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Community Blog Message Queue for Apache RocketMQ Meets Observable Tools: A Visualization of the Core Business Process

Message Queue for Apache RocketMQ Meets Observable Tools: A Visualization of the Core Business Process

This article describes the best practices of observable tools of Message Queue for Apache RocketMQ in online production environments.

By Wenting and Buzhou

This article describes the best practices of observable tools of Message Queue for Apache RocketMQ in online production environments. Message Queue for Apache RocketMQ overtakes similar products in the industry in observability. The features of Message Queue for Apache RocketMQ, such as dashboards and messaging tracing, protect the core business process and effectively cope with scenarios, including capacity planning, troubleshooting of message sending and receiving, and customized monitoring, which are encountered during online mass production and utilization.

An Introduction to Message Queue

Alibaba Cloud provides a large number of message products. The message product matrix covers the fields of various business scenarios, such as the Internet, big data, and IoT, offering multi-dimensional message solutions to cloud customers. No matter which MQ product is, its core is to help you solve the asynchrony and decoupling of the business and system and cope with load shifting during traffic peaks. It is also a distributed product with high throughput, low latency, and high scalability.

However, message products have different focuses on customer-oriented applications. Briefly speaking, Message Queue for Apache RocketMQ is the preferred message channel in the business field, Kafka is an indispensable message product in the big data field, MQTT is a message solution in the IoT field, RabbitMQ focuses on the traditional business message field, MQ MNS completes cloud-native product integration and event flow channel, and EventBridge is an event hub on Alibaba Cloud to build the event center in a unified way.

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This article describes the preferred message channel in the business field, Message Queue for Apache RocketMQ, which was born in the e-commerce system of Alibaba Group. It has capabilities for high performance, low latency, and load shifting. It also provides a wide range of features to deal with instantaneous traffic peaks in business and message scenarios. It is integrated into the core business process for you.

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Message Queue for Apache RocketMQ is required to have very high observability to process a message in the core business process. Thanks to the observability, you can monitor and locate abnormal fluctuations in time and troubleshoot specific business data problems. Therefore, observability has gradually become one of the core capabilities of Message Queue for Apache RocketMQ.

Observability

When it comes to observability, you can think of its three elements: metrics, tracing, and logging.

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Combined with the understanding of MQ, the three elements of observability are explained below:

Metrics: Dashboard

1) A Wide Range of Metrics: The metrics include the number of messages, the amount of accumulation, and the duration of each phase. Each metric is aggregated and displayed from multiple dimensions, including Instance, Topics, and Consumption Group IDs.

2) Best Practice Templates of Message Team: It provides the best templates for you, especially in complex message consumption scenarios. It also offers a wide range of metrics to help you quickly locate problems and continuously realize updates and iterations.

3) Prometheus and Grafana: Data that complies with Prometheus standard data formats can be collected and displayed in Grafana. In addition to templates, you can customize dashboards.

Tracing: Messaging Tracing

1) Tracing Standard -- OpenTelemetry: The tracing standard of Message Queue for Apache RocketMQ has been merged into the open-source standard of OpenTelemetry to regulate and enrich definitions of scenarios for messaging tracing.

2) Customized Display of the Message Field: It reorganizes abstract request span data from the message dimension to display one-to-many consumption information and multiple consumption information, which is intuitive and easy to understand.

3) Connecting Upstream and Downstream Tracing Processes: The tracing of a message can inherit and call contexts and add it to the complete call process. The message process information concatenates the information of upstream and downstream processes of the asynchronous process.

Logging: Log Standardization for Client

1) Error Code Standardization: Different errors have a unique error code.

2) Complete Error Message: It contains the complete error messages and the resource information required for sorting.

3) Error Level Standardization: The log levels of different error messages are refined, allowing you to configure more appropriate and monitor alerts based on levels such as error and warn.

After understanding the basic concepts of MQ and observability, let's take a look at what will happen when Message Queue for Apache RocketMQ encounters observability.

An Introduction to the Observability Tools of Message Queue for Apache RocketMQ

From the preceding introduction, the observability of Message Queue for Apache RocketMQ can help you troubleshoot messages during production and consumption according to error messages. The following section will introduce some concepts in the production and consumption processes of messages to help you understand the application of features.

Concepts of Production and Consumption Processes of Message

First of all, let's clarify the following concepts:

  • Topic: Message topic is the first-level message type, and messages will be classified according to Topic.
  • Message: The carrier of information transmission in MQ
  • Broker: A message transit role responsible for storing and forwarding messages
  • Producer: A message producer (also known as a message publisher) responsible for producing and sending messages
  • Consumer: A message consumer (also known as a message subscriber) that receives and consumes messages

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In short, the production and consumption processes of messages say the producer sends messages to the MQ of the topic for storage. Then, the consumer consumes the messages on the MQ. What is the lifecycle of complete message production with multiple consumers?

Here, we take a scheduled message as an example. The producer sends a message to MQ Server after a certain duration, and MQ Server stores the message in the MQ. At this time, there is a storage time in the queue. If it is a scheduled message, it needs a certain scheduled time to be consumed by the consumer. It is the time when the message is ready. After the scheduled time, the consumer starts to consume the message, and the consumer pulls the message from the MQ. Then, the message reaches the consumer client after a certain duration of the network. At this time, it is not a low code for consumption. There will be a process of waiting for the resource thread of the consumer, and the business message processing will start after waiting for the thread resources of the consumer.

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From the introduction above, business messages are processed for a certain duration, and the ACK result is not returned to the Server until the business message processing is finished. In the entire process of production and consumption, the most complicated part is consumption. Due to duration and other reasons, scenarios where messages are accumulated often occur. The following part focuses on the meaning of each metric in scenarios of message accumulation.

Message Accumulation Scenarios

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As shown in the preceding figure, the gray part indicates the number of completed messages in the MQ; the messages that the consumer has processed and returned to ACK. The orange part indicates those messages that have been pulled to the consumer client and are being processed, but the processing result has not been returned. This message has a very important metric, the message processing duration. The green part indicates that those messages have been stored in the MQ that has occurred. They are already in a state that can be consumed by the consumer called ready messages.

The Number of Ready Messages:
Meaning: It indicates how many ready messages are.
Feature: The number of messages reflects the scale of the message that has not been consumed. When the consumer experiences exceptions, the number of ready messages increases.

Queue Time:
Meaning: The time lag between the ready time and the current time of the earliest ready message
Feature: This time reflects the delay of messages that have not been processed. It is a vital metric for time-sensitive business.

An Introduction to the Features of Observability Tools of Message Queue for Apache RocketMQ

Combined with the concept of the observability of Message Queue for Apache RocketMQ described above, the following part describes the two core features of the observability tools of Message Queue for Apache RocketMQ.

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An Introduction to the Features of Observability Tools — Dashboard

The dashboard allows you to view the specified metric data based on various parameters. The main metric data includes the following three points:

1) Overview

  • View the total number of messages sent and received, TPS, and the distribution of message types of the instance
  • View the current distribution and sorting of each metric: The topic for which the largest number of messages are sent, the Group ID with the largest number of consumed messages, the Group ID with the largest number of accumulated messages, and the Group ID with the longest queue time

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2) Topic (Message Sending)

  • View the curve graph of the number of messages sent for a specified Topic
  • View the curve graph of the success rate of sending messages for a specified Topic
  • View the curve graph of the sending duration of a specified Topic

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3) Group ID (Message Consumption):

  • View the curve graph of the number of messages of a specified Group subscribing to a specified Topic
  • View the consumption success rate of a specified Group subscribing to a specified Topic
  • View metrics, such as the consumption duration of a specified Group subscribing to a specified Topic
  • View metrics related to message accumulation of a specified group subscribing to a specified Topic

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An Introduction to the Features of Observability Tools — Messaging Tracing

In terms of tracing, the observability tools have the feature of messaging tracing, which includes the following three capabilities:

1) Convenient Query Capability: You can query relevant tracing based on the basic information of messages. In addition, you can filter queries based on the result status and duration to find valid tracing to quickly locate problems.

2) Detailed Tracing Information: In addition to the time and duration data of each lifecycle, the messaging tracing provides the accounts and machine information of producers and consumers.

3) Optimized Display Effect: The messaging tracing displays different message types, scenarios where there are multiple consumption Group IDs, and scenarios where the same consumption GroupID is recommitted multiple times.

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Best Practices

Scenario 1: Troubleshooting

1) Target: Health status of message production and consumption

2) Principles

  • Level-1 Metric: It is used for alerting and recognized as a non-objectionable metric.
  • Level-2 Metric: When a Level-1 metric changes, you can quickly locate the causes of the problem by viewing the Level-2 metric.
  • Level-3 Metric: It is used to locate the reasons for the fluctuation of the Level-2 metric. It is added according to respective characteristics and experience of business.

Based on the target and principles, the troubleshooting and analysis for producers and consumers are listed below:

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Scenario 2: Capacity Planning

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In scenarios of capacity planning, you only need to solve the following three issues:

Issue 1: How to Evaluate the Instance Capacity

Solutions:

  • You can view the statistics of the specified instance data on the Instance Details page Also, you can see the TPS peak of message sending and receiving in the selected period.
  • Instances of Platinum Edition can add alert monitoring and judgment service based on this data.

Issue 2: How to Check the Consumption of Instances of Standard Edition

Solution:

  • You can view the module of the total number of messages in the Overview.

Issue 3: Which Messages Have Been Offline and Need to Be Cleaned up Resources?

Solutions:

  • You can sort Topics by the number of sending messages from smallest to largest in a specified period (such as in the latest week) to check whether the Topics whose number of sending messages is 0. The business related to these Topics may have been offline.
  • You can sort Group IDs by the amount of message consumption from smallest to largest in a specified period of time (such as in the latest week) to check whether Group IDs whose amount of message consumption is 0. The business related to these Group IDs may have been offline.

Scenario 3: Business Planning

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In scenarios of business planning, you mainly need to solve the following three issues:

Issue 1: How to View the Distribution of the Business Peak

Solutions:

  • View the daily peak period of the number of messages received for a Topic
  • View the difference between the number of messages received on weekends and weekdays
  • View the changes in the number of messages received for a Topic during holidays

Issue 2: How to Determine Which Business Is Currently on the Rise

Solution:

  • View the number of messages to assist in determining the trend of business.

Issue 3: How to Optimize the Performance of the Consumer System

Solution:

  • View the message processing duration to determine whether there is room for improvement within a reasonable range.

This article introduces MQ, concepts, and the features of observability of Message Queue for Apache RocketMQ and the best practices. It shows the capabilities for visualization of observability tools of Message Queue for Apache RocketMQ in the core business process. It is expected to help with daily online troubleshooting and operations and maintenance.

Click here to experience the observability tools of Message Queue for Apache RocketMQ

Apache middleware mqtt RocketMQ Big Data Best practices Message Queue Tracing Analysis Observability Visualization EventBridge Messaging
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