By Robbie Strickland
Apache Cassandra is a vastly scalable, peer-to-peer database designed for one hundred pc uptime, with deployments within the tens of millions of nodes assisting petabytes of data. This ebook deals readers a pragmatic perception into construction hugely on hand, real-world purposes utilizing Apache Cassandra.
The booklet starts off with the basics, supporting you to appreciate how the structure of Apache Cassandra permits it to accomplish one hundred pc uptime whilst different structures fight to take action. you will have a very good knowing of knowledge distribution, replication, and Cassandra's hugely tunable consistency version. this is often via an in-depth examine Cassandra's powerful aid for a number of information facilities, and the way to scale out a cluster. subsequent, the publication explores the area of software layout, with chapters discussing the local motive force and information modeling. finally, you will find out the right way to avoid universal antipatterns and reap the benefits of Cassandra's skill to fail gracefully.
What you'll learn:
- Understand how the middle structure of Cassandra permits hugely to be had applications
- Use replication and tunable consistency degrees to stability consistency, availability, and performance
- Set up a number of info facilities to allow failover, load balancing, and geographic distribution
- Add skill in your cluster with 0 down time
- Take benefit of excessive availability positive factors 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 process operating good even in the course of failure scenarios
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Extra info for Cassandra High Availability
We’ll start with a discussion of Cassandra’s data placement strategy in the next chapter. Consistent hashing is the core of this strategy as it enables all nodes to understand where data exists in the cluster without complicated coordination mechanisms. Let’s begin with some basics about hash tables in general, and then we can delve deeper into Cassandra’s distributed hash table implementation. Hash tables store data by applying a hash function to the object, which determines its placement in an underlying array.
So in this case, a rebuild operation involves three nodes, placing a high load on all three. Even worse, token ranges A and B reside entirely on nodes that are being taxed by this process, which can result in overburdening the entire cluster due to slow response times for these operations. This means each individual node is doing less work than without vnodes, resulting in greater operational stability. This is especially problematic when adding or removing nodes, as it would become necessary to recompute the tokens to achieve a proper balance.
This causes a significant amount of administrative overhead for a large cluster. We’ll discuss this in detail later in this chapter. Hotspots: In some cases, the relatively large range assigned to each node can cause hotspots if data is not evenly distributed. Attempting to subdivide ranges to deal with nodes of varying sizes is a difficult and error-prone task. For existing installations, migrating to vnodes will improve the performance, reliability, and administrative requirements of your cluster, especially during topology changes and failure scenarios.