Episode 179: Cassandra with Jonathan Ellis
Recording Venue: O’Reilly Scala 2011, Santa Clara California
Guest: Jonathan Ellis
Host: Robert
Cassandra is a distributed, scalable non-relational data store influenced by the Google BigTable project and many of the distributed systems techniques pioneered by the Amazon Dynamo paper. Guest Jonathan Ellis, the program chair of the Apache Cassandra project, discusses Cassandra’s data model, storage model, techniques used to achieve high availability and provides some insight into the trend away from relational databases.
- Amazon Dynamo
- Google Bigtable
- Apache Cassandra
- Log-Structured Merge Tree
- Cassandra research paper by Lakshman and Malik
- The Phi Failure Detector
- DataStax
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SE Radio Alerts
The audio gets better after ~03:00.
The audio quality is pretty poor, the poorest for quite a long time, which is a shame because this is a very interesting podcast.
I think that SE-Radio need to review how they capture audio from participants in remote locations in order to ensure that they can deliver high quality podcasts consistently. In this day and age, there’s no excuse for putting out podcasts with such poor audio.
Yes, audio quality very poor. I had to give myself a break every 15 minutes.
I am wondering whether a comparison with relational databases would be more complete by including the concept of materialized views or indexed views on the relational side. The big RDBMS vendors offer this as a way of denormalizing and physically partitioning/distributing data for queries (although you can actually update them as well under some circumstances).
It is their answer to the scale problem, and works very well in my experience.
I haven’t worked on a problem of Amazon / Ebay / Google scale myself, but how many people do?
I can see that NoSQL solutions are critical for those systems that have extraordinary scaling requirements, but because it is the new cool kid on the block I get a strong feeling that it may be misapplied.
The audio quality is very unfortunate. I usually listen to my podcasts while riding the bike, but it’s simply impossible to understand anything while outside.
Please try to record the audio separately on both sides and merge them together afterwards. Such an interesting podcast deserves to be listened to.
The audio is a disaster, I literally couldn’t understand 80% of it (gave up after 10 mins). Shame.
Audio is a total disaster, I tried to listen to it in my car and gave up after about 5 minutes. I wasn’t able to understand anything.
Since the audio is so bad, could you post a transcript?
Very helpful and detailed. Thank you.
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