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Copy file name to clipboardExpand all lines: docs/GettingStarted/Benchmarking.md
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@@ -9,25 +9,53 @@ LingoDB supports common OLAP benchmarks such as TPC-H, TPC-DS, JOB and SSB.
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* Do *not* manually create Apache Arrow files, but instead use the `sql` command to define tables and import data. If you miss relevant metadata information (e.g., primary keys), LingoDB will not be able to apply many optimizations and performance will be suboptimal.
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* Use a release build of LingoDB for benchmarking. Debug builds are significantly slower.
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## Data Generation
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For some benchmarks, the LingoDB repository contains scripts to generate data and load them:
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## Manual steps
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### Data Generation
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For some benchmarks, the LingoDB repository contains scripts to generate data:
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```sh
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# LINGODB_BINARY_DIR is the directory containing at least the `sql` binary
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# OUTPUT_DIR is the directory where the database should be stored
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# DATA_DIR is the directory where the csv files should be stored
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# SF is the scale factor, e.g., 1 for 1GB, 10 for 10GB, etc.
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# Generate TPC-H database
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bash tools/generate/tpch.sh LINGODB_BINARY_DIR OUTPUT_DIR SF
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bash tools/generate/tpch.sh DATA_DIR SF
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# Generate TPC-DS database
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bash tools/generate/tpcds.sh LINGODB_BINARY_DIR OUTPUT_DIR SF
There is a convenience Makefile target for both generating and loading a set of datasets.
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```sh
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make resources/data/[NAME]-[SF]/.stamp
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# For example
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make resources/data/tpch-1/.stamp
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make resources/data/tpch-10/.stamp
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make resources/data/tpcds-1/.stamp
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make resources/data/ssb-1/.stamp
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make resources/data/job/.stamp
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```
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This will prepare the requested dataset (with scale factor) in the `resources/data/[NAME]-[SF]` directory
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## Running LingoDB
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Afterward, queries can be for examle run with the `sql` command that also reports execution times when the `LINGODB_SQL_REPORT_TIMES` environment variable is set:
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