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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.

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Social Security's Old-Age, Survivors, and Disability Insurance (OASDI) program limits the amount of earnings subject to taxation for a given year. The same annual limit also applies when those earnings are used in a benefit computation. This limit changes each year with changes in the national average wage index. We call this annual limit the contribution and benefit base. This amount is also commonly referred to as the taxable maximum. For earnings in 2024, this base is $168,600.

Base is a fully featured desktop database management system, designed to meet the needs of a broad array of users, from tracking a personal CD collections, to producing a corporate monthly departmental sales reports.

Base offers wizards to help users new to database design (or Base) to create Tables, Queries, Forms and Reports, along with a set of predefined table definitions for tracking Assets, Customers, Sales Orders, Invoices and much more.

When a personal use database is all you need, Base offers the full HSQL relational database engine, configured for single user, with the data stored right in the Base file, as well as native support for dBase flat files.

For power users in the enterprise, Base delivers native support drivers for a variety of multi-user database engines: MySQL, Adabas D, MS Access and PostgreSQL. In addition, support for JDBC and ODBC standard drivers allows you to connect to virtually any database.

On May 5, 2013, Russian Valery Rozov jumped off Changtse (the northern peak of the Mount Everest massif) from a height of 7,220 metres (23,690 ft). Using a specially-developed Red Bull wingsuit, he glided down to the Rongbuk glacier more than 1,000 meters below, setting a new world record for highest altitude base jump.[17] He had previously jumped off mountains in Asia, Antarctica and South America in 2004, 2007, 2008, 2010 and 2012.

You cannot change the beginning date of your claim or adjust your base period after establishing a valid claim. If you have questions about your claim start date, call us at 1-800-480-3287 before filing your claim.

A base period covers 12 months and is divided into four consecutive quarters. The base period includes wages subject to SDI tax that were paid about 5 to 18 months before your disability claim began. The base period does not include wages paid at the time your disability begins. For a DI claim to be valid, you must have at least $300 in wages in the base period. The following information may be used to determine the base period for your claim.

January, February, or March:

The base period is the 12 months ending last September 30.

Example: A claim beginning February 14, 2022, uses a base period of October 1, 2020, through September 30, 2021.

April, May, or June:

The base period is the 12 months ending last December 31.

Example: A claim beginning June 20, 2022, uses a base period of January 1, 2021, through December 31, 2021.

July, August, or September:

The base period is the 12 months ending last March 31.

Example: A claim beginning September 27, 2022, uses a base period of April 1, 2021, through March 31, 2022.

October, November, or December:

The base period is the 12 months ending last June 30.

Example: A claim beginning November 2, 2022, uses a base period of July 1, 2021, through June 30, 2022.

If you're new to Airtable, we recommend starting with a template so you can see how bases are typically structured. Choose between an existing template that is already tailored, or you can customize one to meet your project's needs.

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.

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.

What if estimates are not acceptable, though? How can you still decouple the user and transaction updates? Introducing a persistent message queue solves the problem. There are several choices for implementing persistent messages. The most critical factor in implementing the queue, however, is ensuring that the backing persistence is on the same resource as the database. This is necessary to allow the queue to be transactionally committed without involving a 2PC. Now the SQL operations look a bit different, as shown in figure 5.

This example takes some liberties with syntax and oversimplifying the logic to illustrate the concept. By queuing a persistent message within the same transaction as the insert, the information needed to update the running balances on the user has been captured. The transaction is contained on a single database instance and therefore will not impact system availability. ff782bc1db