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Cassandra High Availability

By : Strickland

4.6 (14)

Cassandra High Availability

4.6 (14)

By: Strickland

Overview of this book

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Cassandra's Approach to High Availability

1. Cassandra's Approach to High Availability

ACID

The monolithic architecture

The master-slave architecture

Cassandra's solution

Cassandra's architecture

Summary

2. Data Distribution

2. Data Distribution

Hash table fundamentals

Consistent hashing

Token assignment

Partitioners

Summary

3. Replication

3. Replication

The replication factor

Snitches

Consistency conflicts

Balancing the replication factor with consistency

Summary

4. Data Centers

4. Data Centers

Use cases for multiple data centers

Data center setup

Replication across data centers

Summary

5. Scaling Out

5. Scaling Out

Choosing the right hardware configuration

Scaling out versus scaling up

Growing your cluster

How to scale out

How to scale up

Removing nodes

Other data migration scenarios

Snitch changes

Summary

6. High Availability Features in the Native Java Client

6. High Availability Features in the Native Java Client

Thrift versus the native protocol

Setting up the environment

Connecting to the cluster

Executing statements

Handling asynchronous requests

Load balancing

Failing over to a remote data center

Tying it all together

Summary

7. Modeling for High Availability

7. Modeling for High Availability

How Cassandra stores data

Understanding compaction

CQL under the hood

Understanding queries

How collections are stored

Working with time-series data

Designing for immutability

Working with geospatial data

Summary

8. Antipatterns

8. Antipatterns

Multikey queries

Secondary indices

Distributed joins

Deleting data

Unbounded row growth

Summary

9. Failing Gracefully

9. Failing Gracefully

Knowledge is power

Logging

Monitoring node metrics

When a node goes down

Backing up data

Summary

Index

Index

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Deleting data

We have established that Cassandra employs a log-structured storage engine, where all writes are immutable appends to the log. The implication is that data cannot actually be deleted at the time a DELETE statement is issued. Cassandra solves this by writing a marker, called a tombstone, with a timestamp greater than the previous value. This has the effect of overwriting the previous value with an empty one, which will then be compiled in subsequent queries for that column in the same manner as any other update. The actual value of the tombstone is set to the time of deletion, which is used to determine when the tombstone can be removed.

Garbage collection

Eventually these tombstones are reconciled with earlier values as part of the compaction process, where the earlier values are discarded. Refer to Chapter 7, Modeling for High Availability, for more details on how compaction works. There are two possibilities for when data can be physically deleted and tombstones collected.

If...

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