mongo

Query failed with error code 2 and error message 'Field 'locale' is invalid in

错误写法 ： @Document(collection = "bookEntity")

正确写法一 ： @Document("bookEntity")

正确写法二 ：

• 1. 将时间进行转换
• 2. 分组求和
• 3. 字段显示

db.mg_order.aggregate({
    $group: {
        _id: {
            date: {
                "$dateToString": {
                    format: "%Y-%m-%d", date: "$paytime", timezone: "+08:00", onNull: "unspecified",
                }
            }
            , lotSn: "$parkingLotSn"
        },
        money:{$sum: "$money"}
        ,
        count: { $sum: 1 }

    }
},{
    $project: {date:"$_id_date",count:"$count",money:"$money"}
})

两次分组

db.mg_order.aggregate([
    {$group: {"_id": { "lotSn": "$parkingLotSn", "car": "$carPlate" }}},
    {$group: {"_id": { "car":"$_id.car" },count: { $sum: 1 }}
    {$sort: {count:-1}}
 ])

常用分组案例

db.mg_order.aggregate([
    {
        $group: {
            _id: {
                date: {
                    "$dateToString": {
                        format: "%Y-%m-%d", date: "$paytime", timezone: "+08:00", onNull: "unspecified",
                    }
                }
                , lotSn: "$parkingLotSn"
            },
            money: { $sum: "$money" },
            count: { $sum: 1 }
        }
    },
    {
        $project: { date: "$_id.date", lotSn: "$_id.lotSn", money: "$money", count: "$count", _id: 0 }
    },
    {
        $group: { _id: "$date", lots_ids:{$addToSet: {date:"$date",lotSn:"$lotSn",money:"$money",count:"$count"}}}
    },
    {
        $project: {date:"$_id.date", lots_ids:"$lots_ids"}
    }
])

Criteria criteria = Criteria.where("lotSn").in(lotSns).and("statisticTime").gte(start).lte(end);
MatchOperation match = Aggregation.match(criteria);
GroupOperation group = Aggregation.group("statisticDate").sum("totalTransactions").as("count")
				.sum("totalRealIncome").as("money").first("statisticTime").as("time");
SortOperation sort = Aggregation.sort(Sort.Direction.ASC, "time");
ProjectionOperation project = Aggregation.project("statisticDate", "count", "money", "time");
Aggregation aggregation = Aggregation.newAggregation(match, group, project, sort);

andExpression

ProjectionOperation subPayTime = Aggregation.project("parkingLotSn", "money")
				.andExpression("{$dateToString:{ format:'%Y-%m-%d',date: '$paytime'}}").as("subPayTime");
GroupOperation group = Aggregation.group("subPayTime", "parkingLotSn").count().as("count").sum("money")
				.as("money");
Aggregation aggregation = Aggregation.newAggregation(match, subPayTime, group);

聚合操作之管道操作与MapReduce

数据准备

db.orders.insertMany([
   { _id: 1, cust_id: "Ant O. Knee", ord_date: new Date("2020-03-01"), price: 25, items: [ { sku: "oranges", qty: 5, price: 2.5 }, { sku: "apples", qty: 5, price: 2.5 } ], status: "A" },
   { _id: 2, cust_id: "Ant O. Knee", ord_date: new Date("2020-03-08"), price: 70, items: [ { sku: "oranges", qty: 8, price: 2.5 }, { sku: "chocolates", qty: 5, price: 10 } ], status: "A" },
   { _id: 3, cust_id: "Busby Bee", ord_date: new Date("2020-03-08"), price: 50, items: [ { sku: "oranges", qty: 10, price: 2.5 }, { sku: "pears", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 4, cust_id: "Busby Bee", ord_date: new Date("2020-03-18"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 5, cust_id: "Busby Bee", ord_date: new Date("2020-03-19"), price: 50, items: [ { sku: "chocolates", qty: 5, price: 10 } ], status: "A"},
   { _id: 6, cust_id: "Cam Elot", ord_date: new Date("2020-03-19"), price: 35, items: [ { sku: "carrots", qty: 10, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 7, cust_id: "Cam Elot", ord_date: new Date("2020-03-20"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 8, cust_id: "Don Quis", ord_date: new Date("2020-03-20"), price: 75, items: [ { sku: "chocolates", qty: 5, price: 10 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 9, cust_id: "Don Quis", ord_date: new Date("2020-03-20"), price: 55, items: [ { sku: "carrots", qty: 5, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 }, { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 10, cust_id: "Don Quis", ord_date: new Date("2020-03-23"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" }
])

使用mapReduce

var mapFunction1 = function() {
   emit(this.cust_id, this.price);
};


var reduceFunction1 = function(keyCustId, valuesPrices) {
   return Array.sum(valuesPrices);
};


db.orders.mapReduce(
   mapFunction1,
   reduceFunction1,
   { out: "map_reduce_example" }
)

使用管道操作

// 根据分组将结果输出
db.orders.aggregate([
   { $group: { _id: "$cust_id", value: { $sum: "$price" } } },
   { $out: "agg_alternative_1" }
])

使用mapReduce

var mapFunction2 = function() {
    for (var idx = 0; idx < this.items.length; idx++) {
           var key = this.items[idx].sku;
       var value = { count: 1, qty: this.items[idx].qty };

       emit(key, value);
    }
};

var reduceFunction2 = function(keySKU, countObjVals) {
   var reducedVal = { count: 0, qty: 0 };

   for (var idx = 0; idx < countObjVals.length; idx++) {
       reducedVal.count += countObjVals[idx].count;
       reducedVal.qty += countObjVals[idx].qty;
   }

   return reducedVal;
};

var finalizeFunction2 = function (key, reducedVal) {
  reducedVal.avg = reducedVal.qty/reducedVal.count;
  return reducedVal;
};

db.orders.mapReduce(
   mapFunction2,
   reduceFunction2,
   {
     out: { merge: "map_reduce_example2" },
     query: { ord_date: { $gte: new Date("2020-03-01") } },
     finalize: finalizeFunction2
   }
 );

使用管道

db.orders.aggregate( [
   { $match: { ord_date: { $gte: new Date("2020-03-01") } } }, // 根据条件进行过滤
   { $unwind: "$items" }, // 将items中的数据进行展开
   { $group: { _id: "$items.sku", qty: { $sum: "$items.qty" }, orders_ids: { $addToSet: "$_id" } }  }, // 根据sku进行分组,计算qty总和,将sku放入集合
   // 数据投影: count,qty,avg=devide/count
   { $project: { value: { count: { $size: "$orders_ids" }, qty: "$qty", avg: { $divide: [ "$qty", { $size: "$orders_ids" } ] } } } },
   // 将上一步的结果存入collection中,如果匹配到进行更新，否则进行插入
   { $merge: { into: "agg_alternative_3", on: "_id", whenMatched: "replace",  whenNotMatched: "insert" } }
] )

// $addToSet 将数据放入到set集合中；  $size 统计集合中数据个数

分区间进行统计

db.mg_parkin.aggregate([
    {$match: {ioType:1}},
    {$addFields: {
        timePart:{$function: {
          body: function(stayTime) {
             if(stayTime != null){
                 if(stayTime == 1){
                     return "0-0.5h";
                 }else if(stayTime / 2 <= 2){
                     return "1-2h";
                 }else if(stayTime / 2 <= 6){
                     return "2-6h";
                 }else {
                     return "6h以上"
                 }
             } 
             return "未知"
          },
          args: [ "$stayTime"],
          lang: "js"
        }}
    }},
    {$group: { _id: "$timePart", count:{$sum: 1}}}
    ])

addFields

一次使用

AggregationOperation addFields = new AggregationOperation() {

    @Override
    public Document toDocument(AggregationOperationContext context) {
        return new Document("$addFields", new Document("name", "$name.am"));
    }

};

泛型

public AggregationOperation aggregateAddFields(final String field, final String valueExpresion) {

    AggregationOperation addFields = new AggregationOperation() {

        @Override
        public Document toDocument(AggregationOperationContext context) {
            return new Document("$addFields", new Document(field, valueExpresion));
        }
    };

    return addFields;
}
...
AggregationOperation addFields = aggregateAddFields("name", "$name.am");

---

导入BSON文件

mongorestore --host localhost --port 27017 --db dbname --collection collection_name  文件路径

地理位置使用geojson

mongodb建立地理位置索引出现错误longitude/latitude is out of bound
解决方法：
在mongodb中存储GeoJSON的格式是先存经度后存纬度，经度它的范围在±180以内，而维度在±90以内，所以出现这种问题直接找是不是经纬度顺序存储反了即可。（上面的存储顺序是正确的大家不必纠结）