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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 6 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, and these popular System design YouTube channels, which have many great System design courses and tutorials.
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!!
