如何解析具有动态键名的嵌套 json
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【中文标题】如何解析具有动态键名的嵌套 json【英文标题】：how to parse a nested json with dynamic key names 【发布时间】：2017-07-13 15:44:30 【问题描述】：我有以下格式的 JSON 结果，JSON Lint 将其显示为“有效响应”。
我的问题是：如何访问“聚合”的内容并从嵌套子对象中提取值，其中键是动态的。我的响应对象预期如下：
[
"metric_date":"2017-06-01 12:06:00",
"date_type":"Month",
"datacenter":"AWS-US-West-Portland",
"avg_avail_secs":1036800
,
"metric_date":"2017-06-01 12:06:00",
"date_type":"Month",
"datacenter":"Colo-US-East-Ashburn",
"avg_avail_secs":1036800
,
.....
]
我尝试了几种组合来使用 Jackson 递归解析 json，但不知道如何获取动态键并形成键/值对映射。
private Object parseValue(Object inputObject, List<String> keys) throws JSONException
Object outputObject = null;
if (null != inputObject)
if (inputObject instanceof JSONArray)
outputObject = parseJSONArray( (JSONArray) inputObject, keys);
else if (inputObject instanceof JSONObject)
outputObject = parseJSONObject( (JSONObject) inputObject, keys);
else if (inputObject instanceof String || inputObject instanceof Boolean || inputObject instanceof Integer)
outputObject = inputObject;
return outputObject;
JSON
"took": 201,
"timed_out": false,
"_shards":
"total": 5,
"successful": 5,
"failed": 0
,
"hits":
"total": 63612,
"max_score": 0,
"hits": []
,
"aggregations":
"metric_date":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": 1496318760000,
"key_as_string": "2017-06-01 12:06:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 1036800
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 1036800
]
]
,
"key": 1493640300000,
"key_as_string": "2017-05-01 12:05:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1491048240000,
"key_as_string": "2017-04-01 12:04:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2592000
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2592000
]
]
,
"key": 1488369780000,
"key_as_string": "2017-03-01 12:03:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1485950520000,
"key_as_string": "2017-02-01 12:02:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2419200
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2419200
]
]
,
"key": 1483272060000,
"key_as_string": "2017-01-01 12:01:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1480594320000,
"key_as_string": "2016-12-01 12:12:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1478002260000,
"key_as_string": "2016-11-01 12:11:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2592000
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2592000
]
]
,
"key": 1475323800000,
"key_as_string": "2016-10-01 12:10:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1472731740000,
"key_as_string": "2016-09-01 12:09:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2592000
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2592000
]
]
,
"key": 1470053280000,
"key_as_string": "2016-08-01 12:08:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1467374820000,
"key_as_string": "2016-07-01 12:07:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
,
"key": 1464782760000,
"key_as_string": "2016-06-01 12:06:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2592000
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2592000
]
]
,
"key": 1462104300000,
"key_as_string": "2016-05-01 12:05:00",
"doc_count": 3534,
"date_type":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "Month",
"doc_count": 3534,
"datacenter":
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
"key": "AWS-US-West-Portland",
"doc_count": 1830,
"avg_avail_secs":
"value": 2678400
,
"key": "Colo-US-East-Ashburn",
"doc_count": 1372,
"avg_avail_secs":
"value": 2678400
]
]
]
【问题讨论】：
您使用的是什么 JSON 库？到目前为止的 Java 代码在哪里？ @cricket_007，我正在使用 jackson，并且添加了有问题的 java 代码。 【参考方案1】：好的，找到问题并找到解决方案。应该是递归的json解析，先获取所有的bucket，然后遍历得到各个key。
【讨论】：
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