } -] -``` - -## Get data model -`GET /model/{modelName}` - -#### Path Variable -* modelName - `required` `string` Data model name, by default it should be the same with cube name. - -#### Response Sample -```sh -{ - "uuid": "ff527b94-f860-44c3-8452-93b17774c647", - "name": "test_kylin_with_slr_model_desc", - "lookups": [ - { - "table": "EDW.TEST_CAL_DT", - "join": { - "type": "inner", - "primary_key": [ - "CAL_DT" - ], - "foreign_key": [ - "CAL_DT" - ] - } - }, - { - "table": "DEFAULT.TEST_CATEGORY_GROUPINGS", - "join": { - "type": "inner", - "primary_key": [ - "LEAF_CATEG_ID", - "SITE_ID" - ], - "foreign_key": [ - "LEAF_CATEG_ID", - "LSTG_SITE_ID" - ] - } - } - ], - "capacity": "MEDIUM", - "last_modified": 1442372116000, - "fact_table": "DEFAULT.TEST_KYLIN_FACT", - "filter_condition": null, - "partition_desc": { - "partition_date_column": "DEFAULT.TEST_KYLIN_FACT.CAL_DT", - "partition_date_start": 0, - "partition_date_format": "yyyy-MM-dd", - "partition_type": "APPEND", - "partition_condition_builder": "org.apache.kylin.metadata.model.PartitionDesc$DefaultPartitionConditionBuilder" - } -} -``` - -## Build cube -`PUT /cubes/{cubeName}/rebuild` - -#### Path Variable -* cubeName - `required` `string` Cube name. - -#### Request Body -* startTime - `required` `long` Start timestamp of data to build, e.g. 1388563200000 for 2014-1-1 -* endTime - `required` `long` End timestamp of data to build -* buildType - `required` `string` Supported build type: 'BUILD', 'MERGE', 'REFRESH' - -#### Response Sample -``` -{ - "uuid":"c143e0e4-ac5f-434d-acf3-46b0d15e3dc6", - "last_modified":1407908916705, - "name":"test_kylin_cube_with_slr_empty - 19700101000000_20140731160000 - BUILD - PDT 2014-08-12 22:48:36", - "type":"BUILD", - "duration":0, - "related_cube":"test_kylin_cube_with_slr_empty", - "related_segment":"19700101000000_20140731160000", - "exec_start_time":0, - "exec_end_time":0, - "mr_waiting":0, - "steps":[ - { - "interruptCmd":null, - "name":"Create Intermediate Flat Hive Table", - "sequence_id":0, - "exec_cmd":"hive -e \"DROP TABLE IF EXISTS kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6;\nCREATE EXTERNAL TABLE IF NOT EXISTS kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6\n(\nCAL_DT date\n,LEAF_CATEG_ID int\n,LSTG_SITE_ID int\n,META_CATEG_NAME string\n,CATEG_LVL2_NAME string\n,CATEG_LVL3_NAME string\n,LSTG_FORMAT_NAME string\n,SLR_SEGMENT_CD smallint\n,SELLER_ID bigint\n,PRICE decimal\n)\nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\177'\nSTORED AS SEQUENCEFILE\nLOCATION '/tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6';\nSET mapreduce.job.split.metainfo.maxsize=-1;\nSET mapred.compress.map.output=true;\nSET mapred.map.output.compression.codec=com.hadoop.compression.lzo.LzoCodec;\nSET mapred.output.compress=true;\nSET ma pred.output.compression.codec=com.hadoop.compression.lzo.LzoCodec;\nSET mapred.output.compression.type=BLOCK;\nSET mapreduce.job.max.split.locations=2000;\nSET hive.exec.compress.output=true;\nSET hive.auto.convert.join.noconditionaltask = true;\nSET hive.auto.convert.join.noconditionaltask.size = 300000000;\nINSERT OVERWRITE TABLE kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6\nSELECT\nTEST_KYLIN_FACT.CAL_DT\n,TEST_KYLIN_FACT.LEAF_CATEG_ID\n,TEST_KYLIN_FACT.LSTG_SITE_ID\n,TEST_CATEGORY_GROUPINGS.META_CATEG_NAME\n,TEST_CATEGORY_GROUPINGS.CATEG_LVL2_NAME\n,TEST_CATEGORY_GROUPINGS.CATEG_LVL3_NAME\n,TEST_KYLIN_FACT.LSTG_FORMAT_NAME\n,TEST_KYLIN_FACT.SLR_SEGMENT_CD\n,TEST_KYLIN_FACT.SELLER_ID\n,TEST_KYLIN_FACT.PRICE\nFROM TEST_KYLIN_FACT\nINNER JOIN TEST_CAL_DT\nON TEST_KYLIN_FACT.CAL_DT = TEST_CAL_DT.CAL_DT\nINNER JOIN TEST_CATEGORY_GROUPINGS\nON TEST_KYLIN_FACT.LEAF_CATEG_ID = TEST_CATEGORY_GROUPINGS.LEAF_CATEG_ID AN D TEST_KYLIN_FACT.LSTG_SITE_ID = TEST_CATEGORY_GROUPINGS.SITE_ID\nINNER JOIN TEST_SITES\nON TEST_KYLIN_FACT.LSTG_SITE_ID = TEST_SITES.SITE_ID\nINNER JOIN TEST_SELLER_TYPE_DIM\nON TEST_KYLIN_FACT.SLR_SEGMENT_CD = TEST_SELLER_TYPE_DIM.SELLER_TYPE_CD\nWHERE (test_kylin_fact.cal_dt < '2014-07-31 16:00:00')\n;\n\"", - "interrupt_cmd":null, - "exec_start_time":0, - "exec_end_time":0, - "exec_wait_time":0, - "step_status":"PENDING", - "cmd_type":"SHELL_CMD_HADOOP", - "info":null, - "run_async":false - }, - { - "interruptCmd":null, - "name":"Extract Fact Table Distinct Columns", - "sequence_id":1, - "exec_cmd":" -conf C:/kylin/Kylin/server/src/main/resources/hadoop_job_conf_medium.xml -cubename test_kylin_cube_with_slr_empty -input /tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6 -output /tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/test_kylin_cube_with_slr_empty/fact_distinct_columns -jobname Kylin_Fact_Distinct_Columns_test_kylin_cube_with_slr_empty_Step_1", - "interrupt_cmd":null, - "exec_start_time":0, - "exec_end_time":0, - "exec_wait_time":0, - "step_status":"PENDING", - "cmd_type":"JAVA_CMD_HADOOP_FACTDISTINCT", - "info":null, - "run_async":true - }, - { - "interruptCmd":null, - "name":"Load HFile to HBase Table", - "sequence_id":12, - "exec_cmd":" -input /tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/test_kylin_cube_with_slr_empty/hfile/ -htablename KYLIN-CUBE-TEST_KYLIN_CUBE_WITH_SLR_EMPTY-19700101000000_20140731160000_11BB4326-5975-4358-804C-70D53642E03A -cubename test_kylin_cube_with_slr_empty", - "interrupt_cmd":null, - "exec_start_time":0, - "exec_end_time":0, - "exec_wait_time":0, - "step_status":"PENDING", - "cmd_type":"JAVA_CMD_HADOOP_NO_MR_BULKLOAD", - "info":null, - "run_async":false - } - ], - "job_status":"PENDING", - "progress":0.0 -} -``` - -## Enable Cube -`PUT /cubes/{cubeName}/enable` - -#### Path variable -* cubeName - `required` `string` Cube name. - -#### Response Sample -```sh -{ - "uuid":"1eaca32a-a33e-4b69-83dd-0bb8b1f8c53b", - "last_modified":1407909046305, - "name":"test_kylin_cube_with_slr_ready", - "owner":null, - "version":null, - "descriptor":"test_kylin_cube_with_slr_desc", - "cost":50, - "status":"ACTIVE", - "segments":[ - { - "name":"19700101000000_20140531160000", - "storage_location_identifier":"KYLIN-CUBE-TEST_KYLIN_CUBE_WITH_SLR_READY-19700101000000_20140531160000_BF043D2D-9A4A-45E9-AA59-5A17D3F34A50", - "date_range_start":0, - "date_range_end":1401552000000, - "status":"READY", - "size_kb":4758, - "source_records":6000, - "source_records_size":620356, - "last_build_time":1407832663227, - "last_build_job_id":"2c7a2b63-b052-4a51-8b09-0c24b5792cda", - "binary_signature":null, - "dictionaries":{ - "TEST_CATEGORY_GROUPINGS/CATEG_LVL2_NAME":"/dict/TEST_CATEGORY_GROUPINGS/CATEG_LVL2_NAME/16d8185c-ee6b-4f8c-a919-756d9809f937.dict", - "TEST_KYLIN_FACT/LSTG_SITE_ID":"/dict/TEST_SITES/SITE_ID/0bec6bb3-1b0d-469c-8289-b8c4ca5d5001.dict", - "TEST_KYLIN_FACT/SLR_SEGMENT_CD":"/dict/TEST_SELLER_TYPE_DIM/SELLER_TYPE_CD/0c5d77ec-316b-47e0-ba9a-0616be890ad6.dict", - "TEST_KYLIN_FACT/CAL_DT":"/dict/PREDEFINED/date(yyyy-mm-dd)/64ac4f82-f2af-476e-85b9-f0805001014e.dict", - "TEST_CATEGORY_GROUPINGS/CATEG_LVL3_NAME":"/dict/TEST_CATEGORY_GROUPINGS/CATEG_LVL3_NAME/270fbfb0-281c-4602-8413-2970a7439c47.dict", - "TEST_KYLIN_FACT/LEAF_CATEG_ID":"/dict/TEST_CATEGORY_GROUPINGS/LEAF_CATEG_ID/2602386c-debb-4968-8d2f-b52b8215e385.dict", - "TEST_CATEGORY_GROUPINGS/META_CATEG_NAME":"/dict/TEST_CATEGORY_GROUPINGS/META_CATEG_NAME/0410d2c4-4686-40bc-ba14-170042a2de94.dict" - }, - "snapshots":{ - "TEST_CAL_DT":"/table_snapshot/TEST_CAL_DT.csv/8f7cfc8a-020d-4019-b419-3c6deb0ffaa0.snapshot", - "TEST_SELLER_TYPE_DIM":"/table_snapshot/TEST_SELLER_TYPE_DIM.csv/c60fd05e-ac94-4016-9255-96521b273b81.snapshot", - "TEST_CATEGORY_GROUPINGS":"/table_snapshot/TEST_CATEGORY_GROUPINGS.csv/363f4a59-b725-4459-826d-3188bde6a971.snapshot", - "TEST_SITES":"/table_snapshot/TEST_SITES.csv/78e0aecc-3ec6-4406-b86e-bac4b10ea63b.snapshot" - } - } - ], - "create_time":null, - "source_records_count":6000, - "source_records_size":0, - "size_kb":4758 -} -``` - -## Disable Cube -`PUT /cubes/{cubeName}/disable` - -#### Path variable -* cubeName - `required` `string` Cube name. - -#### Response Sample -(Same as "Enable Cube") - -## Purge Cube -`PUT /cubes/{cubeName}/purge` - -#### Path variable -* cubeName - `required` `string` Cube name. - -#### Response Sample -(Same as "Enable Cube") - -*** - -## Resume Job -`PUT /jobs/{jobId}/resume` - -#### Path variable -* jobId - `required` `string` Job id. - -#### Response Sample -``` -{ - "uuid":"c143e0e4-ac5f-434d-acf3-46b0d15e3dc6", - "last_modified":1407908916705, - "name":"test_kylin_cube_with_slr_empty - 19700101000000_20140731160000 - BUILD - PDT 2014-08-12 22:48:36", - "type":"BUILD", - "duration":0, - "related_cube":"test_kylin_cube_with_slr_empty", - "related_segment":"19700101000000_20140731160000", - "exec_start_time":0, - "exec_end_time":0, - "mr_waiting":0, - "steps":[ - { - "interruptCmd":null, - "name":"Create Intermediate Flat Hive Table", - "sequence_id":0, - "exec_cmd":"hive -e \"DROP TABLE IF EXISTS kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6;\nCREATE EXTERNAL TABLE IF NOT EXISTS kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6\n(\nCAL_DT date\n,LEAF_CATEG_ID int\n,LSTG_SITE_ID int\n,META_CATEG_NAME string\n,CATEG_LVL2_NAME string\n,CATEG_LVL3_NAME string\n,LSTG_FORMAT_NAME string\n,SLR_SEGMENT_CD smallint\n,SELLER_ID bigint\n,PRICE decimal\n)\nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\177'\nSTORED AS SEQUENCEFILE\nLOCATION '/tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6';\nSET mapreduce.job.split.metainfo.maxsize=-1;\nSET mapred.compress.map.output=true;\nSET mapred.map.output.compression.codec=com.hadoop.compression.lzo.LzoCodec;\nSET mapred.output.compress=true;\nSET ma pred.output.compression.codec=com.hadoop.compression.lzo.LzoCodec;\nSET mapred.output.compression.type=BLOCK;\nSET mapreduce.job.max.split.locations=2000;\nSET hive.exec.compress.output=true;\nSET hive.auto.convert.join.noconditionaltask = true;\nSET hive.auto.convert.join.noconditionaltask.size = 300000000;\nINSERT OVERWRITE TABLE kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6\nSELECT\nTEST_KYLIN_FACT.CAL_DT\n,TEST_KYLIN_FACT.LEAF_CATEG_ID\n,TEST_KYLIN_FACT.LSTG_SITE_ID\n,TEST_CATEGORY_GROUPINGS.META_CATEG_NAME\n,TEST_CATEGORY_GROUPINGS.CATEG_LVL2_NAME\n,TEST_CATEGORY_GROUPINGS.CATEG_LVL3_NAME\n,TEST_KYLIN_FACT.LSTG_FORMAT_NAME\n,TEST_KYLIN_FACT.SLR_SEGMENT_CD\n,TEST_KYLIN_FACT.SELLER_ID\n,TEST_KYLIN_FACT.PRICE\nFROM TEST_KYLIN_FACT\nINNER JOIN TEST_CAL_DT\nON TEST_KYLIN_FACT.CAL_DT = TEST_CAL_DT.CAL_DT\nINNER JOIN TEST_CATEGORY_GROUPINGS\nON TEST_KYLIN_FACT.LEAF_CATEG_ID = TEST_CATEGORY_GROUPINGS.LEAF_CATEG_ID AN D TEST_KYLIN_FACT.LSTG_SITE_ID = TEST_CATEGORY_GROUPINGS.SITE_ID\nINNER JOIN TEST_SITES\nON TEST_KYLIN_FACT.LSTG_SITE_ID = TEST_SITES.SITE_ID\nINNER JOIN TEST_SELLER_TYPE_DIM\nON TEST_KYLIN_FACT.SLR_SEGMENT_CD = TEST_SELLER_TYPE_DIM.SELLER_TYPE_CD\nWHERE (test_kylin_fact.cal_dt < '2014-07-31 16:00:00')\n;\n\"", - "interrupt_cmd":null, - "exec_start_time":0, - "exec_end_time":0, - "exec_wait_time":0, - "step_status":"PENDING", - "cmd_type":"SHELL_CMD_HADOOP", - "info":null, - "run_async":false - }, - { - "interruptCmd":null, - "name":"Extract Fact Table Distinct Columns", - "sequence_id":1, - "exec_cmd":" -conf C:/kylin/Kylin/server/src/main/resources/hadoop_job_conf_medium.xml -cubename test_kylin_cube_with_slr_empty -input /tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/kylin_intermediate_test_kylin_cube_with_slr_desc_19700101000000_20140731160000_c143e0e4_ac5f_434d_acf3_46b0d15e3dc6 -output /tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/test_kylin_cube_with_slr_empty/fact_distinct_columns -jobname Kylin_Fact_Distinct_Columns_test_kylin_cube_with_slr_empty_Step_1", - "interrupt_cmd":null, - "exec_start_time":0, - "exec_end_time":0, - "exec_wait_time":0, - "step_status":"PENDING", - "cmd_type":"JAVA_CMD_HADOOP_FACTDISTINCT", - "info":null, - "run_async":true - }, - { - "interruptCmd":null, - "name":"Load HFile to HBase Table", - "sequence_id":12, - "exec_cmd":" -input /tmp/kylin-c143e0e4-ac5f-434d-acf3-46b0d15e3dc6/test_kylin_cube_with_slr_empty/hfile/ -htablename KYLIN-CUBE-TEST_KYLIN_CUBE_WITH_SLR_EMPTY-19700101000000_20140731160000_11BB4326-5975-4358-804C-70D53642E03A -cubename test_kylin_cube_with_slr_empty", - "interrupt_cmd":null, - "exec_start_time":0, - "exec_end_time":0, - "exec_wait_time":0, - "step_status":"PENDING", - "cmd_type":"JAVA_CMD_HADOOP_NO_MR_BULKLOAD", - "info":null, - "run_async":false - } - ], - "job_status":"PENDING", - "progress":0.0 -} -``` - -## Discard Job -`PUT /jobs/{jobId}/cancel` - -#### Path variable -* jobId - `required` `string` Job id. - -#### Response Sample -(Same as "Resume job") - -## Get job step output -`GET /{jobId}/steps/{stepId}/output` - -#### Path Variable -* jobId - `required` `string` Job id. -* stepId - `required` `string` Step id; the step id is composed by jobId with step sequence id; for example, the jobId is "fb479e54-837f-49a2-b457-651fc50be110", its 3rd step id is "fb479e54-837f-49a2-b457-651fc50be110-3", - -#### Response Sample -``` -{ - "cmd_output":"log string" -} -``` - -*** - -## Get Hive Table -`GET /tables/{tableName}` - -#### Request Parameters -* tableName - `required` `string` table name to find. - -#### Response Sample -```sh -{ - uuid: "69cc92c0-fc42-4bb9-893f-bd1141c91dbe", - name: "SAMPLE_07", - columns: [{ - id: "1", - name: "CODE", - datatype: "string" - }, { - id: "2", - name: "DESCRIPTION", - datatype: "string" - }, { - id: "3", - name: "TOTAL_EMP", - datatype: "int" - }, { - id: "4", - name: "SALARY", - datatype: "int" - }], - database: "DEFAULT", - last_modified: 1419330476755 -} -``` - -## Get Hive Table (Extend Info) -`GET /tables/{tableName}/exd-map` - -#### Request Parameters -* tableName - `optional` `string` table name to find. - -#### Response Sample -``` -{ - "minFileSize": "46055", - "totalNumberFiles": "1", - "location": "hdfs://sandbox.hortonworks.com:8020/apps/hive/warehouse/sample_07", - "lastAccessTime": "1418374103365", - "lastUpdateTime": "1398176493340", - "columns": "struct columns { string code, string description, i32 total_emp, i32 salary}", - "partitionColumns": "", - "EXD_STATUS": "true", - "maxFileSize": "46055", - "inputformat": "org.apache.hadoop.mapred.TextInputFormat", - "partitioned": "false", - "tableName": "sample_07", - "owner": "hue", - "totalFileSize": "46055", - "outputformat": "org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat" -} -``` - -## Get Hive Tables -`GET /tables` - -#### Request Parameters -* project- `required` `string` will list all tables in the project. -* ext- `optional` `boolean` set true to get extend info of table. - -#### Response Sample -```sh -[ - { - uuid: "53856c96-fe4d-459e-a9dc-c339b1bc3310", - name: "SAMPLE_08", - columns: [{ - id: "1", - name: "CODE", - datatype: "string" - }, { - id: "2", - name: "DESCRIPTION", - datatype: "string" - }, { - id: "3", - name: "TOTAL_EMP", - datatype: "int" - }, { - id: "4", - name: "SALARY", - datatype: "int" - }], - database: "DEFAULT", - cardinality: {}, - last_modified: 0, - exd: { - minFileSize: "46069", - totalNumberFiles: "1", - location: "hdfs://sandbox.hortonworks.com:8020/apps/hive/warehouse/sample_08", - lastAccessTime: "1398176495945", - lastUpdateTime: "1398176495981", - columns: "struct columns { string code, string description, i32 total_emp, i32 salary}", - partitionColumns: "", - EXD_STATUS: "true", - maxFileSize: "46069", - inputformat: "org.apache.hadoop.mapred.TextInputFormat", - partitioned: "false", - tableName: "sample_08", - owner: "hue", - totalFileSize: "46069", - outputformat: "org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat" - } - } -] -``` - -## Load Hive Tables -`POST /tables/{tables}/{project}` - -#### Request Parameters -* tables - `required` `string` table names you want to load from hive, separated with comma. -* project - `required` `String` the project which the tables will be loaded into. - -#### Response Sample -``` -{ - "result.loaded": ["DEFAULT.SAMPLE_07"], - "result.unloaded": ["sapmle_08"] -} -``` - -*** - -## Wipe cache -`GET /cache/{type}/{name}/{action}` - -#### Path variable -* type - `required` `string` 'METADATA' or 'CUBE' -* name - `required` `string` Cache key, e.g the cube name. -* action - `required` `string` 'create', 'update' or 'drop' - http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/howto/howto_use_restapi_in_js.md ---------------------------------------------------------------------- diff --git a/website/_docs/howto/howto_use_restapi_in_js.md b/website/_docs/howto/howto_use_restapi_in_js.md deleted file mode 100644 index f41b3c5..0000000 --- a/website/_docs/howto/howto_use_restapi_in_js.md +++ /dev/null @@ -1,48 +0,0 @@ ---- -layout: docs -title: How to Use Restful API in Javascript -categories: howto -permalink: /docs/howto/howto_use_restapi_in_js.html -version: v1.2 -since: v0.7.1 ---- -Kylin security is based on basic access authorization, if you want to use API in your javascript, you need to add authorization info in http headers. - -## Example on Query API. -``` -$.ajaxSetup({ - headers: { 'Authorization': "Basic eWFu**********X***ZA==", 'Content-Type': 'application/json;charset=utf-8' } // use your own authorization code here - }); - var request = $.ajax({ - url: "http://hostname/kylin/api/query";, - type: "POST", - data: '{"sql":"select count(*) from SUMMARY;","offset":0,"limit":50000,"acceptPartial":true,"project":"test"}', - dataType: "json" - }); - request.done(function( msg ) { - alert(msg); - }); - request.fail(function( jqXHR, textStatus ) { - alert( "Request failed: " + textStatus ); - }); - -``` - -## Keypoints -1. add basic access authorization info in http headers. -2. use right ajax type and data synax. - -## Basic access authorization -For what is basic access authorization, refer to [Wikipedia Page](http://en.wikipedia.org/wiki/Basic_access_authentication). -How to generate your authorization code (download and import "jquery.base64.js" from [https://github.com/yckart/jquery.base64.js](https://github.com/yckart/jquery.base64.js)). - -``` -var authorizationCode = $.base64('encode', 'NT_USERNAME' + ":" + 'NT_PASSWORD'); - -$.ajaxSetup({ - headers: { - 'Authorization': "Basic " + authorizationCode, - 'Content-Type': 'application/json;charset=utf-8' - } -}); -``` http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/index.cn.md ---------------------------------------------------------------------- diff --git a/website/_docs/index.cn.md b/website/_docs/index.cn.md deleted file mode 100644 index 053524c..0000000 --- a/website/_docs/index.cn.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -layout: docs-cn -title: æ¦è¿° -categories: docs -permalink: /cn/docs/index.html ---- - -欢è¿æ¥å° Apache Kylin⢠------------- -> Extreme OLAP Engine for Big Data - -Apache Kylinâ¢æ¯ä¸ä¸ªå¼æºçåå¸å¼åæå¼æï¼æä¾Hadoopä¹ä¸çSQLæ¥è¯¢æ¥å£åå¤ç»´åæï¼OLAPï¼è½å以æ¯æè¶ å¤§è§æ¨¡æ°æ®ï¼æåç±eBay Inc.å¼å并贡ç®è³å¼æºç¤¾åºã - -å®è£ ------------- -请åèå®è£ æ档以å®è£ Apache Kylin: [å®è£ å导](/cn/docs/install/) - - - - - - http://git-wip-us.apache.org/repos/asf/kylin/blob/0a74e9cb/website/_docs/index.md ---------------------------------------------------------------------- diff --git a/website/_docs/index.md b/website/_docs/index.md deleted file mode 100644 index 89b5024..0000000 --- a/website/_docs/index.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -layout: docs -title: Overview -categories: docs -permalink: /docs/index.html ---- - -Welcome to Apache Kylin⢠------------- -> Extreme OLAP Engine for Big Data - -Apache Kylin⢠is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets, original contributed from eBay Inc. - -Installation & Setup ------------- - -Please follow installation & tutorial in the navigation panel. - -Advanced Topics -------- - -#### Connectivity - -1. [How to use Kylin remote JDBC driver](howto/howto_jdbc.html) -2. [SQL reference](http://calcite.apache.org/) - ---- - -#### REST APIs - -1. [Kylin Restful API list](howto/howto_use_restapi.html) -2. [Build cube with Restful API](howto/howto_build_cube_with_restapi.html) -3. [How to consume Kylin REST API in javascript](howto/howto_use_restapi_in_js.html) - ---- - -#### Operations - -1. [Backup/restore Kylin metadata store](howto/howto_backup_metadata.html) -2. [Cleanup storage (HDFS & HBase tables)](howto/howto_cleanup_storage.html) -3. [Advanced env configurations](install/advance_settings.html) -3. [How to upgrade](howto/howto_upgrade.html) - ---- - -#### Technical Details - -1. [New meta data model structure](/development/new_metadata.html) - - - -
