I want to test that 3000 users received the text messages I sent them from my product to them.
We are using Twilio multiple numbers (10) to speed up the delivery.
Now I want to test that when each user receives the text and if all 3000 users received it or not.
Basically, I am looking for a solution for fake numbers (at least 3000). So I can send the message to those test numbers and check the received messages as well.
Related
What should be the ramp up time when i need to send 10k request[each request has 6http subsequent calls], I need to send 1 request per minute. and i need to send one request for every 20seconds
Your post contains 4 questions which are mutually exclusive so I provide a generic answer:
If you want to send X requests at exactly the same moment:
Add X thread (virtual users) to Thread Group
Add Synchronizing Timer to the request and set Number of Simultaneous Users to Group by to X
If you want to send 1 request per minute - add Constant Throughput Timer to your request and set Target Throughput to 1
If you want to send 1 request each 20 seconds - the same approach as for point 2 but Target Throughput will be 3
Ramp-up is the time period for starting virtual users, normally normal people use it for gradual load increase, for example if you have 10 users and 10 seconds ramp-up JMeter will start 1 user each second, if you have 10 users and 20 seconds ramp-up JMeter will start each user each 2 seconds, etc.
I am new to hyperledger and online payment.
One scenario I have in mind is: if A is an online shop, B is a consumer. B orders an item from A's shop and pays with Bitcoin/hyperledger. A ships the item to B once the payment is received.
With hyperledger, the process goes:
B sends A the payment via blockchain;
A is able to query how much money A owns;
But the question is:
How does A know if the transaction was from B?
How does A know how much money was sent by B?
How does A know if the transaction from B is for the specific ordered item?
Thanks,
What you'd usually do in Bitcoin is to create a new payment address that is bound to the specific order. The invoice would include that address with instructions to send the specified amount to that address and the recipient could simply wait for an incoming transaction with an output destined for that address.
At this point you'd know which customer has paid for which order, to check the amount just check the output value field in the transaction and compare it to the invoice.
I have a scenario where i need to get three different type of messages. They all contains same information. Lets take for e.g
I have 3 diffrent dealers of car Ford,Honda, Nissan.
They all send me message about car and its specs.
Would you create three queses
ABCCarCompany.E.Direct.Honda
ABCCarCompany.E.Direct.Nissan
ABCCarCompany.E.Direct.Ford
OR just one
ABCCarCompany.E.Direct.Cars
and have them send the car manufacturer as parameter..
What are pros of creating 3 queses vs 1 ..
I recommend that you send all data into a single exchange, call it CarEx. 1 vs 3 queues depends on how you want to use the data. If you need to do the exact same thing with each car that comes in (like put it in a database) then you only need one queue. If you need to do something different for each car (like put into a database for Ford but send an alert for Nissan) then you would want 3 different queues.
If you have 3 different queues you can route the messages into them based on the routing key.
I like this article: http://technet.microsoft.com/en-us/library/dd576261(v=sql.100).aspx because of the receive top (10000) into a table variable. Processing a table variable with 10000 messages would give me a giant boost in performance.
receive top (10000) message_type_name, message_body, conversation_handle<br>
from MySSBLabTestQueue<br>
into #receive
From reading, the receive provides messages given a single conversation_handle. I have 200+ stores all sending messages with the same message type and contract to the same server. Can I implement the server to get all the messages from these stores on a single call to receive?
Thanks
A target can consolidate multiple conversations into few conversation groups, using the MOVE CONVERSATION. The RECEIVE restricts the result set to one single conversation group so moving many individual conversation into a single group can result in bigger result sets, as you desire.
For the records, initiators can also consolidate conversations using MOVE CONVERSATION, there is nothing role specific here. But initiators can also use the RELATED_CONVERSATION_GROUP clause of BEGIN DIALOG to start the conversation directly in the desired group, achieving consolidation and thus bigger result sets w/o having to use MOVE. This is useful because you can simply reverse the roles in the app, ie. instead of stores starting the dialogs with central server, have the central server start the dialogs with each store (thus reversing the roles) and the central server can start the dialogs in as few conversation groups as it likes, even 1. This removes the need to issue MOVE CONVERSATION.
Node.js & Redis:
I have a LIST (users:waiting) storing a queue of users waiting to join games.
I have SORTED SET (games:waiting) of games waiting for users. This is updated by the servers every 30s with a new date. This way I can ensure if a server crashes, the game is no longer used. If the server is running and fills up, it'll remove itself from the sorted set.
Each game has a SET (game:id:users) containing the users that are in it. Each game can accept no more than 6 players.
Multiple servers are using BRPOP to pick up users from the LIST (users:waiting).
Once a server has a user id, it gets the waiting games ids, then proceeds to run SCARD on their game:id:users SET. If the result of this is less than 6, it adds them to the set.
The problem:
If multiple servers are doing this at once, we could end up with more than 6 users being added to a set at a time. For example if one server requests SCARD and immediately after another runs SADD, the number in the set will have increased but the first server won't know.
Is there anyway of preventing this?
You need transactions, which redis supports: http://redis.io/topics/transactions
in your case in particular, you want to pay attention to the watch command: http://redis.io/topics/transactions#cas