Alibaba Cloud Lens for ALB Best Practices
At present, facing the operation and maintenance of cloud products, enterprises lack effective means, mainly including incomplete data, low data granularity, and less time granularity filtering dimensions. Secondly, cloud product instance data is scattered, which makes it difficult for users to get through. Customers are not familiar with ALB cloud products, and it is difficult to get effective performance analysis through data. The threshold for problem analysis is high.
From the perspective of competitive products, cloud products mainly focus on independent data sources such as logs, indicators, usage, and query visualization capabilities. However, the storage Lens proposed by the friends are mostly based on multi cloud, which makes it possible to get through logs, measurements, indicators and other data, focusing on a certain advantageous field, such as cost analysis and performance diagnosis.
SLS has a wealth of observable data sources, such as logs, cloud monitoring indicators, cloud product measurement cost center, etc. It also has the ability to conduct real-time query and analysis of large-scale observable data developed by itself.
Cloud Lens for ALB is not only to obtain the basic observable data, but also to get through the data at a low threshold, make correlation analysis and gain insight, and assist in the optimization of ALB use. Data sources mainly include ALB access logs and SLS indicator data.
In addition, we provide Cloud Lens for ALB unified portal and ALB console instance portal, providing users with scenario based inside functions such as access analysis, performance monitoring, exception detection, and custom analysis.
The Lens for ALB architecture provides ALB's 7-tier access log, second level monitoring, indicator analysis, real-time alarm, and automatic anomaly patrol. It supports centralized management of the collection status of all ALB instances and logs under the account. Provide real-time storage, query and analysis of ALB access logs, and extract PV, average delay, inbound and outbound traffic indicators and other data in real time.
At the same time, based on the intelligent patrol function, it provides rich visual reports and abnormal patrol, supports user-defined alarm configuration, and has the notification capability of SMS, email, voice, nail and other channels.
The advantages of Lens for ALB are as follows:
Simple operation: one-stop opening, centralized use, no need to care about log collection and storage, development and operation and maintenance personnel can focus more on business development. The ability to configure pre aggregation can be customized to greatly improve the query speed.
Massive data and elastic storage: Alibaba Cloud load balancing combines the powerful big data computing capabilities of the log service to analyze and process real-time logs at the second level, meeting the real-time requirements of DevOps, monitoring, alarm and other scenarios. The capacity of the Logstore can be dynamically scaled. It supports instance level opening or closing of access logs and arbitrary setting of log storage time.
Real time query and intelligent inspection: based on the intelligent AIOps algorithm of Dharma Institute, it provides the automatic inspection function of ALB indicators, which helps to find and locate problems faster and more accurately.
Alibaba Cloud Lens for ALB consists of four modules, namely access management module, alarm management module, query analysis module and report center.
The access management module provides the global centralized management of ALB instances. The instance access shows the global instances of all ALBs in the account, and provides the one click open operation of access logs. The target storage gathers the projects and logstores that access the log storage, and supports the periodic modification of the log storage.
Fifteen alarm rules are built in the alarm management module, providing baseline alarm, same loop ratio alarm and intelligent alarm, covering high frequency scenarios such as QPS, delay, error rate and traffic.
The analysis module provides real-time query and analysis of ALB access logs.
The report center provides five reports, including 19 real-time monitoring data, 8 dimensions of second level monitoring data, and the distribution of abnormal indicators.
The access overview provides the overall status of the ALB instance, including the month on day and week on week ratio of PV and UV.
Log in to the SLS console, and under the label of the log application cloud product Lens, click Cloud Lens for ALB.
First, enter the access management page. All ALB instances under the account are collected in the ALB instance record of access management, which allows instance access, log opening, and other operations.
After clicking Enable, two methods of log storage will be provided: selecting an existing project or creating a new project.
Click the access log on the right side of the list to go directly to the query page of the access log and directly query the log library. In addition, you can also select the desired ALB instance for query by clicking Query Analysis on the left.
The storage target database gathers the storage information of all target databases enabled by ALB, and the target log database can be modified. The data storage time supports user-defined days, permanent storage or intelligent hot and cold tiered storage.
Based on access logs, you can configure alarms on the alarm management page. Cloud Lens for ALB has fifteen built-in alarm rules.
You can directly select the corresponding alarm rules according to the business needs, click the log, and start the alarm. After opening, click Set to set alarm threshold, black and white list and other information.
The report center provides five reports: monitoring overview, monitoring center, second level monitoring, instance patrol, and access overview. The monitoring overview provides the core indicators, error codes, traffic, access PV, access success rate and other data of the ALB instance.
The monitoring center provides real-time monitoring indicators, including access PV, access success rate, traffic, average delay, top N statistics, etc.
Second level monitoring provides QPS, access delay, request traffic, success rate and various status codes.
The SLS based machine learning algorithm for instance patrol provides anomaly detection data, including PV, outbound traffic and inbound traffic.
The access overview provides a query to access the overall status, including PV, UV and request.
Knowledge Base Team
Knowledge Base Team
Knowledge Base Team
Knowledge Base Team
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