Home / Documentation / Basic Usage / Scoping Resolution

Basic Usage Tutorials 📖

This tutorial is part of a series to help you learn and perform the basic functions of zentity. Each tutorial adds a little more sophistication to the prior tutorials, so you can start simple and learn the more advanced features over time.

  1. Exact Name Matching
  2. Robust Name Matching
  3. Multiple Attribute Resolution
  4. Multiple Resolver Resolution
  5. Cross Index Resolution
  6. Scoping Resolution ← You are here.

Scoping Resolution

A resolution job will attempt to run every resolver for every index in the entity model, unless otherwise instructed. If you use the same entity model for multiple applications, then you might need only some of the resolvers or indices for each application. You can limit the scope of a resolution job to the resolvers and indices that apply to a given use case. This will prevent unnecessary searches, omit unnecessary results, optimize the performance of your resolution jobs, and minimize the load on your cluster.

This tutorial shows how you can scope a resolution job to prevent unnecessary searches.

Let's dive in.

Important

You must install Elasticsearch, Kibana, and zentity to complete this tutorial. This tutorial was tested with zentity-1.0.2-elasticsearch-6.7.0.

1. Prepare for the tutorial

1.1 Install the required plugins

This tutorial uses the phonetic analysis plugin and ICU analysis plugin for Elasticsearch. You will need to stop Elasticsearch, install these plugin, and start Elasticsearch. You can learn more about Elasticsearch plugin management here.

For Linux (in the $ES_HOME directory of a .tar.gz installation):

sudo bin/elasticsearch-plugin install analysis-phonetic
sudo bin/elasticsearch-plugin install analysis-icu

For Windows (in the $ES_HOME directory of a .zip installation):

bin/elasticsearch-plugin.bat install analysis-phonetic
bin/elasticsearch-plugin.bat install analysis-icu

1.2 Open the Kibana Console UI

The Kibana Console UI makes it easy to submit requests to Elasticsearch and read responses.

1.3 Delete any old tutorial indices

Let's start from scratch. Delete any tutorial indices you might have created from other tutorials.

DELETE .zentity-tutorial-*

1.4 Create the tutorial index

Now create the indices for this tutorial. These is the same data used in the prior tutorial on cross index resolution.

Index A

PUT .zentity-tutorial-index-a
{
  "settings": {
    "index": {
      "number_of_shards": 1,
      "number_of_replicas": 0,
      "analysis" : {
        "filter" : {
          "street_suffix_map" : {
            "pattern" : "(st)",
            "type" : "pattern_replace",
            "replacement" : "street"
          },
          "phonetic" : {
            "type" : "phonetic",
            "encoder" : "nysiis"
          },
          "punct_white" : {
            "pattern" : "\\p{Punct}",
            "type" : "pattern_replace",
            "replacement" : " "
          },
          "remove_non_digits" : {
            "pattern" : "[^\\d]",
            "type" : "pattern_replace",
            "replacement" : ""
          }
        },
        "analyzer" : {
          "name_clean" : {
            "filter" : [
              "icu_normalizer",
              "icu_folding",
              "punct_white"
            ],
            "tokenizer" : "standard"
          },
          "name_phonetic" : {
            "filter" : [
              "icu_normalizer",
              "icu_folding",
              "punct_white",
              "phonetic"
            ],
            "tokenizer" : "standard"
          },
          "street_clean" : {
            "filter" : [
              "icu_normalizer",
              "icu_folding",
              "punct_white",
              "trim"
            ],
            "tokenizer" : "keyword"
          },
          "phone_clean" : {
            "filter" : [
              "remove_non_digits"
            ],
            "tokenizer" : "keyword"
          }
        }
      }
    }
  },
  "mappings": {
    "_doc": {
      "properties": {
        "id_a": {
          "type": "keyword"
        },
        "first_name_a": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "name_clean"
            },
            "phonetic": {
              "type": "text",
              "analyzer": "name_phonetic"
            }
          }
        },
        "last_name_a": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "name_clean"
            },
            "phonetic": {
              "type": "text",
              "analyzer": "name_phonetic"
            }
          }
        },
        "street_a": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "street_clean"
            }
          }
        },
        "city_a": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "name_clean"
            }
          }
        },
        "state_a": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          }
        },
        "phone_a": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "phone_clean"
            }
          }
        },
        "email_a": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          }
        }
      }
    }
  }
}

Index B

PUT .zentity-tutorial-index-b
{
  "settings": {
    "index": {
      "number_of_shards": 1,
      "number_of_replicas": 0,
      "analysis" : {
        "filter" : {
          "street_suffix_map" : {
            "pattern" : "(st)",
            "type" : "pattern_replace",
            "replacement" : "street"
          },
          "phonetic" : {
            "type" : "phonetic",
            "encoder" : "nysiis"
          },
          "punct_white" : {
            "pattern" : "\\p{Punct}",
            "type" : "pattern_replace",
            "replacement" : " "
          },
          "remove_non_digits" : {
            "pattern" : "[^\\d]",
            "type" : "pattern_replace",
            "replacement" : ""
          }
        },
        "analyzer" : {
          "name_clean" : {
            "filter" : [
              "icu_normalizer",
              "icu_folding",
              "punct_white"
            ],
            "tokenizer" : "standard"
          },
          "name_phonetic" : {
            "filter" : [
              "icu_normalizer",
              "icu_folding",
              "punct_white",
              "phonetic"
            ],
            "tokenizer" : "standard"
          },
          "street_clean" : {
            "filter" : [
              "icu_normalizer",
              "icu_folding",
              "punct_white",
              "trim"
            ],
            "tokenizer" : "keyword"
          },
          "phone_clean" : {
            "filter" : [
              "remove_non_digits"
            ],
            "tokenizer" : "keyword"
          }
        }
      }
    }
  },
  "mappings": {
    "_doc": {
      "properties": {
        "id_b": {
          "type": "keyword"
        },
        "first_name_b": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "name_clean"
            },
            "phonetic": {
              "type": "text",
              "analyzer": "name_phonetic"
            }
          }
        },
        "last_name_b": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "name_clean"
            },
            "phonetic": {
              "type": "text",
              "analyzer": "name_phonetic"
            }
          }
        },
        "street_b": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "street_clean"
            }
          }
        },
        "city_b": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "name_clean"
            }
          }
        },
        "state_b": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          }
        },
        "phone_b": {
          "type": "text",
          "fields": {
            "clean": {
              "type": "text",
              "analyzer": "phone_clean"
            }
          }
        },
        "email_b": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          }
        }
      }
    }
  }
}

1.5 Load the tutorial data

Add the tutorial data to the index. This is the same data used in the prior tutorial on cross index resolution.

POST _bulk?refresh
{"index": {"_id": "1", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "Washington", "email_a": "[email protected]", "first_name_a": "Allie", "id_a": "1", "last_name_a": "Jones", "phone_a": "202-555-1234", "state_a": "DC", "street_a": "123 Main St"}
{"index": {"_id": "2", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "Washington", "email_b": "", "first_name_b": "Alicia", "id_b": "2", "last_name_b": "Johnson", "phone_b": "202-123-4567", "state_b": "DC", "street_b": "300 Main St"}
{"index": {"_id": "3", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "Washington", "email_a": "", "first_name_a": "Allie", "id_a": "3", "last_name_a": "Jones", "phone_a": "", "state_a": "DC", "street_a": "123 Main St"}
{"index": {"_id": "4", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "", "email_b": "", "first_name_b": "Ally", "id_b": "4", "last_name_b": "Joans", "phone_b": "202-555-1234", "state_b": "", "street_b": ""}
{"index": {"_id": "5", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "Arlington", "email_a": "[email protected]", "first_name_a": "Eli", "id_a": "5", "last_name_a": "Jonas", "phone_a": "", "state_a": "VA", "street_a": "500 23rd Street"}
{"index": {"_id": "6", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "Washington", "email_b": "[email protected]", "first_name_b": "Allison", "id_b": "6", "last_name_b": "Jones", "phone_b": "202-555-1234", "state_b": "DC", "street_b": "123 Main St"}
{"index": {"_id": "7", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "Washington", "email_a": "", "first_name_a": "Allison", "id_a": "7", "last_name_a": "Smith", "phone_a": "+1 (202) 555 1234", "state_a": "DC", "street_a": "555 Broad St"}
{"index": {"_id": "8", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "Washington", "email_b": "[email protected]", "first_name_b": "Alan", "id_b": "8", "last_name_b": "Smith", "phone_b": "202-000-5555", "state_b": "DC", "street_b": "555 Broad St"}
{"index": {"_id": "9", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "Washington", "email_a": "[email protected]", "first_name_a": "Alan", "id_a": "9", "last_name_a": "Smith", "phone_a": "2020005555", "state_a": "DC", "street_a": "555 Broad St"}
{"index": {"_id": "10", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "Washington", "email_b": "", "first_name_b": "Alison", "id_b": "10", "last_name_b": "Smith", "phone_b": "202-555-9876", "state_b": "DC", "street_b": "555 Broad St"}
{"index": {"_id": "11", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "", "email_a": "[email protected]", "first_name_a": "Alison", "id_a": "11", "last_name_a": "Jones-Smith", "phone_a": "2025559867", "state_a": "", "street_a": ""}
{"index": {"_id": "12", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "Washington", "email_b": "[email protected]", "first_name_b": "Allison", "id_b": "12", "last_name_b": "Jones-Smith", "phone_b": "", "state_b": "DC", "street_b": "555 Broad St"}
{"index": {"_id": "13", "_index": ".zentity-tutorial-index-a", "_type": "_doc"}}
{"city_a": "Arlington", "email_a": "[email protected]", "first_name_a": "Allison", "id_a": "13", "last_name_a": "Jones Smith", "phone_a": "703-555-5555", "state_a": "VA", "street_a": "1 Corporate Way"}
{"index": {"_id": "14", "_index": ".zentity-tutorial-index-b", "_type": "_doc"}}
{"city_b": "Arlington", "email_b": "[email protected]", "first_name_b": "Elise", "id_b": "14", "last_name_b": "Jonas", "phone_b": "703-555-5555", "state_b": "VA", "street_b": "1 Corporate Way"}

Here's what the tutorial data looks like.

Index A

id_a first_name_a last_name_a street_a city_a state_a phone_a email_a
1 Allie Jones 123 Main St Washington DC 202-555-1234 [email protected]
3 Allie Jones 123 Main St Washington DC
5 Eli Jonas 500 23rd Street Arlington VA [email protected]
7 Allison Smith 555 Broad St Washington DC +1 (202) 555 1234
9 Alan Smith 555 Broad St Washington DC 2020005555 [email protected]
11 Alison Jones-Smith 2025559867 [email protected]
13 Allison Jones Smith 1 Corporate Way Arlington VA 703-555-5555 [email protected]

Index B

id_b first_name_b last_name_b street_b city_b state_b phone_b email_b
2 Alicia Johnson 300 Main St Washington DC 202-123-4567
4 Ally Joans 202-555-1234
6 Allison Jones 123 Main St Washington DC 202-555-1234 [email protected]
8 Alan Smith 555 Broad St Washington DC 202-000-5555 [email protected]
10 Alison Smith 555 Broad St Washington DC 202-555-9876
12 Allison Jones-Smith 555 Broad St Washington DC [email protected]
14 Elise Jonas 1 Corporate Way Arlington VA 703-555-5555 [email protected]

2. Create the entity model

Let's use the Models API to create the entity model below. This is the same model used in the prior tutorial on cross index resolution. This tutorial will show how to omit some of the resolvers and indices during a resolution job.

PUT _zentity/models/zentity-tutorial-person
{
  "attributes": {
    "first_name": {
      "type": "string"
    },
    "last_name": {
      "type": "string"
    },
    "street": {
      "type": "string"
    },
    "city": {
      "type": "string"
    },
    "state": {
      "type": "string"
    },
    "phone": {
      "type": "string"
    },
    "email": {
      "type": "string"
    }
  },
  "resolvers": {
    "name_street_city_state": {
      "attributes": [ "first_name", "last_name", "street", "city", "state" ]
    },
    "name_phone": {
      "attributes": [ "first_name", "last_name", "phone" ]
    },
    "name_email": {
      "attributes": [ "first_name", "last_name", "email" ]
    },
    "email_phone": {
      "attributes": [ "email", "phone" ]
    }
  },
  "matchers": {
    "simple": {
      "clause": {
        "match": {
          "{{ field }}": "{{ value }}"
        }
      }
    },
    "fuzzy": {
      "clause": {
        "match": {
          "{{ field }}": {
            "query": "{{ value }}",
            "fuzziness": "1"
          }
        }
      }
    },
    "exact": {
      "clause": {
        "term": {
          "{{ field }}": "{{ value }}"
        }
      }
    }
  },
  "indices": {
    ".zentity-tutorial-index-a": {
      "fields": {
        "first_name_a.clean": {
          "attribute": "first_name",
          "matcher": "fuzzy"
        },
        "first_name_a.phonetic": {
          "attribute": "first_name",
          "matcher": "simple"
        },
        "last_name_a.clean": {
          "attribute": "last_name",
          "matcher": "fuzzy"
        },
        "last_name_a.phonetic": {
          "attribute": "last_name",
          "matcher": "simple"
        },
        "street_a.clean": {
          "attribute": "street",
          "matcher": "fuzzy"
        },
        "city_a.clean": {
          "attribute": "city",
          "matcher": "fuzzy"
        },
        "state_a.keyword": {
          "attribute": "state",
          "matcher": "exact"
        },
        "phone_a.clean": {
          "attribute": "phone",
          "matcher": "fuzzy"
        },
        "email_a.keyword": {
          "attribute": "email",
          "matcher": "exact"
        }
      }
    },
    ".zentity-tutorial-index-b": {
      "fields": {
        "first_name_b.clean": {
          "attribute": "first_name",
          "matcher": "fuzzy"
        },
        "first_name_b.phonetic": {
          "attribute": "first_name",
          "matcher": "simple"
        },
        "last_name_b.clean": {
          "attribute": "last_name",
          "matcher": "fuzzy"
        },
        "last_name_b.phonetic": {
          "attribute": "last_name",
          "matcher": "simple"
        },
        "street_b.clean": {
          "attribute": "street",
          "matcher": "fuzzy"
        },
        "city_b.clean": {
          "attribute": "city",
          "matcher": "fuzzy"
        },
        "state_b.keyword": {
          "attribute": "state",
          "matcher": "exact"
        },
        "phone_b.clean": {
          "attribute": "phone",
          "matcher": "fuzzy"
        },
        "email_b.keyword": {
          "attribute": "email",
          "matcher": "exact"
        }
      }
    }
  }
}

3. Resolve an entity

3.1 Control the scope of indices

Let's use the Resolution API to resolve a person with a known first name, last name, and phone number. These are the same attributes used in the prior tutorial on cross index resolution, which returned nine results. This time, we are going to control the scope of the indices searched during the resolution job. Let's limit the job to the ".zentity-tutorial-index-a" index.

POST _zentity/resolution/zentity-tutorial-person?pretty
{
  "attributes": {
    "first_name": [ "Allie" ],
    "last_name": [ "Jones" ],
    "phone": [ "202-555-1234" ]
  },
  "scope": {
    "include": {
      "indices": [
        ".zentity-tutorial-index-a"
      ]
    }
  }
}

The results will look like this:

{
  "took" : 7,
  "hits" : {
    "total" : 2,
    "hits" : [ {
      "_index" : ".zentity-tutorial-index-a",
      "_type" : "_doc",
      "_id" : "1",
      "_hop" : 0,
      "_attributes" : {
        "city" : "Washington",
        "email" : "[email protected]",
        "first_name" : "Allie",
        "last_name" : "Jones",
        "phone" : "202-555-1234",
        "state" : "DC",
        "street" : "123 Main St"
      },
      "_source" : {
        "city_a" : "Washington",
        "email_a" : "[email protected]",
        "first_name_a" : "Allie",
        "id_a" : "1",
        "last_name_a" : "Jones",
        "phone_a" : "202-555-1234",
        "state_a" : "DC",
        "street_a" : "123 Main St"
      }
    }, {
      "_index" : ".zentity-tutorial-index-a",
      "_type" : "_doc",
      "_id" : "3",
      "_hop" : 1,
      "_attributes" : {
        "city" : "Washington",
        "email" : "",
        "first_name" : "Allie",
        "last_name" : "Jones",
        "phone" : "",
        "state" : "DC",
        "street" : "123 Main St"
      },
      "_source" : {
        "city_a" : "Washington",
        "email_a" : "",
        "first_name_a" : "Allie",
        "id_a" : "3",
        "last_name_a" : "Jones",
        "phone_a" : "",
        "state_a" : "DC",
        "street_a" : "123 Main St"
      }
    } ]
  }
}

As expected, we retrieved results only from ".zentity-tutorial-index-a". There are only two results, which is less than the nine results of the prior tutorial on cross index resolution because some of those matches required searching both indices.

3.2 Control the scope of resolvers

Let's use the Resolution API to resolve a person with a known first name, last name, and phone number. These are the same attributes used in the prior tutorial on cross index resolution, which returned nine results. This time, we are going to control the scope of the resolvers searched during the resolution job. Let's exclude only the "name_street_city_state" resolver. Let's also search across both indices.

POST _zentity/resolution/zentity-tutorial-person?pretty
{
  "attributes": {
    "first_name": [ "Allie" ],
    "last_name": [ "Jones" ],
    "phone": [ "202-555-1234" ]
  },
  "scope": {
    "include": {
      "indices": [
        ".zentity-tutorial-index-a",
        ".zentity-tutorial-index-b"
      ]
    },
    "exclude": {
      "resolvers": [
        "name_street_city_state"
      ]
    }
  }
}

The results will look like this:

{
  "took" : 42,
  "hits" : {
    "total" : 6,
    "hits" : [ {
      "_index" : ".zentity-tutorial-index-a",
      "_type" : "_doc",
      "_id" : "1",
      "_hop" : 0,
      "_attributes" : {
        "city" : "Washington",
        "email" : "[email protected]",
        "first_name" : "Allie",
        "last_name" : "Jones",
        "phone" : "202-555-1234",
        "state" : "DC",
        "street" : "123 Main St"
      },
      "_source" : {
        "city_a" : "Washington",
        "email_a" : "[email protected]",
        "first_name_a" : "Allie",
        "id_a" : "1",
        "last_name_a" : "Jones",
        "phone_a" : "202-555-1234",
        "state_a" : "DC",
        "street_a" : "123 Main St"
      }
    }, {
      "_index" : ".zentity-tutorial-index-b",
      "_type" : "_doc",
      "_id" : "4",
      "_hop" : 0,
      "_attributes" : {
        "city" : "",
        "email" : "",
        "first_name" : "Ally",
        "last_name" : "Joans",
        "phone" : "202-555-1234",
        "state" : "",
        "street" : ""
      },
      "_source" : {
        "city_b" : "",
        "email_b" : "",
        "first_name_b" : "Ally",
        "id_b" : "4",
        "last_name_b" : "Joans",
        "phone_b" : "202-555-1234",
        "state_b" : "",
        "street_b" : ""
      }
    }, {
      "_index" : ".zentity-tutorial-index-b",
      "_type" : "_doc",
      "_id" : "6",
      "_hop" : 1,
      "_attributes" : {
        "city" : "Washington",
        "email" : "[email protected]",
        "first_name" : "Allison",
        "last_name" : "Jones",
        "phone" : "202-555-1234",
        "state" : "DC",
        "street" : "123 Main St"
      },
      "_source" : {
        "city_b" : "Washington",
        "email_b" : "[email protected]",
        "first_name_b" : "Allison",
        "id_b" : "6",
        "last_name_b" : "Jones",
        "phone_b" : "202-555-1234",
        "state_b" : "DC",
        "street_b" : "123 Main St"
      }
    }, {
      "_index" : ".zentity-tutorial-index-a",
      "_type" : "_doc",
      "_id" : "11",
      "_hop" : 2,
      "_attributes" : {
        "city" : "",
        "email" : "[email protected]",
        "first_name" : "Alison",
        "last_name" : "Jones-Smith",
        "phone" : "2025559867",
        "state" : "",
        "street" : ""
      },
      "_source" : {
        "city_a" : "",
        "email_a" : "[email protected]",
        "first_name_a" : "Alison",
        "id_a" : "11",
        "last_name_a" : "Jones-Smith",
        "phone_a" : "2025559867",
        "state_a" : "",
        "street_a" : ""
      }
    }, {
      "_index" : ".zentity-tutorial-index-a",
      "_type" : "_doc",
      "_id" : "7",
      "_hop" : 3,
      "_attributes" : {
        "city" : "Washington",
        "email" : "",
        "first_name" : "Allison",
        "last_name" : "Smith",
        "phone" : "+1 (202) 555 1234",
        "state" : "DC",
        "street" : "555 Broad St"
      },
      "_source" : {
        "city_a" : "Washington",
        "email_a" : "",
        "first_name_a" : "Allison",
        "id_a" : "7",
        "last_name_a" : "Smith",
        "phone_a" : "+1 (202) 555 1234",
        "state_a" : "DC",
        "street_a" : "555 Broad St"
      }
    }, {
      "_index" : ".zentity-tutorial-index-b",
      "_type" : "_doc",
      "_id" : "10",
      "_hop" : 3,
      "_attributes" : {
        "city" : "Washington",
        "email" : "",
        "first_name" : "Alison",
        "last_name" : "Smith",
        "phone" : "202-555-9876",
        "state" : "DC",
        "street" : "555 Broad St"
      },
      "_source" : {
        "city_b" : "Washington",
        "email_b" : "",
        "first_name_b" : "Alison",
        "id_b" : "10",
        "last_name_b" : "Smith",
        "phone_b" : "202-555-9876",
        "state_b" : "DC",
        "street_b" : "555 Broad St"
      }
    } ]
  }
}

This time we retrieved six results total from two indices, which is less than the nine results of the prior tutorial on cross index resolution because some of those matches required using the "name_street_city_state" resolver that we excluded in this job.

Tip

If you want to include all indices in your resolution job, then you can omit "scope.include.indices" altogether as we did in prior tutorials. But when you begin to use the same entity model for different applications, then you might not need to search every index in every application. As a best pratice, use "scope.include.indices" to prevent unnecessary searches and results. Conversely, as a best pratice, use "scope.exclude.resolvers" and "scope.exclude.attributes" to exclude unnecessary details about the entity. You can be more liberal with your use of resolvers and attributes, because they will only be used for the indices that support them.

Conclusion

Congratulations! You learned how to resolve an entity using a controlled scope of indices and resolvers. This is an important concept to understand and implement when you use zentity in production.

 


Continue Reading

Cross Index Resolution Advanced Usage
© 2018 Dave Moore.
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