Hdv 9 Base

[Henry Grimard]

The BASE provides student-athletes access to exceptional year-round athletic training, conditioning and competition. In baseball, basketball, and softball, all players have opportunities to earn their spots on teams that play locally and travel to regional and national tournaments. Beyond the games, we leverage the power of sports to engage young people in a culture of hard work and success.

The BASE provides academic support to ensure our student-athletes succeed in school and graduate with a winning game plan for success after graduation. We surround them in a college-going culture and set expectations for continued education or training after high school. STEM programs and skill-based learning opportunities are offered for all students, ages 6-19.

The BASE partners with businesses of all kinds to introduce our student-athletes to the many industries and professions that make up the economic engine in Greater Boston. Through internships, career days, interactive workshops, worksite tours and more, we ensure our young people get to see and experience the kinds of professional opportunities that will be available to them as they continue their education and training.

The BASE Health & Wellness Initiative builds on our longstanding work supplying the community with healthy foods, with the goal of creating a unique Urban Health and Wellness Center. This critical work will support the health of the entire community through access to nutritious foods and educational wellness programs.

On June 26th we celebrated our graduating class of seniors, bringing together families, friends, and supporters of The BASE. The annual Graduation Celebration is one of our cherished traditions and this year was another special evening of pride, joy, and celebration!

Base is a full-featured desktop database front end, designed to meet the needs of a broad array of users. Base caters to power users and enterprise requirements, providing native-support drivers for some of the most widely employed multi-user database engines: MySQL/MariaDB, Adabas D, MS Access and PostgreSQL. In addition, the built-in support for JDBC- and ODBC-standard drivers allows you to connect to virtually any other existing database engine as well.

Base comes configured with the full HSQL relational database engine. It's an ideal solution for everyday jobs, and for people requiring an easy-to-understand, simple-to-use system: data is stored right inside the Base file, and you also get native support for dBase flat files. LibreOffice Base provides wizards to help users who are new to database design (or just new to the software) to create tables, queries, forms and reports, and it comes supplied with a set of pre-defined table definitions for tracking assets, customers, sales orders, invoices and many other common and useful items.

There are two strategies for scaling any application. The first, and by far the easiest, is vertical scaling: moving the application to larger computers. Vertical scaling works reasonably well for data but has several limitations. The most obvious limitation is outgrowing the capacity of the largest system available. Vertical scaling is also expensive, as adding transactional capacity usually requires purchasing the next larger system. Vertical scaling often creates vendor lock, further adding to costs.

Horizontal scaling offers more flexibility but is also considerably more complex. Horizontal data scaling can be performed along two vectors. Functional scaling involves grouping data by function and spreading functional groups across databases. Splitting data within functional areas across multiple databases, or sharding,1 adds the second dimension to horizontal scaling. The diagram in figure 1 illustrates horizontal data-scaling strategies.

As figure 1 illustrates, both approaches to horizontal scaling can be applied at once. Users, products, and transactions can be in separate databases. Additionally, each functional area can be split across multiple databases for transactional capacity. As shown in the diagram, functional areas can be scaled independently of one another.

Functional partitioning is important for achieving high degrees of scalability. Any good database architecture will decompose the schema into tables grouped by functionality. Users, products, transactions, and communication are examples of functional areas. Leveraging database concepts such as foreign keys is a common approach for maintaining consistency across these functional areas.

Relying on database constraints to ensure consistency across functional groups creates a coupling of the schema to a database deployment strategy. For constraints to be applied, the tables must reside on a single database server, precluding horizontal scaling as transaction rates grow. In many cases, the easiest scale-out opportunity is moving functional groups of data onto discrete database servers.

Schemas that can scale to very high transaction volumes will place functionally distinct data on different database servers. This requires moving data constraints out of the database and into the application. This also introduces several challenges that are addressed later in this article.

Eric Brewer, a professor at the University of California, Berkeley, and cofounder and chief scientist at Inktomi, made the conjecture that Web services cannot ensure all three of the following properties at once (signified by the acronym CAP):2

Specifically, a Web application can support, at most, only two of these properties with any database design. Obviously, any horizontal scaling strategy is based on data partitioning; therefore, designers are forced to decide between consistency and availability.

Database vendors long ago recognized the need for partitioning databases and introduced a technique known as 2PC (two-phase commit) for providing ACID guarantees across multiple database instances. The protocol is broken into two phases:

If any database vetoes the commit, then all databases are asked to roll back their portions of the transaction. What is the shortcoming? We are getting consistency across partitions. If Brewer is correct, then we must be impacting availability, but how can that be?

The availability of any system is the product of the availability of the components required for operation. The last part of that statement is the most important. Components that may be used by the system but are not required do not reduce system availability. A transaction involving two databases in a 2PC commit will have the availability of the product of the availability of each database. For example, if we assume each database has 99.9 percent availability, then the availability of the transaction becomes 99.8 percent, or an additional downtime of 43 minutes per month.

BASE is diametrically opposed to ACID. Where ACID is pessimistic and forces consistency at the end of every operation, BASE is optimistic and accepts that the database consistency will be in a state of flux. Although this sounds impossible to cope with, in reality it is quite manageable and leads to levels of scalability that cannot be obtained with ACID.

The availability of BASE is achieved through supporting partial failures without total system failure. Here is a simple example: if users are partitioned across five database servers, BASE design encourages crafting operations in such a way that a user database failure impacts only the 20 percent of the users on that particular host. There is no magic involved, but this does lead to higher perceived availability of the system.

So, now that you have decomposed your data into functional groups and partitioned the busiest groups across multiple databases, how do you incorporate BASE into your application? BASE requires a more in-depth analysis of the operations within a logical transaction than is typically applied to ACID. What should you be looking for? The following sections provide some direction.

Following Brewer's conjecture, if BASE allows for availability in a partitioned database, then opportunities to relax consistency have to be identified. This is often difficult because the tendency of both business stakeholders and developers is to assert that consistency is paramount to the success of the application. Temporal inconsistency cannot be hidden from the end user, so both engineering and product owners must be involved in picking the opportunities for relaxing consistency.

Figure 2 is a simple schema that illustrates consistency considerations for BASE. The user table holds user information including the total amount sold and bought. These are running totals. The transaction table holds each transaction, relating the seller and buyer and the amount of the transaction. These are gross oversimplifications of real tables but contain the necessary elements for illustrating several aspects of consistency.

In general, consistency across functional groups is easier to relax than within functional groups. The example schema has two functional groups: users and transactions. Each time an item is sold, a row is added to the transaction table and the counters for the buyer and seller are updated. Using an ACID-style transaction, the SQL would be as shown in figure 3.

The total bought and sold columns in the user table can be considered a cache of the transaction table. It is present for efficiency of the system. Given this, the constraint on consistency could be relaxed. The buyer and seller expectations can be set so their running balances do not reflect the result of a transaction immediately. This is not uncommon, and in fact people encounter this delay between a transaction and their running balance regularly (e.g., ATM withdrawals and cellphone calls).

How the SQL statements are modified to relax consistency depends upon how the running balances are defined. If they are simply estimates, meaning that some transactions can be missed, the changes are quite simple, as shown in figure 4.

We've now decoupled the updates to the user and transaction tables. Consistency between the tables is not guaranteed. In fact, a failure between the first and second transaction will result in the user table being permanently inconsistent, but if the contract stipulates that the running totals are estimates, this may be adequate.

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