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 helping petabytes of data. This booklet deals readers a realistic perception into development hugely on hand, real-world purposes utilizing Apache Cassandra.
The e-book begins with the basics, assisting you to appreciate how the structure of Apache Cassandra permits it to accomplish 100% uptime while different structures fight to take action. you should have a superb realizing of information distribution, replication, and Cassandra's hugely tunable consistency version. this can be by means of an in-depth examine Cassandra's strong aid for a number of facts facilities, and the way to scale out a cluster. subsequent, the ebook explores the area of software layout, with chapters discussing the local driving force and knowledge modeling. finally, you can find out easy methods to keep away from universal antipatterns and benefit from Cassandra's skill to fail gracefully.
What you'll learn:
- Understand how the middle 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 info facilities to let failover, load balancing, and geographic distribution
- Add means for your cluster with 0 down time
- Take good thing about 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 process operating good even in the course of failure scenarios
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Extra resources for Cassandra High Availability
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. In fact, two of the nodes own almost no data at all. ”. Thus it’s a common mistake to build a time-series model using time as a key, and rely on ordering from the ByteOrderedPartitioner to perform range queries.
This is accomplished by using a distributed hash table (DHT) design based on the Amazon Dynamo architecture. Keys are assigned to a specific node using a process called consistent hashing, which allows nodes to be added or removed without having to rehash every key based on the new range. Cassandra ships with several partitioner implementations or developers can define their own by implementing a Java interface. These topics will be covered in greater detail in the next chapter. Such systems must therefore be able to replicate data across multiple nodes, making the occurrence of such loss less likely.
Perhaps you’re increasing capacity for an anticipated growth in data or transaction volume, or maybe you’re adding a data center for increased availability. Equally important is to ensure that new nodes receive a balanced share of the data. As a result, machines involved in the transfer end up under less load than without vnodes, thus increasing availability of those ranges. As a result, the ring becomes naturally balanced on its own. Cassandra provides a mechanism to automatically rebuild a failed node using replicated data.