Today we are releasing a new data integrity testing suite for the open source community. Those who will have the most direct utility for this software will be those testing Scylla and Cassandra databases, or, more broadly, other CQL-compliant databases.
In this post we introduce the new Scylla workload prioritization mechanism, explaining the vision behind developing this feature and how it is implemented, and most importantly, we show you test results of how it performs in a real-world setting.
Anyone who’s tried to build such a solution knows that one of the chief difficulties is encompassing the sheer number and complexity of existing data sources. In order to deliver a true solution, we need to be able to bring this disparate data together. A graph data system, built with JanusGraph and backed by the power of Scylla, is a great fit for solving this problem.
Scylla’s March 2019 webinar on database migration drew broad interest and will likely remain a popular topic for years to come. So, you’ve decided to adopt Scylla (or Cassandra). What’s the best way to get your Big Data uploaded into your new cluster? What strategies, tools and techniques can you use to get your terabytes or petabytes from point A to point B? Those were the questions of the day for Dan Yasny, Field Engineer of ScyllaDB.
Another week, another Spark and Scylla post! This time, we’re back again with the Scylla Spark Migrator; we’ll take a short tour through its innards to see how it is implemented. Read why we implemented the Scylla Spark Migrator in this blog. Overview When developing the Migrator, we had several design goals in mind. First, the Migrator should be highly efficient in terms of resource usage. Resource efficiency in the land of Spark applications usually translates to avoiding data shuffles between nodes. Data shuffles are destructive to Spark’s performance, as they incur more I/O costs. Moreover, shuffles usually get slower […]
CHECK OUT PART ONE OF THIS BLOG We covered the basics of Elasticsearch and how Scylla is a perfect complement for it in part one of this blog. Today we want to give you specific how-tos on connecting Scylla and Elasticsearch, including use cases and sample code. Use Case #1 If combining a persistent, highly available datastore with full text search engine is a market requirement, then implementing a single, integrated solution is an ultimate goal that requires time and resources. To answer this challenge we describe below a way for users to use best-of-breed solutions that support full text […]
We needed a Python interpreter that can be shipped everywhere. You won’t believe what happened next! “When I said I wanted portable Python, this is NOT what I meant!” In theory, Python is a portable language. You can write your script locally and distribute it to other machines with the Python interpreter. In practice, things can go wrong for a variety of reasons. The first and simpler problem is the module system: for a script to run, all of the modules it uses must be installed. For Python-savvy users, installing them is not a problem. But for a software vendor […]
Welcome to a whole new chapter in our Spark and Scylla series! This post will introduce the Scylla Migrator project – a Spark-based application that will easily and efficiently migrate existing Cassandra tables into Scylla. Over the last few years, ScyllaDB has helped many customers migrate from existing Cassandra installations to a Scylla deployment. The migration approach is detailed in this document. Briefly, the process is comprised of several phases: Create an identical schema in Scylla to hold the data; Configure the application to perform dual writes; Snapshot the historical data from Cassandra and load it into Scylla; Configure the […]
The Internet is not just connecting people around the world. Through the Internet of Things (IoT), it is also connecting humans to the machines all around us and directly connecting machines to other machines. In this blog post we’ll share an emerging machine-to-machine (M2M) architecture pattern in which MQTT, Apache Kafka and Scylla all work together to provide an end-to-end IoT solution. We’ll also provide demo code so you can try it out for yourself. IoT Scale IoT is a fast-growing market, already known to be over $1.2 trillion in 2017 and anticipated to grow to over $6.5 trillion […]
This blog post is based on a talk I gave last month at the third annual Scylla Summit in San Francisco. It explains how Scylla ensures that ingestion of data proceeds as quickly as possible, but not quicker. It looks into the existing flow-control mechanism for tables without materialized views, and into the new mechanism for tables with materialized views, which is introduced in Scylla Open Source 3.0. Introduction In this post we look into ingestion of data into a Scylla cluster. What happens when we make a large volume of update (write) requests? We would like the ingestion to […]