Scylla Summit 2017: Saving Thousands by Running Scylla on EC2 Spot Instances

Frank will share the motivation behind the 3D XPoint memory, the current shipping Optane SSD product and key values of why it is better than NAND-based SSDs, and a few use cases that exist in the Open Source space for Database usages of Optane SSDs.

This document outlines a presentation on using the GoCQL driver to execute queries against Cassandra and Scylla databases. It discusses connecting to a Cassandra cluster, executing queries, iterating over results, and using asynchronous queries. It also mentions some additional Cassandra libraries built on top of GoCQL, including gocqlx for data binding and queries, and gocassa for queries and migrations. The presentation aims to explain how GoCQL works behind the scenes and how to get started with basic querying functionality.

Kubernetes is a declarative system for automatically deploying, managing, and scaling applications and their dependencies. In this short talk, I'll demonstrate a small Scylla cluster running in Google Compute Engine via Kubernetes and our publicly-published Docker images.

Zenly (recently acquired by Snap) makes a social map app. Their team has been running Scylla in production for the past eight months. Get an overview of the reasons they chose Scylla, its deployment on Google Cloud, the performances they achieved, plus learn as they share some of the few hiccups they hit along the way.

Duarte Nunes presented on distributed materialized views in ScyllaDB. He discussed the challenges of implementing materialized views in a distributed system without a single master, including propagating updates from base tables to views, handling consistency when tables can diverge, and managing concurrent updates safely. His proposed solution uses asynchronous replica-based propagation paired with repair mechanisms and locking or optimistic concurrency to address these issues. Materialized views provide powerful indexing capabilities but also introduce performance overhead that is difficult to avoid given Scylla's data model.

Shlomi Livne, VP of R&D at ScyllaDB, presented on the performance benefits of using user-defined types (UDTs) in ScyllaDB. He explained that with traditional columns, each column has overhead and flexibility comes at a price. However, with frozen UDTs, the columns are treated as a single unit, sharing metadata and improving performance. Livne showed results of a test where UDTs with many fields outperformed traditional columns with the same number of fields. However, he noted that Scylla's row cache and Java driver performance need improvement for UDTs.

What happens to a request that reaches Scylla, and why should one care? Understanding how Scylla executes your queries can help you make better architectural decisions and also better understand the performance of your application. Are my rows too big? Should I make that other column a part of my partition key instead? This talk will cover the interaction between nodes, shards and the role of Scylla's internal components like memtables, cache and sstables. I will explain how different types of queries are executed and how to plan your queries for maximum performance.

In this presentation, I'll speak of the benefits of running Scylla on our Big Data environment which stores over 500TB of data as well as using Scylla as the indexing engine to replace MongoDB and Cassandra for our log data analysis platform.

When working with streaming data, stateful operations are a common use case. If you would like to perform data de-duplication, calculate aggregations over event-time windows, track user activity over sessions, you are performing a stateful operation. Apache Spark provides users with a high level, simple to use DataFrame/Dataset API to work with both batch and streaming data. The funny thing about batch workloads is that people tend to run these batch workloads over and over again. Structured Streaming allows users to run these same workloads, with the exact same business logic in a streaming fashion, helping users answer questions at lower latencies. In this talk, we will focus on stateful operations with Structured Streaming and we will demonstrate through live demos, how NoSQL stores can be plugged in as a fault tolerant state store to store intermediate state, as well as used as a streaming sink, where the output data can be stored indefinitely for downstream applications.

The document appears to be a presentation on optimizing inter-data center communication. It discusses key topics like what inter-data center communication involves, the costs associated with it, best practices for setting snitches, keyspaces, client drivers and consistency levels for queries to optimize performance between data centers. It recommends using network topology replication strategies over simple strategies for multi-region deployments, setting load balancing and consistency levels appropriately in clients, and enabling internode compression to reduce costs of communication between data centers. The presentation encourages reviewing client locations, data access patterns, who is reading/writing data, and having conversations between operations and development teams to determine the best use cases.

On a quest to build the fastest durable log broker in the west, we had to rethink all of the components needed to deliver on this promise. First, we began by building the fastest RPC system in the west, SMF. SMF is a new RPC mechanism, IDL-compiler, and libraries that make using Seastar easy. In this talk, I will cover SMF in detail and show a live demo on how you can get started using it to build your next application so you can live in the future.

Are you a MySQL DBA or DevOps individual being asked to run Cassandra or Scylla? Feeling overwhelmed? In this talk, I will present Cassandra/Scylla operations in terms that directly relate to MySQL. I will show you comparisons between the Information Schema and the Cassandra/Scylla System keyspace(s). I will also talk about metrics available in MySQL versus Cassandra/Scylla and how to retrieve them. Finally, I will talk about how MySQL replication compares with Cassandra replication. Hopefully, when I am done you will be able to relate to Cassandra operations in a practical and useful way.

We will share Scylla adoption practices in equipment sensor data management of MES, Data Modeling Tips, Data Architecture using Scylla, configurations, and tunings.

In this talk, I will explain how HPC is beginning to evolve and how we use supercomputers to monitor supercomputers. First we will look at how HPC is different from cloud computing in terms of infrastructure and application architecture. Then I will discuss how those things are changing and why. Finally, I will dive into a use case of monitoring supercomputers as an application area for Scylla.

Presentation on Scylla's and Cassandra's compaction, why it is needed and how it works, and the different compaction strategies: their strengths and weaknesses, and the different types of "amplification" and how to use them to reason about the different compaction strategies. And finally, what Scylla does better than Cassandra in this area. These slides were presented at a meetup in Tel-Aviv, a joint meetup of the following two groups: https://www.meetup.com/Israel-Cassandra-Users/events/259322355/ https://www.meetup.com/Big-things-are-happening-here/events/259495379/