Top Skills To Thrive as a Distributed Computing Expert

Distributed systems have become essential to enterprise computing. Virtually all web applications are built on a distributed computing system. Consequently, all computing jobs, be it database management or programming video games, will require distributed computing skills, both now and in the future. 

Distributed computing is used in everything from electronic banking systems, online payments to multiplayer online games. Industrial uses for distributed real-time systems include:

• Airline flight control systems
• Uber and Lyft dispatch systems
• Automation control systems used in manufacturing
• Tracking systems used by logistics and e-commerce companies

What Is Distributed Computing?

Distributed computing uses software to coordinate tasks that are performed on multiple computers simultaneously. The machines appear as if they are one computer to the user. One machine can fail without bringing down the entire system. These days, most implementations are designed to function through the Internet, particularly the cloud. 

Distributed systems have many models and architectures, from the traditional client-server systems that include multiple networked computers interacting with a central server to cell phone networks that share workloads with smartphones, switching systems and other connected devices. Peer-to-peer networks, which also distribute workloads on hundreds of computers, are a distributed system architecture. However, the more common distributed systems run on the web and pass on workloads to several virtual server instances on the cloud created as per requirement and terminated when their work is done.  

Why Distribute a System?

Deploying, debugging and maintaining distributed systems can be tricky. So why do it? The main reason is scalability. Your application can only scale so far on a single system, and no system upgrade can handle increasing workloads. What distributed computing gives you is the ability to add more computers to improve performance, rather than having to upgrade a single one. 

During performance drops, you can add another machine. It also provides you with a greater level of fault tolerance over a single machine. Suppose one or more machines fail, the application switches to those that are still running. 

Distributed systems also provide for low latency. So instead of all users in Europe having to access a single machine in Australia and vice versa, you can locate one machine on each continent, allowing them access to the nearest nodes. This would also reduce latency. In short, distributed systems allow shared information while maintaining consistency between redundant software or hardware components. This improves fault tolerance, accessibility and reliability. 

Learn more: Non-Cloud Infrastructure Spending May Flatten Out by 2025: IDC 

Challenges to Distributed Systems Design

Distributed systems come with several trade-offs. The more machines you add to the network, the more complex the system becomes. Thus, in the designing of distributed systems, the significant trade-off is complexity against the desired upgrade to performance. For example, you might add two replica database servers in sync with the central database server to enhance read performance. Every time you enter or alter any piece of information in the primary database server, it asynchronously apprises all replicas of the changes so they can save the changes too. 

What if someone tries to fetch the data before it adequately syncs up to the replicas? They will produce the wrong data! 

One of the biggest challenges to distributed systems is evidenced in the CAP theorem. According to CAP theorem, a distributed data store is not capable of being consistent, available and network partition tolerant at the same time. Consistency means you get what you expect from a sequential read or write; availability means that the entire system does not fail when one node goes down; and partition tolerance means that the system can function and ensure consistency and availability despite network partitions. Since consistency or availability is not feasible without partition tolerance, the trade-off is between a strongly consistent system or a highly available one. 

Challenges also arise when implementing database transactions in distributed systems as they need consensus from each node on whether to stop or carry out an action. However, getting a correct consensus within a set time frame can be tricky in case of process crashes, network partitioning, and loss or duplication of messages. 

Since distributed systems operate without a global clock, they require diligent programming, so the processes are correctly synchronized, and there are no transmission delays. This reduces the risk of errors and data corruption.

Furthermore, in a poorly designed system, the crashing of a single node can cause the entire system to come down. Compared to single server-based systems, a higher number of components in distributed systems widens the attack surface, which opens up organizations to security threats. You will have to balance the risk of an attack with the cost incurred on deploying prevention mechanisms. 

Learn more: What Is IT Infrastructure? Definition, Building Blocks, and Management Best Practices

Top Skills Required To Succeed in Distributed Computing

To make a distributed system work, you should have tailor-made software for running concurrently on multiple computers and handle the challenges mentioned above. Here are the top skills you will need to master to design distributed computing systems that work: 

1. Sharding

Rather than use database replicas that are readable, sharding (or partitioning) can increase performance for both reads and writes. Sharding splits a server into multiple smaller servers, known as shards, each of which can carry different records. You can decide which documents will go into which shard to ensure data is uniformly distributed throughout the overall system. Sharding lets you increase your write traffic by the number of shards. You need to know how to choose the sharding key to avoid a single shard from becoming a “hot spot” receiving more requests than the others. There are many ways in which sharding can be done, and you will need to know which ones to use to get the best performance.  

2. Distributed Databases

Since distributed databases are NoSQL in nature, they are limited to key-value semantics. They suit distributed systems because they can muster a high level of performance and scalability but risk losing out on consistency or availability. In contrast to traditional databases’ ACID properties, distributed databases provide BASE properties, as follows:

Basically Available–When the system always gives a response.

Soft state–The system can change, even when there is no input due to Eventual consistency. 

Eventual consistency–With no input, the data will be eventually dispersed to every node–thus finally becoming consistent.

Examples of distributed databases with BASE properties include: Cassandra, Riak, and Voldemort. If you need stronger consistency, you should consider HBASE, Couchbase, Redid, or Zookeeper.  

3. Paxos/Raft algorithms

Two algorithms that are used to solve the challenge of reaching consensus on a non-reliable network are Paxos and Raft. The Paxos algorithm is used to achieve consensus among a distributed set of computers that communicates via an asynchronous network. One or more clients propose a value to Paxos, and there is a consensus when most systems running Paxos agree on one of the proposed values. Raft is a consensus algorithm used as a substitute to Paxos algorithms.  

4. Tools for Distributed Computation

Distributed computation tools split a huge computational function beyond the capacity of a single computer into many smaller ones, then have them executed on multiple machines simultaneously, followed by aggregation of data and output of the solution. MapReduce was the first such tool that maps the data and makes it more meaningful.Kafka Streams, Apache Spark, Apache Storm and Apache Samza are some of the other such tools. 

5. Distributed File Systems

A distributed file system stores large swathes of data on multiple machines that appear as one. Unlike distributed databases that require a custom API, they use the same interfaces and semantics locally. Some examples include Hadoop Distributed File System (HDFS) used for distributed computing on the Hadoop framework. Interplanetary File System (IPFS), a peer-to-peer network that leverages Blockchain technology. It employs a decentralized architecture that has no single owner or point of failure. 

Learn more: How to Protect Hybrid IT Infrastructure Against Physical and Environmental Threats 

6. Remote Procedure Call (RPC)

RPCs are inter-process communications used in distributed systems where one computer’s RPC is responsible for a procedure to be performed in another address space on a different computer on the same shared network. An RPC is coded to look like a local procedure call minus any explicit instruction for remote interaction. It is implemented through a request-response message-passing system.  

7. Distributed Messaging Systems

Messaging systems allocate a central place where all messages and events can be stored and propagated. They enable the decoupling of applications, so they cannot directly talk to other systems. When a message is sent out from an application to the platform, it is read by multiple applications. A messaging platform is the best mechanism to spread the message of the event. Examples of messaging systems are RabbitMQ, Kafka, Apache ActiveMQ, and Amazon SQS. 

8. Distributed Ledgers

A distributed ledger is a non-changeable, append-only database replicated, synchronized and shared with all nodes on a distributed network. Blockchain is one of the key technologies used for distributed ledgers behind a distributed payment protocol called Bitcoin. Other uses of distributed ledgers include Proof of Existence to establish document integrity, ownership and timestamp. It also enables Decentralized Authentication for storing your identity on the blockchain and enabling single sign-on (SSO) everywhere.  

9. Access Control Mechanisms

To manage access control efficiently in a distributed computing environment, you should become familiar with the various mechanisms for it, including access control lists (ACL), attribute-based access control (ABAC)and role-based access control (RBAC).  

Conclusion: The Future of Computing Is Distributed

Almost any application or service will someday leverage distributed computing in some form. The need for always-on, available anywhere computing makes this inevitable, especially with the growing use of mobile devices to accomplish everyday tasks. Enterprise developers will rely more and more on them to streamline development, application management and deployment of systems and infrastructure. Will you be ready with the right skills to meet the challenge of distributed computing?

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