AWS Compute Blog

Message Filtering Operators for Numeric Matching, Prefix Matching, and Blacklisting in Amazon SNS

This blog was contributed by Otavio Ferreira, Software Development Manager for Amazon SNS

Message filtering simplifies the overall pub/sub messaging architecture by offloading message filtering logic from subscribers, as well as message routing logic from publishers. The initial launch of message filtering provided a basic operator that was based on exact string comparison. For more information, see Simplify Your Pub/Sub Messaging with Amazon SNS Message Filtering.

Today, AWS is announcing an additional set of filtering operators that bring even more power and flexibility to your pub/sub messaging use cases.

Message filtering operators

Amazon SNS now supports both numeric and string matching. Specifically, string matching operators allow for exact, prefix, and “anything-but” comparisons, while numeric matching operators allow for exact and range comparisons, as outlined below. Numeric matching operators work for values between -10e9 and +10e9 inclusive, with five digits of accuracy right of the decimal point.

  • Exact matching on string values (Whitelisting): Subscription filter policy   {"sport": ["rugby"]} matches message attribute {"sport": "rugby"} only.
  • Anything-but matching on string values (Blacklisting): Subscription filter policy {"sport": [{"anything-but": "rugby"}]} matches message attributes such as {"sport": "baseball"} and {"sport": "basketball"} and {"sport": "football"} but not {"sport": "rugby"}
  • Prefix matching on string values: Subscription filter policy {"sport": [{"prefix": "bas"}]} matches message attributes such as {"sport": "baseball"} and {"sport": "basketball"}
  • Exact matching on numeric values: Subscription filter policy {"balance": [{"numeric": ["=", 301.5]}]} matches message attributes {"balance": 301.500} and {"balance": 3.015e2}
  • Range matching on numeric values: Subscription filter policy {"balance": [{"numeric": ["<", 0]}]} matches negative numbers only, and {"balance": [{"numeric": [">", 0, "<=", 150]}]} matches any positive number up to 150.

As usual, you may apply the “AND” logic by appending multiple keys in the subscription filter policy, and the “OR” logic by appending multiple values for the same key, as follows:

  • AND logic: Subscription filter policy {"sport": ["rugby"], "language": ["English"]} matches only messages that carry both attributes {"sport": "rugby"} and {"language": "English"}
  • OR logic: Subscription filter policy {"sport": ["rugby", "football"]} matches messages that carry either the attribute {"sport": "rugby"} or {"sport": "football"}

Message filtering operators in action

Here’s how this new set of filtering operators works. The following example is based on a pharmaceutical company that develops, produces, and markets a variety of prescription drugs, with research labs located in Asia Pacific and Europe. The company built an internal procurement system to manage the purchasing of lab supplies (for example, chemicals and utensils), office supplies (for example, paper, folders, and markers) and tech supplies (for example, laptops, monitors, and printers) from global suppliers.

This distributed system is composed of the four following subsystems:

  • A requisition system that presents the catalog of products from suppliers, and takes orders from buyers
  • An approval system for orders targeted to Asia Pacific labs
  • Another approval system for orders targeted to European labs
  • A fulfillment system that integrates with shipping partners

As shown in the following diagram, the company leverages AWS messaging services to integrate these distributed systems.

  • Firstly, an SNS topic named “Orders” was created to take all orders placed by buyers on the requisition system.
  • Secondly, two Amazon SQS queues, named “Lab-Orders-AP” and “Lab-Orders-EU” (for Asia Pacific and Europe respectively), were created to backlog orders that are up for review on the approval systems.
  • Lastly, an SQS queue named “Common-Orders” was created to backlog orders that aren’t related to lab supplies, which can already be picked up by shipping partners on the fulfillment system.

The company also uses AWS Lambda functions to automatically process lab supply orders that don’t require approval or which are invalid.

In this example, because different types of orders have been published to the SNS topic, the subscribing endpoints have had to set advanced filter policies on their SNS subscriptions, to have SNS automatically filter out orders they can’t deal with.

As depicted in the above diagram, the following five filter policies have been created:

  • The SNS subscription that points to the SQS queue “Lab-Orders-AP” sets a filter policy that matches lab supply orders, with a total value greater than $1,000, and that target Asia Pacific labs only. These more expensive transactions require an approver to review orders placed by buyers.
  • The SNS subscription that points to the SQS queue “Lab-Orders-EU” sets a filter policy that matches lab supply orders, also with a total value greater than $1,000, but that target European labs instead.
  • The SNS subscription that points to the Lambda function “Lab-Preapproved” sets a filter policy that only matches lab supply orders that aren’t as expensive, up to $1,000, regardless of their target lab location. These orders simply don’t require approval and can be automatically processed.
  • The SNS subscription that points to the Lambda function “Lab-Cancelled” sets a filter policy that only matches lab supply orders with total value of $0 (zero), regardless of their target lab location. These orders carry no actual items, obviously need neither approval nor fulfillment, and as such can be automatically canceled.
  • The SNS subscription that points to the SQS queue “Common-Orders” sets a filter policy that blacklists lab supply orders. Hence, this policy matches only office and tech supply orders, which have a more streamlined fulfillment process, and require no approval, regardless of price or target location.

After the company finished building this advanced pub/sub architecture, they were then able to launch their internal procurement system and allow buyers to begin placing orders. The diagram above shows six example orders published to the SNS topic. Each order contains message attributes that describe the order, and cause them to be filtered in a different manner, as follows:

  • Message #1 is a lab supply order, with a total value of $15,700 and targeting a research lab in Singapore. Because the value is greater than $1,000, and the location “Asia-Pacific-Southeast” matches the prefix “Asia-Pacific-“, this message matches the first SNS subscription and is delivered to SQS queue “Lab-Orders-AP”.
  • Message #2 is a lab supply order, with a total value of $1,833 and targeting a research lab in Ireland. Because the value is greater than $1,000, and the location “Europe-West” matches the prefix “Europe-“, this message matches the second SNS subscription and is delivered to SQS queue “Lab-Orders-EU”.
  • Message #3 is a lab supply order, with a total value of $415. Because the value is greater than $0 and less than $1,000, this message matches the third SNS subscription and is delivered to Lambda function “Lab-Preapproved”.
  • Message #4 is a lab supply order, but with a total value of $0. Therefore, it only matches the fourth SNS subscription, and is delivered to Lambda function “Lab-Cancelled”.
  • Messages #5 and #6 aren’t lab supply orders actually; one is an office supply order, and the other is a tech supply order. Therefore, they only match the fifth SNS subscription, and are both delivered to SQS queue “Common-Orders”.

Although each message only matched a single subscription, each was tested against the filter policy of every subscription in the topic. Hence, depending on which attributes are set on the incoming message, the message might actually match multiple subscriptions, and multiple deliveries will take place. Also, it is important to bear in mind that subscriptions with no filter policies catch every single message published to the topic, as a blank filter policy equates to a catch-all behavior.

Summary

Amazon SNS allows for both string and numeric filtering operators. As explained in this post, string operators allow for exact, prefix, and “anything-but” comparisons, while numeric operators allow for exact and range comparisons. These advanced filtering operators bring even more power and flexibility to your pub/sub messaging functionality and also allow you to simplify your architecture further by removing even more logic from your subscribers.

Message filtering can be implemented easily with existing AWS SDKs by applying message and subscription attributes across all SNS supported protocols (Amazon SQS, AWS Lambda, HTTP, SMS, email, and mobile push). SNS filtering operators for numeric matching, prefix matching, and blacklisting are available now in all AWS Regions, for no extra charge.

To experiment with these new filtering operators yourself, and continue learning, try the 10-minute Tutorial Filter Messages Published to Topics. For more information, see Filtering Messages with Amazon SNS in the SNS documentation.