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MySQL 8 for Big Data
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Summary
ThisbookisintendedforMySQLdatabaseadministratorsandBigDataprofessionalslookingtointegrateMySQL8andHadooptoimplementahighperformanceBigDatasolution.SomepreviousexperiencewithMySQLwillbehelpful,althoughthebookwillhighlightthenewerfeaturesintroducedinMySQL8.
目錄(366章)
倒序
- coverpage
- Title Page
- Copyright
- MySQL 8 for Big Data
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- Introduction to Big Data and MySQL 8
- The importance of Big Data
- Social media
- Politics
- Science and research
- Power and energy
- Fraud detection
- Healthcare
- Business mapping
- The life cycle of Big Data
- Volume
- Variety
- Velocity
- Veracity
- Phases of the Big Data life cycle
- Collect
- Store
- Analyze
- Governance
- Structured databases
- Basics of MySQL
- MySQL as a relational database management system
- Licensing
- Reliability and scalability
- Platform compatibility
- Releases
- New features in MySQL 8
- Transactional data dictionary
- Roles
- InnoDB auto increment
- Supporting invisible indexes
- Improving descending indexes
- SET PERSIST
- Expanded GIS support
- The default character set
- Extended bit-wise operations
- InnoDB Memcached
- NOWAIT and SKIP LOCKED
- Benefits of using MySQL
- Security
- Scalability
- An open source relational database management system
- High performance
- High availability
- Cross-platform capabilities
- Installing MySQL 8
- Obtaining MySQL 8
- MySQL 8 installation
- MySQL service commands
- Evolution of MySQL for Big Data
- Acquiring data in MySQL
- Organizing data in Hadoop
- Analyzing data
- Results of analysis
- Summary
- Data Query Techniques in MySQL 8
- Overview of SQL
- Database storage engines and types
- InnoDB
- Important notes about InnoDB
- MyISAM
- Important notes about MyISAM tables
- Memory
- Archive
- Blackhole
- CSV
- Merge
- Federated
- NDB cluster
- Select statement in MySQL 8
- WHERE clause
- Equal To and Not Equal To
- Greater than and Less than
- LIKE
- IN/NOT IN
- BETWEEN
- ORDER BY clause
- LIMIT clause
- SQL JOINS
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- CROSS JOIN
- UNION
- Subquery
- Optimizing SELECT statements
- Insert replace and update statements in MySQL 8
- Insert
- Update
- Replace
- Transactions in MySQL 8
- Aggregating data in MySQL 8
- The importance of aggregate functions
- GROUP BY clause
- HAVING clause
- Minimum
- Maximum
- Average
- Count
- Sum
- JSON
- JSON_OBJECTAGG
- JSON_ARRAYAGG
- Summary
- Indexing your data for High-Performing Queries
- MySQL indexing
- Index structures
- Bitmap indexes
- Sparse indexes
- Dense indexes
- B-Tree indexes
- Hash indexes
- Creating or dropping indexes
- UNIQUE | FULLTEXT | SPATIAL
- Index_col_name
- Index_options
- KEY_BLOCK_SIZE
- With Parser
- COMMENT
- VISIBILITY
- index_type
- algorithm_option
- lock_option
- When to avoid indexing
- MySQL 8 index types
- Defining a primary index
- Primary indexes
- Natural keys versus surrogate keys
- Unique keys
- Defining a column index
- Composite indexes in MySQL 8
- Covering index
- Invisible indexes
- Descending indexes
- Defining a foreign key in the MySQL table
- RESTRICT
- CASCADE
- SET NULL
- NO ACTION
- SET DEFAULT
- Dropping foreign keys
- Full-text indexing
- Natural language fulltext search on InnoDB and MyISAM
- Fulltext indexing on InnoDB
- Fulltext search in Boolean mode
- Differentiating full-text indexing and like queries
- Spatial indexes
- Indexing JSON data
- Generated columns
- Virtual generated columns
- Stored generated columns
- Defining indexes on JSON
- Summary
- Using Memcached with MySQL 8
- Overview of Memcached
- Setting up Memcached
- Installation
- Verification
- Using of Memcached
- Performance tuner
- Caching tool
- Easy to use
- Analyzing data stored in Memcached
- Memcached replication configuration
- Memcached APIs for different technologies
- Memcached with Java
- Memcached with PHP
- Memcached with Ruby
- Memcached with Python
- Summary
- Partitioning High Volume Data
- Partitioning in MySQL 8
- What is partitioning?
- Partitioning types
- Horizontal partitioning
- Vertical partitioning
- Horizontal partitioning in MySQL 8
- Range partitioning
- List partitioning
- Hash partitioning
- Column partitioning
- Range column partitioning
- List column partitioning
- Key partitioning
- Sub partitioning
- Vertical partitioning
- Splitting data into multiple tables
- Data normalization
- First normal form
- Second normal form
- Third normal form
- Boyce-Codd normal form
- Fourth normal form
- Fifth normal form
- Pruning partitions in MySQL
- Pruning with list partitioning
- Pruning with key partitioning
- Querying on partitioned data
- DELETE query with the partition option
- UPDATE query with the partition option
- INSERT query with the partition option
- Summary
- Replication for building highly available solutions
- High availability
- MySQL replication
- MySQL cluster
- Oracle MySQL cloud service
- MySQL with the Solaris cluster
- Replication with MySQL
- Benefits of replication in MySQL 8
- Scalable applications
- Secure architecture
- Large data analysis
- Geographical data sharing
- Methods of replication in MySQL 8
- Replication using binary logs
- Replication using global transaction identifiers
- Replication configuration
- Replication with binary log file
- Replication master configuration
- Replication slave configuration
- Replication with GTIDs
- Global transaction identifiers
- The gtid_executed table
- GTID master's side configurations
- GTID slave's side configurations
- MySQL multi-source replication
- Multi-source replication configuration
- Statement-based versus row-based replication
- Group replication
- Requirements for group replication
- Group replication configuration
- Group replication settings
- Choosing a single master or multi-master
- Host-specific configuration settings
- Configuring a Replication User and enabling the Group Replication Plugin
- Starting group replication
- Bootstrap node
- Summary
- MySQL 8 Best Practices
- MySQL benchmarks and configurations
- Resource utilization
- Stretch your timelines of benchmarks
- Replicating production settings
- Consistency of throughput and latency
- Sysbench can do more
- Virtualization world
- Concurrency
- Hidden workloads
- Nerves of your query
- Benchmarks
- Best practices for MySQL queries
- Data types
- Not null
- Indexing
- Search fields index
- Data types and joins
- Compound index
- Shorten up primary keys
- Index everything
- Fetch all data
- Application does the job
- Existence of data
- Limit yourself
- Analyze slow queries
- Query cost
- Best practices for the Memcached configuration
- Resource allocation
- Operating system architecture
- Default configurations
- Max object size
- Backlog queue limit
- Large pages support
- Sensitive data
- Restrict exposure
- Failover
- Namespaces
- Caching mechanism
- Memcached general statistics
- Best practices for replication
- Throughput in group replication
- Infrastructure sizing
- Constant throughput
- Contradictory workloads
- Write scalability
- Summary
- NoSQL API for Integrating with Big Data Solutions
- NoSQL overview
- Changing rapidly over time
- Scaling
- Less management
- Best for big data
- NoSQL versus SQL
- Implementing NoSQL APIs
- NoSQL with the Memcached API layer
- Prerequisites
- NoSQL API with Java
- NoSQL API with PHP
- NoSQL API with Python
- NoSQL API with Perl
- NDB Cluster API
- NDB API for NodeJS
- NDB API for Java
- NDB API with C++
- Summary
- Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop
- Case study for log analysis
- Using MySQL 8 and Hadoop for analyzing log
- Apache Sqoop overview
- Integrating Apache Sqoop with MySQL and Hadoop
- Hadoop
- MapReduce
- Hadoop distributed file system
- YARN
- Setting up Hadoop on Linux
- Installing Apache Sqoop
- Configuring MySQL connector
- Importing unstructured data to Hadoop HDFS from MySQL
- Sqoop import for fetching data from MySQL 8
- Incremental imports using Sqoop
- Loading structured data to MySQL using Apache Sqoop
- Sqoop export for storing structured data from MySQL 8
- Sqoop saved jobs
- Summary
- Case study: Part II - Real time event processing using MySQL applier
- Case study overview
- MySQL Applier
- SQL Dump and Import
- Sqoop
- Tungsten replicator
- Apache Kafka
- Talend
- Dell Shareplex
- Comparison of Tools
- MySQL Applier overview
- MySQL Applier installation
- libhdfs
- cmake
- gcc
- FindHDFS.cmake
- Hive
- Real-time integration with MySQL Applier
- Organizing and analyzing data in Hadoop
- Summary 更新時(shí)間：2021-08-20 10:06:58
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