Basis Database

[Saurabh Cloudas]

TheBASIS Database Management System (DBMS) provides the developer with relational access and high speed direct navigational access to their data. The BASIS DBMS provides robust and powerful functionality allowing developers the flexibility to combine powerful SQL statements and relational data access methods with the performance and control of navigational operations such as direct table and index access and direct table movement. In addition, the BASIS SQL engine enables developers to execute queries and other SQL statements from third party products. Combining these two powerful data access methods returns the result set from an SQL query or from a table opened directly on the server via the highly optimized BASIS ISAM database engine. The result is an easy-to-use interface that supports SQL commands and direct-result-set navigation, all in one integrated solution.

Data dictionaries are organized, formal descriptions of data files that store physical and logical file attributes. The BASIS Data Dictionary is a powerful tool that holds a central description for tables in a database and describes the characteristics of one or more of the BBx file components of a DMBS.

The BBj ODBC Driver supports Minimum + SQL Grammar and Level 1 ODBC API. This translates into an ODBC driver that handles SQL in a more standardized way and a shorter learning curve for employees who already understand SQL functionality. At these two levels of compliance, the BBj ODBC Driver can supply over 30 scalar functions, including ASCII, POWER (double, integer), RAND (double), REPLACE (char, char, char), CURTIME and DATABASE.

For the BBj SQL engine to best optimize queries, it needs to know certain information about the records in the tables involved. BBj addresses this need with a feature called database analysis. Administrators perform this analysis in the BBj Enterprise Manager when first setting up the database and then again anytime the structure of the data changes significantly.

During this analysis, the BBj SQL engine determines the average number of distinct values for various numbers of segments of a particular key. At runtime, the SQL engine uses this information to determine which key to use for iteration. For example, assume there is a key on the LAST_NAME column in a table and one on the STATE column. Now assume that all people in the table live in New Mexico, Colorado, or Texas. Also, assume that most of the people do not have the same last name. If the SQL engine knows that there are more distinct values in the LAST_NAME column than there are in the STATE column, it can conclude that searching on the LAST_NAME key is probably going to require that it read fewer records. If the SQL engine does not know that LAST_NAME is more distinct than STATE, it has no way of knowing which key is more efficient for searching.

A relational view is a mechanism to create a virtual table that has built-in projection, join, and/or restrictions that do not physically exist on disk. For example, a view may look at the customer table, but only display the customer last name and the customer first name fields. Alternatively, a view may look at the customer table but only display those rows that correspond to customers who are in a particular region. For most practical purposes, treat a view as a table. Additionally, a view may represent the customer table joined with the order table. Any combination of these preceeding examples may occur in a view.

Views are advantageous because they allow the arrangement of non-normalized data and show specific parts of the data without changing the code. If there is a table with dozens of columns but only five columns need to be accessible, simply create a view for the end-user to present these five columns only. Views show only specific parts of the data selected by the user.

The demand for views involves non-normalized data, which is the intermixing of record types into a single file. Non-normalized data is a common practice in legacy BBx Applications. These applications require a mechanism to view one physical file as more than one logical file without actually creating two new files.

The BASIS DBMS preserves the investment made in existing data file design by combining the powerful Views feature with a variety of new functions and capabilities. The Views feature lets you create virtual tables defined from multiple record types, tailor the specific rows and columns displayed to each end user after a query, and save the table for future queries.

Several features, such as nested SELECTS and outer joins along with Views, help you better manage non-normalized data and create more focused and powerful queries. In addition, a multilevel logging feature enhances your ability to diagnose problems effectively.

Read/write records are based on file type. read record and write record are a few multiple table types that support direct results set navigation. Together, these table/file types form the underlying data structures within the BASIS DBMS. These file types include MKEYED, XKEYED, JKEYED, and VKEYED, debuting in BBj.

VKEYED, XKEYED, and MKEYED files are similar to regular keyed files except VKEYED, XKEYED, and MKEYED files grow dynamically by specifying a record count of 0 and XKEYED can contain an unlimited number of keys and segments per record. There is no limit on the length of the key.

MKEYED files have the following restrictions: 16 keys per record and a total of 48 segments per record. A single field or part of a field is called a segment. A key can be composed from one or more segments. The total size of a key cannot be more than 120 bytes.

BASIS first introduced ESQL (Exclusive SQL) tables with the release of BBj 6.0. As the name suggests, ESQL tables are only accessible via SQL statements so the traditional verbs like OPEN() and READ() do not apply to these files. Instead, developers can create these files via SQL CREATE TABLE statements and add or read rows by SQL INSERT, UPDATE, DELETE, and SELECT statements. Additionally, ESQL also offers true SQL data types such as DECIMAL with a defined precision and scale, DATE, and TIMESTAMP, to name a few. ESQL tables offer a host of other features as well, such as variable length records, dynamic index creation, and full support for transactions including commit and rollback.

I've got a Postgres 9.2 database (production) which is almost 90 GB, I need to restore production database to development postgres server every day, what I think is to schedule a backup an restore script as cron job on linux, but the problem is it won't work in long term, as the database is growing too fast, so base backup and restore every night would take longer and longer as the size of database is increasing.

90 gb, sounds like the db design is not 3rd normal form relational, or has a lot of blobs which might be better off as files, if you've got a growing db problem like this your bank account must be also growing, maybe invest in some design optimisations.

The NCDB is a clinical oncology database sourced from hospital registry data collected in more than 1,500 Commission on Cancer-accredited facilities. These data are used to analyze and track patients with malignant neoplastic diseases, their treatments, and outcomes. Online reporting tools are available to provide your program with comparative benchmarks for similar programs. We also have reporting tools for quality improvement, quality assurance, and surveillance measures.

The new RCRS replaces the RQRS and annual NCDB Call for Data submissions. With the RCRS, cancer programs can submit new and updated cases on a frequent basis (similar to the RQRS procedure), and the annual NCDB Call for Data will be phased out. The RCRS now serves as the new, single source of data submission for all CoC-accredited hospital registries. Cases submitted to the RCRS will include all disease sites from 2004 through current.

The NCDB maintains a number of web-based data applications for data access. These tools can be used by CoC-accredited programs to evaluate and compare the cancer care delivered to patients at their facilities.

NCDB PUFs are HIPAA-compliant data files containing cases submitted to the NCDB. They are designed to provide investigators with a data resource they can use to review and advance the quality of care delivered to cancer patients. Available only through an application process.

The CoC and the NCDB developed the NCDB Quality Reporting Tools to provide CoC-accredited cancer programs with mechanisms to evaluate the cancer care delivered to their patients and assess comparatively with those treated at other CoC-accredited cancer programs.

A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). Together, the data and the DBMS, along with the applications that are associated with them, are referred to as a database system, often shortened to just database.

Data within the most common types of databases in operation today is typically modeled in rows and columns in a series of tables to make processing and data querying efficient. The data can then be easily accessed, managed, modified, updated, controlled, and organized. Most databases use structured query language (SQL) for writing and querying data.

SQL is a programming language used by nearly all relational databases to query, manipulate, and define data, and to provide access control. SQL was first developed at IBM in the 1970s with Oracle as a major contributor, which led to implementation of the SQL ANSI standard, SQL has spurred many extensions from companies such as IBM, Oracle, and Microsoft. Although SQL is still widely used today, new programming languages are beginning to appear.

Databases have evolved dramatically since their inception in the early 1960s. Navigational databases such as the hierarchical database (which relied on a tree-like model and allowed only a one-to-many relationship), and the network database (a more flexible model that allowed multiple relationships), were the original systems used to store and manipulate data. Although simple, these early systems were inflexible. In the 1980s, relational databases became popular, followed by object-oriented databases in the 1990s. More recently, NoSQL databases came about as a response to the growth of the internet and the need for faster speed and processing of unstructured data. Today, cloud databases and self-driving databases are breaking new ground when it comes to how data is collected, stored, managed, and utilized.

3a8082e126