5.4.5 Cassandra Node Level Read Operation
by Sourabh Mukherjee, Amit Kumar Das, Sayan Goswami
Big Data Simplified
- Cover
- About Pearson
- Tittle
- Copyright
- Dedication
- Brief Contents
- Contents (1/2)
- Contents (2/2)
- Preface
- Acknowledgements
- About the Authors
- Model Syllabus for Big Data
- Lesson Plan
- Chapter 1 A Closer Look at Data
- Chapter 2 Introducing Big Data
- 2.1 Introduction
- 2.2 The Transition to Big Data
- 2.3 The Definition of Big Data
- 2.4 The V’s
- 2.5 Sources of Big Data
- 2.6 Common Applications of Big Data
- 2.7 An Introduction to Big Data Technologies
- 2.8 An Overview of Popular Vendors
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 3 Introducing Hadoop
- 3.1 Introduction
- 3.2 An Overview of Hadoop
- 3.3 Configuring a Hadoop Cluster (1/2)
- 3.3 Configuring a Hadoop Cluster (2/2)
- 3.4 Storing Data with HDFS
- 3.5 HDFS Technical Commands
- 3.6 Hadoop Distributions
- 3.7 Hadoop in the Cloud
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 4 Introducing MapReduce
- 4.1 Introduction
- 4.2 Processing Data with MapReduce
- 4.3 Parallelism in Map and Reduce Phases
- 4.4 Optimize the Map Phase Using a Combiner
- 4.5 What is YARN?
- 4.6 Example Use Case on MapReduce: Development and Execution Step-by-step (1/2)
- 4.6 Example Use Case on MapReduce: Development and Execution Step-by-step (2/2)
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 5 Introducing NoSQL
- 5.1 Introduction
- 5.2 NoSQL Databases in the Light of CAP Theorem
- 5.3 NoSQL Product Categories
- 5.4 NoSQL Database: Cassandra
- 5.4.1 Characteristics of Cassandra
- 5.4.2 Cassandra Architecture
- 5.4.3 Components of Cassandra
- 5.4.4 Cassandra Write Operations at a Node Level
- 5.4.5 Cassandra Node Level Read Operation
- 5.4.6 KEYSPACE in Cassandra
- 5.4.7 Starting Cassandra Server and Cqlsh Query Editor
- 5.4.8 DataStax Distribution Package
- 5.5 NoSQL Databases in the Cloud
- 5.6 NoSQL – Do’s and Don’ts
- 5.7 Business Intelligence and NoSQL
- 5.8 Big Data and NoSQL
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 6 Introducing Spark and Kafka
- 6.1 Introducing Spark
- 6.2 Working with Kafka
- 6.2.1 What is Apache Kafka
- 6.2.2 Kafka Architecture
- 6.2.3 Need of Apache Kafka in Big Data
- 6.2.4 Kafka Use Cases
- 6.2.5 Why is Kafka so Fast?
- 6.2.6 Kafka Needs ZooKeeper
- 6.2.7 Different Components in Kafka
- 6.2.8 Difference between Apache Kafka and Apache Flume
- 6.2.9 Kafka Demonstration—How Messages are Passing from Publisher to Consumer through a Topic
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 7 Other BigData Tools and Technologies
- Chapter 8 Working with Big Data in R
- 8.1 Prerequisites
- 8.2 Exploratory Data Analysis
- 8.3 R Libraries for Dealing with Large Data Sets
- 8.4 Integrating Hadoop with R
- 8.5 Simple R Program with Hadoop
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 9 Working with Big Data in Python
- Chapter 10 Big Data Applied
- Chapter 11 Big Data Strategy
- 11.1 Introduction
- 11.2 Two Typical Big Data Use Cases
- 11.3 Data Warehouses vs. Data Lakes—What is Your Strategy?
- 11.4 Key Questions to Ask
- 11.5 Getting Ready for a Big Data Program
- 11.6 Making Technology Choices
- 11.7 Making Tooling Choices
- Summary
- Short-answer Type Questions (5 Marks Questions)
- Long-answer Type Questions (10 Marks Questions)
- Chapter 12 Case Study: Retail Near Real-time Analytics
- 12.1 Introduction to Retail Domain
- 12.2 Near Real-time Analytics: Problem Statement
- 12.3 NRT Analytics: Solution Approach
- 12.4 NRT Analytics: Details of Solution Implemented (1/3)
- 12.4 NRT Analytics: Details of Solution Implemented (2/3)
- 12.4 NRT Analytics: Details of Solution Implemented (3/3)
- Summary
- Multiple-choice Questions (1 Mark Questions)
- Short-answer Type Questions (5 Marks Questions)
- Appendix (1/2)
- Appendix (2/2)
- Index
Introducing NoSQL | 107
1. It reaches its maximum allocated size in memory.
2. The number of minutes a memtable can stay in memory elapses.
3. Manually flushed by a user.
5.4.5 Cassandra Node Level Read Operation
Every Column Family stores data in a number of SSTables. Thus, data for a particular row can be
located in a number of SSTables and the memtable. Cassandra consults a bloom lter that checks
the probability of the table having the needed data. Therefore, for every read request, Cassandra
needs to read the data from all applicable SSTables (all SSTables for a column family through
compaction) and scan the memtable for applicable data fragments. This data is then merged and
returned to the coordinator. The read process of Cassandra has been depicted in Figure 5.8.
Figure 5.7 Cassandra write process
Write
mutation
Memory
Memtable
SSTable
Index
Flush
Key,
offset
pairs
Bloom
filters
Commit
log
Disk
Figure 5.8 Cassandra read process
Bloom
filter
Memory
Disk
Client
request
Merge
data
Memtable
SSTable SSTable
M05 Big Data Simplified XXXX 01.indd 107 5/20/2019 7:42:47 PM
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