Cassandra o que nao te contaram
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Rafael Monteiro discusses common mistakes made when using Cassandra as an infrastructure for databases. Some key mistakes include sharing storage across nodes, putting the commit log and data on the same disks, and putting a load balancer between the client and cluster. Other design mistakes include using Cassandra as a relational database when the data is not denormalized, having too many materialized views, and filtering by columns that are not in the primary key. Proper data modeling and running nodetool repair can help avoid issues. The presentation encourages using the right tool for the job.
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The Java EE 6 platform is an extreme makeover from the previous versions. It breaks the “one size fits all” approach with Profiles and improves on the Java EE 5 developer productivity features. It enables extensibility by embracing open source libraries and frameworks such that they are treated as first class citizens of the platform. NetBeans, Eclipse, and IntelliJ provide extensive tooling for Java EE 6.
But how can you leverage all of this on a cloud ?
GlassFish v3, the Reference Implementation of Java EE 6, can easily run on multiple cloud infrastructures. This talk will provide a brief introduction to Java EE 6 and GlassFish v3. The attendees will learn how to create a simple Java EE 6 sample application and deploy them on GlassFish v3 running locally. Then it will deploy that sample using Amazon, RightScale, Joyent, and Elastra cloud infrastructures. It will also show how servers are dynamically provisioned in some environments to meet the demand. The talk will also explain the advantages of each approach enabling you to choose the optimal strategy for your environment.
Takeaways from the session
The attendees will be able to learn how to deploy a Java EE 6 application in different cloud environments. They’ll also learn about the pros/cons of these infrastructures.
Scylla Summit 2022: Learning Rust the Hard Way for a Production Kafka+ScyllaD...
Numberly operates business-critical data pipelines and applications where failure and latency means "lost money" in the best-case scenario. Most of those data pipelines and applications are deployed on Kubernetes and rely on Kafka and ScyllaDB, where Kafka acts as the message bus and ScyllaDB as the source of some data enrichment. The availability and latency of both systems are thus very important because they mix and match data in the early stage of their pipelines to be consumed by their platforms.
Most of their applications are developed using Python. But they always felt that they could benefit from a lower-level programming language to squeeze the performance of their hardware even further for some of the most demanding applications. So, when an important part of their data pipeline was to be adjusted to reflect some important changes in their platforms, they thought it was a great opportunity to rewrite it in Rust!
Moving to Rust was hard, not only because of the language itself, but because being at a lower level allowed them to see, test, and demonstrate things that they could not pinpoint or explain that well using Python. They spent a lot of time analyzing the latency impacts of code patterns and client driver settings and ended up contributing to Apache Avro as they went down the rabbit hole.
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To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
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Cassandra o que nao te contaram
- 1. Cassandra o que não te contaram Rafael Monteiro e Pereira Software Engineer
- 2. About me • Rafael Monteiro • Mestrando em Inteligência Artificial • Software Engineer
- 5. Lets talk about ... • Cassandra
- 6. Common infrastructure mistakes • Shared Storage • SAN • NAS
- 7. Common infrastructure mistakes • Put Commit Log and Data on the same Disks
- 8. Common infrastructure mistakes • Put a Load Balancer between the cliente and the cluster
- 9. Common infrastructure mistakes • Huge JVM HEAPS! • 4-8GB = Low • 8-12GB = Ok • 12-16GB = Careful • >16 GB = Bad Idea
- 11. Design Mistakes • If your data is relational – USE a RDBMS
- 12. Design Mistakes • Use Cassandra as a Key – Value Store • KEY - PAYLOAD
- 13. Design Mistakes • Too many materialized views
- 14. Design Mistakes • Filter by a column that is not on the key.
- 15. Conclusion • Model it RIGHT and you will be fine! • Don’t forget running nodetool repair ! • Use the right tool for the job
- 16. Obrigado!!!! Feedback ? Contato ? E-mail: rafael@monteiro.io Github: github.com/rafamonteiro Twitter: @monteiro_io LinkedIn: lnked.in/rafamonteiro