By C.Y. Kan
Design, construct, and learn your facts intricately utilizing Cassandra
About This Book
- Build specialist info versions in Cassandra utilizing CQL and applicable indexes
- Grasp the Model-By-Query thoughts via operating examples
- Step-by-step educational of a inventory industry technical research application
Who This e-book Is For
If you have an interest in Cassandra and need to increase real-world research functions, then this publication is ideal for you. it'd be invaluable to have previous wisdom of NoSQL database.
What you are going to Learn
- Discover the original manner of query-driven info modeling in Cassandra
- Explore the variations among an information version of a relational database and that of Cassandra
- Master the proper makes use of of the first index, composite key, compound key, and secondary index
- Design a high-performance Cassandra facts model
- Develop a whole, real-world technical-analysis program for the inventory market
- Grasp the suggestions of evolving a knowledge version in production
- Determine powerful functionality tuning, replication, and system-monitoring strategies
Starting with a short creation to Cassandra, this e-book flows via a number of elements similar to basic information modeling methods, number of information varieties, designing a knowledge version, making a choice on compatible keys and indexes via to a real-world software, the entire whereas utilising the easiest practices coated during this book.
Although the appliance is small, you may be fascinated with the total improvement lifestyles cycle. you'll wade through the layout issues of arising with a versatile and sustainable info version for a inventory industry technical-analysis software written in Python. As company adjustments constantly and so does a knowledge version, additionally, you will study the strategies of evolving a knowledge version to handle new company requisites. operating a web-scale Cassandra cluster calls for many cautious concerns akin to evolving an information version, functionality tuning, and method tracking. This e-book is a useful instructional for a person who desires to undertake Cassandra.
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Additional info for Cassandra Data Modeling and Analysis
Before we go into the details of Cassandra's architecture, let us walk through each of them first. [ 14 ] Chapter 1 Google BigTable Google BigTable is Google's core technology, particularly addressing data persistence and management on web-scale. It runs the data stores for many Google applications, such as Gmail, YouTube, and Google Analytics. It was designed to be a web-scale data store without sacrificing real-time responses. It has superb read and write performance, linear scalability, and continuous availability.
Typical use cases are for the generation of security token and one-time token, automatic purging of outdated columns, and so on. [ 35 ] Cassandra Data Modeling Secondary index One important thing you need to remember is that the secondary index in Cassandra is not identical to that in a relational database. The secondary index in Cassandra can be created to query a column that is not a part of the primary key. A column family can have more than one secondary index. Behind the scenes, it is implemented as a separate hidden table which is maintained automatically by Cassandra's internal process.
In other words, the replicas are updated asynchronously. Cassandra's high-level architecture Cassandra runs on a peer-to-peer architecture which means that all nodes in the cluster have equal responsibilities except that some of them are seed nodes for other non-seed nodes to obtain information about the cluster during startup. Each node holds a partition of the database. Cassandra provides automatic data distribution and replication across all nodes in the cluster. Parameters are provided to customize the distribution and replication behaviors.