'High-Performance Java Persistence' Part One
'High-Performance Java Persistence' Part One
The 'High-Performance Java Persistence' book is done! Part one at least. The initial section looks at app developers vs. database admins.
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Four months, one week and two days and 114 pages; that’s how much it took to write the first part of the High-Performance Java Persistence book.
As previously stated, the book is developed in an Agile fashion. Each part represents a milestone, which is accompanied by a release. This way, the readers can get access to the book content prior to finishing the whole book (which might take a year or so).
Table of Contents
1.1 The database server and the connectivity layer
1.2 The application data access layer
1.2.1 The ORM framework
1.2.2 The native query builder framework
2. Performance and Scaling
2.1 Response time and Throughput
2.2 Database connections boundaries
2.3 Scaling up and scaling out
2.3.1 Master-Slave replication
2.3.2 Multi-Master replication
3. JDBC Connection Management
3.2.1 Why is pooling so much faster?
3.3 Queuing theory capacity planning
3.4 Practical database connection provisioning
3.4.1 A real-life connection pool monitoring example
220.127.116.11 Concurrent connection request count metric
18.104.22.168 Concurrent connection count metric
22.214.171.124 Maximum pool size metric
126.96.36.199 Connection acquisition time metric
188.8.131.52 Retry attempts metric
184.108.40.206 Overall connection acquisition time metric
220.127.116.11 Connection lease time metric
4. Batch Updates
4.1 Batching Statements
4.2 Batching PreparedStatements
4.2.1 Choosing the right batch size
4.2.2 Bulk operations
4.3 Retrieving auto-generated keys
4.3.1 Sequences to the rescue
5. Statement Caching
5.1 Statement lifecycle
18.104.22.168 Execution plan visualization
5.2 Caching performance gain
5.3 Server-side statement caching
5.3.1 Bind-sensitive execution plans
5.4 Client-side statement caching
6. ResultSet Fetching
6.1 ResultSet scrollability
6.2 ResultSet changeability
6.3 ResultSet holdability
6.4 Fetching size
6.5 ResultSet size
6.5.1 Too many rows
22.214.171.124 SQL limit clause
126.96.36.199 JDBC max rows
188.8.131.52 Less is more
6.5.2 Too many columns
7.3.1 Concurrency control
184.108.40.206 Two-phase locking
220.127.116.11 Multi-Version Concurrency Control
18.104.22.168 Dirty write
22.214.171.124 Dirty read
126.96.36.199 Non-repeatable read
188.8.131.52 Phantom read
184.108.40.206 Read skew
220.127.116.11 Write skew
18.104.22.168 Lost update
7.3.3 Isolation levels
22.214.171.124 Read Uncommitted
126.96.36.199 Read Committed
188.8.131.52 Repeatable Read
7.5 Read-only transactions
7.5.1 Read-only transaction routing
7.6 Transaction boundaries
7.6.1 Distributed transactions
184.108.40.206 Two-phase commit
7.6.2 Declarative transactions
7.7 Application-level transactions
7.7.1 Pessimistic and optimistic locking
220.127.116.11 Pessimistic locking
18.104.22.168 Optimistic locking
The first part is about closing the gap between an application developer and a database administrator. This book is focused on data access, and for this purpose, it explains the inner-workings of both the database engine and the JDBC drivers of the four most common relational databases (Oracle, SQL Server, MySQL, and PostgreSQL).
I explain what performance and scalability means and the thin relation between response time and throughput. Being a big fan of Neil J. Gunther, I couldn’t not write about the Universal Scalability Law and how this equation manages to associate capacity with contention and coherency.
From hardware to distributed systems, queues are everywhere, and Queuing theory provides an invaluable equation for understanding how queues affects throughput. Connection management is one area where queuing plays a very important role and monitoring connection usage is of paramount importance to providing responsive and scalable services.
Like any other client-server communication, the data access layer can benefit from batching requests. Database drivers, like other database-related topics, are very specific when it comes to batching statements. For this purpose, I explained how you can leverage batching based on the database system in use.
Statement caching is very important for high-performance enterprise applications, both on the server-side and the client-side. This books explains how statement caching is implemented in the most common RDBMS and how you can activate this optimization using the JDBC API.
A good data fetch plan can make a difference between a high-performance data access layer and one that barely crawls. For this reason, I explained how the fetch size and the result set size affect transaction performance.
Transactions is a very complex topic. This chapter goes beyond the SQL standard phenomena and isolation levels, and it explains all possible non-serializable data anomalies and various concurrency control mechanisms. Transactions are important, not just for ensuring data effectiveness and avoiding data integrity issues but for efficiently access data too.
There is also a sample chapter, which you can read it for free and get a feeling of what this book can offer you. The sample chapter can be either read online, or it can be downloaded as PDF, mobi or epub (just like the actual book).
The book price varies between a minimum value, a suggested price, and a maximum value (VAT might be added according to the laws of the country where you make the purchase). Once you purchase the book, every update comes for free. Early buyers are recompensed with a lower acquisition cost, the price growing whenever a new part is being released.
Enjoy reading it and let me know what you think.
Published at DZone with permission of Vlad Mihalcea . See the original article here.
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