hw2 (part 1): done, report done for ex1

Assignment2_part1/queries/query3c.http

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+GET /restaurants/_search
+{
+  "size": 0,
+  "query": {
+    "geo_distance": {
+      "distance": "9000km",
+      "location": {
+        "lat": 28.642449499999998,
+        "lon": 77.10684570000001
+      }
+    }
+  },
+  "aggs": {
+    "city_term": {
+      "terms": {
+        "field": "ratingText.keyword",
+        "size": 10000,
+        "shard_size": 10000
+      },
+      "aggs": {
+        "max_vote_count": {
+          "max": {
+            "field": "votes"
+          }
+        },
+        "max_vote_bucket_sort": {
+          "bucket_sort": {
+            "sort": [
+              { "max_vote_count": { "order": "desc" } } 
+            ]                              
+          }
+        }
+      }
+    }
+  }
+}

Assignment2_part1/report/build.sh

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+#!/bin/bash
+
+set -e
+
+SCRIPT_DIR=$(cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd)
+
+cd "$SCRIPT_DIR"
+pandoc main.md -o main.pdf

Assignment2_part1/report/main.md

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+---
+author: Claudio Maggioni
+title: Visual Analytics -- Assignment 2 -- Part 1
+geometry: margin=2cm,bottom=3cm
+---
+
+# Indexing
+
+The first step of indexing is to convert the given CSV dataset (stored in 
+`data/restaurants.csv`) into a JSON-lines file which can be directly used as the 
+HTTP request body of Elasticsearch document insertion requests.
+
+The conversion is performed by the script `./convert.sh`. The converted file 
+is stored in `data/restaurants.jsonl`.
+
+The gist of the conversion script is the following invocation of the _jq_ tool:
+
+```shell
+jq -s --raw-input --raw-output \
+  'split("\n") | .[1:-1] | map(split(",")) |
+    map({
+      "id": .[0],
+      "name": .[1],
+      "city": .[2],
+      "location": {
+        "lon": .[8] | sub("^\"\\["; "") | sub("\\s*"; "") | tonumber,
+        "lat": .[9] | sub("\\]\"$"; "") | sub("\\s*"; "") | tonumber,
+      },
+      "averageCostForTwo": .[3],
+      "aggregateRating": .[4],
+      "ratingText": .[5],
+      "votes": .[6],
+      "date": .[7]
+    })' "$input"
+```
+
+Here the CSV file is read as raw text, splitted into lines, has its first and 
+last line discarded (as they are respectively the CSV header and a terminating
+blank line), splitted into columns by the `,` (comma) delimiter character,
+and each line is converted into a JSON object by _jq_. Note that 
+_jq_ is invoked in `slurp` mode so that the output is elaborated in one go.
+
+Location coordinate strings are represented in the CSV with the pattern:
+
+```
+"[{longitude}, {latitude}]"
+```
+
+(with `{longitude}` and `{latitude}` being two JSON formatted `float`s). 
+Therefore, the comma split performed by _jq_ divides each cell value in two pieces.
+I exploit this side effect
+by simply removing the spurious non-numeric characters (like `[]"` and space),
+converting the obtained strings into `float`s and storing them in the `lon` and `lat`
+properties of `location`.
+
+After the conversion, the JSON-lines dataset is uploaded as an _Elasticsearch_ index
+named `restaurants` by the script `upload.sh`. The script assumes _Elasticsearch_ is 
+deployed locally, uses HTTPS authentication and has HTTP basic authentication turned
+on. Installation parameters for my machine are hardcoded in variables at the start
+of the script and may be adapted to the local installation to run it.
+
+The upload script, in order:
+
+- Tries to `DELETE` (ignoring failures, e.g. if the index does not exist) and 
+    `POST`s the `/restaurants` index, which will be used to store the documents.
+- Field mappings are `POST`ed at the URI `/restaurants/_mappings/`. Mappings 
+    are defined in the `mappings.json` file.
+- The lines of the dataset are read one-by-one, and then the correspoding
+    document is `POST`ed at the URI `/restaurants/_doc/{id}` where `{id}` is 
+    the value of the `id` field for the document/line.
+
+The mappings map the `id` field to type `long`, all other numeric fields to type
+`float`, the `location` field to type `geo_point`, and the `date` field to type
+`date` by using non-strict ISO 8601 with optional time as a parsing format. All 
+string fields are stored as type `text`, while also defining a `.keyword` alias
+for each to allow exact match queries on each field.