Set Up System Cube

Available since Apache Kylin v2.3.0

Main content of this section:

• [What is System Cube](#What is System Cube)
• [How to Set Up System Cube](#How to Set Up System Cube)
• [Automatically create System Cube](#Automatically create System Cube)
• [Details of System Cube](#Details of System Cube)

What is System Cube

For better supporting self-monitoring, a set of system Cubes are created under the system project, called "KYLIN_SYSTEM". Currently, there are five Cubes. Three are for query metrics, "METRICS_QUERY", "METRICS_QUERY_CUBE", "METRICS_QUERY_RPC". And the other two are for job metrics, "METRICS_JOB", "METRICS_JOB_EXCEPTION".

How to Set Up System Cube

In this section, we will introduce the method of manually enabling the system cube. If you want to automatically enable the system cube through shell scripts, please refer to [Automatically Create System Cube](#Automatically Create System Cube).

1. Prepare

Create a configuration file SCSinkTools.json in KYLIN_HOME directory.

For example:

[
    {
       "sink": "hive",
       "storage_type": 2,
       "cube_desc_override_properties": {
         "kylin.cube.algorithm": "INMEM",
         "kylin.cube.max-building-segments": "1"
       }
    }
]

2. Generate Metadata

Run the following command in KYLIN_HOME folder to generate related metadata:

./bin/kylin.sh org.apache.kylin.tool.metrics.systemcube.SCCreator \
-inputConfig SCSinkTools.json \
-output <output_folder>

By this command, the related metadata will be generated and its location is under the directory <output_folder>. The details are as follows, system_cube is our <output_folder>:

3. Set Up Datasource

Running the following command to create source hive tables:

hive -f <output_folder>/create_hive_tables_for_system_cubes.sql

By this command, the related hive table will be created.

4. Upload Metadata for System Cubes

Then we need to upload metadata to hbase by the following command:

./bin/metastore.sh restore <output_folder>

5. Reload Metadata

Finally, we need to reload metadata in Kylin web UI.

Then, a set of system Cubes will be created under the system project, called "KYLIN_SYSTEM".

6. System Cube build

When the system Cube is created, we need to build the Cube regularly.

Step 1. Create a shell script that builds the system Cube by calling org.apache.kylin.tool.job.CubeBuildingCLI

For example:

#!/bin/bash

dir=$(dirname ${0})
export KYLIN_HOME=${dir}/../

CUBE=$1
INTERVAL=$2
DELAY=$3
CURRENT_TIME_IN_SECOND=`date +%s`
CURRENT_TIME=$((CURRENT_TIME_IN_SECOND * 1000))
END_TIME=$((CURRENT_TIME-DELAY))
END=$((END_TIME - END_TIME%INTERVAL))

ID="$END"
echo "building for ${CUBE}_${ID}" >> ${KYLIN_HOME}/logs/build_trace.log
sh ${KYLIN_HOME}/bin/kylin.sh org.apache.kylin.tool.job.CubeBuildingCLI --cube ${CUBE} --endTime ${END} > ${KYLIN_HOME}/logs/system_cube_${CUBE}_${END}.log 2>&1 &

Step 2. Then run this shell script regularly. For example, add a cron job as follows:

0 */2 * * * sh ${KYLIN_HOME}/bin/system_cube_build.sh KYLIN_HIVE_METRICS_QUERY_QA 3600000 1200000

20 */2 * * * sh ${KYLIN_HOME}/bin/system_cube_build.sh KYLIN_HIVE_METRICS_QUERY_CUBE_QA 3600000 1200000

40 */4 * * * sh ${KYLIN_HOME}/bin/system_cube_build.sh KYLIN_HIVE_METRICS_QUERY_RPC_QA 3600000 1200000

30 */4 * * * sh ${KYLIN_HOME}/bin/system_cube_build.sh KYLIN_HIVE_METRICS_JOB_QA 3600000 1200000

50 */12 * * * sh ${KYLIN_HOME}/bin/system_cube_build.sh KYLIN_HIVE_METRICS_JOB_EXCEPTION_QA 3600000 12000

Automatically create System Cube

Kylin provides system-cube.sh from v2.6.0, users can automatically create system cube by executing this script.

• Create System Cube：sh bin/system-cube.sh setup

• Build System Cube：sh bin/system-cube.sh build

• Add crontab job for System Cube：sh bin/system-cube.sh cron

Note: System-cube.sh will call ${KYLIN_HOME}/bin/build-incremental-cube.sh to submit build job. In build-incremental-cube.sh, the user name and password of ADMIN:KYLIN are used by default as authentication to call kylin's rebuild API. If you have changed the password of the ADMIN user or want to use a user other than ADMIN to submit the build job, please find the ADMIN:KYLIN in build-incremental-cube.sh and replace it with the correct user name and password.

Details of System Cube

Common Dimension

For all of these Cube, admins can query at four time granularities. From higher level to lower, it's as follows:

KYEAR_BEGIN_DATE	year
KMONTH_BEGIN_DATE	month
KWEEK_BEGIN_DATE	week
KDAY_DATE	date

METRICS_QUERY

This Cube is for collecting query metrics at the highest level. The details are as follows:

Dimension
HOST	the host of server for query engine
KUSER	the user who executes the query
PROJECT	the project where the query executes
REALIZATION	the cube which the query hits. In Kylin, there are two OLAP realizations: Cube, or Hybrid of Cubes
REALIZATION_TYPE
QUERY_TYPE	users can query on different data sources: CACHE, OLAP, LOOKUP_TABLE, HIVE
EXCEPTION	when doing query, exceptions may happen. It's for classifying different exception types

Measure
COUNT
MIN, MAX, SUM, PERCENTILE_APPROX of QUERY_TIME_COST	the time cost for the whole query
MAX, SUM of CALCITE_SIZE_RETURN	the row count of the result Calcite returns
MAX, SUM of STORAGE_SIZE_RETURN	the row count of the input to Calcite
MAX, SUM of CALCITE_SIZE_AGGREGATE_FILTER	the row count of Calcite aggregates and filters
COUNT DISTINCT of QUERY_HASH_CODE	the number of different queries

METRICS_QUERY_RPC

This Cube is for collecting query metrics at the lowest level. For a query, the related aggregation and filter can be pushed down to each rpc target server. The robustness of rpc target servers is the foundation for better serving queries. The details are as follows:

Dimension
HOST	the host of server for query engine
PROJECT	the project where the query executes
REALIZATION	the cube which the query hits
RPC_SERVER	the rpc related target server
EXCEPTION	the exception of a rpc call. If no exception，"NULL" is used

Measure
COUNT
MAX, SUM, PERCENTILE_APPROX of CALL_TIME	the time cost of a rpc all
MAX, SUM of COUNT_SKIP	based on fuzzy filters or else, a few rows will be skiped. This indicates the skipped row count
MAX, SUM of SIZE_SCAN	the row count actually scanned
MAX, SUM of SIZE_RETURN	the row count actually returned
MAX, SUM of SIZE_AGGREGATE	the row count actually aggregated
MAX, SUM of SIZE_AGGREGATE_FILTER	the row count actually aggregated and filtered, = SIZE_SCAN - SIZE_RETURN

METRICS_QUERY_CUBE

This Cube is for collecting query metrics at the Cube level. The most important are cuboids related, which will serve for Cube planner. The details are as follows:

Dimension
CUBE_NAME
SEGMENT_NAME
CUBOID_SOURCE	source cuboid parsed based on query and Cube design
CUBOID_TARGET	target cuboid already precalculated and served for source cuboid
IF_MATCH	whether source cuboid and target cuboid are equal
IF_SUCCESS	whether a query on this Cube is successful or not

Measure
COUNT
WEIGHT_PER_HIT
MAX, SUM of STORAGE_CALL_COUNT	the number of rpc calls for a query hit on this Cube
MAX, SUM of STORAGE_CALL_TIME_SUM	sum of time cost for the rpc calls of a query
MAX, SUM of STORAGE_CALL_TIME_MAX	max of time cost among the rpc calls of a query
MAX, SUM of STORAGE_COUNT_SKIP	the sum of row count skipped for the related rpc calls
MAX, SUM of STORAGE_COUNT_SCAN	the sum of row count scanned for the related rpc calls
MAX, SUM of STORAGE_COUNT_RETURN	the sum of row count returned for the related rpc calls
MAX, SUM of STORAGE_COUNT_AGGREGATE	the sum of row count aggregated for the related rpc calls
MAX, SUM of STORAGE_COUNT_AGGREGATE_FILTER	the sum of row count aggregated and filtered for the related rpc calls, = STORAGE_SIZE_SCAN - STORAGE_SIZE_RETURN

METRICS_JOB

In Kylin, there are mainly three types of job:

• "BUILD", for building Cube segments from HIVE.
• "MERGE", for merging Cube segments in HBASE.
• "OPTIMIZE", for dynamically adjusting the precalculated cuboid tree base on the base cuboid in HBASE.

This Cube is for collecting job metrics. The details are as follows:

Dimension
HOST	the host of server for job engine
KUSER	the user who run the job
PROJECT	the project where the job runs
CUBE_NAME	the cube with which the job is related
JOB_TYPE	build, merge or optimize
CUBING_TYPE	in kylin, there are two cubing algorithms, Layered & Fast(InMemory)

Measure
COUNT
MIN, MAX, SUM, PERCENTILE_APPROX of DURATION	the duration from a job start to finish
MIN, MAX, SUM of TABLE_SIZE	the size of data source in bytes
MIN, MAX, SUM of CUBE_SIZE	the size of created Cube segment in bytes
MIN, MAX, SUM of PER_BYTES_TIME_COST	= DURATION / TABLE_SIZE
MIN, MAX, SUM of WAIT_RESOURCE_TIME	a job may includes serveral MR(map reduce) jobs. Those MR jobs may wait because of lack of Hadoop resources.
MAX, SUM of step_duration_distinct_columns
MAX, SUM of step_duration_dictionary
MAX, SUM of step_duration_inmem_cubing
MAX, SUM of step_duration_hfile_convert

METRICS_JOB_EXCEPTION

This Cube is for collecting job exception metrics. The details are as follows:

Dimension
HOST	the host of server for job engine
KUSER	the user who run a job
PROJECT	the project where the job runs
CUBE_NAME	the cube with which the job is related
JOB_TYPE	build, merge or optimize
CUBING_TYPE	in kylin, there are two cubing algorithms, Layered & Fast(InMemory)
EXCEPTION	when running a job, exceptions may happen. It's for classifying different exception types

Measure
COUNT