Adding instructions for mini jobs to README files (#160)

Summary:
Pull Request resolved: #160

This diff adss instructions for running Spark, Django, and Mediawiki mini job

Differential Revision: D78453571

Author: marziehlenjaniMeta

packages/django_workload/README.md

@@ -78,6 +78,15 @@ If running on ARM platform, please use the job `django_workload_arm`:
 ./benchpress_cli.py run django_workload_arm -r standalone
 ```
 
+## Running  Django_workload_arm_mini and Django_workload_mini
+
+These jobs are shrunken versions of the Django_workload_arm and Django_workload jobs. They reuse the dataset generated by the full Django_workload_arm and Django_workload jobs and have a shorter runtime because they run only one iteration with the number of repetitions set to 100.
+To run these jobs follow the following steps:
+
+1. Run either Django_workload_arm or Django_workload (not the mini versions).
+This will generate the necessary dataset and store it in /data/cassandra/data. We recommend to keep a backup of this data to avoid data loss.
+2. Run Django_workload_arm_mini or Django_workload_mini on the emulator or real system. These mini jobs will reuse the dataset generated in the previous step.
+
 ## Reporting
 
 Once the benchmark finishes on the django benchmarking machine, benchpress will

packages/mediawiki/README.md

@@ -133,6 +133,22 @@ you can run the following:
 ./benchpress_cli.py run oss_performance_mediawiki_mlp -i '{"scale_out": 3}'
 ```
 
+## Running oss_performance_mediawiki_mini
+
+This job is a shrunken version of oss_performance_mediawiki job. To run it, you can simply execute the following command:
+
+```bash
+./benchpress run oss_performance_mediawiki_mini
+```
+
+The oss_performance_mediawiki_mini job introduces additional options that allow you to:(i) reuse the temporary folder between runs, (ii) adjust the load generator seed, (iii) configure load generation durations for warmup, (iv) set the interval for JIT retranslation, and (v) specify a deterministic number of warmup iterations.
+
+To reuse the temporary directory, run the oss_performance_mediawiki_mini job once and locate the path to the temp folder in the log output. Then, use that path for subsequent runs by specifying the temp_dir option. For example:
+
+```bash
+./benchpress run oss_performance_mediawiki_mini -i '{"temp_dir":"/tmp/hhvm-nginxtIdDIr"}'
+```
+
 ### Reporting
 
 After the benchmark finishes, benchpress will report the benchmark results in the

packages/spark_standalone/README.md

@@ -372,6 +372,45 @@ faster with the already built database. Therefore, it's normal that the first ru
 takes much longer time than what the benchmark eventually reports. The database will
 be stored in `/flash23/warehouse`.
 
+### Running SparkBench Mini
+
+SparkBench Mini is a shrunken version of SparkBench that aims to reduce execution time to
+less than 30s and can potentially be used for emulations. To run the mini version of
+SparkBench, please follow the following steps:
+
+1. Download one of the down-sampled version of the dataset:
+    - 5GB variant `bpc_t93586_s2_synthetic_5GB`
+    - 1GB variant `bpc_t93586_s2_synthetic_1GB`
+    - You can download these datasets from Github by following the instructions in
+    [Download dataset](#download-dataset) or from the Manifold bucket
+    [benchpress_artifacts/tree/spark_standalone/dataset](https://www.internalfb.com/manifold/explorer/benchpress_artifacts/tree/spark_standalone/dataset).
+
+2. Make sure that you get the latest version of DCPerf. If you use DCPerf from Github,
+please check out the latest commit in `v2-beta` branch. If you use the DCPerf fbpkg
+internally, please do `fbpkg.fetch cea.chips.benchpress` to get the latest version. If
+you've installed SparkBench with an older version of DCPerf, we recommend you clean and
+re-install SparkBench.
+
+3. If you have run SparkBench with a different dataset, please remove the data from previous runs so that SparkBench can rebuild database with the new dataset:
+
+```bash
+rm -rf /flash23/warehouse
+rm -rf <benchpressPath>/benchmarks/spark_standalone/spark-2.4.5-bin-hadoop2.7/metastore_db
+```
+
+4. Create the /flash23 folder. Copy the bpc_t93586_s2_synthetic_5GB or bpc_t93586_s2_synthetic_1GB in the /flash23 folder. Note that SparkBench mini does not
+require the high I/O throughput as the regular version, so it's OK to put `/flash23`
+folder on your system drive.
+
+5. Run `spark_standalone_remote_mini` job on a real machine. This will create data in `/flash23/warehouse` and `<benchpressPath>/benchmarks/spark_standalone/spark-2.4.5-bin-hadoop2.7/metastore_db`. Create a backup of these two folders. By default, this job uses the 5GB dataset. If you want to use the 1GB dataset, run the job with specifying the
+`dataset_name` parameter like this:
+
+```bash
+./benchpress run spark_standalone_remote_mini -i '{"dataset_name":"bpc_t93586_s2_synthetic_1GB"}'
+```
+
+6. Run Spark mini on a real machine/emulator for the second time to reuse data, with the same commands and options (See [Reusing database on another machine](#reusing-database-on-another-machine).
+
 ## Reusing database on another machine
 
 Building database takes a considerable amount of time, so it's advisable to consider