Only the data blocks containing rows are compressed. No compression is done on the table headers, the master index, the cylinder index and the WAL write-ahead log.
In this chapter, we present primary and near-primary sources for several of the most important core concepts in database system design: The ideas in this chapter are so fundamental to modern database systems that nearly every mature database system implementation contains them.
Three of the papers in this chapter are far and away the canonical references on their respective topics. Moreover, in contrast with the prior chapter, this chapter focuses on broadly applicable techniques and algorithms rather than whole systems.
Query Optimization Query optimization is important in relational database architecture because it is core to enabling data-independent query processing. To do so, the optimizer relies on both pre-computed statistics about the contents of each relation stored in the system catalog as well as a set of heuristics for determining the cardinality size of the query output e.
As an exercise, consider these heuristics in detail: How might they be improved? Using these cost estimates, the optimizer uses a dynamic programming algorithm to construct a plan for the query.
The optimizer defines a set of physical operators that implement a given logical operator e. This avoids having to consider all possible orderings of operators but is still exponential in the plan size; as we discuss in Chapter 7modern query optimizers still struggle with large plans e.
Additionally, while the Selinger et al.
Like almost all query optimizers, the Selinger et al. The relational optimizer is closer in spirit to code optimization routines within modern language compilers i. Concurrency Control Our first paper on transactions, from Gray et al.
The paper in fact reads as two separate papers. First, the paper presents the concept of multi-granularity locking. The problem here is simple: When should we lock at a coarse granularity e. While Gray et al. Second, the paper develops the concept of multiple degrees of isolation.
As Gray et al. Classically, database systems used serializable transactions as a means of enforcing consistency: However, serializability is often considered too expensive to enforce. To improve performance, database systems often instead execute transactions using non-serializable isolation. In the paper here, holding locks is expensive: Therefore, as early asdatabase systems such as IMS and System R began to experiment with non-serializable policies.
In a lock-based concurrency control system, these policies are implemented by holding locks for shorter durations. This allows greater concurrency, may lead to fewer deadlocks and system-induced aborts, and, in a distributed setting, may permit greater availability of operation.
In the second half of this paper, Gray et al. Today, they are prevalent; as we discuss in Chapter 6non-serializable isolation is the default in a majority of commercial and open source RDBMSs, and some RDBMSs do not offer serializability at all.
The paper also discusses the important notion of recoverability: All but Degree 0 transactions satisfy this property. A wide range of alternative concurrency control mechanisms followed Gray et al. As hardware, application demands, and access patterns have changed, so have concurrency control subsystems.
However, one property of concurrency control remains a near certainty:Full-text index can be defined on a view. A.
SQL server utilizes Ans) Write-ahead logging mechanism The lowest/smallest unit of input/output performed by SQL . Which are the top machine learning technologies sought by employers? Update Cancel. ad by Udacity. Become a data scientist - no PhD required. Is hive Hadoop better than teradata?
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