Distributed Systems: An Overview for Data Engineers
As data continues to grow, data engineers are faced with new challenges of managing and processing large data sets beyond the capabilities of a single machine. This is where distributed systems come in.
Distributed systems are a collection of independent computers that work together to achieve a common goal. They allow for the processing of large amounts of data by breaking it down into smaller chunks and processing it across multiple machines. This results in better performance, fault tolerance, and scalability.
In this article, we will explore the fundamental concepts of distributed systems, the different types of distributed systems, and some of the popular tools used in building distributed systems.
Fundamental Concepts of Distributed Systems
Consistency
Consistency refers to the property of a distributed system where all nodes in the system see the same data at the same time. Achieving consistency in a distributed system is a complex problem as nodes are spread across different locations and communicate with each other over a network.
There are two approaches to consistency: strong consistency and eventual consistency. Strong consistency ensures that all nodes see the same data at the same time, whereas eventual consistency allows nodes to have different views of the data but eventually converge to a common state.
Availability
Availability refers to the property of a distributed system where nodes are always ready to respond to requests. Achieving high availability is important in distributed systems as failures can occur at any time.
To ensure availability, systems can be built with redundancy so that if one node fails, another node can take its place. By having multiple nodes operating simultaneously, the system can continue to operate despite the failure of one or more nodes.
Partition Tolerance
Partition tolerance refers to the ability of a distributed system to continue to operate even if some communication links between nodes fail. This is important in distributed systems as the nodes are spread across different locations and communicate with each other over a network.
To achieve partition tolerance, a system can be designed to allow for multiple nodes to operate independently without the need for constant communication. When communication is re-established, the system can be updated to reflect changes made to other nodes during the period of partition.
CAP Theorem
CAP theorem states that it is impossible for a distributed system to simultaneously achieve consistency, availability, and partition tolerance. Instead, a system can only achieve two of these characteristics at the same time.
For example, a system that prioritizes consistency and partition tolerance may not be as available as a system that prioritizes availability and partition tolerance. Understanding the tradeoffs involved in CAP theorem is important when designing distributed systems.
Types of Distributed Systems
Shared-Nothing Architecture
In a shared-nothing architecture, each node in the distributed system has its own dedicated processors, storage, and memory. This approach ensures that each node operates independently and does not rely on other nodes to complete a task.
Shared-nothing architecture is often used in distributed databases like Apache Cassandra, as it allows for horizontal scalability and fault tolerance.
Shared-Disk Architecture
In a shared-disk architecture, nodes in the distributed system share a common disk storage. This approach allows for a faster data access and better consistency compared to the shared-nothing architecture.
Shared-disk architecture is often used in clustered file systems like GlusterFS, as it allows for a unified view of the file system across multiple systems.
Shared-Memory Architecture
In a shared-memory architecture, nodes in the distributed system share a common memory. This approach allows for faster data access and better consistency compared to the shared-disk or shared-nothing architectures.
Shared-memory architecture is often used in high-performance computing like scientific computation or computer graphics.
Tools for Distributed Systems
Apache Kafka
Apache Kafka is a distributed streaming platform used to manage large streams of data across multiple nodes. Kafka is designed to be fault-tolerant, scalable, and high-performance.
Apache Hadoop
Apache Hadoop is a big data framework that allows for the distributed processing of large data sets. Hadoop's distributed file system (HDFS) allows for the storage of large data sets across multiple nodes, while Hadoop MapReduce allows for the distributed processing of the data.
Apache Spark
Apache Spark is a distributed computing system used to process large amounts of data in parallel across multiple nodes. Spark's in-memory caching system allows for faster data access and processing.
Apache Cassandra
Apache Cassandra is a distributed NoSQL database used to manage large amounts of data across multiple nodes. Cassandra's shared-nothing architecture allows for horizontal scalability and fault tolerance.
Consul
Consul is a distributed service mesh used to connect and secure services across multiple nodes. Consul allows for service discovery, load balancing, and health checking.
Conclusion
Distributed systems are becoming more and more important as data sets continue to grow. Understanding the fundamental concepts of distributed systems, the different types of distributed systems, and the popular tools used in building distributed systems is important for data engineers to design and manage scalable, fault-tolerant data pipelines.
Category: Distributed System