A beginner’s guide to Read and Write Skew phenomena
Introduction In my article about ACID and database transactions, I introduced the three phenomena described by the SQL standard: dirty read non-repeatable read phantom read While these are good to differentiate the four isolation levels (Read Uncommitted, Read Committed, Repeatable Read and Serializable), in reality, there are more phenomena to take into consideration as well. The 1995 paper (A Critique of ANSI SQL Isolation Levels) introduces the other phenomena that are omitted from the standard specification. In my High-Performance Java Persistence book, I decided to insist on the Transaction chapter as it… Read More
How to prevent lost updates in long conversations
Introduction All database statements are executed within the context of a physical transaction, even when we don’t explicitly declare transaction boundaries (BEGIN/COMMIT/ROLLBACK). Data integrity is enforced by the ACID properties of database transactions. Logical vs Physical transactions A logical transaction is an application-level unit of work that may span over multiple physical (database) transactions. Holding the database connection open throughout several user requests, including user think time, is definitely an anti-pattern. A database server can accommodate a limited number of physical connections, and often those are reused by using connection pooling. Holding… Read More
A beginner’s guide to database locking and the lost update phenomena
Introduction A database is highly concurrent system. There’s always a chance of update conflicts, like when two concurring transactions try to update the same record. If there would be only one database transaction at any time then all operations would be executed sequentially. The challenge comes when multiple transactions try to update the same database rows as we still have to ensure consistent data state transitions. The SQL standard defines three consistency anomalies (phenomena): Dirty reads, prevented by Read Committed, Repeatable Read and Serializable isolation levels Non-repeatable read, prevented by Repeatable Read… Read More
A beginner’s guide to natural and surrogate database keys
Types of primary keys All database tables must have one primary key column. The primary key uniquely identifies a row within a table therefore it’s bound by the following constraints: UNIQUE NOT NULL IMMUTABLE When choosing a primary key we must take into consideration the following aspects: the primary key may be used for joining other tables through a foreign key relationship the primary key usually has an associated default index, so the more compact the data type the less space the index will take the primary key assignment must ensure uniqueness… Read More
The data knowledge stack
Concurrency is not for the faint-hearted We all know concurrency programming is difficult to get it right. That’s why threading tasks are followed by extensive design and code review sessions. You never assign concurrent issues to inexperienced developers. The problem space is carefully analyzed, a design emerges and the solution is both documented and reviewed. That’s how threading related tasks are usually addressed. You will naturally choose a higher level abstraction since you don’t want to get tangled up in low-level details. That’s why the java.util.concurrent is usually better (unless you build… Read More
Time to break free from the SQL-92 mindset
Are you stuck in the 90s? If you are only using the SQL-92 language reference, then you are overlooking so many great features like: Window Functions PIVOT MERGE INSTEAD OF triggers Some test data In my previous article I imported some CSV Dropwizard metrics into PostgreSQL for further analysis.
How to import CSV data into PostgreSQL
Introduction Many database servers support CSV data transfers and this post will show one way you can import CSV files to PostgreSQL. SQL aggregation rocks! My previous post demonstrated FlexyPool metrics capabilities and all connection related statistics were exported in CSV format. When it comes to aggregation tabular data SQL is at its best. If your database engine supports SQL:2003 windows functions you should definitely make use of this great feature.
JOOQ Facts: SQL functions made easy
Introduction The JDBC API has always been cumbersome and error-prone and I’ve never been too fond of using it. The first major improvement was brought by the Spring JDBC framework which simply revitalized the JDBC usage with its JdbcTemplate or the SqlFunction classes, to name a few. But Spring JDBC doesn’t address the shortcoming of using string function or input parameters names and this opened the door for type-safe SQL wrappers such as jOOQ. JOOQ is the next major step towards a better JDBC API and ever since I started using it… Read More
