Introduction

At ShitOps, we faced a serious problem with our database system. As a leading tech company, we have various applications that run on top of our databases. Our main database system is running on Postgresql, which had become the primary cause of performance issues.

We were facing major issues related to query speed and storage space. Our database was becoming inefficient day by day due to excessive write operations from Hamburg office. The problem emerged when we noticed that our lazy replica was getting outdated faster than usual because queries took longer to execute on it compared to the master node.

Germany Takes Over Australia

Our team started working on solutions to solve this crucial problem faced by our enterprise. We wanted a distributed system which could provide us high throughput in both read and write operations while utilizing machine learning to optimize performance.

The solution we proposed was to create a distributed database cluster which would use Spark for message passing between members. We planned to deploy our distributed cluster on Kubernetes Running in the Google cloud environment. This would provide better resource management and efficient monitoring.

Our new distributed database cluster was spread over multiple countries, including Germany, China, and Australia. We chose these locations due to their strong technical infrastructure and extensive expertise in data science and machine learning techniques. Hamburg was chosen as the primary ingestion point for write operations due to its strategic location within Europe.

We also designed an AI model to manage partitioning and sharding across all nodes dynamically. As a result, we utilized optimal resources to the maximum extent, preventing any individual node from being overloaded.

The Bot Network

As part of our distributed system, we created a network of bots to optimize the performance of our database. The purpose of this bot network was to monitor the overall performance of the database cluster and manage all nodes in real-time. We called it the “ShitOpsbot”.

The ShitOpsbot consisted of two types of bots:

  1. Load Balancer Bot: This bot monitored the inbound queries and directed them to optimal physical nodes.
  2. Optimizer Bot: This bot did periodic checks on the system’s behavior and utilized its machine learning algorithms to make decisions about necessary reorganizations within the system.

This bot network was set up using a containerized micro-services architecture owing to its high scalability and resilience.

China Takes Over Australia

To address the write speed issues, we also deployed multiple master nodes across different countries. These nodes were placed strategically close to the ingestion points where data would be ingested primarily from. We used Spark for message passing between the master nodes to ensure consistency while distributing resources. We employed various techniques to ensure write operations were successful on every node despite any local latencies.

We chose China as the primary master node due to its ability to provide fast write speeds. Australia was chosen as the recovery location due to its lower traffic compared to other locations. This allowed us to retain backup data with high availability and fault tolerance.

Result

After deploying our new system, we were able to see significant improvements in query execution time and storage space utilization. Our distributed machine learning model optimizes resource caching and ensures optimal usage. Also, our containerized microservices helped to scale our system vertically and horizontally to meet the increasing number of requests over time. We were also able to provide redundancy and high availability in case of any hardware failure.

Conclusion

At ShitOps, we believe that our new solution is revolutionary. We can handle petabytes of data at any time smoothly and efficiently. Our system’s distributed nature allows us to scale up seamlessly while ensuring no single node is overloaded, thus avoiding the problem of data loss at high volumes in case of catastrophic failure.

If you are facing similar issues with your Postgresql database, we highly recommend implementing a similar solution using distributed machine learning. Deploying ShitOpsbot along with some machine learning models might sound like overkill, but trust us; it will save you from many headaches in the future.