By Robbie Strickland
Apache Cassandra is a hugely scalable, peer-to-peer database designed for 100% uptime, with deployments within the tens of hundreds of thousands of nodes aiding petabytes of data. This publication deals readers a pragmatic perception into construction hugely to be had, real-world functions utilizing Apache Cassandra.
The e-book starts off with the basics, aiding you to appreciate how the structure of Apache Cassandra permits it to accomplish 100% uptime while different platforms fight to take action. you should have a great knowing of information distribution, replication, and Cassandra's hugely tunable consistency version. this is often through an in-depth examine Cassandra's powerful help for a number of info facilities, and the way to scale out a cluster. subsequent, the booklet explores the area of software layout, with chapters discussing the local motive force and information modeling. finally, you will find out tips on how to stay away from universal antipatterns and make the most of Cassandra's skill to fail gracefully.
What you are going to learn:
- Understand how the center structure of Cassandra allows hugely to be had applications
- Use replication and tunable consistency degrees to stability consistency, availability, and performance
- Set up a number of information facilities to allow failover, load balancing, and geographic distribution
- Add means in your cluster with 0 down time
- Take benefit of excessive availability positive aspects within the local driver
- Create info types that scale good and maximize availability
- Understand universal anti-patterns so that you can keep away from them
- Keep your method operating good even in the course of failure scenarios
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Additional info for Cassandra High Availability
This value becomes the index in an array of street addresses. We can look up the street address of a given name by computing its hash, then accessing the resulting array index. There are additional complexities in hash table design, specifically around avoiding hash collisions, but the basic concept remains straightforward. Let’s examine the distributed hash table architecture and the means by which it solves this problem. Each node in the DHT must share the same hash function so that hash results on one node match the results on all others.
ByteOrderedPartitioner: This places keys in byte order (lexically) around the ring. This partitioner should generally be avoided for reasons explained in this section. The only reason to switch from the default Murmur3Partitioner to ByteOrderedPartitioner would be to enable range queries on keys (range queries on columns are always possible). However, this decision must be carefully weighed as there is a high likelihood that you’ll end up with hotspots. If we presume that both reads and writes follow the same distribution as the data itself (which is a logical assumption in this specific case), the heavier data nodes will also be required to handle more operations than the lighter data nodes.
While the distribution of data in this model might be balanced (or it might not, depending on whether the application is busier at certain times), the workload will always experience hotspots. For now, consider it sufficient that you understand the implications of choosing the ByteOrderedPartitioner over one of the other options that uses a random hash function. 2) should be used with great caution, and can usually be avoided by altering the data model. In practice, it’s rarely necessary to store keys in order if you model your data correctly.
Cassandra High Availability by Robbie Strickland