Customers use Amazon OpenSsearch Service to store their operational and telemetry signal data. They use this data to monitor the health of their applications and infrastructure, so when a production problem occurs, they can quickly identify this cause. The mere volume and variety of data often make this process complex and time consuming, leading to a high average time to repair (MTTR).
To speed up and transform this process as developers interact with their operational data, today we have introduced the support of Amazon Q developers in the OpenSearch Service. With this analysis with the help of AI, new and experienced users can go through comprehensive operational data without training, analyze problems and get information about a fraction of that time. Amazon Q Developer in OpenSearch Services reduces MTTR by integrating generative capacitive AI directly into OpenSearch workflows, so you can improve operational skills without scalping your specialized teams. Now you can explore, analyze patterns and create visualizations using interactions in context and natural language.
In this post we share how to start using Amazon Q Developer in OpenSearch Service and explore some of its key abilities.
Solutions
Setting data on observability signal for many steps includes analysis, including tools code, creating complex queries, creating visualization and control panels, configuring notifications of suitable and often detectors based on machine learning anomalies. These important requirements in investment in time, sources and expertise. Amazon Q Developer in OpenSearch is a survey of natural language and generative tools based on AI within OpenSearch, which simplifies initial settings and ongoing operations. Customers are already using generations based on natural language to help construct questions OpenSearch; Amazon Q in OpenSearch Service brings the following additional abilities:
- Visualization based on a natural language
- Summary results for Quiers generated with Questions of Natural Language
- Detector’s proposals anomaly
- Alert Summary and knowledge
- Instructions for proven procedures
Let’s explore each of these abilities in detail to understand how they help traditional observability workflows and make the data analysis process in the centralized OpenSearch user interface more efficient.
Visualization based on a natural language
Visualization based on the natural language with Amazon Q for OpenSSearch fundamentally transforms how users create and interact with data visualizations. You do not need to know the specialized languages of questions that are currently used in OpenSearch Service DashBoards to create complex visualizations. For example, you can specify the requirements as “Show me a chart of errors in the last 24 hours with a Région” or “Create a chart showing HTTP response distribution” and Amazon Q automatically generates a reasonable visualization.
You want to start with this feature, choose Visualization In the navigation pane and choose Create a new visualization. OpenSearch UI has types of visualization Maya. To use a new visualization based on a natural language, select Previewer of natural language.
This will bring a new visualization page with a text box where you can specify a question in a natural language.
In the drop -down menu, select the Index pattern (openSearch_dashabords_sample_data_logs in this case). Amazon Q interprets your intention, identified fields, automatically selects the most visualization types and uses the correct formatting and style. Amazon Q can also understand multiple dimensions in data, aggregation of different methods and different time ranges.
You are now ready to build your visualization in natural language. For example, for a question “Show me the number of different IP addresses per day in protocols,” we see the following visualization.
Amazon Q generates visualization according to the instruction. UI also gives the possibility to update any part of data, transformations, marks and coding for visualization. This window also shows the Generate Query for Data in PPL. For this example Amazon Q generated this question
source=opensearch_dashboards_sample_data_logs*| stats DISTINCT_COUNT(`ip`) as unique_ips by span(`timestamp`, 1d)
With this interactive user interface, you can customize different aspects of visualization if NedDD. For example, if you prefer to use the type type of what Amazon Q, you can change mark Enter bar and choose UpdateGold is choosing Adjust the visual and a specific new set of instructions for this visualization (for example, “Change to Column Chart”).
After adding visualization to your satisfaction, you can save and load later. What makes this feature particularly powerful is its ability to understand context and propose to specify updates of your challenges – initial visualization does not meet your needs, you can describe the required changes using Adjust the visual choice.
Summary
Amazon Q acts as an interpretation layer that processes inquiry, results into a condensed structured summary. It can also identify formulas and other meaningful trends in data by observing qualitative and quantitative characteristics of results. The efficiency of the system largely depends on the quality of basic data, the specificity of the initial query and the characteristics of the generation of questions, among other things. Amazon Q also samples a set of results for generating this summary of results. These summaries are a good starting point for analysis. For example, for the same question we used for the last time (“Show me the number of different IP addresses per day in protocol”), Amazon Q analyzes the result set in Amazon Q Summary section.
Detector’s proposals anomaly
As it responds to your question, Amazon Q can propose to create an anomaly detector based on a selected data source. It does this by recommending to receive the field of your operational data with confirmation of one click to create a detector.
Functions are aggregations of fields or scripts that determine what represents anomaly. Identifying functions and creating a detector for the use of these functions usually require a deep technical understanding of spikes, decreases, slingshots and relationship between multiple functions. Amazon Q helps reduce this traditional complexity in creating a detector automatically identify these functions as shown below. You can also change the proposed detector to fine -tune your needs.
Alert of summary and inspection
In addition to warnings, the Amazon Q icon generates a brief summary that includes warning definitions, specific conditions that led to its activation, and an overview of the current state of the monitored system or service.
The Insights component provides a higher level of insight into the alert by emphasizing the importance of these alerts, typical conditions that result in these warnings, along with recommendations that help alleviate the conditions of these warnings. If you want to get an overview of the warnings, you have to provide more information about your knowledge environment. You can find instructions to generate knowledge in the View Summary and Extension Summary.
By selecting Show in the discoveryYou can dive deeper into the data for warning by a single click, which makes it easier to transition from a detailed investigation in Discover. Function of knowledge and summary helps to speed up your investigation; Care should be taken to identify the main cause of the problem because it is likely to require human intervention.
Instructions for proven procedures
Amazon Q Developer in Opensearch Service not only simplifies operations, but also serves as an intelligent assistant to implement the proven OpenSearch Service procedures. Amazon Q for OpenSSearch has been trained for development and product documentation, so it can propose proven procedures for the operation of the OpenSearch Service Domains, collections without Amazon OpenSearch and configuration based on your capacity and compliance needs. You want to start, select the Amazon Q icon at the top right. The assistant maintains the history of interviews. For the instructions it provides, the assistant quotes his resources and provides a useful connection with the documentation. It also makes suggestions on how to continue the conversation. You can ask about accessing access to access, index status manager, dimensioning leading nodes or other proven procedures or operating questions about OpenSearch.
Consistency
UI OpenSearch UI is available for use without other related costs. Amazon Q developer for OpenSsearch Service is available within UI OpenSsearch in the following AWS regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt) (London) (London) (London). Because it includes free level, there are no connected costs.
Conclusion
Amazon Q developers’ support on OpenSearch brings A-Powred capacity to help alvat traditional barriers that teams face when setting up, monitoring and solving their applications. This allows teams of all levels of experience to use the full power of OpenSsearch.
We are excited about how to use these new capabilities to transform workflows for better operational results. Want to start with Amazon Q Developer on Opensearch service
About the authors
Muthu pitchiamani He is a search specialist with Amazon OpenSsearch Service. It creates extensive search applications and solutions. Muthhu is interested in network and security topics and is based from Austin in Texas.
Dagney Braun He is a senior product manager in the Amazon Web Services OpenSSearch team. It is enthusiastic about the easy use of OpenSearch and the extension of available tools for better support for all customers.