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In the realm of distributed systems, global state collection is a technique used to gather information about the overall state of the system at a given point in time.
A distributed system consists of multiple processes or nodes that communicate with each other to perform tasks. Each process maintains its local state, which includes information about its current status, data, and any ongoing computations. The global state of the system is a combination of the local states of all processes, along with the state of communication channels between them.
Collecting the global state can be challenging due to the asynchronous nature of distributed systems, where processes may be executing concurrently and messages may be delayed or lost. One approach to address this issue is the use of snapshot algorithms, such as the Chandy-Lamport algorithm.
The Chandy-Lamport algorithm works as follows:
A designated process initiates the snapshot by recording its local state and marking its incoming channels as empty.
The initiating process sends a “snapshot” message to all its neighboring processes through its outgoing channels.
Upon receiving the “snapshot” message, a process records its local state if it has not done so already. It then marks the incoming channel from which it received the message as empty and forwards the “snapshot” message to its neighbors.
Each process continues to record the state of its incoming channels, capturing any messages received after recording its local state but before receiving the “snapshot” message on that channel.
Once the algorithm is complete, the collected local states and channel states can be combined to form a consistent global state of the distributed system. This global state can be used for various purposes, such as debugging, monitoring, and recovery.
In distributed computing, a gossip protocol is a type of communication protocol that allows state sharing in a distributed system 1. It is based on the way epidemics spread, where each node in the system randomly selects a few other nodes to share information with, and those nodes in turn share with others, eventually disseminating the information to all nodes in the system. Gossip protocols are often used for tasks such as dissemination of data, failure detection, and state synchronization in large-scale distributed systems. They can help improve system scalability, fault tolerance, and resilience in the face of network failures or other types of disruptions. However, like all distributed systems designs, gossip protocols have trade-offs, such as increased network overhead and potential delays in propagating updates to all nodes.
In distributed systems, replica management refers to the process of maintaining consistency and coordination of multiple copies of data, services or application instances across different nodes or servers in the system.
One replica management strategy is primary-backup replication, where one node (the primary) is designated as the master copy and all updates to the data/service are applied to this primary node, with the changes then propagated to the backup replicas. This approach provides strong consistency guarantees, but has the downside of requiring a failover process if the primary node fails.
Another strategy is active-active replication, where multiple replicas are actively serving traffic and responding to requests simultaneously. This approach can improve system performance, but may lead to data consistency issues if updates on different replicas occur concurrently.
A third strategy is read replica architecture, where multiple replicas in the system can serve only read requests, while a single primary replica serves both read and write requests. This approach can help scale read-heavy workloads and provide better response times, but may lead to data consistency issues if multiple replicas are updated concurrently.
Quorum-based replication is a technique in distributed systems used to ensure consistency and availability of replicated data. It involves creating a quorum of replicas, where a majority of replicas must be in agreement for a write operation to be considered successful. This technique ensures that any updates to shared data are propagated to the majority of replicas, and the changes are not lost during a network partition or a node failure. Quorum-based replication is often used in distributed databases and other distributed systems where consistency is critical for write and read operations. It helps to ensure that data consistency is maintained while also aiming to provide high availability of data. Quorum-based replication is comparatively easy to implement and is a popular technique for achieving strong data consistency in various distributed systems.
Self-stabilization is a concept of fault-tolerance in distributed systems . It refers to a property of a distributed algorithm or system that guarantees that even if it starts from an arbitrary initial state, it will converge to a correct and stable state within a finite time. In other words, a self-stabilizing system can recover from arbitrary faults, errors, or failures that may occur in the system, and still maintain its correctness and stability.
Self-stabilization is particularly useful in distributed systems where failures and faults are common, and where it can be difficult to ensure that all processes are synchronized and consistent with each other. Self-stabilization can provide a robust and fault-tolerant foundation for many distributed system applications, such as routing protocols, consensus algorithms, and fault-tolerant computing.
There are several types of self-stabilizing algorithms, such as leader election, mutual exclusion, and clock synchronization, that provide different kinds of safety and liveness guarantees. The design and analysis of self-stabilizing algorithms can be challenging due to the non-deterministic and asynchronous nature of distributed systems, and requires a deep understanding of distributed computing theory and practice.
HyperLogLog is a probabilistic algorithm used for estimating the number of distinct elements in a large dataset or multiset, which becomes impractical to keep track of using traditional methods due to the required amount of memory. The algorithm uses significantly less memory compared to exact methods, and is able to estimate cardinalities of more than a billion with a typical accuracy of 2% using about 1.5 kB of memory. HyperLogLog is an extension of the earlier LogLog algorithm, which is based on a probabilistic approach to estimate the unique count of elements in a dataset. HyperLogLog has various applications in big data analytics, machine learning, distributed query engines, and data recommenders, among others.
It is frequently used in Postgres, Redis, and other distributed systems, and can allow for efficient rollup tables with HyperLogLog, which permit storage and access of data structure concurrently.
The HyperLogLog algorithm works by hashing each element in the dataset to a 64-bit value. The algorithm then examines the binary representation of the hash value to determine the position of the first zero bit. This position is used to assign the element to one of several “buckets” or “registers”.
The algorithm maintains a set of counters, with each counter corresponding to a specific range of register values. For example, one counter might count the number of registers that have a value of zero, while another counter might count the number of registers that have a value of one.
The HyperLogLog algorithm uses a “bias correction” step to reduce the estimation error that occurs when the number of registers is small. The bias correction involves adjusting the estimate by a constant value that depends on the number of registers and the distribution of the hashed elements.
