![]() ![]() "body": "This is a slack message that I am sending to the #bottestchannel", An example Slack message might look like: For instance, “ to” in the context of Slack means the channel to post the message to while to in the context of email (SMTP) means an email address. Each Message Service implementation is responsible for interpreting what the attributes mean in the context of its system. See the Message Attributes section in the Appendix below for the full listing of attributes. The Messaging API supports the generic concept of a message, which consists of attributes like to, from, subject, body and other fields. We’ll cover the API approach in the next subsection and the pipeline approach in the section below on pipeline stages. Given a configured service instance (hint, the logging service is setup by default), we can then send messages either via a pipeline stage or via the APIs. This can be done via the Fusion Admin (System->Messaging Services) UI, as in the screen grab below or via the API. To leverage the new messaging service, the service must first be setup. The Messaging Service currently supports three services: email, logging and Slack. See the Appendix below on Message Service Configuration for more information on this and other configuration options. Furthermore, it can even save all messages to Solr so that they can be searched later. It also enables the scheduling of the sending of messages by integrating with Fusion’s built in scheduler. The Messaging Service provides implementations to send messages of specific types (e.g. To enable alerting, we added a new service for delivering messages, called the Messaging Service, via one or more service providers as well as several new pipeline stages to enable alerting during both indexing and querying. These services are deployed in almost all cases just as Solr is deployed (see the architecture diagram below for more detail). With the demo now out of the way, let’s dig into how the system works.įusion is made up of a number of services that work together to enable search, recommendations, large scale storage, alerting and other key services for sophisticated search apps. For this demo, I’m going to assume you have downloaded Fusion, unpacked it and logged into the admin console. ![]() Let’s take a closer look with a video demo at the functionality and then I’ll show you the underpinnings of how this works in Fusion. Currently, Fusion ships with support for sending emails and slack messages, but support for other integrations will be coming soon. Since our alerting mechanism is fully integrated into our pipeline architecture, any upstream stage can affect how an alert message gets sent. While systems like Percolator (for Elasticsearch) and Luwak (for Solr) use techniques to manage and execute standing (alerting) queries (by storing them as documents), our approach makes it possible to not only match incoming data streams against standing queries, but also include regular expressions, database lookups and other criteria for deciding whether something is alert-worthy or not. To address the needs of streaming data and alerting, our latest release of Lucidworks Fusion, now includes a fully integrated messaging service as well as pipeline stages for quickly and easily setting up alerts at both index time and search time. ![]() However, until recently, with the massive increase in content being ingested by search engines, there hasn’t been much focus on the problem outside of some niche areas. Historians of information retrieval will also note an interest in document routing or document filtering to match documents as they flow in against standing queries from the very early days of search. We’ve long known that search is great for helping rank what is important in content. At the same time, our attention span remains finite, so we need a way to separate the proverbial wheat from the chaff, the noise from the signal, the garbage from the gold. are flowing in all around us all the time, begging us to take action on everything that passes by. ![]()
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