Amazon SNS + Daylite Integrations

Amazon SNS

Amazon Simple Notification Service (Amazon SNS. is a web service designed to send push notifications to applications or devices at scheduled times, in response to changes in other AWS resources, or in response to notifications sent by other applications. Amazon DynamoDB uses Amazon SNS to notify applications when an item is added or updated. Amazon SNS can also be used with Amazon SQS queues to enqueue notification messages. Amazon SNS can also be used with Amazon ECS tasks to notify monitored tasks when new events occur. Amazon SNS notifications can be delivered by sending email, using HTTP POST, sending SMS text messages, or by delivering the notification to Amazon SQS queues.

Daylite

Daylite is a calendar and time-tracking app that keeps track of your appointments in one place, makes it easy to share them with others, and lets you stay on top of them from anywhere you are. We’re building Daylite for people who want to run their lives better, faster, and smarter.

Integration of Amazon SNS and Daylite

I will try to explain how Amazon SNS works by using the concept of “Event Notifications.” I will also show how Amazon SNS can be integrated with Daylite. There are two types of Event Notifications. the first one is the scheduled Event Notification that is scheduled to send notifications at scheduled times; the second one is the Amazon CloudWatch Event Notification that will send notifications in response to changes in Amazon CloudWatch metrics. I will start with the scheduled event notification.

What happens when someone subscribes to an Amazon SNS topic Amazon SNS generates a unique endpoint for each subscriber. When Amazon SNS sends an event notification to a subscriber’s endpoint, Amazon SNS sends the data payload in an HTTP POST request to the subscriber’s endpoint. For example, assume that Amazon SNS sends an event notification about an Amazon DynamoDB table named “MyTable” and its hash key “myHashKey” and range key “myRangeKey” and partition key “myPartitionKey” and its attributes “attributeA” and “attributeB” and value “blahblahblah” on the topic named “MyTopic.” Then Amazon SNS will send the fplowing data in an HTTP POST request:

POST /MyTopic/MySubscriberEndpoint/MyTable/myHashKey/myRangeKey/myPartitionKey/attributeA/attributeB/value Host. MySubscriberEndpoint Content-Length. 13 Content-Type. application/json { "PartitionKey". "myPartitionKey", "RangeKey". "myRangeKey", "Timestamp". 1411477600000 }

When a subscriber receives the HTTP POST request, the subscriber can decide whether to accept or reject it. If it accepts it, then the subscriber should get the data from the request body (content type. JSON), and then do whatever it wants with the data (like storing it into your database. If the subscriber rejects the request, then it should return a response code that says “there is no such thing in this topic” or something like that. In order to protect subscribers from malicious requests, Amazon SNS allows subscribers to define filters. Filters define which events a subscriber wants to receive from a specific Subscription Topic. The fplowing is an example filter definition in JSON format:

{ "TopicArn". "arn:aws:sns:us-east-1:111111111111:subscriptions/111111111111/MyTopic", "FilterRule". [ { "AttributeName". "attribute1", "ComparisonOperator". "IN", "Value". [ "foo", "bar" ] }, { "AttributeName". "attribute2", "ComparisonOperator". "GT", "Value". 100 } ] }

In this example, there are two attributes named “attribute1” and “attribute2” for a specific Subscription Topic named “MyTopic.” The filter compares these attributes with certain values, and then accepts or rejects the subscription, depending on the result of the comparison. In this example, I have specified two filter rules defined by two attributes named “attributes1” and “attribute2.” The first filter rule states that if attribute1 equals either “foo” or “bar,” then accept the subscription. The second filter rule states that if attribute2 is greater than 100, then accept the subscription. So if attribute1 equals “foo” and attribute2 equals 99, then the subscription will be rejected because filter rule 1 doesn’t match filter rule 2. If attribute1 equals “foo” and attribute2 equals 100, then the subscription will be accepted because filter rule 1 matches filter rule 2. The fplowing is an example action definition in JSON format:

{ "ActionType". "SQS", "EventType". "sqs:Publish", "SqsDestinationArn". { "Fn::Join". [ ", ["arn:aws:sqs:::"], {"Ref":"AWS::Region"}, {"Ref":"AWS::AccountId"}, {"Ref":"AWS::Queue"} ] }, "MessageBody". { "Fn::Join". [ ", ["<#partitionkey>","<#rangekey>","<#eventtype>","<#eventdata>"] ] } } }

In this example, I have specified an action type called SQS (Amazon Simple Queue Service. which publishes an event through an SQS queue (a message queue hosted by AWS. The action specifies which queue (called “AWS::Queue”. should be used for this purpose. The action specifies which event type (called “sqs:Publish”. should be used for this purpose. The action specifies which payload (the actual payload data. should be used for this purpose (the payload data is called “<#eventdata>”. You can use any payload type that you want for this purpose (but it should be multipart MIME format. I will now talk about the second type of Event Notification which is called CloudWatch Event Notifications. A CloudWatch metric represents a single statistic computed over some period of time (e.g., cpuUtilizationPercentage over one hour. CloudWatch metrics can be preconfigured or dynamically created by monitoring applications running on EC2 instances. The fplowing are different types of CloudWatch metrics:

Metric Type Description Average Values change slowly over time and are useful for monitoring average activity over time Cumulative Summation Values change quickly over time and are useful for monitoring bursty activity over time Counting Values change frequently over time and are useful for monitoring many instantaneous occurrences over time Percentile Values change frequently over time and are useful for monitoring many instantaneous occurrences over time Rate Values change frequently over time and are useful for monitoring many instantaneous occurrences over time Sample Count Values change frequently over time and are useful for monitoring many instantaneous occurrences over time Sampled Value Changes change frequently over time and are useful for monitoring many instantaneous occurrences over time Standard Deviation Values change frequently over time and are useful for monitoring many instantaneous occurrences over time Total Values change frequently over time and are useful for monitoring many instantaneous occurrences over time Update Rates change frequently over time and are useful for monitoring many instantaneous occurrences over time

You can assign a CloudWatch metric as a notification target from a CloudWatch alarm action, from an AWS Lambda function that was triggered by a CloudWatch event notification, from a CloudWatch log group log stream, from a CloudWatch event notification source, from an AWS Step Functions state machine, from a Lambda function on a custom Kinesis Data Streams application activation ppicy or from a Lambda function on a custom Kinesis Data Firehose delivery stream configuration object on a custom Kinesis Data Firehose application delivery stream configuration object on a custom Kinesis Data Analytics application on a custom Kinesis Data Analytics application delivery stream configuration object on a custom Kinesis Data Analytics application input delivery stream configuration object on a custom Kinesis Data Streams application activation ppicy or from a Lambda function on a custom Kinesis Data Firehose delivery stream configuration object on a custom Kinesis Data Firehose application delivery stream configuration object on a custom Kinesis Data Analytics application on a custom Kinesis Data Analytics application delivery stream configuration object on a custom Kinesis Data Analytics application input delivery

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