这更多是关于表现。这是场景:
此应用程序用于控制组织中的PC清单。所以应用程序有一个由32个字段和1个关系组成的模型。该模型已经保存了2650条记录。我还有一个将所有记录导出到谷歌表的过程。即使它工作正常,从我的角度来看,导出会消耗太多时间。
所以我的逻辑包括获取所有记录,循环遍历每个记录并获取每个字段的数据。然后将所有字段放入一行,最后将其保存到google工作表中;因此它看起来像这样:
var allRows ="";
header = ["Property Tag", "Status", "Building", "Department", "Floor", "Area", "Specific Location", "Serial Number", "Model", "Purchase Date", "Warranty End", "HD Size"];
header.push("Processor", "RAM", "PC Name", "MAC Address", "Monitor 1", "Monitor 1 Model", "Monitor 2", "Monitor 2 Model", "Notes", "Office", "Last Inventoried","SSO Type");
header.push("Static/Reserved IP Address", "Static IP Reason","Card Reader Installed", "Last Repair Issue", "Last Repair Date", "Created By", "Created On");
header.push("Last Modified By", "Last Modified On", "Item Type");
allRows += header.join() + "\r\n";
//get all pcItems and save them to google sheet
var pcItems = app.models.pcItems.newQuery().run();
for(i=0; i<pcItems.length; i++){
item = pcItems[i];
propTag = (item.propertyTag) ? ("'" + item.propertyTag) : "";
status = item.status || "";
building = item.building || "";
dept = item.department || "";
floor = item.floor || "";
area = item.area || "";
specLoc = (item.specificLocation) ? "'" + item.specificLocation : "";
serialNum = (item.serialNumber) ? "'" + item.serialNumber : "";
model = item.model || "";
purchase = (item.purchaseDate) ? Utilities.formatDate(item.purchaseDate, "GMT-6", "MM/dd/yyyy") : "";
warranty = (item.warrantyEnd) ? Utilities.formatDate(item.warrantyEnd, "GMT-6", "MM/dd/yyyy") : "";
hd = (item.hdSize) ? "'" + item.hdSize : "";
processor = item.processor || "";
ram = item.ram || "";
pcName = (item.pcName) ? "'" + item.pcName : "";
macAdd = (item.macAddress) ? "'" + item.macAddress : "";
monOne = (item.monitor1) ? "'" + item.monitor1 : "";
monOneMod = item.monitor1Model || "";
monTwo = (item.monitor2) ? "'" + item.monitor2 : "";
monTwoMod = item.monitor2Model || "";
notes = (item.notes) ? "'" + item.notes : "";
office = item.officeVersion || "";
lastInv = (item.lastInventoried) ? "'" + item.lastInventoried : "";
ssoType = item.ssoType || "";
staticIp = item.staticIpAddress || "";
staticIpReason = item.staticIpReason || "";
var cardReader = (item.cardReaderInstalled === true) ? true : (item.cardReaderInstalled === false) ? false : "";
createdBy = item.createdBy || "";
createdOn = (item.created) ? "'" + Utilities.formatDate(item.created, "GMT-6", "MM/dd/yyyy HH:mm") : "";
lastRepairDate = (item.lastRepairDate) ? Utilities.formatDate(item.lastRepairDate, "GMT-6", "MM/dd/yyyy") : "";
lastRepairIssue = item.lastRepairIssue || "";
hist = item.itemHistory;
if(hist.length){
lastModifiedBy = hist[hist.length-1].modifiedBy;
lastModifiedOn = (hist[hist.length-1].modified) ? ("'" + Utilities.formatDate(hist[hist.length-1].modified, "GMT-6", "MM/dd/yyyy HH:mm")) : "";
} else {
lastModifiedBy = "";
lastModifiedOn = "";
}
row = [propTag, status, building, dept, floor, area, specLoc, serialNum, model, purchase, warranty, hd];
row.push(processor, ram, pcName, macAdd, monOne, monOneMod, monTwo, monTwoMod, notes, office, lastInv, ssoType);
row.push(staticIp, staticIpReason, cardReader, lastRepairIssue, lastRepairDate, createdBy, createdOn, lastModifiedBy, lastModifiedOn, "PC");
formattedRow = [];
for(d=0; d<row.length; d++){
cellData = row[d];
if((typeof(cellData) === "string") && (cellData.indexOf(",") > -1)){
cellData = '"'+cellData+'"';
} else if(typeof(cellData) === "object"){
cellData = Utilities.formatDate(cellData, "GMT", "MM/dd/yyyy");
}
formattedRow.push(cellData);
}
csvRow = formattedRow.join();
allRows += csvRow+"\r\n";
}
var data = Utilities.newBlob("").setDataFromString(allRows, "UTF-8").setContentType("text/csv");
var newFile = Drive.Files.insert({title: fileName}, data, {convert: true});
var ss = SpreadsheetApp.openById(newFile.id);
var sheet = ss.getActiveSheet();
var fileHeader = sheet.getRange(1, 1, 1, sheet.getLastColumn());
fileHeader.setBackground("#efefef").setFontWeight("Bold").setVerticalAlignment("Middle");
sheet.setRowHeight(1, 30);
sheet.setFrozenRows(1);
var allData = sheet.getRange(1, 1, sheet.getLastRow(), sheet.getLastColumn());
allData.setNumberFormat("@");
sheet.autoResizeColumns(1, sheet.getLastColumn());
sheet.deleteColumns(sheet.getLastColumn(), 3);
return ss.getUrl();
此过程大约需要8-10分钟才能完成。我相信这可以更快地完成。我知道这是因为如果我去设置&gt;部署&gt;导出数据并导出所有数据,只需 1:30分钟。这非常快,因为它也会导出其他数据。
所以我的问题是......有谁知道更好的方法可以帮助我完成这项任务?对于此事的任何意见,我都非常感谢!
答案 0 :(得分:3)
首先,我建议您在代码中找到瓶颈。例如,您可以尝试使用console.time
和console.timeEnd
来记录执行时间。一旦你知道算法中最慢的部分,你可以解决如何改进它们。
要尝试的第二件事是使用预取。看来,现在您的脚本会调用数据库来访问每条记录的关系。因此,对DB的调用总数为N * M + 1
,其中N是记录总数,M是每条记录的关系数,1是获取无关系记录的初始调用。
var query = app.models.pcItems.newQuery();
query.prefetch.myModel._add();
var pcItems = query.run();
for (...) {
...
// after adding prefetch this line should not cause additional
// call to the database
hist = item.itemHistory;
...
}