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Background for this question

  1. I have a machine learning pipeline where all our Features and pre-calculated prediction scores are stored in PostgreSQL database.
  2. I have in excess of 800 columns as features for my Machine Learning models - so having them in PGSQL is probably not a good idea because a. SQL is probably not ideal to have such a wide list of columns - i.e. columnar data b. Feature Data may be better stored in distributed database

Typical Use Case

  1. I need to read the features (not immediately) after I have written them - in batches - so that I can run them through my ML models and calculate scores (and store them in Database)
  2. Typically, I am talking about reading a batch of 100k records at a time, but it might be smaller - however, it might involve reading many, many columns (as I said above)

Why I wanted to look into Cassandra

  1. AP database - from CAP
  2. Column-based
  3. Good for fast writing ( may be reading too?)
  4. Updated My data volume is growing faster than I thought - already nearing 0.5 TB.

What I read online

  1. Cassandra is very good for writing data fast
  2. Reading from Cassandra is not as efficient as write

So what are your question(s) ?

  1. Why is Cassandra so good in writing, if I cannot read what I've written fast enough?
  2. What areas should I check in Cassandra, before giving it a try for my ML Feature Store for models?

My apologies if this question is too general, or too specific. Happy to review it again based on feedback.

Thanks in advance,

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  • And technically, Cassandra is a "partitioned rowstore," not a columnstore.
    – Aaron
    Sep 26, 2022 at 15:40

1 Answer 1

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You are misinformed. Cassandra is designed for high-velocity reads at internet scale.

This statement is incorrect:

Reading from Cassandra is not as efficient as write

Quite the opposite. If you have modelled your data correctly for Cassandra, the tables are optimised for super-fast reads.

Reading 100K records is not a lot for production Cassandra clusters I've worked on. It is realistic to scale a Cassandra cluster for 1M reads/sec or even 10M reads/sec.

Cassandra scales linearly. If you have a 3-node cluster that is capable of handling 50K reads/sec and you need to do 100K reads/sec, you just double the size of your cluster to 6 nodes. If the traffic to your app doubles to 200K reads/sec, then all you need to do is add another 6 nodes for a total of 12 nodes.

When done correctly, you can keep scaling Cassandra to meet your throughput requirements. Cheers!

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  • Can there be situations where cassandra does reads 3 times faster than writes? Or is it a sign that the cluster is not configured properly? I have a 5 node cluster and my reads are 60k/s but writes are 18k/s. My data is a table with a bigint (as primary key) and 2 lists (100 elements in each list, in each row). I don't have clustering keys and my reads just retrieve rows by the bigint id column, without any filtering. 2 days ago
  • This is different to what was asked above so please consider posting a new question otherwise the comments section will end up being a never-ending thread. Cheers! yesterday

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