I have a meteor app that takes the text of an article and splits it into paragraphs, sentences, words, and characters and then stores that in a json which I then save as a document in a collection. The document I am testing now ends up as 15133 bytes in mongodb.
When I insert the document it takes about 20 or 30 seconds to insert. Then sometimes it starts going through my article creation routine again and inserts another document. Sometimes it ends up inserting 3 or more documents. Sometimes it behaves as it should and only inserts 1 document into the collection.
What should I be looking for that could be causing this behavior?
Here is my code, as requested:
Meteor.methods({
'createArticle': function (text, title) {
var article = {}
article.title = title
article.userID = "sdfgsdfg"
article.text = text
article.paragraphs = []
var paragraphs = splitArticleIntoParagraphs(text)
console.log("paragraphs", paragraphs)
_.each(paragraphs, function (paragraph, p) {
if (paragraph !== "") {
console.log("paragraph", paragraph)
article.paragraphs[p] = {}
article.paragraphs[p].read = false
article.paragraphs[p].text = paragraph
console.log("paragraphs[p]", article.paragraphs[p])
var sentences = splitParagraphIntoSentences(paragraph)
article.paragraphs[p].sentences = []
}
_.each(sentences, function (sentence, s) {
if (sentence !== "") {
article.paragraphs[p].sentences[s] = {}
console.log("sentence", sentence)
article.paragraphs[p].sentences[s].text = sentence
article.paragraphs[p].sentences[s].read = false
console.log("paragraphs[p].sentences[s]", article.paragraphs[p].sentences[s])
var wordsForward = splitSentenceIntoWordsForward(sentence)
console.log("wordsForward", JSON.stringify(wordsForward))
article.paragraphs[p].sentences[s].forward = {}
article.paragraphs[p].sentences[s].forward.words = wordsForward
// var wordsReverse = splitSentenceIntoWordsReverse(sentence)
_.each(wordsForward, function (word, w) {
if (word) {
// console.log("word", JSON.stringify(word))
// article.paragraphs[p].sentences[s] = {}
// article.paragraphs[p].sentences[s].forward = {}
// article.paragraphs[p].sentences[s].forward.words = []
article.paragraphs[p].sentences[s].forward.words[w] = {}
article.paragraphs[p].sentences[s].forward.words[w].wordID = word._id
article.paragraphs[p].sentences[s].forward.words[w].simp = word.simp
article.paragraphs[p].sentences[s].forward.words[w].trad = word.trad
console.log("word.simp", word.simp)
var characters = word.simp.split('')
console.log("characters", characters)
article.paragraphs[p].sentences[s].forward.words[w].characters = []
_.each(characters, function (character, c) {
if (character) {
console.log("character", character, p, s, w, c)
article.paragraphs[p].sentences[s].forward.words[w].characters[c] = {}
article.paragraphs[p].sentences[s].forward.words[w].characters[c].text = character
article.paragraphs[p].sentences[s].forward.words[w].characters[c].wordID = Words.findOne({simp: character})._id
}
})
}
})
}
})
})
// console.log("article", JSON.stringify(article))
// console.log(JSON.stringify(article.paragraphs[10].sentences[1].forward))//.words[4].characters[0])
console.log("done")
var id = Articles.insert(article)
console.log("id", id)
return id
}
})
I call the method here:
Template.articleList.events({
"click #addArticle": function(event) {
event.preventDefault();
var title = $('#title').val();
var text = $('#text').val();
$('#title').value = '';
$('#text').value = '';
$('#text').attr('rows', '3');
Meteor.call('createArticle', text, title);
}
})
An important thing to keep in mind is that meteor methods do not work very well when it comes to perform CPU intensive tasks. Since your meteor server only works in a single thread, any kind of blocking computations - like yours - will affect all client connections, e.g. delaying DDP heartbeats. This - in turn - can result in clients thinking that the connection was dropped.
As #ghybs suggested in one of the comments your method is probably triggered several times by an impatient DDP client who thinks that server has disconnected. The easiest way to prevent this behavior is by adding noRetry flag to Meteor.apply as explained here:
https://docs.meteor.com/api/methods.html#Meteor-apply
I believe Meteor.call does not have this option.
Another strategy would be trying to make sure that your methods are idempotent, i.e. calling them more than once should not produce any additional effects. This is usually true - at least when you use method simulation - because retrying db insert will re-use the same document id which will fail on the second try. For some reason this is not happening in your case.
Finally, the problem you described clearly shows that probably a different pattern should be used for a computationally expensive task like yours. If I were you, I would start by splitting the job in several steps:
First I would make sure that the document is uploaded to the server with a POST request rather than through DDP.
Then I would implement a "process file" server side method that grabs the file which is already on the server or in the database (in case you used files collection). The first thing the method should do would be calling this.unblock(), but thats not all.
Ideally the computational task should be executed in a separated process. Only when that process is completed the method would return telling the actual caller that the job is done. But since we called this.unblock() that caller can perform different tasks, e.g. calling other methods/subscriptions while waiting for the result.
Sometimes, having a separated process will not be good enough. I've experienced situations where I had to delegate the task to another worker server(s).
Related
Below is the code written by my collegue who doesnt work in the firm anymore. I am inserting records in object with data loader and I can see success message but I do not see any records in my object. I am not able to understand what below trigger is doing.Please someone help me understand as I am new to salesforce.
trigger DataLoggingTrigger on QMBDataLogging__c (after insert) {
Map<string,Schema.RecordTypeInfo> recordTypeInfo = Schema.SObjectType.QMB_Initial_Letter__c.getRecordTypeInfosByName();
List<QMBDataLogging__c> logList = (List<QMBDataLogging__c>)Trigger.new;
List<Sobject> sobjList = (List<Sobject>)Type.forName('List<'+'QMB_Initial_Letter__c'+'>').newInstance();
Map<string, QMBLetteTypeToVfPage__c> QMBLetteTypeToVfPage = QMBLetteTypeToVfPage__c.getAll();
Map<String,QMBLetteTypeToVfPage__c> mapofLetterTypeRec = new Map<String,QMBLetteTypeToVfPage__c>();
set<Id>processdIds = new set<Id>();
for(string key : QMBLetteTypeToVfPage.keyset())
{
if(!mapofLetterTypeRec.containsKey(key)) mapofLetterTypeRec.put(QMBLetteTypeToVfPage.get(Key).Letter_Type__c, QMBLetteTypeToVfPage.get(Key));
}
for(QMBDataLogging__c log : logList)
{
Sobject logRecord = (sobject)log;
Sobject QMBLetterRecord = new QMB_Initial_Letter__c();
if(mapofLetterTypeRec.containskey(log.Field1__c))
{
string recordTypeId = recordTypeInfo.get(mapofLetterTypeRec.get(log.Field1__c).RecordType__c).isAvailable() ? recordTypeInfo.get(mapofLetterTypeRec.get(log.Field1__c).RecordType__c).getRecordTypeId() : recordTypeInfo.get('Master').getRecordTypeId();
string fieldApiNames = mapofLetterTypeRec.containskey(log.Field1__c) ? mapofLetterTypeRec.get(log.Field1__c).FieldAPINames__c : '';
//QMBLetterRecord.put('Letter_Type__c',log.Name);
QMBLetterRecord.put('RecordTypeId',tgh);
processdIds.add(log.Id);
if(string.isNotBlank(fieldApiNames) && fieldApiNames.contains(','))
{
Integer i = 1;
for(string fieldApiName : fieldApiNames.split(','))
{
string logFieldApiName = 'Field'+i+'__c';
fieldApiName = fieldApiName.trim();
system.debug('fieldApiName=='+fieldApiName);
Schema.DisplayType fielddataType = getFieldType('QMB_Initial_Letter__c',fieldApiName);
if(fielddataType == Schema.DisplayType.Date)
{
Date dateValue = Date.parse(string.valueof(logRecord.get(logFieldApiName)));
QMBLetterRecord.put(fieldApiName,dateValue);
}
else if(fielddataType == Schema.DisplayType.DOUBLE)
{
string value = (string)logRecord.get(logFieldApiName);
Double dec = Double.valueOf(value.replace(',',''));
QMBLetterRecord.put(fieldApiName,dec);
}
else if(fielddataType == Schema.DisplayType.CURRENCY)
{
Decimal decimalValue = Decimal.valueOf((string)logRecord.get(logFieldApiName));
QMBLetterRecord.put(fieldApiName,decimalValue);
}
else if(fielddataType == Schema.DisplayType.INTEGER)
{
string value = (string)logRecord.get(logFieldApiName);
Integer integerValue = Integer.valueOf(value.replace(',',''));
QMBLetterRecord.put(fieldApiName,integerValue);
}
else if(fielddataType == Schema.DisplayType.DATETIME)
{
DateTime dateTimeValue = DateTime.valueOf(logRecord.get(logFieldApiName));
QMBLetterRecord.put(fieldApiName,dateTimeValue);
}
else
{
QMBLetterRecord.put(fieldApiName,logRecord.get(logFieldApiName));
}
i++;
}
}
}
sobjList.add(QMBLetterRecord);
}
if(!sobjList.isEmpty())
{
insert sobjList;
if(!processdIds.isEmpty()) DeleteDoAsLoggingRecords.deleteTheProcessRecords(processdIds);
}
Public static Schema.DisplayType getFieldType(string objectName,string fieldName)
{
SObjectType r = ((SObject)(Type.forName('Schema.'+objectName).newInstance())).getSObjectType();
DescribeSObjectResult d = r.getDescribe();
return(d.fields.getMap().get(fieldName).getDescribe().getType());
}
}
You might be looking in the wrong place. Check if there's an unit test written for this thing (there should be one, especially if it's deployed to production), it should help you understand how it's supposed to be used.
You're inserting records of QMBDataLogging__c but then it seems they're immediately deleted in DeleteDoAsLoggingRecords.deleteTheProcessRecords(processdIds). Whether whatever this thing was supposed to do succeeds or not.
This seems to be some poor man's CSV parser or generic "upload anything"... that takes data stored in QMBDataLogging__c and creates QMB_Initial_Letter__c out of it.
QMBLetteTypeToVfPage__c.getAll() suggests you could go to Setup -> Custom Settings, try to find this thing and examine. Maybe it has some values in production but in your sandbox it's empty and that's why essentially nothing works? Or maybe some values that are there are outdated?
There's some comparison if what you upload into Field1__c can be matched to what's in that custom setting. I guess you load some kind of subtype of your QMB_Initial_Letter__c in there. Record Type name and list of fields to read from your log record is also fetched from custom setting based on that match.
Then this thing takes what you pasted, looks at the list of fields in from the custom setting and parses it.
Let's say the custom setting contains something like
Name = XYZ, FieldAPINames__c = 'Name,SomePicklist__c,SomeDate__c,IsActive__c'
This thing will look at first record you inserted, let's say you have the CSV like that
Field1__c,Field2__c,Field3__c,Field4__c
XYZ,Closed,2022-09-15,true
This thing will try to parse and map it so eventually you create record that a "normal" apex code would express as
new QMB_Initial_Letter__c(
Name = 'XYZ',
SomePicklist__c = 'Closed',
SomeDate__c = Date.parse('2022-09-15'),
IsActive__c = true
);
It's pretty fragile, as you probably already know. And because parsing CSV is an art - I expect it to absolutely crash and burn when text with commas in it shows up (some text,"text, with commas in it, should be quoted",more text).
In theory admin can change mapping in setup - but then they'd need to add new field anyway to the loaded file. Overcomplicated. I guess somebody did it to solve issue with Record Type Ids - but there are better ways to achieve that and still have normal CSV file with normal columns and strong type matching, not just chucking everything in as strings.
In theory this lets you have "jagged" csv files (row 1 having 5 fields, row 2 having different record type and 17 fields? no problem)
Your call whether it's salvageable or you'd rather ditch it and try normal loading of QMB_Initial_Letter__c records. (get back to your business people and ask for requirements?) If you do have variable number of columns at source - you'd need to standardise it or group the data so only 1 "type" of records (well, whatever's in that "Field1__c") goes into each file.
I've got an observable watching a log that is continuously being written too. Each line is a new onNext call. Sometimes the log outputs a single log item over multiple lines. Detecting this is easy, I just can't find the right RX call.
I'd like to find a way to collect the single log items into a List of lines, and onNext the list when the single log item is complete.
Buffer doesn't seem right as this isn't time based, it's algorithm based.
GroupBy might be what I want, but the documentation is confusing for it. It also seems that the observables it creates probably won't have onComplete called until the completion of the source observable.
This solution can't delay the log much (preferably not at all). I need to be reading the log as close to real time as possible, and order matters.
Any push in the right direction would be great.
This is a typical reactive parsing problem. You could use Rxx Parsers, or for a native solution you can build your own state machine with either Scan or by defining an async iterator. Scan is preferable for simple parsers and often uses a Scan-Where-Select pattern.
Async iterator state machine example: Turnstile
Scan parser example (untested):
IObservable<string> lines = ReadLines();
IObservable<IReadOnlyList<string>> parsed = lines.Scan(
new
{
ParsingItem = (IEnumerable<string>)null,
Item = (IEnumerable<string>)null
},
(state, line) =>
// I'm assuming here that items never span lines partially.
IsItem(line)
? IsItemLastLine(line)
? new
{
ParsingItem = (IEnumerable<string>)null,
Item = (state.ParsingItem ?? Enumerable.Empty<string>()).Concat(line)
}
: new
{
ParsingItem = (state.ParsingItem ?? Enumerable.Empty<string>()).Concat(line),
Item = (List<string>)null
}
: new
{
ParsingItem = (IEnumerable<string>)null,
Item = new[] { line }
})
.Where(result => result.Item != null)
.Select(result => result.Item.ToList().AsReadOnly());
I have a publication, essentially what's below:
Meteor.publish('entity-filings', function publishFunction(cik, queryArray, limit) {
if (!cik || !filingsArray)
console.error('PUBLICATION PROBLEM');
var limit = 40;
var entityFilingsSelector = {};
if (filingsArray.indexOf('all-entity-filings') > -1)
entityFilingsSelector = {ct: 'filing',cik: cik};
else
entityFilingsSelector = {ct:'filing', cik: cik, formNumber: { $in: filingsArray} };
return SB.Content.find(entityFilingsSelector, {
limit: limit
});
});
I'm having trouble with the filingsArray part. filingsArray is an array of regexes for the Mongo $in query. I can hardcode filingsArray in the publication as [/8-K/], and that returns the correct results. But I can't get the query to work properly when I pass the array from the router. See the debugged contents of the array in the image below. The second and third images are the client/server debug contents indicating same content on both client and server, and also identical to when I hardcode the array in the query.
My question is: what am I missing? Why won't my query work, or what are some likely reasons it isn't working?
In that first screenshot, that's a string that looks like a regex literal, not an actual RegExp object. So {$in: ["/8-K/"]} will only match literally "/8-K/", which is not the same as {$in: [/8-K/]}.
Regexes are not EJSON-able objects, so you won't be able to send them over the wire as publish function arguments or method arguments or method return values. I'd recommend sending a string, then inside the publish function, use new RegExp(...) to construct a regex object.
If you're comfortable adding new methods on the RegExp prototype, you could try making RegExp an EJSON-able type, by putting this in your server and client code:
RegExp.prototype.toJSONValue = function () {
return this.source;
};
RegExp.prototype.typeName = function () {
return "regex";
}
EJSON.addType("regex", function (str) {
return new RegExp(str);
});
After doing this, you should be able to use regexes as publish function arguments, method arguments and method return values. See this meteorpad.
/8-K/.. that's a weird regex. Try /8\-K/.
A minus (-) sign is a range indicator and usually used inside square brackets. The reason why it's weird because how could you even calculate a range between 8 and K? If you do not escape that, it probably wouldn't be used to match anything (thus your query would not work). Sometimes, it does work though. Better safe than never.
/8\-K/ matches the string "8-K" anywhere once.. which I assume you are trying to do.
Also it would help if you would ensure your publication would always return something.. here's a good area where you could fail:
if (!cik || !filingsArray)
console.error('PUBLICATION PROBLEM');
If those parameters aren't filled, console.log is probably not the best way to handle it. A better way:
if (!cik || !filingsArray) {
throw "entity-filings: Publication problem.";
return false;
} else {
// .. the rest of your publication
}
This makes sure that the client does not wait unnecessarily long for publications statuses as you have successfully ensured that in any (input) case you returned either false or a Cursor and nothing in between (like surprise undefineds, unfilled Cursors, other garbage data.
From what I understood here, "V8 has a generational garbage collector. Moves objects aound randomly. Node can’t get a pointer to raw string data to write to socket." so I shouldn't store data that comes from a TCP stream in a string, specially if that string becomes bigger than Math.pow(2,16) bytes. (hope I'm right till now..)
What is then the best way to handle all the data that's comming from a TCP socket ? So far I've been trying to use _:_:_ as a delimiter because I think it's somehow unique and won't mess around other things.
A sample of the data that would come would be something_:_:_maybe a large text_:_:_ maybe tons of lines_:_:_more and more data
This is what I tried to do:
net = require('net');
var server = net.createServer(function (socket) {
socket.on('connect',function() {
console.log('someone connected');
buf = new Buffer(Math.pow(2,16)); //new buffer with size 2^16
socket.on('data',function(data) {
if (data.toString().search('_:_:_') === -1) { // If there's no separator in the data that just arrived...
buf.write(data.toString()); // ... write it on the buffer. it's part of another message that will come.
} else { // if there is a separator in the data that arrived
parts = data.toString().split('_:_:_'); // the first part is the end of a previous message, the last part is the start of a message to be completed in the future. Parts between separators are independent messages
if (parts.length == 2) {
msg = buf.toString('utf-8',0,4) + parts[0];
console.log('MSG: '+ msg);
buf = (new Buffer(Math.pow(2,16))).write(parts[1]);
} else {
msg = buf.toString() + parts[0];
for (var i = 1; i <= parts.length -1; i++) {
if (i !== parts.length-1) {
msg = parts[i];
console.log('MSG: '+msg);
} else {
buf.write(parts[i]);
}
}
}
}
});
});
});
server.listen(9999);
Whenever I try to console.log('MSG' + msg), it will print out the whole buffer, so it's useless to see if something worked.
How can I handle this data the proper way ? Would the lazy module work, even if this data is not line oriented ? Is there some other module to handle streams that are not line oriented ?
It has indeed been said that there's extra work going on because Node has to take that buffer and then push it into v8/cast it to a string. However, doing a toString() on the buffer isn't any better. There's no good solution to this right now, as far as I know, especially if your end goal is to get a string and fool around with it. Its one of the things Ryan mentioned # nodeconf as an area where work needs to be done.
As for delimiter, you can choose whatever you want. A lot of binary protocols choose to include a fixed header, such that you can put things in a normal structure, which a lot of times includes a length. In this way, you slice apart a known header and get information about the rest of the data without having to iterate over the entire buffer. With a scheme like that, one can use a tool like:
node-buffer - https://github.com/substack/node-binary
node-ctype - https://github.com/rmustacc/node-ctype
As an aside, buffers can be accessed via array syntax, and they can also be sliced apart with .slice().
Lastly, check here: https://github.com/joyent/node/wiki/modules -- find a module that parses a simple tcp protocol and seems to do it well, and read some code.
You should use the new stream2 api. http://nodejs.org/api/stream.html
Here are some very useful examples: https://github.com/substack/stream-handbook
https://github.com/lvgithub/stick
I've got a piece of code that opens a data reader and for each record (which contains a url) downloads & processes that page.
What's the simplest way to make it multi-threaded so that, let's say, there are 10 slots which can be used to download and process pages in simultaneousy, and as slots become available next rows are being read etc.
I can't use WebClient.DownloadDataAsync
Here's what i have tried to do, but it hasn't worked (i.e. the "worker" is never ran):
using (IDataReader dr = q.ExecuteReader())
{
ThreadPool.SetMaxThreads(10, 10);
int workerThreads = 0;
int completionPortThreads = 0;
while (dr.Read())
{
do
{
ThreadPool.GetAvailableThreads(out workerThreads, out completionPortThreads);
if (workerThreads == 0)
{
Thread.Sleep(100);
}
} while (workerThreads == 0);
Database.Log l = new Database.Log();
l.Load(dr);
ThreadPool.QueueUserWorkItem(delegate(object threadContext)
{
Database.Log log = threadContext as Database.Log;
Scraper scraper = new Scraper();
dc.Product p = scraper.GetProduct(log, log.Url, true);
ManualResetEvent done = new ManualResetEvent(false);
done.Set();
}, l);
}
}
You do not normally need to play with the Max threads (I believe it defaults to something like 25 per proc for worker, 1000 for IO). You might consider setting the Min threads to ensure you have a nice number always available.
You don't need to call GetAvailableThreads either. You can just start calling QueueUserWorkItem and let it do all the work. Can you repro your problem by simply calling QueueUserWorkItem?
You could also look into the Parallel Task Library, which has helper methods to make this kind of stuff more manageable and easier.