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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Managing fault-tolerant, scalable data with high performance

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Product type Paperback
Published in Apr 2017
Publisher
ISBN-13 9781787127296
Length 360 pages
Edition 2nd Edition
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Author (1):
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Sandeep Yarabarla Sandeep Yarabarla
Author Profile Icon Sandeep Yarabarla
Sandeep Yarabarla
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Table of Contents (15) Chapters Close

Preface 1. Getting Up and Running with Cassandra FREE CHAPTER 2. The First Table 3. Organizing Related Data 4. Beyond Key-Value Lookup 5. Establishing Relationships 6. Denormalizing Data for Maximum Performance 7. Expanding Your Data Model 8. Collections, Tuples, and User-Defined Types 9. Aggregating Time-Series Data 10. How Cassandra Distributes Data 11. Cassandra Multi-Node Cluster 12. Application Development Using the Java Driver 13. Peeking under the Hood 14. Authentication and Authorization

Why not relational databases?

Relational database systems (RDBMS) have been the primary data store for enterprise applications for 20 years. Lately, NoSQL databases have been picking up a lot of steam, and businesses are slowly seeing a shift towards non-relational databases. There are a few reasons why relational databases don't seem like a good fit for modern big data web applications:

  • Relational databases are not designed for clustered solutions. There are some solutions that shard data across servers, but these are fragile, complex, and generally don't work well.

Sharding solutions implemented by RDBMS are as follows:

  • MySQL's product MySQL cluster provides clustering support which adds many capabilities of non-relational systems. It is actually an NoSQL solution that integrates with the MySQL relational database. It partitions the data onto multiple nodes, and the data can be accessed via different APIs.
  • Oracle provides a clustering solution, Oracle RAC, which involves multiple nodes running an Oracle process accessing the same database files. This creates a single point of failure as well as resource limitations in accessing the database itself.
  • They are not a good fit for current hardware and architectures. Relational databases are usually scaled up using larger machines with more powerful hardware and maybe clustering and replication among a small number of nodes. Their core architecture is not a good fit for commodity hardware and thus doesn't work with scale-out architectures.

Scale-out versus scale-up architecture:

  • Scaling out means adding more nodes to a system, such as adding more servers to a distributed database or filesystem. This is also known as horizontal scaling.
  • Scaling up means adding more resources to a single node within the system, such as adding more CPU, memory, or disks to a server. This is also known as vertical scaling.
You have been reading a chapter from
Learning Apache Cassandra - Second Edition
Published in: Apr 2017
Publisher:
ISBN-13: 9781787127296
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