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4. Create the `/flash23` folder. Copy `bpc_t93586_s2_synthetic_5GB`
@@ -408,7 +408,7 @@ Note that SparkBench mini does not require the high I/O throughput
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5. Run `spark_standalone_remote_mini` job on a real machine.
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This will create data in `/flash23/warehouse`
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and `<benchpressPath>/benchmarks/spark_standalone/spark-2.4.5-bin-hadoop2.7/metastore_db`.
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and `<benchpressPath>/benchmarks/spark_standalone/spark-4.0.0-bin-hadoop3/metastore_db`.
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Create a backup of these two folders. By default, this job uses the 5GB dataset.
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If you want to use the 1GB dataset, run the job with specifying the
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`dataset_name` parameter like this:
@@ -425,12 +425,12 @@ with the same commands and options.
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Building database takes a considerable amount of time, so it's advisable to consider
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using the same set of storage nodes and NVMe drives when running SparkBench on another
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compute node server. If you choose to reuse the Spark database in `/flash23/warehouse`,
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please also make sure to copy the folder `metastore_db` under `benchmarks/spark_standalone/spark-2.4.5-bin-hadoop2.7`
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please also make sure to copy the folder `metastore_db` under `benchmarks/spark_standalone/spark-4.0.0-bin-hadoop3`
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to the new machine's same location, for example:
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```
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# Under the DCPerf folder
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rsync -a benchmarks/spark_standalone/spark-2.4.5-bin-hadoop2.7/metastore_db root@<target-hostname>:~/DCPerf/benchmarks/spark_standalone/spark-2.4.5-bin-hadoop2.7/
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rsync -a benchmarks/spark_standalone/spark-4.0.0-bin-hadoop3/metastore_db root@<target-hostname>:~/DCPerf/benchmarks/spark_standalone/spark-4.0.0-bin-hadoop3/
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```
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If you do not copy over the `metastore_db` folder, you will see errors like the following
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