Cassandra Data Modeling and Analysis by C.Y. Kan

By C.Y. Kan

Design, construct, and study your facts intricately utilizing Cassandra

About This Book

  • Build expert information types in Cassandra utilizing CQL and applicable indexes
  • Grasp the Model-By-Query strategies via operating examples
  • Step-by-step instructional of a inventory industry technical research application

Who This ebook Is For

If you have an interest in Cassandra and need to boost real-world research functions, then this ebook is ideal for you. it might be valuable to have past wisdom of NoSQL database.

What you are going to Learn

  • Discover the original approach of query-driven info modeling in Cassandra
  • Explore the diversities among an information version of a relational database and that of Cassandra
  • Master the right kind 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 strategies of evolving an information version in production
  • Determine powerful functionality tuning, replication, and system-monitoring strategies

In Detail

Starting with a brief creation to Cassandra, this booklet flows via a number of facets similar to primary information modeling techniques, choice of information varieties, designing a knowledge version, settling on compatible keys and indexes via to a real-world program, all of the whereas making use of the simplest practices lined during this book.

Although the appliance is small, you'll be fascinated with the complete improvement lifestyles cycle. you'll wade through the layout concerns of bobbing up with a versatile and sustainable information version for a inventory industry technical-analysis software written in Python. As enterprise adjustments continuously and so does an information version, additionally, you will research the concepts of evolving an information version to handle new company requisites. operating a web-scale Cassandra cluster calls for many cautious issues reminiscent of evolving an information version, functionality tuning, and method tracking. This ebook is a useful instructional for somebody who desires to undertake Cassandra.

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Additional resources for Cassandra Data Modeling and Analysis

Sample text

A column-oriented store is a multidimensional map. Specifically, it is a data structure known as Map. An example of the declaration of map data structure is as follows: Map> The Map data structure gives efficient key lookup, and the sorted nature provides efficient scans. RowKey is a unique key and can hold a value. The inner SortedMap data structure allows a variable number of ColumnKey values. This is the trick that Cassandra uses to be schemaless and to allow the data model to evolve organically over time.

It is worth noting that for a column family storing skinny rows, the column key is repeatedly stored in each column. Although it wastes some storage space, it is not a problem on inexpensive commodity hard disks. Bucketing Even though a wide row can accommodate up to 2 billion variable columns, it is still a hard limit that cannot prevent voluminous data from filling up a node. In order to break through the 2 billion column limit, we can use a workaround technique called bucketing to split the data across multiple nodes.

We will briefly go through its building blocks, the main differences to the relational data model, and examples of constructing queries on a Cassandra data model. Cassandra describes its data model components by using the terms that are inherited from the Google BigTable parent, for example, column family, column, row, and so on. Some of these terms also exist in a relational data model. They, however, have completely different meanings. It often confuses developers and administrators who have a background in the relational world.

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