这是我的db集合文档。
{
"_id" : ObjectId("55e86e98f493590878bb45d7"),
"RecordID" : 84096380,
"FN" : "Michael",
"MI" : "",
"LN" : "Horn",
"NAME_PRE" : "MR",
"ADDR" : "160 Yankee Camp Rd",
"CITY" : "Telford",
"ST" : "TN",
"ZIP" : 37690,
"APT" : "",
"Z4" : 2200,
"DPC" : 605,
"CAR_RTE" : "R001",
"WALK_SEQ" : 228,
"LOT" : "0136A",
"FIPS_ST" : 47,
"FIPS_CTY" : 179,
"LATITUDE" : 36.292787,
"LONGITUDE" : -82.568171,
"ADDR_TYP" : 1,
"MSA" : 3660,
"CBSA" : 27740,
"ADDR_LINE" : 3,
"DMA_SUPPR" : "",
"GEO_MATCH" : 1,
"CENS_TRACT" : 61900,
"CENS_BLK_GRP" : 1,
"CENS_BLK" : 17,
"CENS_MED_HOME_VALUE" : 953,
"CENS_MED_HH_INCOME" : 304,
"CRA" : "",
"Z4_TYP" : "S",
"DSF_IND" : 1,
"DPD_IND" : "N",
"PHONE_FLAG" : "Y",
"PHONE" : NumberLong("4237730233"),
"TIME_ZN" : "E",
"GENDER" : "M",
"NEW_TO_BLD" : "",
"SOURCES" : 19,
"BASE_VER_DT" : 20101,
"COMP_ID" : NumberLong("3769001836"),
"IND_ID" : 1,
"INF_HH_RANK" : 1,
"HOME_OWNR_SRC" : "V",
"DOB_YR" : 1975,
"DOB_MON" : 7,
"DOB_DAY" : 10,
"EXACT_AGE" : 39,
"AGE" : 39,
"HH_INCOME" : "D",
"NET_WORTH" : "G",
"CREDIT_LINES" : "",
"CREDIT_RANGE_NEW" : "",
"EDUC" : "A",
"OCC_OCCUP" : "E",
"OCC_OCCUP_DET" : "",
"OCC_BUSN_OWNR" : "",
"NUM_KIDS" : 3,
"PRES_KIDS" : "Y",
"KIDS_M_0_2" : "",
"KIDS_F_0_2" : "",
"KIDS_U_0_2" : "Y",
"KIDS_M_3_5" : "",
"KIDS_F_3_5" : "",
"KIDS_U_3_5" : "Y",
"KIDS_M_6_10" : "",
"KIDS_F_6_10" : "",
"KIDS_U_6_10" : "",
"KIDS_M_11_15" : "",
"KIDS_F_11_15" : "",
"KIDS_U_11_15" : "",
"KIDS_M_16_17" : "",
"KIDS_F_16_17" : "",
"KIDS_U_16_17" : "Y",
"HH_MARITAL_STAT" : "S",
"HOME_OWNR" : "O",
"LOR" : 7,
"DWELL_TYP" : "S",
"NUM_ADULTS" : 1,
"HH_SIZE" : 2,
"HOME_MKT_VALUE" : "F",
"GENS" : 2,
"ADULTS_M_18_24" : "",
"ADULTS_F_18_24" : "",
"ADULTS_U_18_24" : "",
"ADULTS_M_25_34" : "",
"ADULTS_F_25_34" : "",
"ADULTS_U_25_34" : "",
"ADULTS_M_35_44" : "Y",
"ADULTS_F_35_44" : "",
"ADULTS_U_35_44" : "",
"ADULTS_M_45_54" : "",
"ADULTS_F_45_54" : "",
"ADULTS_U_45_54" : "",
"ADULTS_M_55_64" : "",
"ADULTS_F_55_64" : "",
"ADULTS_U_55_64" : "",
"ADULTS_M_65_74" : "",
"ADULTS_F_65_74" : "",
"ADULTS_U_65_74" : "",
"ADULTS_M_75PLUS" : "",
"ADULTS_F_75PLUS" : "",
"ADULTS_U_75PLUS" : ""
}
我在集合中有大约200000000个文档。
以下是我的角度控制器
$http.post('/api/allsearch',
{
"everything":$scope.filterSearch ,
"searchid":$routeParams.id,
"filterId": $scope.filterId,
"zipgroup" : $scope.zipgroup
},{timeout:600000})
.success(function(data){
for(var i=0; i<data.zips.length;i++){
oneset={
"zip": data.zips[i],
"state": data.states[i],
"city": data.cities[i],
"distance": data.distances[i],
"count": data.counted_results[i]
};
$scope.totalCount+=data.counted_results[i];
$scope.results.push(oneset);
}
angular.forEach($scope.results, function (result) {
result.distance = parseFloat(result.distance);
});
$rootScope.processing=false;
$scope.filterlinkdisplay=true;
});
这些是与请求相关的节点api函数。
api.route('/allsearch')
.post(function(req,res){
var filters=req.body.everything;
var filterid=req.body.filterId;
var searchid=req.body.searchid;
var zipgroup=req.body.zipgroup;
var myObject = new Array();
Searchradius.findOne({"searchid" : searchid, user: req.decoded.id}).exec(function(err, docs) {
var asyncTasks = [];
// Loop through some items
zipgroup.forEach(function(item){
// We don't actually execute the async action here
// We add a function containing it to an array of "tasks"
asyncTasks.push(function(callback){
// Call an async function, often a save() to DB
searchingalgo(item, filters, filterid, function(pers){
myObject[item] = pers;
// Async call is done, alert via callback
callback();
});
});
});
Async.parallel(asyncTasks, function(){
//console.log(myObject);
Searchradius.update({ _id: searchid }, { $set: { ucounteds: myObject , uzips: zipgroup }}, function(err, result){
if(err) {
res.send(err);
return;
}
var fields = ['city', 'state', 'zip','distance', 'count'];
var myresults = [];
var tc=0;
var newMyobj= new Array();
co=0;
zipgroup.forEach(function(item){
tc+=myObject[item];
//myresults.push(jobj);
});
for(i=0;i<zipgroup.length;i++){
newMyobj[i]=myObject[zipgroup[i]];
}
console.log(tc);
Searchfilter.update({ _id: filterid }, { $set: { counted_results: tc }}, function(err, resultupdate){
});
res.json({
success: true,
zips: zipgroup,
states: docs.states,
cities: docs.cities,
distances: docs.distances,
counted_results : newMyobj,
});
}); //update searchradius
}); //getdata function
}); //searchradius findone
});
function searchingalgo(zip, filerobject, filterid, callback){
var query = Consumer1s.count({ "ZIP": zip});
if(filerobject.ac){
var ac = Object.keys(filerobject.ac);
var adultagecriteria= [];
ac.forEach(function(entry) {
var prop={};
prop[entry]="Y";
adultagecriteria.push(prop);
});
query.or(adultagecriteria);
}
if(filerobject.child){
var child = Object.keys(filerobject.child);
var children= [];
child.forEach(function(entry) {
var prop={};
prop[entry]="Y";
children.push(prop);
});
query.or(children);
}
if(filerobject.HH_INCOME){
query.where('HH_INCOME', filerobject.HH_INCOME);
}
if(filerobject.NET_WORTH){
query.where('NET_WORTH', filerobject.NET_WORTH);
}
if(filerobject.GENDER){
query.where('GENDER', filerobject.GENDER);
}
query.exec(function(err,dc){
if(filterid){
Searchfilter.update({ _id: filterid }, { $set: { filters: filerobject }}, function(err, result){
if(err) {
console.log(err);
return;
}
// console.log(result);
//result= dc;
callback(dc);
});
}else{
callback(dc);
}
});
}
zipgroup属于
类型[37663, 37664, 37669, 37671, 37660, 37669, 37667, 37668, 37666, 37665, 37662, 37661 ]
基本上，这个概念是根据ZIP或ADULTS_ *信息计算记录数量，基本上是文档中的所有字段。
我已经完成了对ZIP的索引。当没有过滤器但过滤器（通常喜欢）包括
时，它有助于加速{
GENDER: '',
HH_INCOME: 'A',
OCC_OCCUP: 'B',
ac:
{ ADULTS_F_18_24: true,
ADULTS_F_24_35: true,
ADULTS_M_18_24: true,
ADULTS_M_24_35: true },
child: { KIDS_F_0_2: true, KIDS_F_6_10: true }
}
查询变得太慢，节点应用程序崩溃。 1.我需要增加mongodb查询时间的方法（除了索引）。 2.我需要节点应用程序在崩溃时自动重新启动。 欢迎任何其他建议。
此外，我尝试将old_max_space_size增加到2000000，但这不起作用..