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mongodb - 嵌入式文档的汇总平均值

转载 作者:可可西里 更新时间:2023-11-01 10:28:12 25 4
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我的文档结构如下所示:

{
"_id" : ObjectId("52517d9e6e0af435ddd48219"),
"date" : ISODate("2013-10-06T18:11:26.329Z"),
"engines" : {
"ahnlab" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "ahnlab",
"node_name" : "",
"status" : 1,
"task_id" : "7fac4f67-2bde-49de-980a-4f7fa1d46db7",
"threat" : "EICAR_Test_File"
},
"avast" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "avast",
"node_name" : "",
"status" : 4,
"task_id" : "fdce50e9-9bf3-4cc9-91a4-b674a108d478",
"threat" : ""
},
"avg" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "avg",
"node_name" : "",
"status" : 1,
"task_id" : "4bdb4a37-80ab-4631-8587-edcbde7e2592",
"threat" : "EICAR_Test"
},
"avira" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "avira",
"node_name" : "",
"status" : 4,
"task_id" : "43b43bab-ff67-440c-9919-f6241ccaf539",
"threat" : ""
},
"bitdefender" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "bitdefender",
"node_name" : "",
"status" : 4,
"task_id" : "ba7efe74-ef93-465f-b6c3-6c4fc746934d",
"threat" : ""
},
"comodo" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "comodo",
"node_name" : "",
"status" : 4,
"task_id" : "831d2dc8-704b-4eb7-9a75-9a0364a8ab09",
"threat" : ""
},
"drweb" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "drweb",
"node_name" : "",
"status" : 4,
"task_id" : "ebb758c3-9146-4a99-b36f-0fb6ee024a33",
"threat" : ""
},
"f-prot" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "f-prot",
"node_name" : "",
"status" : 4,
"task_id" : "13590a25-ac0d-4b1b-b93e-bc715009432a",
"threat" : ""
},
"forticlient" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "forticlient",
"node_name" : "",
"status" : 4,
"task_id" : "",
"threat" : ""
},
"kaspersky" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "kaspersky",
"node_name" : "",
"status" : 4,
"task_id" : "",
"threat" : ""
},
"malwarebytes" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "malwarebytes",
"node_name" : "",
"status" : 4,
"task_id" : "a97f3d9a-d6a9-44df-8355-c053e9f4980a",
"threat" : ""
},
"mcafee" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "mcafee",
"node_name" : "",
"status" : 1,
"task_id" : "e7a592bb-84fc-4c47-a1ed-3719874b19cc",
"threat" : "EICAR test file"
},
"msessentials" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "msessentials",
"node_name" : "",
"status" : 4,
"task_id" : "01c9fb71-155a-473d-b45c-91fa117ae649",
"threat" : ""
},
"nod32" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "nod32",
"node_name" : "",
"status" : 4,
"task_id" : "a715cc8f-0e2f-4698-b883-a35fa6add13e",
"threat" : ""
},
"norman" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "norman",
"node_name" : "",
"status" : 4,
"task_id" : "29da2955-0674-45d2-ac4d-c0b3ea401cba",
"threat" : ""
},
"norton" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "norton",
"node_name" : "",
"status" : 4,
"task_id" : "c4cd1e3e-2f0d-4bf6-84cf-ab0962f7f4ed",
"threat" : ""
},
"panda" : {
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "panda",
"node_name" : "",
"status" : 4,
"task_id" : "b0888f7c-e4a4-4b4e-b163-21d283e166f1",
"threat" : ""
}
},
"expiration_date" : ISODate("2013-10-06T18:11:36.329Z"),
"file_name" : "ffgtr.exe",
"scan_status" : "DONE",
"task_id" : "4ce4ae9e-ef0a-476a-8189-92a5bfe328bd"
}

我想通过聚合来投影每个引擎的“日期”和“定义”字段。(“定义”是一个嵌入式字段)

只有我指定了特定的引擎,我才成功地做到了:

cursor = collection.aggregate([
{ "$match": { "date": { "$gte": startdate } } },
{'$project': { "def": "$engines.avast.definitions","date":1,"_id": 0 }}
], allowDiskUse=True)

但我想在不指定特定引擎的情况下对所有引擎运行查询,例如:

cursor = collection.aggregate([
{ "$match": { "date": { "$gte": startdate } } },
{'$project': { "def": "$engines.$elemMatch.definitions","date":1,"_id": 0 }}
], allowDiskUse=True)

(这是行不通的)

我想要找到的是“日期”和每个引擎类型的每个“引擎”定义日期之间的平均差异。

最佳答案

这里的主要问题是数据的结构。为了使用聚合框架并愉快地使用大多数其他 MongoDB 查询操作和索引实践,那么您的数据应该像这样构造。

{
"_id" : ObjectId("52517d9e6e0af435ddd48219"),
"date" : ISODate("2013-10-06T18:11:26.329Z"),
"engines" : [
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "ahnlab",
"node_name" : "",
"status" : 1,
"task_id" : "7fac4f67-2bde-49de-980a-4f7fa1d46db7",
"threat" : "EICAR_Test_File"
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "avast",
"node_name" : "",
"status" : 4,
"task_id" : "fdce50e9-9bf3-4cc9-91a4-b674a108d478",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "avg",
"node_name" : "",
"status" : 1,
"task_id" : "4bdb4a37-80ab-4631-8587-edcbde7e2592",
"threat" : "EICAR_Test"
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "avira",
"node_name" : "",
"status" : 4,
"task_id" : "43b43bab-ff67-440c-9919-f6241ccaf539",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "bitdefender",
"node_name" : "",
"status" : 4,
"task_id" : "ba7efe74-ef93-465f-b6c3-6c4fc746934d",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "comodo",
"node_name" : "",
"status" : 4,
"task_id" : "831d2dc8-704b-4eb7-9a75-9a0364a8ab09",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "drweb",
"node_name" : "",
"status" : 4,
"task_id" : "ebb758c3-9146-4a99-b36f-0fb6ee024a33",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "f-prot",
"node_name" : "",
"status" : 4,
"task_id" : "13590a25-ac0d-4b1b-b93e-bc715009432a",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "forticlient",
"node_name" : "",
"status" : 4,
"task_id" : "",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "kaspersky",
"node_name" : "",
"status" : 4,
"task_id" : "",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "malwarebytes",
"node_name" : "",
"status" : 4,
"task_id" : "a97f3d9a-d6a9-44df-8355-c053e9f4980a",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "mcafee",
"node_name" : "",
"status" : 1,
"task_id" : "e7a592bb-84fc-4c47-a1ed-3719874b19cc",
"threat" : "EICAR test file"
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "msessentials",
"node_name" : "",
"status" : 4,
"task_id" : "01c9fb71-155a-473d-b45c-91fa117ae649",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "nod32",
"node_name" : "",
"status" : 4,
"task_id" : "a715cc8f-0e2f-4698-b883-a35fa6add13e",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "norman",
"node_name" : "",
"status" : 4,
"task_id" : "29da2955-0674-45d2-ac4d-c0b3ea401cba",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "norton",
"node_name" : "",
"status" : 4,
"task_id" : "c4cd1e3e-2f0d-4bf6-84cf-ab0962f7f4ed",
"threat" : ""
},
{
"definitions" : ISODate("2000-01-01T00:00:00.000Z"),
"name" : "panda",
"node_name" : "",
"status" : 4,
"task_id" : "b0888f7c-e4a4-4b4e-b163-21d283e166f1",
"threat" : ""
}
],
"expiration_date" : ISODate("2013-10-06T18:11:36.329Z"),
"file_name" : "ffgtr.exe",
"scan_status" : "DONE",
"task_id" : "4ce4ae9e-ef0a-476a-8189-92a5bfe328bd"
}

然后使用该结构就像处理 $unwind 一样简单然后是 $group为您的结果:

db.collection.aggregate([
{ "$unwind": "$engines" },
{ "$group": {
"_id": "$engines.name",
"avgInterval": { "$avg": {
"$subtract": [ "$date", "$engines.definitions" ]
}}
}}
])

两个日期对象之间的差异将以毫秒为单位。

否则,您将无法使用带有 mapReduce 的 JavaScript 遍历代码中的对象键:

db.collection.mapReduce(
function() {
var doc = this;
Object.keys(doc.engines).forEach(function(key) {
emit( key,
( doc.date.valueOf() -
doc.engines[key].definitions.valueOf())
);
})
},
function(key,values) {
return ( Array.sum(values) / values.length );
},
{ "out": { "inline": 1 } }
);

两者都会为您提供集合或其他查询输入中每个引擎“名称”的平均值。

尝试更改结构,因为它更适合您的持续需求并提供更快的处理速度。

关于mongodb - 嵌入式文档的汇总平均值,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/31228608/

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