As a software engineer, you cannot fully understand replication, databases, key-value stores, NoSQL stores, Hadoop, version control, Paxos, or any other software system without deep knowledge of logs. Also known as commit logs or write-ahead logs, logs have been around almost as long as computers. They are at the heart of many distributed data systems and real-time application architectures.
To build, deploy, and run a distributed graph, a Hadoop installation, a distributed search backend, and a first and second generation key-value store, you need to have a thorough understanding of logs, including what it is, how to use it for data integration, system building, and real-time processing.
What is a log?
A log is the simplest storage abstraction. It stores the records and chronologically arranges them. Records are added to the end of the log, and each entry is assigned a unique log entry number.
The record on the extreme left of the log is the oldest, and the record on the extreme right is the latest. The log record number can be considered a ‘time stamp.’ Remember, you cannot just add records because space will eventually run out.
Logs in the Database
The appearance of a log is as simple as a binary system. The main purpose is to maintain synchronization amongst different data indexes and structures when the system crashes. To do the same, the database needs to modify data. The log records not only the information that needs to be modified but everything that happens.
The log is considered the authoritative source for restoring all the structures in case the system crashes.
Logs in Distributed Systems
Logs solve two important problems in distributed data systems – ordering changes and distributing data. In a data distributed system, agreeing upon orders for updates is one of the core design issues.
The log-centric distributed system processing comes from a simple observation, called the state machine replication principle. According to this principle, if two identical, time-independent processes begin in the same state and receive the same inputs in the same order, they will produce the same outputs and end in the same state. The state of the process refers to whether data is retained on the hard disk.
In a deterministic system, you can rebuild the state of the systems anytime by replaying the input for every single step. There are two ways of leveraging logs in distributed processing and replication:
- State Machine Model:
Also known as the active-active model, changes and operations are written in the log, and each replica picks up the log.
- Primary Backup Model:
Also known as the active-passive model, it is where one node is selected as the master. Upon master failure, the selected replica takes over.
Logs in Data Integration
Logs help in making a company’s data easily accessible in all its storage and processing systems. An organization may have numerous data inputs that gather data and events from various places. A log can serve as a central pipeline for all the different consumers and producers. Acting as an asynchronous messaging system allows consumers and producers to read buffered data from the log.
Logs enable high-performance optimization by doing the following::
- Avoiding unnecessary data copies
- Optimizing high throughput by batching small reads and writes
- Enabling partitioning
Logs in the real-time data processing
Logs simplify real-time stream processing. They enable real-time data collection from different data inputs or events at different speeds. For example, when the output of a log in the processing system becomes the input, it can build complicated data flow graphs.
Logs make each dataset multi-subscriber and order. It provides buffering to the processes for the system to work asynchronously. Hence, every software engineer must know about logs.
Q1: What is a log distributed system?
Ans: The distributed log is defined as the data structure that models the problem of a general agreement.
Q2: Why is logging important?
Ans: Logs are an important part of troubleshooting application and infrastructure performance. They help you by providing visibility into how the applications run on each of the several infrastructure components.
Q3: What are the three benefits of using distributed log collectors?
Ans: Distributed log collectors provide high-volume storage on a hardware appliance. They also enable higher logging rates and provide horizontal scalability and redundancy.
Q4: What is the log data structure, Kafka?
Ans: Log is a data structure used for write-intensive applications. The most popular application is Kafka. It is used in database replication, microservice communication, event sourcing, data streaming, and real-time processing.