| Description |
1 online resource (1 volume) : illustrations |
| Series |
Community experience distilled |
|
Community experience distilled.
|
| Contents |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Cassandra's Approach to High Availability; ACID; The monolithic architecture; The master-slave architecture; Sharding; Master failover; Cassandra's solution; Cassandra architecture; Distributed hash table; Replication; Replication across data centers; Tunable consistency; The CAP theorem; Summary; Chapter 2: Data Distribution; Hash table fundamentals; Distributing hash tables; Consistent hashing; The mechanics of consistent hashing; Token assignment |
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Manually assigned tokensvnodes; How vnodes improve availability; Partitioners; Hotspots; Effects of scaling out using the ByteOrderedPartitioner; A time-series example; Summary; Chapter 3: Replication; The replication factor; Replication strategies; SimpleStrategy; NetworkTopologyStrategy; Snitches; Maintaining the replication factor when a node fails; Consistency conflicts; Consistency levels; Repairing data; Balancing the replication factor with consistency; Summary; Chapter 4: Data Centers; Use cases for multiple data centers; Live backup; Failover; Load balancing; Geographic distribution |
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Online analysisAnalysis using Hadoop; Analysis using Spark; Data center setup; RackInferringSnitch; PropertyFileSnitch; GossipingPropertyFileSnitch; Cloud snitches; Replication across data centers; Setting the replication factor; Consistency in a multiple data center environment; The anatomy of a replicated write; Achieving stronger consistency between data centers; Summary; Chapter 5: Scaling Out; Choosing the right hardware configuration; Scaling out versus scaling up; Growing your cluster; Adding nodes without vnodes; Adding nodes with vnodes; How to scale out; Adding a data center |
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How to scale upUpgrading in place; Scaling up using data center replication; Removing nodes; Removing nodes within a data center; Decommissioning a data center; Other data migration scenarios; Snitch changes; Summary; Chapter 6: High Availability Features in the Native Java Client; Thrift versus the native protocol; Setting up the environment; Connecting to the cluster; Executing statements; Prepared statements; Batched statements; Caution with batches; Handling asynchronous requests; Running queries in parallel; Load balancing; Failing over to a remote data center |
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Downgrading the consistency levelDefining your own retry policy; Token awareness; Tying it all together; Falling back to QUORUM; Summary; Chapter 7: Modeling for High Availability; How Cassandra stores data; Implications of a log-structured storage; Understanding compaction; Size-tiered compaction; Leveled compaction; Date-tiered compaction; CQL under the hood; Single primary key; Compound keys; Partition keys; Clustering columns; Composite partition keys; The importance of the storage model; Understanding queries; Query by key; Range queries; Denormalizing with collections |
| Summary |
If you are a developer or DevOps engineer who understands the basics of Cassandra and are ready to take your knowledge to the next level, then this book is for you. An understanding of the essentials of Cassandra is needed |
| Notes |
Includes index |
|
English |
|
Online resource; title from cover page (Safari, viewed January 23, 2015) |
| SUBJECT |
Apache Cassandra. http://id.loc.gov/authorities/names/n2013073861
|
|
Apache Cassandra fast |
| Subject |
Database management -- Computer programs.
|
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Database Management Systems
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COMPUTERS -- Programming Languages -- SQL.
|
|
Database management -- Computer programs
|
| Form |
Electronic book
|
| ISBN |
9781783989133 |
|
1783989130 |
|
