kyligence/kylin-tpch

Run TPCH Benchmark on Apache Kylin

Run TPCH Benchmark on Apache Kylin

This is a derived work from https://github.com/hortonworks/hive-testbench

This benchmark requires Apache Kylin 2.0 or later. The goal at this stage is functionality rather than performance. Enabling all TPC-H queries is a new milestone of Apache Kylin's SQL capability.

1. Prepare TPCH test data

   • Follow the steps to setup test data in Hive.

     • For example, generate a minimal data with ScaleFactor=2

       git clone https://github.com/hortonworks/hive-testbench.git
       cd hive-testbench/
       ./tpch-build.sh
       ./tpch-setup.sh 2

     • Verify the TPCH tables existed in hive

       echo "use tpch_flat_orc_2; show tables;" | hive

2. Import Kylin model

   • Get this repository.

     git clone https://github.com/Kyligence/kylin-tpch.git

   • Make sure there is a running Kylin instance.

     export KYLIN_HOME=...

   • Run the setup-kylin-model.sh with the SAME SCALE FACTOR to generate hive data, for example:

     cd kylin-tpch
     ./setup-kylin-model.sh 2

   • The script also creates a few simple views on top of the original TPCH tables to allow Kylin pre-calculate some complex measures. The resulted E-R model topology is identical to the original TPCH model.

3. Build Kylin cube

   • In the Kylin web, click "System->Reload Metadata" to refresh the newly imported TPCH model.

   • Select project "tpch" and find the 4 cubes below.

     • lineitem_cube
     • partsupp_cube
     • customer_cube
     • customer_vorder_cube

   • lineitem_cube and partsupp_cube together covers 20 TPCH queries out of the total 22. Besides, customer_vorder_cube is for query 13 and customer_cube is for query 22.

   • Build all cubes within the year range from 1992 to 1998.

   • Verify by counting v_lineitem in Kylin. The row count must match the Hive table lineitem.

       select count(*) from v_lineitem

4. Run TPCH queries

   • Find the TPCH queries under the query folder. Run them via the Kylin web, or Kylin REST API / JDBC / ODBC.
   • The queries are slightly adapted to leverage the views created in Step 2 and work around limitations like KYLIN-2341.
   • The original queries (from hive-testbench) can be found at queries/original-queries. The original and the adapted queries should give the same result in Hive and Kylin respectively.
   • There is a tool to run all queries one by one. It can be found at tools/query-tool.py. For Kylin, it prints query duration. For Hive, it prints query duration and query results.
     • Install dependencies

     pip install requests

     • Run kylin query

     python tools/query-tool.py -s http://127.0.0.1:7070/kylin -u ADMIN -p KYLIN -d queries -o tpch -r 3 -t kylin

     • Run hive query

     python tools/query-tool.py -t hive -c 10

Experiment Result

This benchmark was tested in two environments. One enviroment is HDP sandbox, another is a 4-node CDH cluster. Each query was run multiple times and the final stable response time get recorded for both Kylin and Hive.

And again, the goal at this stage is SQL capability rather than performance. No special performance tuning was done to both Kylin and Hive.

Btw, KyBot is a great tool if you want to analyze Kylin cube and query performance. Upload a Kylin diagnosis pack to this online service and it can show you why a query runs slow or why a cube takes long to build. KyBot is free for trial.

Cube Build Stats	HDP Sandbox (SF=2)	CDH 4-Node Cluster (SF=10)
lineitem_cube	20.1 GB, 125 mins * 7	85.8 GB, 54.69 mins * 7
partsupp_cube	1.6 GB, 27.02 mins	8.3 GB, 18.48 mins
customer_vorder_cube	283 MB, 16.88 mins	1.39 GB, 13.7 mins
customer_cube	7 MB, 3.95 mins	36.98 MB, 3.97 mins

(The lineitem_cube was built incrementally. Each increment builds 1 years data.)

Query Response Time	Kylin on HDP (SF=2)	Hive+Tez on HDP (SF=2)	Kylin on CDH (SF=10)	Hive+MR on CDH (SF=10)
query01	0.43 sec	13.79 sec	0.45 sec	43.45 sec
query02	8.87 sec	17.04 sec	8.77 sec	3570 sec
query03	5.2 sec	16.32 sec	4.84 sec	137.4 sec
query04	0.71 sec	31.23 sec	2.69 sec	90.99 sec
query05	0.33 sec	23.68 sec	0.47 sec	162.79 sec
query06	0.34 sec	8.91 sec	0.39 sec	23.29 sec
query07	0.17 sec	35.23 sec	0.24 sec	245.52 sec
query08	7.42 sec	19.4 sec	8.82 sec	2248.5 sec
query09	10.4 sec	30.75 sec	34.8 sec	19869 sec
query10	21.1 sec	19.03 sec	11.91 sec	120.61 sec
query11	3.42 sec	15.87 sec	2.09 sec	123.3 sec
query12	7.66 sec	12.64 sec	5.73 sec	96.34 sec
query13	20.44 sec	24.61 sec	104.77 sec	135.64 sec
query14	1.1 sec	4.54 sec	1.56 sec	54.42 sec
query15	1.96 sec	13.06 sec	17.02 sec	124.29 sec
query16	3.69 sec	17.36 sec	10.16 sec	144.34 sec
query17	5.49 sec	25.61 sec	10.19 sec	183.56 sec
query18	22.43 sec	65.32 sec	74.88 sec	231.9 sec
query19	21.9 sec	22.5 sec	138.81 sec	186.87 sec
query20	6.46 sec	16.21 sec	42.02 sec	229.01 sec
query21	58.6 sec	84.6 sec	136.3 sec	276.59 sec
query22	23.21 sec	21.79 sec	102.81 sec	154.48 sec

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HDP Sandbox Configuration

• HDP sandbox (version 2.4.0.0-169) VM on VirtualBox
• 4 vcores, 10 GB memory, ScaleFactor=2
• Because of the limited resource, in depth tuning is required to run through the benchmark
• More resource to Tez
  • hive.tez.java.opts -Xmx=512m
  • tez container heap 1G
• More resource to HBase
  • hbase region server heap 4G
• Kylin be easy on HBase
  • kylin.storage.hbase.coprocessor-timeout-seconds=3600
  • kylin.storage.hbase.max-hconnection-threads=5
  • kylin.storage.hbase.core-hconnection-threads=5
  • kylin.storage.hbase.coprocessor-mem-gb=30
  • kylin.storage.partition.aggr-spill-enabled=false
  • kylin.storage.hbase.compression-codec=snappy
  • kylin.query.scan-threshold=1000000000
• YARN config
  • 5 GB memory, 3 vcores
• MR config
  • mapreduce.job.reduce.slowstart.completedmaps=1
  • enable compression org.apache.hadoop.io.compress.SnappyCodec

CDH 4-Node Cluster Configuration

• Cloudera 5.8.3
• 1 Master (32 vcores, 192 GB memory), 3 Slave (32 vcores, 96 GB memory)
• ScaleFactor = 10
• YARN config
  • 200 GB memory, 128 vcores
• Kylin config
  • kylin.storage.hbase.compression-codec=snappy
  • kylin.query.scan-threshold=1000000000
  • kylin.storage.hbase.coprocessor-mem-gb=30
  • set kylin JVM -Xms4g -Xmx8g

kylin-tpch's Issues

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Table of Content

• Cluster Resource
• Kylin Configuration
• Response Time of TPC-H 22 Query

Cluster Resource

Key	Value
Node Memory	64GB
Node Core	16
Node (Instance Type)	m5.4xlarge
Node Disk	400GB * 2, SSD
Node Num	4 Worker + 1 Master
EMR Version	emr-5.31.0
Kylin Version	4.0.0
Yarn Memory	204.80 GB
Yarn Core	52

Kylin Configuration

kylin.properties.txt

Response Time

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Final Report in Chinese

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Table of Content

• Cluster Resource
• Kylin Configuration
• Response Time of TPC-H 22 Query
• Screenshots of Kylin
• Metrics of Cube Building
• Metrics of Query Load Test, thread = 1
• Metrics of Query Load Test, thread = 4

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• Create EMR cluster
• Prepare Data
• Kylin Configuration
• Deploy Kylin
• Cubing
• Check Storage
• Response Time and QPS
• Reference

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• Summary
• Data Load Duration
• Storage Size
• Response Time
• Kylin Configuration
• Screenshots of Kylin
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• Summary
• Data Load Duration
• Storage Size
• Response Time
• Kylin Configuration
• Comment
• Screenshots of Kylin
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