A Java facade to AWS SQS that supports "message time in queue" as an actionable metric via callbacks.
When it comes to queues, be them JMS ones, database tables, or even Amazon's SQS, the most common metric used to evaluate the state of a queue is its length. In essence, one derives an efficiency metric based upon how many messages are residing in a queue at any given time. If there are just a few messages, the queue is operating efficiently. If there are numerous messages, things are inefficient and alarms must be sounded.
And what do you do when those alarms are triggered? Fire up more queue workers, right? If you started with one worker, you've now got more than one; and once the queue's length is back to some low number, those workers go away. Auto-scaling -- it's a beautiful thing.
But what if you're in a consistently busy environment with extreme bursts where queues can fill up rapidly? If you have sufficient workers already running to handle that burst, do you need to fire up more? You can fire up more workers, but it might cost you. That is, you might have to provision new worker instances, such as Heroku worker dynos or AWS AMIs, which will end up costing you money. And sometimes those worker instances take a few moments to fire up and when they're operational, the burst of activity is over and the queue is back to normal -- the initially available workers handled the load adequately.
That is, if you already have sufficient capacity to handle the influx of messages on a queue, then monitoring a queue's length isn't too helpful. In fact, it's a misleading metric and can cause you to take unneeded actions. What's more, a queue's length is a lagging indicator when there's sufficient capacity -- in many cases, by the time you are notified that some threshold was met, enough messages have already been processed, thus triggering a false alarm.
Consequently, a queue's length is not indicative of a system's efficiency when there's already sufficient workers present. Rather, the metric that means something in a high capacity environment is how long a message resides in a queue. That is an actionable metric: if messages are stuck in a queue waiting to be processed, you'll need more processors!
In high capacity, bursty environments where there are usually multiple worker processes reading from queues, a message's time in queue can effectively be leveraged to augment capacity. If the time in queue metric starts rising, then you have an indication that there aren't sufficient processes to work off the load. Consequently, you can bring on more processes to handle the load.
There's an interesting parallel here: the queue will also have a lot of messages in it. That is, queue length monitors will fire correctly, but the action to take is derived not from the queue's length but from the time in queue metric. Thus, while a queue full of messages doesn't necessarily mean you need to take action, an increase in queue wait time is actionable.
By default, SQS doesn't provide the ability to query how long a message has been residing in a queue. In fact, one of the only metrics Amazon exposes with respect to SQS is queue length. That metric is inadequate for certain environments.
Moo provides an interface for clients to obtain and take action on the message time in queue metric. This is done by augmenting an SQS message with a time stamp. That time stamp is then checked when a message is popped off of an SQS queue. If a threshold difference is exceed then a callback is invoked.
Users of Moo will find its usage similar to Ahoy!, which is an asynchronous callback oriented facade on top of AWS's Java SDK. In fact, Moo uses Ahoy! underneath, with the added feature of attaching a "maximum time in queue" asynchronous callback.
Moo supports multiple time in queue thresholds and setting a maximum time in queue threshold is done like so:
//adds a 1 second max threshold (times are in milliseconds)
sqs.addQueueWaitTimeCallback(1000, new QueueWaitTimeCallback() {
public void onThresholdExceeded(long waitTime) {
//waitTime is the actual time in queue
//do something... like fire off a web hook, etc
}
});
Note the addQueueWaitTimeCallback method takes a millisecond maximum time in queue value and an accompanying QueueWaitTimeCallback callback implementation. The onThresholdExceeded method will be invoked during a message receive if the maximum threshold value is exceeded; what's more, the onThresholdExceeded will receive as a parameter the actual queue wait time.
Because Moo augments a sent message, you must use (for now) a Moo client to receive that message. That is, Moo wraps an incoming message, be it XML, JSON, or plain text, with some meta data (namely a time stamp) and parses out the original data upon receive. You do not have to do anything on your part.
Moo, however, will validate the length of a message before sending it as AWS will reject messages larger than 256KB. Moo requires 30 bytes to store a time stamp and to wrap the original message body. Accordingly, to use Moo, your messages must be less than 262,113 bytes.
To fire up an instance of Moo, you have a number of options, including configuring an instance of AWS's AmazonSQS or just passing along a key, secret, and queue name like so:
SQS sqs = new SQS(System.getProperty("key"), System.getProperty("secret"), System.getProperty("queue"));
Next, you can attach zero to many QueueWaitTimeCallback instances like so:
sqs.addQueueWaitTimeCallback(600000, new QueueWaitTimeCallback() {
public void onThresholdExceeded(long actualWaitTime) {
//do something -- fire off SNS message?
}
});
In this case, I've added a callback to be invoked if messages are in a queue longer than 10 minutes. Note, these QueueWaitTimeCallback callbacks are fired by the queue reader instance; accordingly, a QueueWaitTimeCallback can certainly fire up more instances of itself, for example.
Here's a sample JSON document that you might want to throw onto an SQS queue:
{ "employees":[
{ "firstName":"John", "lastName":"Doe" },
{ "firstName":"Anna", "lastName":"Smith" },
{ "firstName":"Peter", "lastName":"Jones" }
]}
Sending and receiving this message are exactly like you'd do if you were using Ahoy!. For example, to send a message, just pass along a String to the send method:
sqs.send(json, new SendCallback() {
public void onSend(String messageId) {
//messageId is from SQS
}
});
Note, the send method takes an optional SendCallback.
Receiving a message is via the receive method, which takes a mandatory ReceiveCallback -- this callback will be invoked asynchronously for each message received off of a queue. Each instance will receive the message placed upon the queue and the message's SQS id.
sqs.receive(new ReceiveCallback() {
public void onReceive(String messageId, String message) {
//do something w/the message -- in this case it's JSON
}
});
Note, if upon the receive of a message, Moo notices that a message has been waiting in a queue for more than the max queue wait time threshold configured for an associated QueueWaitTimeCallback, Moo will invoke it.
Moo is a facade to AWS's Java SDK -- in essence, Moo makes working with SQS easier. Accordingly, to use Moo, you'll also need the AWS Java SDK. Have a look at Ahoy! as well -- Moo uses Ahoy! internally for asynchronous callbacks.
Finally, to see how Moo works, I highly recommend you take a look at the various test cases in the test folder.
To build Moo, you'll need Ant -- just type ant jar and Moo will run a bunch of tests, plus produce a jar file for you.
Finally, you can see how Moo works in the real by running the task functional-test; however, for that to work, you'll need to create a local.properties file that has a few properties (see the default.properties file for more details).
This is important so read up.
receivewill delete the message off of the SQS queuereceivewill listen for 20 seconds and grab up to 10 messages and theonReceivecallback will be invoked for each message- reread that last point, please
Check out these handy-dandy resources:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent