Distributed SQL on PostgreSQL for building WebScale Database Infrastructure

How Distributed SQL on PostgreSQL help you in building WebScale Database Infrastructure for Performance, Scalability and Reliability

Distributed SQL is a method of horizontally scaling a SQL database by distributing the data and query processing across multiple machines. In PostgreSQL, this can be achieved using techniques such as sharding, which divides the data into smaller chunks and stores them on different machines, and replication, which creates multiple copies of the data across different machines.

By distributing the data and query processing across multiple machines, distributed SQL can help to improve the performance and scalability of PostgreSQL. For example, by splitting the data into smaller chunks, it can reduce the amount of data that needs to be read or written by a single query, thus improving query performance. Additionally, by having multiple copies of the data, it can help to ensure high availability and fault tolerance in the event of a machine failure.

Furthermore, distributed SQL also helps in terms of reliability. With a single point of failure, the entire system can crash. However, with a distributed SQL, the data is replicated across multiple servers, which means that if one server fails, the data is still available on other servers.

In summary, Distributed SQL helps PostgreSQL in horizontal scalability, performance and reliability by distributing data and query processing across multiple machines, reducing the amount of data that needs to be read or written by a single query, ensuring high availability and fault tolerance, and providing data replication across multiple servers.

You can download Python script for real-time distributed query processing for PostgreSQL with CLI options, logging, monitoring, and error handling from our GitHub – https://github.com/shiviyer/PostgreSQL-Distributed-SQL/blob/main/psql-dist-sql.py 
Note: This script uses the psycopg2 library to connect to a PostgreSQL database and execute a query passed in as a command line argument. It also uses Python’s built-in logging module to log information, error messages and handle errors. This script can also be used for distributed query processing by running it on multiple servers or instances. You will need to run it with the appropriate command-line arguments, like –host, –port, –user, –password, –database, –query, and so on. This script will also log the status of each step of the process and will exit if any error occurs, so you can check the log for troubleshooting.

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A boutique private-label enterprise-class MySQL, MariaDB, MyRocks, PostgreSQL and ClickHouse consulting, 24*7 consultative support and remote DBA services company with core expertise in performance, scalability and high availability. Our consultants have several years of experience in architecting and building web-scale database infrastructure operations for internet properties from diversified verticals like CDN, Mobile Advertising Networks, E-Commerce, Social Media Applications, SaaS, Gaming and Digital Payment Solutions. Our globally distributed team working on multiple timezones guarantee 24*7 Consulting, Support and Remote DBA Services delivery for MySQL, MariaDB, MyRocks, PostgreSQL and ClickHouse.