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The voice of The Apache Software Foundation

ApacheCon Seville 2016 – Using Monitoring to Understand Cassandra- Alain Rodriguez

February 10, 2017
rbowen

Using Monitoring to Understand Cassandra- Alain Rodriguez

LetÛªs be honest, nobody cares about a fire extinguisher until their house is burning down. In a similar vein, monitoring is an important, yet often undervalued part of running production systems. Distributed databases internals are often complex. Cassandra is not an exception and might sometimes be tricky to understand correctly.

In this presentation Alain will demystify Cassandra internals and demonstrate how monitoring can be used to solve the difficult problems that are unique to distributed databases. WeÛªll explore the options available to empower every operator running this powerful database. Audience members will walk away with a an excellent understanding of how a well thought out monitoring solution can save them from countless hours of tedious debugging and business impacting performance issues and downtime.

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Apache Big Data Seville 2016 – User Defined Functions and Materialized Views in Cassandra 3.0 – DuyHai Doan,

January 19, 2017
rbowen

User Defined Functions and Materialized Views in Cassandra 3.0 – DuyHai Doan,

Cassandra is evolving at a very fast pace and keeps introducing new features that close the gap with traditional SQL world, but they are always designed with a distributed approach in mind.

First we’ll throw an eye at the recent user-defined functions and show how they can improve your application performance and enrich your analytics use-cases.

Next, a tour on the materialized views, a major improvement that drastically changes the way people model data in Cassandra and makes developers’ life easier!

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Apache Big Data Seville 2016 – SASI, Cassandra on the Full Text Search Ride! – DuyHai Doan

January 19, 2017
rbowen

SASI, Cassandra on the Full Text Search Ride! – DuyHai Doan

Apache Cassandra is a scalable database with high availability features. But they come with severe limitations in term of querying capabilities.

Since the introduction of SASI in Cassandra 3.4, the limitations belong to the pass. Now you can create indices on your columns as well as benefit from full text search capabilities with the introduction of the new `LIKE ‘%term%’` syntax.

To illustrate how SASI works, we’ll use a database of 100 000 albums and artists. We’ll also show how SASI can help to accelerate analytics scenarios with Apache Spark using SparkSQL predicate push-down.

We also highlight some use-cases where SASI is not a good fit and should be avoided (there is no magic, sorry)

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