High-Performance Java Persistence by Vlad Mihalcea

By Vlad Mihalcea

This publication is a trip into Java information entry functionality tuning. From connection administration, to batch updates, fetch sizes and concurrency keep an eye on mechanisms, it unravels the interior workings of the commonest Java information entry frameworks.

The first half goals to lessen the distance among program builders and database directors. as a result, it covers either JDBC and the database basics which are of paramount value whilst decreasing transaction reaction times.

The moment half demonstrates how one can make the most of JPA and Hibernate with no compromising software performance.

The 3rd half is devoted to jOOQ and its robust type-safe querying services, like window features or universal desk expressions.

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Additional resources for High-Performance Java Persistence

Sample text

Since a disk access is required for fetching every additional row-level data, indexing is suitable when selecting only a fraction of the whole table data. Most database systems take this decision based on the index selectivity of the current bind parameter values. A covering index bypasses disk access if all requesting columns are scanned by the index. Because each disk access requires reading a whole block of data, accessing to many disparate blocks can actually perform worse than scanning the whole table (random access is slower than sequential scans).

2, a prepared statement was planned and compiled entirely during the prepare phase, so the execution plan was generated in the absence of the actual bind parameter values. Although it attempted to spare database resources, this strategy was very sensitive to skewed data. 2, the prepare phase only parses and rewrites a statement, while the optimization and the planning phase are deferred until execution time. This way, the rewritten syntax tree is optimized according to the actual bind parameter values, and an optimal execution plan is generated.

CREATE TABLE post ( id NUMBER(19,0) NOT NULL GENERATED ALWAYS AS IDENTITY, title VARCHAR2(255 CHAR), version NUMBER(10,0) NOT NULL, PRIMARY KEY (id)); Batch Updates SQL Server Traditionally, SQL Server offered identity column generators, but, since SQL Server 2012, it now supports database sequences as well. 5 doesn’t support identity columns natively, although it offers the SERIAL column type which can emulate an identity column. CREATE TABLE post ( id SERIAL NOT NULL, title VARCHAR(255), version INT4 NOT NULL, PRIMARY KEY (id)); The SERIAL (4 bytes) and BIGSERIAL (8 bytes) types are just a syntactic sugar expression as, behind the scenes, PostgreSQL relies on a database sequence anyway.

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