Software messaging patterns

Paxos Generation Clock Quorum. When data is replicated across cluster nodes, achieving consensus on a single value is not enough. All the replicas need to reach agreement on all the data. This requires executing Paxos multiple times while maintaining strict order. Replicated Log describes how basic Paxos can be extended to achieve this.

This technique is also known as state machine replication to achieve fault tolerance. In state machine replication, the storage services, like a key value store, are replicated on all the servers, and the user inputs are executed in the same order on each server. The key implementation technique used to achieve this is to replicate Write-Ahead Log on all the servers to have a Replicated Log. To provide durability guarantees, you can use the Write-Ahead Log pattern.

This helps with log cleaning, which is handled by Low-Water Mark. Fault tolerance is provided by replicating the write-ahead log on multiple servers. The replication among the servers is managed using the Leader and Followers pattern and Quorum is used to update the High-Water Mark to decide which values are visible to clients.

All the requests are processed in strict order, by using Singular Update Queue. The order is maintained while sending the requests from leaders to followers using Single Socket Channel. To optimize for throughput and latency over a single socket channel, Request Pipeline can be used.

Followers know about availability of the leader via the HeartBeat received from the leader. If the leader is temporarily disconnected from the cluster because of network partition, it is detected by using Generation Clock.

If all the requests are served only by the leader, it might get overloaded. When the clients are read only and tolerate reading stale values, they can be served by the follower servers. Follower Reads allows handling read requests from follower servers. Consensus algorithms are useful when multiple cluster nodes all store the same data. Often, data size is too big to store and process on a single node. So data is partitioned across a set of nodes using various partitioning schemes such as Fixed Partitions or Key-Range Partitions.

To achieve fault tolerance, each partition is also replicated across a few cluster nodes using Replicated Log. Sometimes data across a set of partitions needs to be stored as one atomic operation. If processes storing a partition crash or if there are network delays or process pauses, it might happen that data is copied on a few partitions and failed on a few.

To maintain atomicity, the data needs to be stored and made accessible on all the partitions or none of them. Two Phase Commit is used to guarantee atomicity across a set of partitions. To guarantee atomicity, two-phase-commit often needs to lock the data items involved.

This can severely impact throughput, particularly when there are long running read-only operations holding locks. To allow better throughput without using conflicting locks, two-phase-commit implementations often use Versioned Value based storage. Products like Kubernetes or Kafka's architecture are built around a strongly consistent metadata store. We can understand it as a pattern sequence. Consistent Core is used as a strongly consistent, fault tolerant metadata store.

Lease is used to implement group membership and failure detection of cluster nodes. Cluster nodes use State Watch to get notified when any cluster node fails or updates its metadata The Consistent Core implementation uses Idempotent Receiver to ignore duplicate requests sent by cluster nodes in case of retries on network failure. The Consistent Core is built with a 'Replicated Wal', which is described as a pattern sequence in the above section.

Usage of various types of logical timestamps can also be seen as a pattern sequence. Various products use either a Gossip Dissemination or a Consistent Core for group membership and failure detection of cluster nodes. The data storage uses Versioned Value to be able to determine which values are most recent. If a single server is responsible for updating the values or Leader and Followers is used, then a Lamport Clock can be used as a version, in the Versioned Value.

When the timestamp values need to be derived from the time of the day, a Hybrid Clock is used instead of a simple Lamport Clock.

If multiple servers are allowed to handle client requests to update the same value, a Version Vector is used to be able to detect concurrent writes on different cluster nodes. This way, understanding problems and their recurring solutions in their general form, helps in understanding building blocks of a complete system. Distributed systems is a vast topic. The set of patterns covered here is a small part, covering different categories to showcase how a patterns approach can help understand and design distributed systems.

I will keep adding to this set to broadly include the following categories of problems solved in any distributed system. Many thanks to Martin Fowler for helping me throughout and guiding me to think in terms of patterns.

Some patterns then added over next few weeks. Thoughtworks Insights Careers Products. What this is about For the last several months, I have been conducting workshops on distributed systems at Thoughtworks.

Distributed systems - An implementation perspective Today's enterprise architecture is full of platforms and frameworks which are distributed by nature. There are two aspects: They run on multiple servers. The number of servers in a cluster can vary from as few as three servers to a few thousand servers.

They manage data. So these are inherently 'stateful' systems. Patterns Patterns , a concept introduced by Christopher Alexander, is widely accepted in the software community to document design constructs which are used to build software systems. Problems and Their Recurring Solutions.

Several things can go wrong when data is stored on multiple servers. Process crashes Processes can crash at any time maybe due to hardware faults or software faults. It can be taken down for routine maintenance by system administrators. It can be killed doing some file IO because the disk is full and the exception is not properly handled. In cloud environments, it can be even trickier, as some unrelated events can bring the servers down.

There are two problems to be tackled here. A particular server can not wait indefinitely to know if another server has crashed. There should not be two sets of servers, each considering another set to have failed, and therefore continuing to serve different sets of clients.

This is called the split brain. Process Pauses Even with quorums and leader and followers, there is a tricky problem that needs to be solved. Smart Proxy : To track messages on a service that publishes reply messages to the Return Address specified by the requestor, use a Smart Proxy to store the Return Address supplied by the original requestor and replace it with the address of the Smart Proxy. When the service sends the reply message route it to the original Return Address.

Test Message : To prevent a component garbling outgoing messages due to an internal fault, use Test Message to assure the health of message processing components. Channel Purger : To remove 'left-over' messages on a channel so that they do not disturb tests or running systems, use a Channel Purger to remove unwanted messages from a channel.

Favour idempotent Message processing: a duplicate message is tolerated without causing "double debits". Interchange of information i. Messaging is a ubiquitous part of the world around us. Conventional software methodologies and component technologies overlook messaging and therefore provide an incomplete model. On the other hand, a messaging paradigm and the associated messaging design pattern MDP address this gap and provide a more complete and accurate model of the real world.

As a consequence, software engineering processes and techniques are improved significantly. While designing and manufacturing software, we need to think not only in terms of software components, but also in terms of the messaging being exchanged between these entities. Encapsulation, decoupling and reusability are improved while reducing complexity. Keep in mind that most of the design patterns are, at some level, responsible for interchanging information between participants.

The overall design and UML diagrams are simplified and streamlined making them easier to understand and implement. The resulting software design and implementation are also more robust and straightforward.

Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Message Design Patterns Ask Question. Asked 12 years, 2 months ago. Active 2 years, 7 months ago. Viewed 21k times. For message based passing systems, what are your "message design patterns" e.

Limit directed messages i. Have a system "heartbeat" message Other examples? Add a comment. Active Oldest Votes. Per Enterprise Integration Patterns , the Authors Gregor Hohpe and Bobby Woolf have documented over 60 messaging patterns, grouped into the following six categories : Message Construction Message : To exchange a piece of information between two applications connected by a message channel, package the information into a Message, a data record that the messaging system can transmit through a message channel.

Message Routing Pipes-and-Filters : To perform complex processing on a message while maintaining independence and flexibility, use the Pipes and Filters architectural style to divide a larger processing task into a sequence of smaller, independent processing steps Filters that are connected by channels Pipes.

Message Transformation translation Message Translator : To allow systems using different data formats to communicate with each other using messaging, use a special filter, a Message Translator, between other filters or applications to translate one data format into another. Messaging Endpoint Message Endpoint : To connect an application to a messaging channel to send and receive messages, use a Message Endpoint, a client of the messaging system that the application can then use to send or receive messages.

Messaging Channels Message Channel : To allow one application communicate with another using messaging, connect the applications using a Message Channel, where one application writes information to the channel and the other one reads that information from the channel. Systems Management Monitoring Control Bus : To effectively administer a messaging system that is distributed across multiple platforms and a wide geographic area, use a Control Bus to manage an enterprise integration system.

Avoid large messages - prefer the "baggage-check" idiom Avoid message ordering requirements - greatly simplifies burden on infrastructure. Is you first line correct? I'm not sure I understand the second part correctly? See eaipatterns. Abstract Interchange of information i. Unfortunately the link is dead, is this link the corresponding ressources? Sign up or log in Sign up using Google.

Sign up using Facebook. However, many interactions between systems extend beyond sending a single, stateless message: a request may expect a response; a handshake or authentication are needed first; a reservation is confirmed or expires. Such conversations , stateful exchanges between participants, present new design challenges and patterns. I therefore started documenting Conversation Patterns , which are the starting point for Enterprise Integration Patterns 2.

Architecting integration solutions is a complex task. There are many conflicting drivers and even more possible 'right' solutions. Whether the architecture was in fact a good choice usually is not known until many months or even years later, when inevitable changes and additions put the original architecture to test. Unfortunately, there is no "cookbook" for enterprise integration solutions.

Most integration vendors provide methodologies and best practices, but these instructions tend to be very much geared towards the vendor-provided tool set and often lack treatment of the bigger picture, including underlying guidelines, principles and best practices.

Asynchronous messaging architectures have proven to be the best strategy for enterprise integration because they allow for a loosely coupled solution that overcomes the limitations of remote communication, such as latency and unreliability. Unfortunately, asynchronous messaging is not without pitfalls. Many of the assumptions that hold true when developing single, synchronous applications are no longer valid.

Vendor-independent design guidance helps developers avoid these pitfalls so they can build robust integration architectures based on asynchronous messaging. Each pattern tackles a specific problem by discussing design considerations and presenting an elegant solution that balances often conflicting forces. The solution is not the first approach that comes to mind, but one that has evolved through actual use over time, capturing the experience that senior developers and architects have gained by repeatedly building solutions and learning from their mistakes.

Jeff strikes a great balance between drawing on theoretical foundations cybernetics, complex systems theory and real-world examples to explain why and how traditional IT organizations must transform to support the business in a digital world. A book to hand to all IT managers.