Schema Analyzer

An Open Source joint by Dan Levy ✨

Analyze column type & size summary from any input JSON array

Schema Analyzer is the core library behind Dan's Schema Generator.

Features

The primary goal is to support any input JSON/CSV and infer as much as possible. More data will generally yield better results.

• [x] Heuristic type analysis for arrays of objects.
• [x] Browser-based (local, no server necessary)
• [x] Automatic type detection:
  • [x] ID - Identifier column, by name and unique Integer check (detects BigInteger)
  • [x] ObjectId (MongoDB's 96 bit/12 Byte ID. 32bit timestamp + 24bit MachineID + 16bit ProcessID + 24bit Counter)
  • [x] UUID/GUID (Common 128 bit/16 Byte ID. Stored as a hex string, dash delimited in parts: 8, 4, 4, 4, 12)
  • [x] Boolean (detects obvious strings true, false, Y, N)
  • [x] Date (Smart detection via comprehensive regex pattern)
  • [x] Timestamp (integer, number of milliseconds since unix epoch)
  • [x] Currency (62 currency symbols supported)
  • [x] Float (w/ scale & precision measurements)
  • [x] Number (Integers)
  • [x] Null (sparse column data helps w/ certain inferences)
  • [x] Email (falls back to string)
  • [x] String (big text and varchar awareness)
  • [x] Array (includes min/max/avg length)
  • [x] Object
• [x] Detects column size minimum, maximum and average
• [x] Includes data points at the 30th, 60th and 90th percentiles (for detecting outliers and enum types!)
• [x] Handles some error/outliers
• [x] Quantify # of unique values per column
• [x] Identify enum Fields w/ Values
• [x] Identify Not Null fields
• [ ] Nested data structure & multi-table relational output.

Getting Started

npm install schema-analyzer

import { schemaBuilder } from 'schema-builder'

schemaBuilder(schemaName: String, data: Array<Object>): TypeSummary

Preview Analysis Results

What does this library's analysis look like?

It consists of 3 key top-level properties:

• totalRows - # of rows analyzed.
• fields: FieldTypeSummary - a map of field names with all detected types (includes meta-data for each type detected, with possible overlaps. e.g. an Email is also a String, "42" is a String and Number)

Review the raw results below

Details about each field can be found below.

{
  "totalRows": 5,
  "fields": {
    "id": {
      "types": {
        "Number": {
          "rank": 8,
          "count": 5,
          "value": { "min": 1, "mean": 3, "max": 5, "p25": 2, "p33": 2, "p50": 3, "p66": 4, "p75": 4, "p99": 5 }
        },
        "String": {
          "rank": 12,
          "count": 5,
          "length": { "min": 1, "mean": 1, "max": 1, "p25": 1, "p33": 1, "p50": 1, "p66": 1, "p75": 1, "p99": 1 }
        }
      }
    },
    "name": {
      "types": {
        "String": {
          "rank": 12,
          "count": 5,
          "length": { "min": 3, "mean": 7.2, "max": 15, "p25": 3, "p33": 3, "p50": 5, "p66": 10, "p75": 10, "p99": 15 }
        }
      }
    },
    "role": {
      "types": {
        "String": {
          "rank": 12,
          "count": 5,
          "length": { "min": 4, "mean": 5.4, "max": 9, "p25": 4, "p33": 4, "p50": 5, "p66": 5, "p75": 5, "p99": 9 }
        }
      }
    },
    "email": {
      "types": {
        "Email": {
          "rank": 11,
          "count": 5,
          "length": { "min": 15, "mean": 19.4, "max": 26, "p25": 15, "p33": 15, "p50": 18, "p66": 23, "p75": 23, "p99": 26 }
        }
      }
    },
    "createdAt": {
      "types": {
        "Date": {
          "rank": 4,
          "count": 4,
          "value": { "min": "2001-01-01T00:00:00.000Z", "mean": "2015-04-14T18:00:00.000Z", "max": "2020-02-02T00:00:00.000Z", "p25": "2020-02-02T00:00:00.000Z", "p33": "2020-02-02T00:00:00.000Z", "p50": "2019-12-31T00:00:00.000Z", "p66": "2019-12-31T00:00:00.000Z", "p75": "2001-01-01T00:00:00.000Z", "p99": "2001-01-01T00:00:00.000Z" }
        },
        "String": {
          "rank": 12,
          "count": 1,
          "length": { "min": 6, "mean": 6, "max": 6, "p25": 6, "p33": 6, "p50": 6, "p66": 6, "p75": 6, "p99": 6 }
        }
      }
    },
    "accountConfirmed": {
      "types": {
        "Unknown": {
          "rank": -1,
          "count": 1
        },
        "String": {
          "rank": 12,
          "count": 1,
          "length": { "min": 9, "mean": 9, "max": 9, "p25": 9, "p33": 9, "p50": 9, "p66": 9, "p75": 9, "p99": 9 }
        },
        "Boolean": {
          "rank": 3,
          "count": 4
        }
      }
    }
  }
}

Sample input dataset for the example results above

| id | name | role | email | createdAt | accountConfirmed | |----|-----------------|-----------|------------------------------|------------|------------------| | 1 | Eve | poweruser | [email protected] | 01/20/2020 | undefined | | 2 | Alice | user | [email protected] | 02/02/2020 | true | | 3 | Bob | user | [email protected] | 12/31/2019 | true | | 4 | Elliot Alderson | admin | [email protected] | 01/01/2001 | false | | 5 | Sam Sepiol | admin | [email protected] | 9/9/99 | true |

AggregateSummary

Numeric and String types include a summary of the observed field sizes:

Number & String Range Object Details

Properties

• min the minimum number or string length
• max the maximum number or string length
• mean the average number or string length
• percentiles[25th, 33th, 50th, 66th, 75th, 99th] values from the Nth percentile number or string length

Percentile is based on input data, as-is with out sorting.

Length Range Data

Range data for the length of a String field type:

{
  "rank": 11,
  "count": 5,
  "length": { "min": 15, "mean": 19.4, "max": 26, "p25": 15, "p33": 15, "p50": 18, "p66": 23, "p75": 23, "p99": 26 }
}

This is useful for defining strict length limits or minimums, for example as SQL servers often require..

Range data for a Date fields value:

{
  "rank": 4,
  "count": 4,
  "value": { "min": "2001-01-01T00:00:00.000Z", "mean": "2015-04-14T18:00:00.000Z", "max": "2020-02-02T00:00:00.000Z", "p25": "2020-02-02T00:00:00.000Z", "p33": "2020-02-02T00:00:00.000Z", "p50": "2019-12-31T00:00:00.000Z", "p66": "2019-12-31T00:00:00.000Z", "p75": "2001-01-01T00:00:00.000Z", "p99": "2001-01-01T00:00:00.000Z" }
}

Notes

We recommend you provide at least 100+ rows. Accuracy increases greatly with 1,000 rows.

The following features require a certain minimum # of records:

• Enumeration detection.
  • 100+ Rows Required.
  • Number of unique values must not exceed 20 or 5% of the total number of records. (100 records will identify as Enum w/ 5 values. Up to 20 are possible given 400 or 1,000+.)
• Not Null detection.
  • where rowCount === field count

Full List of Detected Types

• Unknown
• ObjectId
• UUID
• Boolean
• Date
• Timestamp
• Currency
• Float
• Number
• Email
• String
• Array
• Object
• Null