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https://github.com/dharmeshkakadia/tpcds-hdinsight

TPCDS benchmark for various engines
https://github.com/dharmeshkakadia/tpcds-hdinsight

benchmarking hive llap presto spark tpcds

Last synced: 3 months ago
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TPCDS benchmark for various engines

Host: GitHub
URL: https://github.com/dharmeshkakadia/tpcds-hdinsight
Owner: dharmeshkakadia
Created: 2016-10-06T16:18:27.000Z (about 8 years ago)
Default Branch: master
Last Pushed: 2018-01-16T21:15:32.000Z (almost 7 years ago)
Last Synced: 2024-05-19T03:22:57.049Z (6 months ago)
Topics: benchmarking, hive, llap, presto, spark, tpcds
Language: Python
Homepage:
Size: 443 KB
Stars: 7
Watchers: 4
Forks: 23
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesome-hive - TPCDS

README

# tpcds-hdinsight
TPCDS benchmark for Azure HDInsight

## How to use with Hive CLI

1. Clone this repo.

```shell
git clone https://github.com/dharmeshkakadia/tpcds-hdinsight/ && cd tpcds-hdinsight
```
2. Run TPCDSDataGen.hql with settings.hql file and set the required config variables.
```shell
hive -i settings.hql -f TPCDSDataGen.hql -hiveconf SCALE=10 -hiveconf PARTS=10 -hiveconf LOCATION=/HiveTPCDS/ -hiveconf TPCHBIN=resources
```
Here,

`SCALE` is a scale factor for TPCDS. Scale factor 10 roughly generates 10 GB data, Scale factor 1000 generates 1 TB of data and so on.

`PARTS` is a number of task to use for datagen (parrellelization). This should be set to the same value as `SCALE`.

`LOCATION` is the directory where the data will be stored on HDFS.

`TPCHBIN` is where the resources are found. You can specify specific settings in settings.hql file.

3. Now you can create tables on the generated data.
```shell
hive -i settings.hql -f ddl/createAllExternalTables.hql -hiveconf LOCATION=/HiveTPCDS/ -hiveconf DBNAME=tpcds
```
Generate ORC tables and analyze
```shell
hive -i settings.hql -f ddl/createAllORCTables.hql -hiveconf ORCDBNAME=tpcds_orc -hiveconf SOURCE=tpcds
hive -i settings.hql -f ddl/analyze.hql -hiveconf ORCDBNAME=tpcds_orc
```

4. Run the queries !
```shell
hive -database tpcds_orc -i settings.hql -f queries/query12.sql
```

## How to use with Beeline CLI

1. Clone this repo.

```shell
git clone https://github.com/dharmeshkakadia/tpcds-hdinsight && cd tpcds-hdinsight
```

2. Upload the resources to DFS.
```shell
hdfs dfs -copyFromLocal resources /tmp
```

3. Run TPCDSDataGen.hql with settings.hql file and set the required config variables.
```shell
beeline -u "jdbc:hive2://`hostname -f`:10001/;transportMode=http" -n "" -p "" -i settings.hql -f TPCDSDataGen.hql -hiveconf SCALE=10 -hiveconf PARTS=10 -hiveconf LOCATION=/HiveTPCDS/ -hiveconf TPCHBIN=`grep -A 1 "fs.defaultFS" /etc/hadoop/conf/core-site.xml | grep -o "wasb[^<]*"`/tmp/resources
```
Here,

`SCALE` is a scale factor for TPCDS. Scale factor 10 roughly generates 10 GB data, Scale factor 1000 generates 1 TB of data and so on.

`PARTS` is a number of task to use for datagen (parrellelization). This should be set to the same value as `SCALE`.

`LOCATION` is the directory where the data will be stored on HDFS.

`TPCHBIN` is where the resources are found. You can specify specific settings in settings.hql file.

4. Now you can create tables on the generated data.
```shell
beeline -u "jdbc:hive2://`hostname -f`:10001/;transportMode=http" -n "" -p "" -i settings.hql -f ddl/createAllExternalTables.hql -hiveconf LOCATION=/HiveTPCDS/ -hiveconf DBNAME=tpcds
```
Generate ORC tables and analyze
```shell
beeline -u "jdbc:hive2://`hostname -f`:10001/;transportMode=http" -n "" -p "" -i settings.hql -f ddl/createAllORCTables.hql -hiveconf ORCDBNAME=tpcds_orc -hiveconf SOURCE=tpcds
beeline -u "jdbc:hive2://`hostname -f`:10001/;transportMode=http" -n "" -p "" -i settings.hql -f ddl/analyze.hql -hiveconf ORCDBNAME=tpcds_orc
```

5. Run the queries !
```shell
beeline -u "jdbc:hive2://`hostname -f`:10001/tpcds_orc;transportMode=http" -n "" -p "" -i settings.hql -f queries/query12.sql
```

If you want to run all the queries 10 times and measure the times it takes, you can use the following command:

for f in queries/*.sql; do for i in {1..10} ; do STARTTIME="`date +%s`"; beeline -u "jdbc:hive2://`hostname -f`:10001/tpcds_orc;transportMode=http" -i settings.hql -f $f > $f.run_$i.out 2>&1 ; SUCCESS=$? ; ENDTIME="`date +%s`"; echo "$f,$i,$SUCCESS,$STARTTIME,$ENDTIME,$(($ENDTIME-$STARTTIME))" >> times_orc.csv; done; done;

## FAQ

#### Does it work with scale factor 1?

No. The parrellel data generation assumes that scale > 1. If you are just starting out, I would suggest you start with 10 and then move to standard higher scale factors (100, 1000, 10000,..)

#### Do I have to specify PARTS=SCALE ?

Yes.

#### How do I avoid my session getting killed due to network errors while long running benchmark?

Use byobu. Type byobu which will start a new session and then run the command. It will be there when you come back even if your network connection is broken.

#### How do I generate partitioned text tables ?
After generating raw data(step 3a), use the following command:

```
hive -i settings.hql -f ddl/createAllTextTables.hql -hiveconf TEXTDBNAME=tpcds_text -hiveconf SOURCE=tpcds
```

This will generate tpcds_text database with all the tables in text format.

#### How do I generate Parquet data?

After generating raw data(step 3a), use the following command:

```
hive -i settings.hql -f ddl/createAllParquetTables.hql -hiveconf PARQUETDBNAME=tpcds_pqt -hiveconf SOURCE=tpcds
```

This will generate tpcds_pqt database with all the tables in parquet format.

#### How do I run the queries with Spark?

Spark thriftserver listens on 10002 instead of hive thrift server listening on 10001. So replace the connection url appropriately. For example, running the all the queries 10 times with Spark,

```
for f in queries/*.sql; do for i in {1..10} ; do STARTTIME="`date +%s`"; beeline -u "jdbc:hive2://`hostname -f`:10002/tpcds_orc;transportMode=http" -i sparksettings.hql -f $f > $f.run_$i.out 2>&1 ; SUCCESS=$? ; ENDTIME="`date +%s`"; echo "$f,$i,$SUCCESS,$STARTTIME,$ENDTIME,$(($ENDTIME-$STARTTIME))" >> times_orc.csv; done; done;
```

#### How do I run the queries with [Presto](https://github.com/dharmeshkakadia/presto-hdinsight)?

```
presto --schema tpcds_orc -f queries/query12.sql
```

You can run all the queries 10 times with presto with the following command,

```
for f in queries/*.sql; do for i in {1..10} ; do STARTTIME="`date +%s`"; presto --schema tpcds_orc -f $f > $f.run_$i.out 2>&1 ; SUCCESS=$? ; ENDTIME="`date +%s`"; echo "$f,$i,$SUCCESS,$STARTTIME,$ENDTIME,$(($ENDTIME-$STARTTIME))" >> times_orc.csv; done; done;
```