By C.Y. Kan
Design, construct, and learn your info intricately utilizing Cassandra
About This Book
- Build expert information versions in Cassandra utilizing CQL and applicable indexes
- Grasp the Model-By-Query ideas via operating examples
- Step-by-step educational of a inventory industry technical research application
Who This publication Is For
If you have an interest in Cassandra and wish to boost real-world research purposes, then this booklet is ideal for you. it might be invaluable to have previous wisdom of NoSQL database.
What you'll Learn
- Discover the original method 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 info model
- Develop a whole, real-world technical-analysis software for the inventory market
- Grasp the recommendations of evolving an information version in production
- Determine powerful functionality tuning, replication, and system-monitoring strategies
Starting with a brief advent to Cassandra, this ebook flows via numerous features akin to primary info modeling methods, collection of info kinds, designing a knowledge version, picking compatible keys and indexes via to a real-world program, the entire whereas using the easiest practices coated during this book.
Although the appliance is small, you may be curious about the entire improvement existence cycle. you are going to battle 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 company adjustments constantly and so does a knowledge version, additionally, you will examine the concepts of evolving an information version to deal with new enterprise standards. working a web-scale Cassandra cluster calls for many cautious concerns equivalent to evolving a knowledge version, functionality tuning, and process tracking. This ebook is a useful educational for somebody who desires to undertake Cassandra.
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Additional resources for Cassandra Data Modeling and Analysis
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 should be noted that each column has a client-supplied timestamp associated, but it can be ignored during data modeling. Cassandra uses the timestamp internally to resolve transaction conflicts. In a relational database, column names can be only strings and be stored in the table metadata. In Cassandra, both RowKey and ColumnKey can be strings, long integers, Universal Unique IDs, or any kind of byte arrays.
It is exactly what modeling by query means. [ 43 ] Cassandra Data Modeling Data modeling considerations Apart from modeling by query, we need to bear in mind a few important points when designing a Cassandra data model. We can also consider a few good patterns that will be introduced in this section. Data duplication Denormalization is an evil in a relational data model, but not in Cassandra. Indeed, it is a good and common practice. It is solely based on the fact that Cassandra does not use high-end disk storage subsystem.
Even worse, if all the relevant replicas are down, the coordinator stores the hint locally. The hint basically contains the location of the failed replica, the affected row key, and the actual data that is being written. When a node responsible for the token range is up again, the hint will be handed off to resume the write. As such, the update cannot be read before a complete handoff, leading to inconsistent reads. [ 24 ] Chapter 1 Another repair mechanism is called anti-entropy which is a replica synchronization mechanism to ensure up-to-date data on all nodes and is run by the administrators manually.