Remember that our goal is to cluster zip codes by average power consumption per month and then plot the clusters on a map. Right now our data is two separate tables and not aggregated by zip code. You'll just have to get used to it!
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 write ahead logging in teradata aster 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: The optimal strategy is workload-dependent.
However, for analysis of complex systems such as concurrency control, simulation can be a valuable intermediate step between back of the envelope and full-blown systems benchmarking. The Agrawal study is an example of this approach: Several aspects of the evaluation are particularly valuable.
In contrast, virtually every performance study without a crossover point is likely to be uninteresting. Second, the authors consider a wide range of system configurations; they investigate and discuss almost all parameters of their model.
Third, many of the graphs exhibit non-monotonicity i. As the authors illustrate, an assumption of infinite resources leads to dramatically different conclusions.
A less careful model that made this assumption implicit would be much less useful.By disabling write-ahead logging, you increase the performance of write operations, but you can incur data loss if the region servers fail. Your client application is responsible for ensuring data consistency when you use this option.
(a) Convert rows to column segment (b) Store segments in blobs Figure 1: Creation of a column store index . Greenplum Database (from EMC Greenplum) and Aster Database (from Teradata’s Aster . Teradata Aster Analytics includes the Aster Database, Aster Client and the Aster Portfolio that consists of SQL, SQL-MapReduce and Graph functions for multi-genre advanced analytics.
These functions provide everything from data acquisition and preparation to analytic modeling and visualization.
This book, Support Utilities, describes utility programs most often used to support Teradata Database. These utilities are used primarily by Teradata Support personnel and field engineers.
Notice: These utilities manipulate Teradata Database at a low level. Incorrect use of . As messages come in, Kafka is writing latest messages to the very front of that log. Okay, so it’s a write-ahead log, so old data in the back and the newest data in the front.
Note: In Kafka or earlier, don’t try and delete a topic, you’re only going to end up in a bit of frustration. Write ahead logging in teradata jobs; Willie stark as huey long essay; An introduction to the history of the marketing in japan.
There is an article on it in Wikipedia, and a wikibook for learners at Japanese. A more serious matter for the reader of history is counting years. A province was given a local government structure that included a.