mongodb内嵌数组对象字段in查询

KEIL5编译报错L6200E:__stdout重定义问题分析与解决 本文深入分析KEIL5编译报错L6200E中__stdout重定义问题的根源,指出因未完整定义__stderr导致与标准库stdio_streams.o冲突。提供完整重定义标准流、启用MicroLIB库等解决方案,帮助开发者彻底解决Symbol multiply defined错误,提升嵌入式开发效率。 阅读详情

文档结构(部分数据展示):

{
  "_id": ObjectId("5cf774a317d273d688fffffc"),
  "_class": "com.sunshine.aunt.model.po.ContractItemExpenseStatistics",
  "statisticsDatetime": "2019-05-22",
  "statisticsList": [
    {
      "ic": "0600",
      "sit": "2",
      "a1": "660.00",
      "a2": "420.00",
      "a3": "5496.00",
      "a4": "3860.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "2716.00"
    },
    {
      "ic": "0700",
      "sit": "3",
      "a1": "0.00",
      "a2": "1105.00",
      "a3": "3070.00",
      "a4": "3070.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "1105.00"
    },
    {
      "ic": "0400",
      "sit": "2",
      "a1": "330.00",
      "a2": "510.00",
      "a3": "14800.00",
      "a4": "7423.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "8217.00"
    },
    {
      "ic": "0500",
      "sit": "2",
      "a1": "20.00",
      "a2": "384.00",
      "a3": "6028.00",
      "a4": "7180.00",
      "a5": "0.00",
      "a6": "18.00",
      "a7": "400.00",
      "tt": "-748.00"
    },
    {
      "ic": "1200",
      "sit": "2",
      "a1": "1050.00",
      "a2": "1470.00",
      "a3": "29400.00",
      "a4": "21600.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "10320.00"
    },
    {
      "ic": "0100",
      "sit": "1",
      "a1": "21380.00",
      "a2": "27238.00",
      "a3": "310351.00",
      "a4": "187887.00",
      "a5": "150.00",
      "a6": "0.00",
      "a7": "2250.00",
      "tt": "171082.00"
    }
  ],
  "storeId": 1297,
  "parentStoreId": 2,
  "storeType": "B"
},
/*5createdAt: 2019/6/5下午3: 52: 03*/{
  "_id": ObjectId("5cf774a317d273d688fffffb"),
  "_class": "com.sunshine.aunt.model.po.ContractItemExpenseStatistics",
  "statisticsDatetime": "2019-05-18",
  "statisticsList": [
    {
      "ic": "0100",
      "sit": "1",
      "a1": "320.00",
      "a2": "304.00",
      "a3": "3042.00",
      "a4": "0",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "3666.00"
    }
  ],
  "storeId": 20089,
  "parentStoreId": 20079,
  "storeType": "D"
},
/*6createdAt: 2019/6/5下午3: 52: 03*/{
  "_id": ObjectId("5cf774a317d273d688fffffa"),
  "_class": "com.sunshine.aunt.model.po.ContractItemExpenseStatistics",
  "statisticsDatetime": "2019-05-04",
  "statisticsList": [
    {
      "ic": "0600",
      "sit": "2",
      "a1": "310.00",
      "a2": "70.00",
      "a3": "1400.00",
      "a4": "0",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "1780.00"
    },
    {
      "ic": "1500",
      "sit": "2",
      "a1": "0",
      "a2": "0",
      "a3": "0",
      "a4": "3300.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "-3300.00"
    },
    {
      "ic": "0300",
      "sit": "1",
      "a1": "310.00",
      "a2": "150.00",
      "a3": "2600.00",
      "a4": "0",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "3060.00"
    },
    {
      "ic": "0100",
      "sit": "1",
      "a1": "1620.00",
      "a2": "1934.00",
      "a3": "17537.00",
      "a4": "2500.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "18591.00"
    }
  ],
  "storeId": 20082,
  "parentStoreId": 20079,
  "storeType": "D"
},
/*7createdAt: 2019/6/5下午3: 52: 03*/{
  "_id": ObjectId("5cf774a317d273d688fffff9"),
  "_class": "com.sunshine.aunt.model.po.ContractItemExpenseStatistics",
  "statisticsDatetime": "2019-05-10",
  "statisticsList": [
    {
      "ic": "0600",
      "sit": "2",
      "a1": "0.00",
      "a2": "60.00",
      "a3": "1300.00",
      "a4": "1300.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "60.00"
    },
    {
      "ic": "0400",
      "sit": "2",
      "a1": "0.00",
      "a2": "0.00",
      "a3": "0",
      "a4": "646.00",
      "a5": "0.00",
      "a6": "0.00",
      "a7": "646.00",
      "tt": "-646.00"
    },
    {
      "ic": "1500",
      "sit": "2",
      "a1": "300.00",
      "a2": "60.00",
      "a3": "2800.00",
      "a4": "0",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "3160.00"
    },
    {
      "ic": "0500",
      "sit": "2",
      "a1": "310.00",
      "a2": "70.00",
      "a3": "5000.00",
      "a4": "0",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "5380.00"
    },
    {
      "ic": "0300",
      "sit": "1",
      "a1": "510.00",
      "a2": "160.00",
      "a3": "5000.00",
      "a4": "4955.00",
      "a5": "0.00",
      "a6": "0.00",
      "a7": "2255.00",
      "tt": "715.00"
    },
    {
      "ic": "1200",
      "sit": "2",
      "a1": "0.00",
      "a2": "0.00",
      "a3": "0",
      "a4": "3000.00",
      "a5": "0.00",
      "a6": "0.00",
      "a7": "115.00",
      "tt": "-3000.00"
    },
    {
      "ic": "0100",
      "sit": "1",
      "a1": "1540.00",
      "a2": "1490.00",
      "a3": "17400.00",
      "a4": "18804.00",
      "a5": "0",
      "a6": "0",
      "a7": "0",
      "tt": "1626.00"
    }
  ],
  "storeId": 20080,
  "parentStoreId": 20079,
  "storeType": "D"
}

需要实现效果:

where storeId=1297 and statisticsList.ic in ("0100", "0600")

查询出storeId=1297并且内嵌数组对象statisticsList的ic字段等于0100、0600的,不等于的不返回,这个时候就需要使用聚合方法aggregate() :

db.test.aggregate(
{$match: {storeId:1297}}
,{$unwind: "$statisticsList"}
,{$match: {"statisticsList.ic":{$in: ["0100", "0600"]}}}
,{$group: { 
    _id: "$_id",
    storeId:{$first: "$storeId"},
    storeType:{$first: "$storeType"},
    statisticsDatetime:{$first: "$statisticsDatetime"},
    statisticsList:{$push:"$statisticsList"}
}})

结果:

上面聚合代码转换为java的mongoTemplate操作Mongodb的代码为:

//封装查询条件
List<AggregationOperation> operations = new ArrayList<>();        
operations.add(Aggregation.match(Criteria.where("storeId").is(1297)));
operations.add(Aggregation.unwind("statisticsList"));
operations.add(Aggregation.match(Criteria.where("statisticsList.ic").in("0100", "0600")));

operations.add(Aggregation.group("_id")
                          .first("storeId").as("storeId")
                          .first("storeType").as("storeType")
                          .first("statisticsDatetime").as("statisticsDatetime")
                          .push("statisticsList").as("statisticsList"));

 

读懂 ABAP CDS 注解的直接、继承与派生 本文系统梳理了ABAP CDS注解的评估逻辑,重点解析直接注解(Direct)、继承注解(Inherited)和派生注解(Derived)的差异及其在CL_DD_DDL_ANNOTATION_SERVICE中的处理方式。通过视图注解@Metadata.ignorePropagatedAnnotations可调控注解传播行为,避免下游语义影响上层视图。文章还提供了基于标准表的完整示例(包含底层视图、消费视图和ABAP报告程序),帮助开发者在实际系统中验证三层注解的叠加效果与阻断机制。关键点包括:派生注解的数据 阅读详情

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plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

MongoDB CRUD操作:内嵌文档数组查询

摘要:本文介绍了mongodb内嵌文档数组的查询

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