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TBMQ architecture

Introduction

This article explains the architectural structure of TBMQ, breaking down how data moves between different components and outlining the core architectural choices. TBMQ is designed with great care to implement the following attributes:

  • scalability: it is a horizontally scalable platform constructed using cutting-edge open-source technologies;
  • fault tolerance: no single point of failure, each broker (node) within the cluster is identical in terms of functionality;
  • robustness and efficiency: a single server node can manage millions of clients and process hundreds of thousands of messages per second. Remarkably, the TBMQ cluster can handle a huge volume of at least 100M clients and 3M incoming messages per second;
  • durability: making sure data remains accurate and consistent is extremely important. TBMQ relies on Kafka queue implementation to provide exceptionally high message durability, ensuring that data is never lost.

Architecture diagram

The following diagram shows the pivotal parts of the broker and the route of message transmission.

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Motivation

Within the ThingsBoard company, our extensive expertise and clear understanding of diverse IoT requirements and use cases have helped us to identify two main scenarios in which our clients develop their solutions. In the first scenario, numerous devices are generating a substantial volume of messages that are consumed by specific applications, resulting in a fan-in pattern. On the other hand, the second scenario involves numerous devices subscribing to specific updates or notifications. This leads to a few incoming requests that cause a high volume of outgoing data. This case is known as a fan-out pattern. Acknowledging these scenarios, we intentionally designed TBMQ to be exceptionally well-suited for both.

Our design principles focused on ensuring the broker’s fault tolerance and high availability. We wanted to implement a reliable mechanism for message processing, capable of handling any potential failures that may arise among the participants in the data flow. Additionally, we prioritized supporting distributed and partitioned processing, allowing for effortless horizontal scalability as our operations grow.

We wanted our broker to support high-throughput processing, guaranteeing low-latency delivery of messages to clients, and providing the ability to withstand peak loads from publishing clients while ensuring backup storage for offline clients.

Ensuring data durability and replication was crucial in our design. We aimed for a system where once the broker acknowledges receiving a message, it remains safe and won’t be lost. Even if a QoS 0 message is delivered to the broker but hasn’t yet reached the subscriber and the broker restarts, that message is already saved and will be delivered to the subscriber once the broker is operational again.

We did not want to reinvent the wheel to fulfill all of our requirements for the persistence and processing layer. Instead, we wanted to achieve all of those by choosing the world’s leading platform. Considering these factors, we made a strategic decision regarding the choice of the underlying system that could seamlessly integrate within the MQTT layer.

How does TBMQ work in a nutshell

To ensure the fulfillment of the above requirements and prevent message loss in the case of client or broker failures, TBMQ uses the powerful capabilities of Kafka as its underlying infrastructure.

Kafka plays a crucial role in various stages of the MQTT workflow. Notably, client sessions and subscriptions are stored within dedicated Kafka topics. By utilizing these Kafka topics, all broker nodes can readily access the most up-to-date states of client sessions and subscriptions, allowing them to maintain local copies for efficient message processing and delivery. In the case of a client losing connection to a specific broker node, other nodes seamlessly continue operations based on the latest state. Additionally, newly added broker nodes to the cluster get this vital information upon their activation.

Client subscriptions hold significant importance within the MQTT publish/subscribe pattern. To optimize performance, TBMQ employs the Trie data structure, enabling efficient persistence of client subscriptions in memory and facilitating swift access to relevant topic patterns.

Upon a publisher client sending a PUBLISH message, it is stored in the initial Kafka topic called tbmq.msg.all. Once Kafka acknowledges the message’s persistence, the broker promptly responds to the publisher with either a PUBACK/PUBREC message or no response at all, depending on the chosen Quality of Service (QoS) level.

Subsequently, separate threads, functioning as Kafka consumers, retrieve messages from the mentioned Kafka topic and utilize the Subscription Trie data structure to identify the intended recipients. Depending on the client type (DEVICE or APPLICATION) and persistence options described below, the broker either redirects the message to another specific Kafka topic or directly delivers it to the recipient.

Non-persistent client

A client is classified as a non-persistent one when the following conditions are met in the CONNECT packet:

For MQTT v3.x:

  • clean_session flag is set to true.

For MQTT v5:

  • clean_start flag is set to true and sessionExpiryInterval is set to 0 or not specified.

In the case of non-persistent clients, all messages intended for them are published directly without undergoing additional persistence. It is important to note that non-persistent clients can only be of type DEVICE.

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Persistent client

MQTT clients that do not meet the non-persistent conditions mentioned above are categorized as persistent clients. Let’s delve into the conditions for persistent clients:

For MQTT v3.x:

  • clean_session flag is set to false.

For MQTT v5 clients:

  • sessionExpiryInterval is greater than 0 (regardless of the clean_start flag).
  • clean_start flag is set to false and sessionExpiryInterval is set to 0 or not specified.

Client type

Building on our vast knowledge and expertise within the IoT ecosystem and the successful implementation of numerous IoT use cases, we have classified MQTT clients into two distinct categories:

  • The DEVICE clients primarily engaged in publishing a significant volume of messages while subscribing to a limited number of topics with relatively low message rates. These clients are typically associated with IoT devices or sensors that frequently transmit data to TBMQ.

  • The APPLICATION clients specialize in subscribing to topics with high message rates. They often require messages to be persisted when the client is offline with later delivery, ensuring the availability of crucial data. APPLICATION clients are commonly utilized for real-time analytics, data processing, or other application-level functionalities.

Consequently, we made a strategic decision to optimize performance by separating the processing flow for these two types of clients.

DEVICE client

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For DEVICE persistent clients, we use the tbmq.msg.persisted Kafka topic as a means of processing published messages that are extracted from the tbmq.msg.all topic. Dedicated threads, functioning as Kafka consumers, retrieve these messages and store them in a PostgreSQL database utilized for persistence storage. This approach is particularly suitable for DEVICE clients, as they typically do not require extensive message reception and may not be concerned about message loss during offline periods. Using this approach helps us smoothly recover stored messages when a DEVICE client reconnects. At the same time, it ensures good performance for scenarios involving a low incoming message rate.

APPLICATION client

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For APPLICATION persistent clients, we choose a distinct approach. A dedicated Kafka topic is created for each client and every message that is extracted from the tbmq.msg.all topic and is intended for a specific APPLICATION is stored in the corresponding Kafka topic. Subsequently, a separate thread (Kafka consumer) is assigned to each APPLICATION. These threads retrieve messages from the corresponding Kafka topics and deliver them to the respective clients. This approach significantly improves performance by ensuring efficient message delivery.

By utilizing this method, we can provide a guarantee that no messages are lost, even if a client experiences temporary issues. Messages stored within Kafka topics inherently stay available all the time due to their persistence.

It is important to note that APPLICATION clients can only be classified as persistent.

For both types of clients, we provide configurable instruments to control the persistence of messages per client and the duration for which they are stored. You can refer to the following environment variables to adjust these settings:

  • TB_KAFKA_APP_PERSISTED_MSG_TOPIC_PROPERTIES;
  • MQTT_PERSISTENT_SESSION_DEVICE_PERSISTED_MESSAGES_LIMIT;
  • MQTT_PERSISTENT_SESSION_DEVICE_PERSISTED_MESSAGES_TTL.

For more detailed information, please refer to the configurations provided in the following documentation.

Kafka Topics

Below is a comprehensive list of Kafka topics used within TBMQ, along with their respective descriptions.

  • tbmq.msg.all - topic to store all published messages to the broker from MQTT clients.
  • tbmq.msg.app. + ${client_id} - topic to store messages the APPLICATION client should receive based on its subscriptions.
  • tbmq.msg.app.shared. + ${topic_filter} - topic to store messages the APPLICATION clients should receive based on their common shared subscription.
  • tbmq.msg.persisted - topic to store messages the DEVICE persistent clients should receive based on their subscriptions.
  • tbmq.msg.retained - topic to store all retained messages. Related to MQTT Retain messages feature.
  • tbmq.client.session - topic to store sessions of all clients.
  • tbmq.client.subscriptions - topic to store subscriptions of all clients.
  • tbmq.client.session.event.request - topic to store events like CONNECTION_REQUEST, DISCONNECTION_REQUEST, CLEAR_SESSION_REQUEST, etc. for sessions of all clients.
  • tbmq.client.session.event.response. + ${service_id} - topic to store responses to events of the previous topic sent to specific broker node where target client is connected.
  • tbmq.client.disconnect. + ${service_id} - topic to store force client disconnection events (by admin request from UI/API or on sessions conflicts).
  • tbmq.msg.downlink.basic. + ${service_id} - topic used to send messages from one broker node to another to which the DEVICE subscriber is currently connected.
  • tbmq.msg.downlink.persisted. + ${service_id} - topic used to send messages from one broker node to another to which the DEVICE persistent subscriber is currently connected.
  • tbmq.sys.app.removed - topic for events to process removal of APPLICATION client topic. Used when the client changes its type from APPLICATION to DEVICE.
  • tbmq.sys.historical.data - topic for historical data statistics (e.g., number of incoming messages, outgoing messages, etc.) published from each broker node in the cluster to calculate the total values per cluster.

PostgreSQL database

TBMQ uses a PostgreSQL database to store different entities such as users, user credentials, MQTT client credentials, and published messages for DEVICES, among others.

It is important to acknowledge that Postgres, being an SQL database, has certain limitations regarding the speed of message persistence, particularly in terms of the number of writes per second it can handle. It should be noted that Postgres cannot match the performance capabilities of Kafka in this regard. Based on our experience, we have observed limits of approximately 5-6k operations per second, depending on the hardware configuration of the Postgres installation.

Considering these limitations, we recommend using APPLICATION clients for use cases that surpass the aforementioned performance thresholds. APPLICATION clients, with their dedicated Kafka topics and separate threads for message delivery, offer higher performance and scalability for scenarios requiring robust message persistence.

In future releases, we have plans to add more options for third-party persistence storage for client messages. Our goal is to include better, more dependable solutions that meet a wider set of needs.

Web UI

TBMQ offers a user-friendly and lightweight graphical user interface (GUI) that simplifies the administration of the broker in an intuitive and efficient manner. This GUI provides several key features to facilitate broker management:

  • MQTT Client Credential Management: users can easily manage MQTT client credentials through the GUI, allowing for the creation, modification, and deletion of client credentials as needed.
  • Client Session and Subscriptions Control: the GUI enables administrators to monitor and control the state of client sessions, including terminating, and managing active client connections. It also provides functionality to manage client subscriptions, allowing for the addition, removal, and modification of subscriptions.
  • Shared Subscription Management: the GUI includes tools for managing shared subscriptions. Administrators can create and manage Application shared subscription entities, facilitating efficient message distribution to multiple subscribed clients of type APPLICATION.
  • Retained Message Management: the GUI allows administrators to manage retained messages, which are messages that are saved by the broker and delivered to new subscribers.

In addition to these administrative features, the GUI provides monitoring dashboards that offer comprehensive statistics and insights into the broker’s performance. These dashboards provide key metrics and visualizations to facilitate real-time monitoring of essential broker statistics, enabling administrators to gain a better understanding of the system’s health and performance.

The combination of these features makes the GUI an invaluable tool for managing, configuring, and monitoring TBMQ in a user-friendly and efficient manner.

Subscriptions Trie

Within TBMQ, all client subscriptions are consumed from a Kafka topic and stored in a Trie data structure in the memory. The Trie organizes the topic filters hierarchically, with each node representing a level in the topic filter.

When a PUBLISH message is read from Kafka, the broker needs to identify all clients with relevant subscriptions for the topic name of the published message to ensure they receive the message. The Trie data structure enables efficient retrieval of client subscriptions based on the topic name. Once the relevant subscriptions are identified, a copy of the message is forwarded to each respective client.

This approach ensures high-performance message processing as it allows for quick and precise determination of the clients that need to receive a specific message. However, it is worth noting that this method requires increased memory consumption by the broker due to the storage of the Trie data structure.

For more detailed information on the Trie data structure, you can refer to the provided link.

Actor system

TBMQ utilizes an Actor System as the underlying mechanism for implementing actors responsible for handling MQTT clients. The adoption of the Actor model enables efficient and concurrent processing of messages received from clients, thereby ensuring high-performance operation.

The broker uses its own custom implementation of an Actor System, specifically designed to meet the requirements of TBMQ. Within this system, two distinct types of actors are present:

  • Client Actors: for every connected MQTT client, a corresponding Client actor is created. These actors are responsible for processing the main message types, such as CONNECT, SUBSCRIBE, UNSUBSCRIBE, PUBLISH, and others. The Client actors handle the interactions with the MQTT clients and help the execution of the associated message operations.
  • Persisted Device Actors: in addition to the Client actors, an extra Persisted Device actor is created for all DEVICE clients that are categorized as persistent. These actors are specifically designated to manage the persistence-related operations and handle the storage and retrieval of messages for persistent DEVICE clients.

With the Actor System and various actor types, TBMQ efficiently processes messages concurrently, ensuring optimal performance and quick responses when dealing with client interactions.

For further insights into the Actor model, you can refer to the provided link.

Standalone vs cluster mode

TBMQ is designed to be horizontally scalable, allowing for the addition of new broker nodes to the cluster automatically. All nodes within the cluster are identical and share the overall load, ensuring a balanced distribution of client connections and message processing.

The design of the broker eliminates the need for “master” or “coordinator” processes, as there is no hierarchy or central node responsible for managing the others. This decentralized approach removes the presence of a single point of failure, enhancing the system’s overall robustness and fault tolerance.

To handle client connection requests, a load balancer of your choice can be used. The load balancer distributes incoming client connections across all available TBMQ nodes, evenly distributing the workload and maximizing resource utilization.

In the event that a client loses its connection with a specific broker node (e.g., due to node shutdown, removal, or failure), it can easily reconnect to any other healthy node within the cluster. This seamless reconnection capability ensures continuous operation and uninterrupted service for connected clients, as they can establish connections with any available node in the cluster.

By leveraging horizontal scalability, load balancing, and automatic discovery of new nodes, TBMQ provides a highly scalable and resilient architecture for handling MQTT communication in large-scale deployments.

Programming languages

The back end of TBMQ is implemented in Java 17. The front end of TBMQ is developed as a SPA using the Angular 15 framework.