1 point by datajedi 1 year ago flag hide 15 comments
john_doe 1 year ago next
Great article! Real-time analytics is a critical aspect for our business and the first step is to optimize our database performance. We use PostgreSQL and I would be interested in hearing what others have to say about optimizing write-heavy workloads.
data_engineer 1 year ago next
At our company, we've seen great results with using partitioning, column-oriented storage, and compression with PostgreSQL to improve our database performance for real-time analytics.
john_doe 1 year ago next
Thanks for the tips! Partitioning and compression are definitely on our roadmap, and we're considering using Apache Kafka as well. The idea of scaling with Citus is very intriguing, and I'm going to look into that further as well.
big_data 1 year ago prev next
For extreme scaling, we've used Apache Kafka to stream data into our PostgreSQL database, ensuring zero data loss and improved throughput.
big_data 1 year ago next
@systems_architect, Citus sounds like a great option, can you share more about your experiences scaling with it?
database_guy 1 year ago prev next
Adding to that, using indexing strategies like partitioning by time has also significantly helped us in optimizing our query performance.
data_engineer 1 year ago next
Partitioning time-based data is amazing for query performance. Glad to see you're finding these suggestions useful!
systems_architect 1 year ago prev next
We've used Citus as a distributed PostgreSQL database to scale out read and write loads across multiple nodes.
systems_architect 1 year ago prev next
Certainly! Citus provides excellent performance and ease of use. It allows us to shard horizontally, meaning we can distribute data across multiple nodes. Since it is distributed, we can also parallelize queries for faster results.
new_to_hn 1 year ago next
Sounds interesting, do you have any resources to help anyone new to Citus to get started?
systems_architect 1 year ago next
Yes, definitely! The Citus documentation is a fantastic resource to help you get started. They also have a detailed guide on installation, and some good tutorials to help new users learn the ropes.
citus_fan 1 year ago prev next
I have to agree with @systems_architect, Citus is a fantastic tool that has significantly improved our query performance.
dirty_data 1 year ago prev next
When working with real-time analytics, I've had great success with using pre-aggregation and downsampling to reduce the query complexity and processing time.
learn_more 1 year ago next
Could you elaborate more on pre-aggregation? How did you decide on the aggregate metrics, and how did it impact your queries?
dirty_data 1 year ago next
Sure! Pre-aggregation involves creating pre-calculated summaries of your data beforehand. We decided on the aggregate metrics based on the most frequently used metrics, and we saw an average of 70% reduction in query time. The aggregates were pre-calculated using materialized views and Lag.