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diff --git a/sample-apps/blog-tutorial-shared/README.md b/sample-apps/blog-tutorial-shared/README.md new file mode 100644 index 00000000000..09ac61e6b56 --- /dev/null +++ b/sample-apps/blog-tutorial-shared/README.md @@ -0,0 +1,19 @@ +# Vespa tutorial utility scripts + +## From raw JSON to Vespa Feeding format + + $ python parse.py trainPosts.json > somefile.json + +Parses JSON from the file trainPosts.json downloaded from Kaggle during the [blog search tutorial](https://git.corp.yahoo.com/pages/vespa/documentation/documentation/tutorials/blog-search.html) and format it according to Vespa Document JSON format. + + $ python parse.py -p trainPosts.json > somefile.json + +Give it the flag "-p" or "--popularity", and the script also calculates and adds the field `popularity`, as introduced [in the tutorial](https://git.corp.yahoo.com/pages/vespa/documentation/documentation/tutorials/blog-search.html#blog-popularity-signal). + +## Building and running the Spark script for calculating latent factors + +1. Install the latest version of [Apache Spark](http://spark.apache.org/) and [sbt](http://www.scala-sbt.org/download.html). + +2. Clone this repository and build the Spark script with `sbt package` (in the root directory of this repo). + +3. Use the resulting jar file when running spark jobs included in the tutorials.
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