10 Must-Know Distributed Data Structures and Algorithms for System Design Interviews
10 Distributed Data Structures Behind Scalable System Design
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Hi there, if you are preparing for a System Design Interview, then one thing you should focus on is learning different System Design Algorithms and what problems they solve in Distributed Systems and Microservices.
In the past, I have shared 50 System Design Problems and 10 Essential System Design topics and in this article, I am going to tell you 10 System Design algorithms and distributed data structures which every developer should learn.
Without any further ado, here are the 10 System Design algorithms and distributed Data Structures you can use to solve large-scale distributed system problems:
Consistent Hashing
MapReduce
Distributed Hash Tables (DHT)
Bloom Filters
Two-phase commit (2PC)
Paxos
Raft
Gossip protocol
Chord:
CAP theorem
These algorithms and distributed data structures are just a few examples of the many techniques that can be used to solve large-scale distributed system problems.
By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, bugfree.ai and Udemy which have many great System design courses, and these popular System design YouTube channels, which have many great System design courses and tutorials.
10 Distributed Data Structure and System Design Algorithms for Programmers
It’s important to have a good understanding of these algorithms and how to apply them effectively in different scenarios.
So, let’s deep dive into each of them and find out what they are, how they work, and when to use them.
1. Consistent Hashing
Consistent hashing is a technique used in distributed systems to efficiently distribute data among multiple nodes.
It is used to minimize the amount of data that needs to be transferred between nodes when a node is added or removed from the system.
The basic idea behind consistent hashing is to use a hash function to map each piece of data to a node in the system. Each node is assigned a range of hash values, and any data that maps to a hash value within that range is assigned to that node.
When a node is added or removed from the system, only the data that was assigned to that node needs to be transferred to another node. This is achieved by using a concept called virtual nodes.
Instead of assigning each physical node a range of hash values, multiple virtual nodes are assigned to each physical node.
Each virtual node is assigned a unique range of hash values, and any data that maps to a hash value within that range is assigned to the corresponding physical node.
When a node is added or removed from the system, only the virtual nodes that are affected need to be reassigned, and any data that was assigned to those virtual nodes is transferred to another node.
This allows the system to scale dynamically and efficiently, without requiring a full redistribution of data each time a node is added or removed.
Overall, consistent hashing provides a simple and efficient way to distribute data among multiple nodes in a distributed system. It is commonly used in large-scale distributed systems, such as content delivery networks and distributed databases, to provide high availability and scalability.
2. Map reduce
MapReduce is a programming model and framework for processing large datasets in a distributed system. It was originally developed by Google and is now widely used in many big data processing systems, such as Apache Hadoop.
The basic idea behind MapReduce is to break a large dataset into smaller chunks, distribute them across multiple nodes in a cluster, and process them in parallel. The processing is divided into two phases: a Map phase and a Reduce phase.
In the Map phase, the input dataset is processed by a set of Map functions in parallel. Each Map function takes a key-value pair as input and produces a set of intermediate key-value pairs as output.
These intermediate key-value pairs are then sorted and partitioned by key, and sent to the Reduce phase.
In the Reduce phase, the intermediate key-value pairs are processed by a set of Reduce functions in parallel. Each Reduce function takes a key and a set of values as input, and produces a set of output key-value pairs.
Here is an example of how MapReduce can be used to count the frequency of words in a large text file:
Map phase: Each Map function reads a chunk of the input file and outputs a set of intermediate key-value pairs, where the key is a word and the value is the number of occurrences of that word in the chunk.
Shuffle phase: The intermediate key-value pairs are sorted and partitioned by key, so that all the occurrences of each word are grouped together.
Reduce phase: Each Reduce function takes a word and a set of occurrences as input, and outputs a key-value pair where the key is the word and the value is the total number of occurrences of that word in the input file.
The MapReduce framework takes care of the parallel processing, distribution, and fault tolerance of the computation. This allows it to process large datasets efficiently and reliably, even on clusters of commodity hardware.
3. Distributed Hash Tables (DHT)
A Distributed Hash Table (DHT) is a distributed system that provides a decentralized key-value store. It is used in peer-to-peer (P2P) networks to store and retrieve information in a scalable and fault-tolerant manner.
In a DHT, each participating node stores a subset of the key-value pairs, and a mapping function is used to assign keys to nodes.
This allows nodes to locate the value associated with a given key by querying only a small subset of nodes, typically those responsible for keys close to the given key in the mapping space.
DHTs provide several desirable properties, such as self-organization, fault-tolerance, load-balancing, and efficient routing. They are commonly used in P2P file sharing systems, content distribution networks, and distributed databases.
One popular DHT algorithm is the Chord protocol, which uses a ring-based topology and a consistent hashing function to assign keys to nodes. Another widely used DHT is the Kademlia protocol, which uses a binary tree-like structure to locate nodes responsible for a given key.
4. Bloom Filters
Bloom Filters are a probabilistic data structure used for efficient set membership tests. They were introduced by Burton Howard Bloom in 1970.
A Bloom Filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set or not. It uses a bit array and a set of hash functions to store and check for the presence of an element in a set.
The process of adding an element to a Bloom Filter involves passing the element through a set of hash functions which returns a set of indices in the bit array. These indices are then set to 1 in the bit array.
To check whether an element is present in the set or not, the same hash functions are applied to the element and the resulting indices are checked in the bit array.
If all the bits at the indices are set to 1, then the element is considered to be present in the set. However, if any of the bits are not set, the element is considered to be absent from the set.
Since Bloom Filters use hash functions to index the bit array, there is a possibility of false positives, i.e., the filter may incorrectly indicate that an element is present in the set when it is not.
However, the probability of a false positive can be controlled by adjusting the size of the bit array and the number of hash functions used.
The false negative rate, i.e., the probability of a Bloom filter failing to identify an element that is actually present in the set, is zero.
Bloom Filters are widely used in various fields such as networking, databases, and web caching to perform efficient set membership tests.
5. 2 Phase Commit
Two-phase commit (2PC) is a protocol used to ensure the atomicity and consistency of transactions in distributed systems. It is a way to guarantee that all nodes participating in a transaction either commit or rollback together.
The two-phase commit protocol works in two phases:
Prepare Phase: In the prepare phase, the coordinator node sends a message to all participating nodes, asking them to prepare to commit the transaction.
Each participant responds with a message indicating whether it is prepared to commit or not. If any participant cannot prepare, it responds with a message indicating that it cannot participate in the transaction.
Commit Phase: If all participants are prepared to commit, the coordinator sends a message to all nodes asking them to commit. Each participant commits the transaction and sends an acknowledgement to the coordinator.
If any participant cannot commit, it rolls back the transaction and sends a message to the coordinator indicating that it has rolled back.
If the coordinator receives acknowledgements from all participants, it sends a message to all nodes indicating that the transaction has been committed.
If the coordinator receives a rollback message from any participant, it sends a message to all nodes indicating that the transaction has been rolled back.
The two-phase commit protocol ensures that all nodes in a distributed system agree on the outcome of a transaction, even in the presence of failures.
However, it has some drawbacks, including increased latency and the possibility of deadlock. Additionally, it requires a coordinator node, which can be a single point of failure.
6. Paxos
Paxos is a distributed consensus algorithm that allows a group of nodes to agree on a common value, even in the presence of failures. It was introduced by Leslie Lamport in 1998 and has become a fundamental algorithm for distributed systems.
The Paxos algorithm is designed to handle a variety of failure scenarios, including message loss, duplication, reordering, and node failures.
The algorithm proceeds in two phases: the prepare phase and the accept phase. In the prepare phase, a node sends a prepare message to all other nodes, asking them to promise not to accept any proposal with a number less than a certain value.
Once a majority of nodes have responded with promises, the node can proceed to the accept phase. In the accept phase, the node sends an accept message to all other nodes, proposing a certain value.
If a majority of nodes respond with an acceptance message, the value is considered accepted.
Paxos is a complex algorithm, and there are several variations and optimizations of it, such as Multi-Paxos, Fast Paxos, and others.
These variations aim to reduce the number of messages exchanged, optimize the latency of the algorithm, and reduce the number of nodes that need to participate in the consensus.
Paxos is widely used in distributed databases, file systems, and other distributed systems where a high degree of fault tolerance is required.
7. Raft
Raft is a consensus algorithm designed to ensure fault-tolerance in distributed systems. It is used to maintain a replicated log that stores a sequence of state changes across multiple nodes in a cluster.
Raft achieves consensus by electing a leader, which coordinates the communication among the nodes and ensures that the log is consistent across the cluster.
The Raft algorithm consists of three main components: leader election, log replication, and safety. In the leader election phase, nodes in the cluster elect a leader using a randomized timeout mechanism.
The leader then coordinates the log replication by receiving state changes from clients and replicating them across the nodes in the cluster. Nodes can also request entries from the leader to ensure consistency across the cluster.
The safety component of Raft ensures that the algorithm is resilient to failures and ensures that the log is consistent across the cluster.
Raft achieves safety by ensuring that only one node can be the leader at any given time and by enforcing a strict ordering of log entries across the cluster.
Raft is widely used in distributed systems to provide fault-tolerance and high availability. It is often used in systems that require strong consistency guarantees, such as distributed databases and key-value stores.
8. Gossip
The gossip protocol is a peer-to-peer communication protocol used in distributed systems to disseminate information quickly and efficiently.
It is a probabilistic protocol that allows nodes to exchange information about their state with their neighbors in a decentralized manner.
The protocol gets its name from the way it spreads information like a rumor or gossip.
In a gossip protocol, nodes randomly select a set of other nodes to exchange information with. When a node receives information from another node, it then forwards that information to a subset of its neighbors, and the process continues.
Over time, the entire network becomes aware of the information as it spreads from node to node.
One of the key benefits of the gossip protocol is its fault-tolerance. Since the protocol relies on probabilistic communication rather than a central authority, it can continue to function even if some nodes fail or drop out of the network.
This makes it a useful tool in distributed systems where reliability is a critical concern.
Gossip protocols have been used in a variety of applications, including distributed databases, peer-to-peer file sharing networks, and large-scale sensor networks.
They are particularly well-suited to applications that require fast and efficient dissemination of information across a large number of nodes.
9. Chrod
Chord is a distributed hash table (DHT) protocol used for decentralized peer-to-peer (P2P) systems. It provides an efficient way to locate a node (or a set of nodes) in a P2P network given its identifier.
Chord allows P2P systems to scale to very large numbers of nodes while maintaining low overhead.
In a Chord network, each node is assigned an identifier, which can be any m-bit number. The nodes are arranged in a ring, where the nodes are ordered based on their identifiers in a clockwise direction.
Each node is responsible for a set of keys, which can be any value in the range of 0 to 2^m-1.
To find a key in the network, a node first calculates its hash value and then contacts the node whose identifier is the first clockwise successor of that hash value.
If the successor node does not have the desired key, it forwards the request to its successor, and so on, until the key is found. This process is known as a finger lookup, and it typically requires a logarithmic number of messages to find the desired node.
To maintain the consistency of the network, Chord uses a protocol called finger tables, which store information about other nodes in the network.
Each node maintains a finger table that contains the identifiers of its successors at increasing distances in the ring. This allows nodes to efficiently locate other nodes in the network without having to maintain a complete list of all nodes.
Chord also provides mechanisms for maintaining consistency when nodes join or leave the network. When a node joins the network, it notifies its immediate successor, which updates its finger table accordingly.
When a node leaves the network, its keys are transferred to its successor node, and the successor node updates its finger table to reflect the departure.
Overall, Chord provides an efficient and scalable way to locate nodes in a P2P network using a simple and decentralized protocol.
10. CAP Theorem
The CAP theorem, also known as Brewer’s theorem, is a fundamental concept in distributed systems that states that it is impossible for a distributed system to simultaneously guarantee all of the following three properties:
Consistency: Every read receives the most recent write or an error.
Availability: Every request receives a response, without guarantee that it contains the most recent version of the information.
Partition tolerance: The system continues to function and provide consistent and available services even when network partitions occur.
In other words, a distributed system can only provide two out of the three properties mentioned above.
This theorem implies that in the event of a network partition, a distributed system must choose between consistency and availability.
For example, in a partitioned system, if one node cannot communicate with another node, it must either return an error or provide a potentially stale response.
The CAP theorem has significant implications for designing distributed systems, as it requires developers to make trade-offs between consistency, availability, and partition tolerance.
Conclusion
That’s all about the essential System Design Data Structure, Algorithms and Protocol You can learn in 2023. In conclusion, system design is an essential skill for software engineers, especially those working on large-scale distributed systems.
These ten algorithms, data structure, and protocols provide a solid foundation for tackling complex problems and building scalable, reliable systems. By understanding these algorithms and their trade-offs, you can make informed decisions when designing and implementing systems.
Additionally, learning these algorithms can help you prepare for system design interviews and improve their problem-solving skills. However, it’s important to note that these algorithms are just a starting point, and you should continue to learn and adapt as technology evolves.
By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, bugfree.ai and Udemy which have many great System design courses
Also, here is a nice System design template from DesignGuru which you can use to answer any System design question on interviews. It highlights key software architecture components and allows you to express your knowledge well.
All the best for your System design interviews!!
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Awesome write up. Hey covered a few of these topics myself here:
https://open.substack.com/pub/ahmazin