如何使用 BigQuery 提取 JSON 对象中的所有键

Question

BigQuery has facilities to parse JSON in real-time interactive queries: Just store the JSON encoded object as a string, and query in real time, with functions like JSON_EXTRACT_SCALAR.

However, I can't find a way to discover all the keys (properties) in these objects.

Can I use a UDF for this?

Answer 1

Here's something that uses Standard SQL :

CREATE TEMP FUNCTION jsonObjectKeys(input STRING)
RETURNS Array<String>
LANGUAGE js AS """
  return Object.keys(JSON.parse(input));
""";
WITH keys AS (
  SELECT
    jsonObjectKeys(myColumn) AS keys
  FROM
    myProject.myTable
  WHERE myColumn IS NOT NULL
)
SELECT
  DISTINCT k
FROM keys
CROSS JOIN UNNEST(keys.keys) AS k
ORDER BY k

Answer 2

Below version fixes some "issues" in original answer like:
1. only first level of keys was emitted
2. having to manually comppile and than run final query for extracting info based on discovered keys

SELECT type, key, value, COUNT(1) AS weight 
FROM JS(
  (SELECT json, type 
     FROM [fh-bigquery:openlibrary.ol_dump_20151231@0] 
     WHERE type = '/type/edition'
  ),
  json, type,                             // Input columns
  "[{name: 'type', type:'string'},        // Output schema
   {name: 'key', type:'string'},
   {name: 'value', type:'string'}]",
   "function(r, emit) {                    // The function
      x = JSON.parse(r.json);
      processKey(x, '');
      function processKey(node, parent) {
        if (parent !== '') {parent += '.'};
        Object.keys(node).map(function(key) {
          value = node[key].toString();
          if (value !== '[object Object]') {
            emit({type:r.type, key:parent + key, value:value});
          } else {
            processKey(node[key], parent + key);
          };
        });         
      };
    }"
  )
GROUP EACH BY type, key, value
ORDER BY weight DESC
LIMIT 1000

The result is as below

Row          type   key                 value                         weight     
1   /type/edition   type.key            /type/edition               25140209     
2   /type/edition   last_modified.type  /type/datetime              25140209     
3   /type/edition   created.type        /type/datetime              17092292     
4   /type/edition   languages.0.key     /languages/eng              14514830     
5   /type/edition   notes.type          /type/text                  11681480     
6   /type/edition   revision            2                            8714084     
7   /type/edition   latest_revision     2                            8704217     
8   /type/edition   revision            3                            5041680     
9   /type/edition   latest_revision     3                            5040634     
10  /type/edition   created.value       2008-04-01T03:28:50.625462   3579095     
11  /type/edition   revision            1                            3396868     
12  /type/edition   physical_format     Paperback                    3181270     
13  /type/edition   revision            4                            3053266     
14  /type/edition   latest_revision     4                            3053197     
15  /type/edition   revision            5                            2076094     
16  /type/edition   latest_revision     5                            2076072     
17  /type/edition   publish_country     nyu                          1727347     
18  /type/edition   created.value       2008-04-30T09:38:13.731961   1681227     
19  /type/edition   publish_country     enk                          1627969     
20  /type/edition   publish_places      London                       1613755     
21  /type/edition   physical_format     Hardcover                    1495864     
22  /type/edition   publish_places      New York                     1467779     
23  /type/edition   revision            6                            1437467     
24  /type/edition   latest_revision     6                            1437463     
25  /type/edition   publish_country     xxk                          1407624 

Answer 3

The answers above don't work well in the current (2021) version, fail if either the JSON field is null or the JSON has null entries, don't aggregate well (we're trying to get structure, not content), or the like.

So, here's an improved version based on Felipe Hoffa 's answer .

It's fully recursive; checks for null and Array types; suppresses array indices (as [] ); flagged deterministic so it'll get cached; and groups, sorts, & counts the results.

Sample output:

key type    n
""  null    213
avatar  string  1046
blinking    boolean 1046
created_at  string  1046
deprecated_fields   Array   1046
display_name    string  1046
fields  Array   1046
fields.[]   Object  31
fields.[].name  string  31
fields.[].value string  31
fields.[].verified_at   null    27
fields.[].verified_at   string  4
friends_count   number  1046

Note:

1. the empty string key means that the field itself is actually null
2. the deprecated_fields key is one where all examples in the JSON are ..., deprecated_fields: [], ...
3. null is returned as the string "null" , like other types (not SQL null)

It could be improved to detect different types of number (int, bigint, float, decimal), dates, numbers stored as strings, or the like. But eh, this was good enough for my purposes, and that'd require more processing.

Just change the your-* bits in the last couple lines:

CREATE TEMP FUNCTION jsonParsed(input STRING)
RETURNS Array<Struct<key STRING, type STRING>>
DETERMINISTIC LANGUAGE js AS 
"""
function processKey(node, parent) {
    var ary = [];
    if (parent !== '') {
        parent += '.';
    }
    if (node == null) {
        ary.push({
            key: parent,
            type: 'null'
        })
    } else {
        Object.keys(node).map(function(key) {
            var v = node[key];
            if (node.constructor.name == "Array") {
                keytouse = '[]'
            } else {
                keytouse = key
            }
            if ((v == null) || (typeof(v) !== 'object')) {
                if (v == null) { typetouse = 'null';} else {typetouse = typeof(v);}
                ary.push({
                    key: parent + keytouse,
                    type: typetouse
                });
            } else {
                ary.push({
                    key: parent + keytouse,
                    type: v.constructor.name
                });
                ary = [].concat(ary, processKey(v, parent + keytouse));
            }
        });
    }
    return ary;
}
return processKey(JSON.parse(input), '');
""";

with keys as (SELECT jsonParsed(your-json-field) as keys  FROM `your-project-id.your-database-id.your-table-id`)
select key, type, count(*) as n from keys k cross join unnest(k.keys) as kk group by key, type order by key asc;

Answer 4

How to extract all of a JSON object keys using a JavaScript UDF in BigQuery:

SELECT type, key
FROM (
  SELECT * FROM
  js(
    (SELECT json, type FROM [fh-bigquery:openlibrary.ol_dump_20151231]
    ),
    // Input columns.
    json, type,
    // Output schema.
    "[{name: 'key', type:'string'},
     {name: 'type', type:'string'}]",
     // The function.
     "function(r, emit) { 
      x=JSON.parse(r.json)
      Object.keys(x).forEach(function(entry) {
        emit({key:entry, type:r.type,});
      });     
    }"
  )
)
LIMIT 100

Grouped and counted:

Once you've found all the keys you can use, then you can use JSON_EXTRACT_SCALAR on a normal SQL query:

Now that you know the keys, you can extract all information known for a type:

SELECT JSON_EXTRACT_SCALAR(json, '$.key') key,
  JSON_EXTRACT_SCALAR(json, '$.type.key') type,
  JSON_EXTRACT(json, '$.revision') revision,
  JSON_EXTRACT_SCALAR(json, '$.last_modified.value') last_modified,
  JSON_EXTRACT_SCALAR(json, '$.title') title,
  JSON_EXTRACT_SCALAR(json, '$.publish_date') publish_date,
  JSON_EXTRACT(json, '$.publishers') publishers,
  JSON_EXTRACT(json, '$.latest_revision') latest_revision,
  JSON_EXTRACT(json, '$.languages') languages,
  JSON_EXTRACT(json, '$.authors') authors,
  JSON_EXTRACT(json, '$.works') works,
  JSON_EXTRACT(json, '$.number_of_pages') number_of_pages,
  JSON_EXTRACT(json, '$.publish_places') publish_places,
  JSON_EXTRACT(json, '$.publish_country') publish_country,
  JSON_EXTRACT(json, '$.subjects') subjects,
  JSON_EXTRACT_SCALAR(json, '$.created.value') created,
  JSON_EXTRACT_SCALAR(json, '$.pagination') pagination,
  JSON_EXTRACT_SCALAR(json, '$.by_statement') by_statement,
  JSON_EXTRACT(json, '$.isbn_10') isbn_10,
  JSON_EXTRACT_SCALAR(json, '$.isbn_10[0]') isbn_10_0,
  JSON_EXTRACT(json, '$.notes') notes,
  JSON_EXTRACT(json, '$.lc_classifications') lc_classifications,
  JSON_EXTRACT_SCALAR(json, '$.subtitle') subtitle,
  JSON_EXTRACT(json, '$.lccn') lccn,
  JSON_EXTRACT(json, '$.identifiers') identifiers,
  JSON_EXTRACT(json, '$.contributions') contributions,
  JSON_EXTRACT(json, '$.isbn_13') isbn_13,
  JSON_EXTRACT_SCALAR(json, '$.isbn_13[0]') isbn_13_0,
  JSON_EXTRACT(json, '$.physical_format') physical_format,
  JSON_EXTRACT(json, '$.oclc_numbers') oclc_numbers,
  JSON_EXTRACT(json, '$.series') series,
  JSON_EXTRACT(json, '$.source_records') source_records,
  JSON_EXTRACT(json, '$.covers') covers,
  JSON_EXTRACT(json, '$.dewey_decimal_class') dewey_decimal_class,
  JSON_EXTRACT_SCALAR(json, '$.edition_name') edition_name,
  # ...
FROM [fh-bigquery:openlibrary.ol_dump_20151231]
WHERE type='/type/edition'
LIMIT 10

(sample data taken from an Open Library data dump https://openlibrary.org/developers/dumps , based on a reddit conversation )

Answer 5

This is what I came up with (Specifically for StandardSQL).. Not sure if accumulating in a list is the best method... Also.. I simplified for my case where I'm just concerned with keys.

CREATE TEMPORARY FUNCTION Foo(infoo STRING)
RETURNS Array<String>
LANGUAGE js AS """
      blah = [];


      function processKey(node, parent) {
        if (parent !== '') {parent += '.'};
        Object.keys(node).forEach(function(key) {
          value = node[key].toString();
          if (value !== '[object Object]') {
            blah.push(parent+key) 
          } else {
            processKey(node[key], parent + key);
          };
        });                 
      };

    try {     
      x = JSON.parse(infoo);  
      processKey(x,'');
      return blah;
    } catch (e) { return null }      

"""
OPTIONS ();
WITH x as(
select Foo(jsonfield) as bbb from clickstream.clikcs
)
select distinct arr_item from (SELECT arr_item FROM x, UNNEST(bbb) as arr_item)