A Comprehensive Guide to Distributed Systems

As the name implies, it’s a system that is distributed in nature.
The components of this system works together as one cohesive unit.
It is also fault tolerant and horizontally scalable comparatively much more easily when compared to a non-distributed system

Broadly speaking, they allow us to achieve following

No. At least not without incurring heavy costs

This is a well know theorem used to help system designers make informed decisions about trade-offs between available system resources and desired functionality while designing networked shared-data systems (distributed systems)

This is an extension to CAP theorem and helps to make better decision wherein network is NOT partitioned.

It is a set of properties of database transactions intended to guarantee data validity, despite errors, power failures, and other mishaps. Relational databases are primary candidates of such properties, though other types might also support (partially or fully)

Dictionary definition of the term says:

Consensus is a group discussion where everyone’s opinions are heard and understood, and a solution is created that respects those opinions. Consensus is not what everyone agrees to, nor is it the preference of the majority. Consensus results in the best solution that the group can achieve at the time.

In distributed systems, same definition applies. The only difference is in the meaning of the word opinion. The above definition for distributed systems can be stated as:

The goal of a distributed consensus algorithm is to allow a set of computers to all agree on a single value that one of the nodes in the system proposed (as opposed to making up a random value). The challenge in doing this in a distributed system is that messages can be lost or machines can fail.

Note: Above algorithms are pretty detailed in nature and require their own separate space for them to be properly explained and understood.

These strategies use the concept of locking data items. A lock is a variable associated with a data item that determines whether read/write operations can be performed on that data item.

A database storage engine is an internal software component that a database server uses to store, read, update, and delete data in the underlying memory and storage systems. Broadly speaking there are 2 types that are used extensively:

2. Full Table Data Replication

3. Key-Based Incremental Data Replication: This replication strategy leverages the replication key column to identify the new and updated data. A replication key column is a column holding unique values across all dataset. It then carries out the replication process for records that house the updated replication keys.

The fallacies of distributed computing are a set of assertions made by L Peter Deutsch and others at Sun Microsystems describing false assumptions that programmers new to distributed applications invariably make.

The fallacies:

Although this article gives good introduction to distributed systems, there are many more concepts that are useful in building distributed systems, better. I’m working on part 2 of this guide that will try to include those in a similarly easy to understand manner.

That’s all, folks ¯\(ツ)/¯

References

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