Add a track for the NYC taxi rides dataset.

nyc_taxis/README.txt

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+This dataset contains the rides that have been performed in yellow taxis in
+New York in December 2015. It can be downloaded
+from http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml.
+
+This has only de tested with the December 2015 dump, but this should work with
+any dump of the yellow taxis, and should be easy to adapt to the green taxis.
+
+Once downloaded, you can generate the mappings with:
+  python3 parse.py mappings
+
+And the json documents  can be generated with
+  python3 parse.py json file_name.csv > documents.json
+
+Finally the json docs can be compressed with
+  bzip2 -k documents.json

nyc_taxis/mappings.json

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+{
+  "type": {
+    "properties": {
+      "surcharge": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "dropoff_datetime": {
+        "type": "date",
+        "format": "yyyy-MM-dd HH:mm:ss"
+      },
+      "trip_type": {
+        "type": "keyword"
+      },
+      "mta_tax": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "rate_code_id": {
+        "type": "keyword"
+      },
+      "passenger_count": {
+        "type": "integer"
+      },
+      "pickup_datetime": {
+        "type": "date",
+        "format": "yyyy-MM-dd HH:mm:ss"
+      },
+      "tolls_amount": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "tip_amount": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "payment_type": {
+        "type": "keyword"
+      },
+      "extra": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "vendor_id": {
+        "type": "keyword"
+      },
+      "store_and_fwd_flag": {
+        "type": "keyword"
+      },
+      "improvement_surcharge": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "fare_amount": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "ehail_fee": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "cab_color": {
+        "type": "keyword"
+      },
+      "dropoff_location": {
+        "type": "geo_point"
+      },
+      "vendor_name": {
+        "type": "text"
+      },
+      "total_amount": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "trip_distance": {
+        "scaling_factor": 100,
+        "type": "scaled_float"
+      },
+      "pickup_location": {
+        "type": "geo_point"
+      }
+    },
+    "_all": {
+      "enabled": false
+    },
+    "dynamic": "strict"
+  }
+}

nyc_taxis/parse.py

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+import json
+import csv
+import sys
+import re
+
+types = {}
+for f in ["vendor_id","cab_color","payment_type","trip_type","rate_code_id","store_and_fwd_flag"]:
+  types[f] = 'keyword'
+for f in ["vendor_name"]:
+  types[f] = 'text'
+for f in ["passenger_count"]:
+  types[f] = 'integer'
+for f in ["pickup_location", "dropoff_location"]:
+  types[f] = 'geo_point'
+for f in ["trip_distance", "fare_amount", "surcharge", "mta_tax", "extra", "ehail_fee", "improvement_surcharge", "tip_amount", "tolls_amount", "total_amount"]:
+  types[f] = 'scaled_float'
+for f in ["pickup_datetime", "dropoff_datetime"]:
+  types[f] = 'date'
+
+def write_mappings():
+  mappings = {}
+  for (k, v) in types.items():
+    mappings[k] = { "type": v }
+    if v == 'date':
+      mappings[k]['format'] = "yyyy-MM-dd HH:mm:ss"
+    elif v == 'scaled_float':
+      mappings[k]['scaling_factor'] = 100
+  mappings = { "properties": mappings }
+  mappings['_all'] = { "enabled": False }
+  mappings['dynamic'] = 'strict'
+  mappings = { "type": mappings }
+  print(json.dumps(mappings, indent=2))
+
+def to_geo_point(d, f):
+  lat_field = f + "_latitude"
+  lon_field = f + "_longitude"
+  if lat_field in d and lon_field in d:
+    longitude = float(d[lon_field])
+    latitude = float(d[lat_field])
+    if longitude < -180 or longitude > 180 or latitude < -90 or latitude > 90:
+      raise Exception("Malformed coordinates")
+    d[f + '_location'] = [float(d[lon_field]), float(d[lat_field])]
+    del d[lon_field]
+    del d[lat_field]
+
+def to_underscore(s):
+  s = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', s)
+  return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s).lower()
+
+def to_json(f):
+  fields = []
+  for field in f.readline().strip().split(','):
+    field = to_underscore(field)
+    if field.startswith('tpep_') or field.startswith('lpep_'):
+      field = field[5:]
+    elif field == 'ratecode_id':
+      field = 'rate_code_id'
+    fields.append(field)
+  for line in f.readlines():
+    cols = line.strip().split(',')
+    if len(cols) < len(fields):
+      raise Exception("Cannot parse '%s': number of fields does not match '%s'" %(line, ",".join(fields)))
+
+    try:
+      d = {}
+      for i in range(len(fields)):
+        field = fields[i]
+        value = cols[i]
+        if value != '': # the way csv says the field does not exist
+          d[field] = value
+
+      to_geo_point(d, 'pickup')
+      to_geo_point(d, 'dropoff')
+
+      for (k, v) in d.items():
+        if k not in types:
+          raise Exception("Unknown field '%s'" %k)
+        t = types[k]
+        try:
+          if t == 'integer':
+            d[k] = int(v)
+          elif t == 'float':
+            d[k] = float(v)
+        except Exception as cause:
+          raise Exception("Cannot parse (%s,%s)" %(k, v)) from cause
+
+      print(json.dumps(d))
+    except KeyboardInterrupt:
+      break
+    except Exception as e:
+      print("Skipping malformed entry '%s' because of %s" %(line, str(e)), file=sys.stderr)
+
+if sys.argv[1] == "json":
+  for file_name in sys.argv[2:]:
+    with open(file_name) as f:
+      to_json(f)
+elif sys.argv[1] == "mappings":
+  write_mappings()
+else:
+  raise Exception("Expected 'json' or 'mappings' but got %s" %sys.argv[1])

nyc_taxis/track.json

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+{
+  "meta": {
+    "short-description": "Trip records completed in yellow and green taxis in New York in 2015",
+    "description": "This test indexes 165M taxi rides using 8 client threads and 10,000 docs per bulk request against Elasticsearch",
+    "data-url": "http://benchmarks.elasticsearch.org.s3.amazonaws.com/corpora/nyc_taxis"
+  },
+  "indices": [
+    {
+      "name": "nyc_taxis",
+      "types": [
+        {
+          "name": "type",
+          "mapping": "mappings.json",
+          "documents": "documents.json.bz2",
+          "document-count": 165346692,
+          "compressed-bytes": 4812721501,
+          "uncompressed-bytes": 79802445255
+        }
+      ]
+    }
+  ],
+  "operations": [
+    {
+      "name": "index",
+      "type": "index",
+      "index-settings": {
+        "index.number_of_shards": 1,
+        "index.codec": "best_compression",
+        "index.number_of_replicas": 0,
+        "index.refresh_interval": "30s",
+        "index.translog.flush_threshold_size": "4g"
+      },
+      "bulk-size": 10000,
+      "force-merge": false,
+      "clients": {
+        "count": 8
+      }
+    },
+    {
+      "name": "search",
+      "type": "search",
+      "target-throughput": 1,
+      "warmup-iterations": 100,
+      "iterations": 100,
+      "clients": {
+        "count": 1
+      },
+      "queries": [
+        {
+          "name": "default",
+          "body": {
+            "query": {
+              "match_all": {}
+            }
+          }
+        },
+        {
+          "name": "range",
+          "body": {
+            "query": {
+              "range": {
+                "total_amount": {
+                  "gte": 5,
+                  "lt": 15
+                }
+              }
+            }
+          }
+        },
+        {
+          "name": "distance_amount_agg",
+          "cache": false,
+          "body": {
+            "size": 0,
+            "aggs": {
+              "distance_histo": {
+                "histogram": {
+                  "field": "distance",
+                  "interval": 1
+                },
+                "aggs": {
+                  "total_amount_stats": {
+                    "stats": {
+                      "field": "total_amount"
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }        
+      ]
+    }
+  ],
+  "challenges": [
+    {
+      "name": "append-no-conflicts",
+      "description": "index data and then run a couple search requests",
+      "schedule": [
+        "index",
+        "search"
+      ]
+    }
+
+  ]
+}
+