I have an EA set in place that loops history trades and builds one large string with trade information. I then send this string every second from MT4 to the python backend using a plain PUSH/PULL pattern.
For whatever reason, the data isn't received on the pull side when the string transferred becomes too long. The backend PULL-socket slices each string and further processes it.
Any chance that the PULL-side is too slow to grab and process all the data which then causes an overflow (so that a delay arises due to the processing part)?
Talking about file sizes we are well below 5kb per second.
This is the PULL-socket, which manipulates the data after receiving it:
while True:
# check 24/7 for available data in the pull socket
try:
msg = zmq_socket.recv_string()
data = msg.split("|")
print(data)
# if data is available and msg is account info, handle as follows
if data[0] == "account_info":
[...]
except zmq.error.Again:
print("\nResource timeout.. please try again.")
sleep(0.000001)
I am a bit curious now since the pull socket seems to not even be able to process a string containing 40 trades with their according information on a single MT4 client - Python connection. I actually planned to set it up to handle more than 5.000 MT4 clients - python backend connections at once.
Q : Any chance that the pull side is too slow to grab and process all the data which then causes an overflow (so that a delay arises due to the processing part)?
Zero chance.
Sending 640 B each second is definitely no showstopper ( 5kb per second - is nowhere near a performance ceiling... )
The posted problem formulation is otherwise undecidable.
Step 1) POSACK/NACK prove whether a PUSH side accepts the payload for sending error-free.
Step 2) prove the PULL side is not to be blamed - [PUSH.send(640*chr(64+i)) for i in range( 10 )] via a python-2-python tcp://-transport-class solo-channel crossing host-to-host hop, over at least your local physical network ( no VMCI/emulated vLAN, no other localhost colocation )
Step 3) if either steps above got POSACK-ed, your next chances are the ZeroMQ configuration space and/or the MT4-based PUSH-side incompatibility, most probably "hidden" inside a (not mentioned) third party ZeroMQ wrapper used / first-party issues with string handling / processing ( which you must have already read about, as it has been so many times observed and mentioned in the past posts about this trouble with well "hidden" MQL4 internal eco-system changes ).
Anyway, stay tuned. ZeroMQ is a sure bet and a truly horsepower for professional and low-latency designs in distributed-system's domain.
I Am going to build a system for flash sale which will share the same Redis instance and will run on 15 servers at a time.
So the algorithm of Flash sale will be.
Set Max inventory for any product id in Redis
using redisTemplate.opsForValue().set(key, 400L);
for every request :
get current inventory using Long val = redisTemplate.opsForValue().get(key);
check if it is non zero
if (val == null || val == 0) {
System.out.println("not taking order....");
}
else{
put order in kafka
and decrement using redisTemplate.opsForValue().decrement(key)
}
But the problem here is concurrency :
If I set inventory 400 and test it with 500 request thread,
Inventory becomes negative,
If I make function synchronized I cannot manage it in distributed servers.
So what will be the best approach to it?
Note: I can not go for RDBMS and set isolation level because of high request count.
Redis is monothreaded, so running a Lua Script on it is always atomic.
You can define then a Lua script on your Redis instance and running it from your Spring instances.
Your Lua script would just be a sequence of operations to execute against your redis instance (the only one to have the correct value of your stock) and returns the new value for instance or an error if the value is negative.
Your Lua script is basically a Redis transaction, there are other methods to achieve Redis transaction but IMHO Lua is the simplest above all (maybe the least performant, but I have found that in most cases it is fast enough).
I need to remove duplicates from a flow I've developed, it can receive the same ${filename} multiple times. I tried using HBase_1_1_2_ClientMapCacheService with DetectDuplicate (I am using NiFi v1.4), but found that it lets a few duplicates through. If I use DistributedMapCache (ClientService and Server), I do not get any duplicates. Why would I receive some duplicates with the HBase Cache?
As a test, I listed a directory (ListSFTP) with 20,000 files on all cluster nodes (4 nodes) and passed to DetectDuplicate (using the HBase Cache service). It routed 20,020 to "non-duplicate", and interestingly the table actually has 20,000 rows.
Unfortunately I think this is due to a limitation in the operations that are offered by HBase.
The DetectDuplicate processor relies on an operation "getAndPutIfAbsent" which is expected to return the original value, and then set the new value if it wasn't there. For example, first time through it would return null and set the new value, indicating it wasn't a duplicate.
HBase doesn't natively support this operation, so the implementation of this method in the HBase map cache client does this:
V got = get(key, keySerializer, valueDeserializer);
boolean wasAbsent = putIfAbsent(key, value, keySerializer, valueSerializer);
if (! wasAbsent) return got;
else return null;
So because it is two separate calls there is a possible race condition...
Imagine node 1 calls the first line and gets null, but then node 2 performs the get and the putIfAbsent, now when node 1 calls putIfAbsent it gets false because node 2 just populated the cache, so now node 1 returns the null value from the original get... both of these look like non-duplicates to DetectDuplicate.
In the DistributedMapCacheServer, it locks the entire cache per operation so it can provide an atomic getAndPutIfAbsent.
I have two different sources of data which I need to marry together. Data set A will have a foo_key attribute which can map to Data set B's bar_key attribute with a one to many relationship.
Data set A:
[{ foo_key: 12345, other: 'blahblah' }, ...]
Data set B:
[{ bar_key: 12345, other: '' }, { bar_key: 12345, other: '' }, { bar_key: 12345, other: '' }, ...]
Data set A is coming from a SQS queue and any relationships with data set B will be available as I poll A.
Data set B is coming from a separate SQS queue that I am trying to dump into a memcached cache to do quick look ups on when an object drops into data set A.
Originally I was planning on setting the memcached key to be bar_key from the objects in data set B but then realized that if I did that it would be possible to overwrite the value since there can be many of the same bar_key value. Then I was thinking well I can create a key bar_key and the value just be an array of the SQS messages. But since I have multiple hosts polling the SQS queue I think it might be possible that when I check to see if the key is in memcached, check it out, append the new message to it, and then set it, that another host could be trying to preform the same operation and thus the first host's attempt at appending the value would just be overwritten.
I've looked around at memcached key locking but I'm not sure I understand it entirely. Would the solution be that when I get the key/value pair from memcached I create a temporary dummy lock on a new key called bar_key_dummy that expires in x seconds, and if I try to fetch a key that has a bar_key_dummy lock active I just send the SQS message back to the queue without deleting to try again in x seconds?
Here's some pseudocode for what I have going on in my head. Does this make any sense?
store = MemCache.new(host)
sqs_messages.poll do |message|
dummy_key = "#{message.bar_key}_dummy"
sqs.dont_delete_message && next unless store.get(dummy_key).nil?
# set dummy_key in memcache with a value of 1 for 3 seconds
store.set(dummy_key, 1, 3)
temp_data = store.get(message.bar_key) || []
temp_data << message
store.set(message.bar_key, temp_data, 300)
# delete dummy key when done in case shorter than x seconds
store.delete(dummy_key)
end
Thanks for any help!
Memcached has a special operation - cas Compare and Swap.
Command gets returns Item along with its unique CAS value.
Then dataset can be searched and update must be issued with the cas command which takes original unique CAS value.
If CAS was changed in between two command, update operation will fail with the EXISTS error
Retrieving data from mongo takes too long, even for small datasets. For bigger datasets we get out of memory errors of the javascript engine. We've tried several schema designs and several ways to retrieve data. How do we optimize mongoDB/mapReduce function/MongoWire to retrieve more data quicker?
We're not very experienced with MongoDB yet and are therefore not sure whether we're missing optimization steps or if we're just using the wrong tools.
1. Background
For graphing and playback purposes we want to store changes for several objects over time. Currently we have tens of objects per project, but expectations are we need to store thousands of objects. The objects may change every second or not change for long periods of time. A Delphi backend writes to and reads from MongoDB through MongoWire and SuperObjects, the data is displayed in a web frontend.
2. Schema design
We're storing the object changes in minute-second-millisecond objects in a record per hour. The schema design is like described here. Sample:
o: object1,
dt: $date,
v: {0: {0:{0: {speed: 8, rate: 0.8}}}, 1: {0:{0: {speed: 9}}}, …}
We've put indexes on {dt: -1, o: 1} and {o:1}.
3. Retrieving data
We use a mapReduce to construct a new date based on the minute-second-millisecond objects and to put the object back in v:
o: object1,
dt: $date,
v: {speed: 8, rate:0.8}
An average document is about 525 kB before the mapReduce function and has had ~29000 updates. After mapReduce of such a document, the result is about 746 kB.
3.1 Retrieving data from through mongo shell with mapReduce
We're using the following map function:
function mapF(){
for (var i = 0; i < 3600; i++){
var imin = Math.floor(i / 60);
var isec = (i % 60);
var min = ''+imin;
var sec = ''+isec;
if (this.v.hasOwnProperty(min) && this.v[min].hasOwnProperty(sec)) {
for (var ms in this.v[min][sec]) {
if (imin !== 0 && isec !== 0 && ms !== '0' && this.v[min][sec].hasOwnProperty(ms)) {// is our keyframe
var currentV = this.v[min][sec][ms];
//newT is new date computed by the min, sec, ms above
if (toDate > newT && newT > fromDate) {
if (fields && fields.length > 0) {
for (var p = 0, length = fields.length; p < length; p++){
//check if field is present and put it in newV
}
if (newV) {
emit(this.o, {vs: [{o: this.o, dt: newT, v: newV}]});
}
} else {
emit(this.o, {vs: [{o: this.o, dt: newT, v: currentV}]});
}
}
}
}
}
}
};
The reduce function basically just passes the data on. The call to mapReduce:
db.collection.mapReduce( mapF,reduceF,
{out: {inline: 1},
query: {o: {$in: objectNames]}, dt: {$gte: keyframeFromDate, $lt: keyframeToDate}},
sort: {dt: 1},
scope: {toDate: toDateWithinKeyframe, fromDate: fromDateWithinKeyframe, fields: []},
jsMode: true});
Retrieving 2 objects over 1 hour: 2,4 seconds.
Retrieving 2 objects over 5 hour: 8,3 seconds.
For this method we would have to write js and bat files runtime and read the json data back in. We have not measured times fort his yet, because frankly, we don’t like the idea very much.
Another problem with this method is that we get out of memory errors of the v8 javascript engine when we try to retrieve data for longer periods and/or more objects. Using a pc with more RAM works to some extend in preventing out of memory, but it doesn't make retrieving data faster.
This article mentions splitVector, which we might use to devide the workload. But we're not sure on how to use the keyPattern and maxChunkSizeBytes options. Can we use a keyPattern for both o and dt?
We might use multiple collections, but our dataset isn’t that big to start with at the moment, so we’re worried about how much collections we’d need.
3.2 Retrieving data through mongoWire with mapReduce
For retrieving data through mongoWire with mapReduce, we use the same mapReduce functions as above. We use the following Delphi code to start te query:
FMongoWire.Get('$cmd',BSON([
'mapreduce', ‘collection’,
'map', bsonJavaScriptCodePrefix + FMapVCRFunction.Text,
'reduce', bsonJavaScriptCodePrefix + FReduceVCRFunction.Text,
'out', BSON(['inline', 1]),
'query', mapquery,
'sort', BSON(['dt', -1]),
'scope', scope
]));
Retrieving data with this method is about 3-4 times (!) slower. And then the data has to be translated from BSON (IBSONDocument to JSON (SuperObject), which is a major time consuming part in this method. For retrieving raw data we use TMongoWireQuery which translates the BSONdocument in parts, while this mapReduce function uses TMongoWire directly and tries to translate the complete result. This might explain why this takes so long, while normally it's quite fast. If we can reduce the time it takes for the mapReduce to return results, this might be a next step for us to focus on.
3.3 Retrieving raw data and parsing in Delphi
Retrieving raw data to Delphi takes a bit longer then the previous method, but probably because of the use of TMongoWireQuery, the translation from BSON to JSON is much quicker.
4. Questions
Can we do further optimizations on our schema design?
How can we make the mapReduce function faster?
How can we prevent the out of
memory errors of the v8 engine? Can someone give more information on
the splitVector function?
How can we best use of mapReduce from Delphi? Can we use
MongoWireQuery in stead of MongoWire?
5. Specs
MongoDB 3.0.3
MongoWire from 2015 (recently updated)
Delphi 2010 (got XE5 as well)
4GB RAM (tried on 8GB RAM as well, less out of memory, but reading times are about the same)
Phew what a question! First up: I'm not an expert at MongoDB. I wrote TMongoWire as a way to get to know MongoDB a little. Also I really (really) dislike when wrappers have a plethora of overloads to do the same thing but for all kinds of specific types. A long time ago programmers didn't have generics, but we did have Variant. So I built a MongoDB wrapper (and IBSONDocument) based around variants. That said, I apparently made something people like to use, and by keeping it simple performs quite well. (I haven't been putting much time in it lately, but on the top of the list is catering for the new authentication schemes since version 3.)
Now, about your specific setup. You say you use mapreduce to get from 500KB to 700KB? I think there's a hint there you're using the wrong tool for the job. I'm not sure what the default mongo shell does differently than when you do the same over TMongoWire.Get, but if I assume mapReduce assembles the response first before sending it over the wire, that's where the performance gets lost.
So here's my advice: you're right with thinking about using TMongoWireQuery. It offers a way to process data faster as the server will be streaming it in, but there's more.
I strongly suggest to use an array to store the list of seconds. Even if not all seconds have data, store null on the seconds without data so each minute array has 60 items. This is why:
One nicety that turned up in designing TMongoWireQuery, is the assumption you'll be processing a single (BSON) document at a time, and that the contents of the documents will be roughly similar, at least in the value names. So by using the same IBSONDocument instance when enumerating the response, you actually save a lot of time by not having to de-allocate and re-allocate all those variants.
That goes for simple documents, but would actually be nice to have on arrays as well. That's why I created IBSONDocumentEnumerator. You need to pre-load an IBSONDocument instance with an IBSONDocumentEnumerator in the place where you're expecting the array of documents, and you need to process the array in roughly the same way as with TMongoWireQuery: enumerate it using the same IBSONDocument instance, so when subsequent documents have the same keys, time is saved not having to re-allocate them.
In your case though, you would still need to pull the data of an entire hour through the wire just to select the seconds you need. As I said before, I'm not a MongoDB expert, but I suspect there could be a better way to store data like this. Either with a separate document per second (I guess this would let the indexes do more of the work, and MongoDB can take that insert-rate), or with a specific query construction so that MongoDB knows to shorten the seconds array into just that data you're requesting (is that what $splice does?)
Here's an example of how to use IBSONDocumentEnumerator on documents like {name:"fruit",items:[{name:"apple"},{name:"pear"}]}
q:=TMongoWireQuery.Create(db);
try
q.Query('test',BSON([]));
e:=BSONEnum;
d:=BSON(['items',e]);
d1:=BSON;
while q.Next(d) do
begin
i:=0;
while e.Next(d1) do
begin
Memo1.Lines.Add(d['name']+'#'+IntToStr(i)+d1['name']);
inc(i);
end;
end;
finally
q.Free;
end;