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Oracle ODBC driver for Oracle 8, 9i, 10g, XE, 11g and 12c. Available on Linux, Unix and Windows platforms. To build and run an application as a 32-bit application on a 64-bit operating system, you must create the ODBC data source with the ODBC Administrator in %windir. A document-oriented database, or document store, is a computer program designed for storing, retrieving and managing document-oriented information, also known as semi. A remote stack buffer overflow was discovered in the Firebird Server during March, 2013 that allows an unauthenticated user to crash the server and. Document- oriented database - Wikipedia. This article is about the software type. For deployed applications of the software type, see Full text database. A document- oriented database, or document store, is a computer program designed for storing, retrieving and managing document- oriented information, also known as semi- structured data. Document- oriented databases are one of the main categories of No. SQL databases, and the popularity of the term . XML databases are a subclass of document- oriented databases that are optimized to work with XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal. Document- oriented databases are inherently a subclass of the key- value store, another No. SQL database concept. The difference lies in the way the data is processed; in a key- value store the data is considered to be inherently opaque to the database, whereas a document- oriented system relies on internal structure in the document in order to extract metadata that the database engine uses for further optimization. Although the difference is often moot due to tools in the systems. Relational databases generally store data in separate tables that are defined by the programmer, and a single object may be spread across several tables. Document databases store all information for a given object in a single instance in the database, and every stored object can be different from every other. This makes mapping objects into the database a simple task, normally eliminating anything similar to an object- relational mapping. This makes document stores attractive for programming web applications, which are subject to continual change in place, and where speed of deployment is an important issue. Documents. While each document- oriented database implementation differs on the details of this definition, in general, they all assume documents encapsulate and encode data (or information) in some standard format or encoding. Encodings in use include XML, YAML, JSON, and BSON, as well as binary forms like PDF and Microsoft Office documents (MS Word, Excel, and so on). Documents in a document store are roughly equivalent to the programming concept of an object. They are not required to adhere to a standard schema, nor will they have all the same sections, slots, parts, or keys. Generally, programs using objects have many different types of objects, and those objects often have many optional fields. Every object, even those of the same class, can look very different. Document stores are similar in that they allow different types of documents in a single store, allow the fields within them to be optional, and often allow them to be encoded using different encoding systems. For example, the following is a document, encoded in JSON. The structure and text and other data inside the document are usually referred to as the document's content and may be referenced via retrieval or editing methods, (see below). Unlike a relational database where every record contains the same fields, leaving unused fields empty; there are no empty 'fields' in either document (record) in the above example. This approach allows new information to be added to some records without requiring that every other record in the database share the same structure. Step by step instruction how to download and install the ODBC drivers for Oracle 11g release 2. Setting up an Oracle ODBC Driver and Data Source requires more steps than the Microsoft SQL Server database setup. This article explains the steps including. Document databases typically provide for additional metadata to be associated with and stored along with the document content. That metadata may be related to facilities the datastore provides for organizing documents, providing security, or other implementation specific features. CRUD operations. This key is a simple identifier (or ID), typically a string, a URI, or a path. The key can be used to retrieve the document from the database. Typically the database retains an index on the key to speed up document retrieval, and in some cases the key is required to create or insert the document into the database. Retrieval. For example, you may want a query that retrieves all the documents with a certain field set to a certain value. The set of query APIs or query language features available, as well as the expected performance of the queries, varies significantly from one implementation to another. Likewise, the specific set of indexing options and configuration that are available vary greatly by implementation. It is here that the document store varies most from the key- value store. In theory, the values in a key- value store are opaque to the store, they are essentially black boxes. They may offer search systems similar to those of a document store, but may have less understanding about the organization of the content. Document stores use the metadata in the document to classify the content, allowing them, for instance, to understand that one series of digits is a phone number, and another is a postal code. This allows them to search on those types of data, for instance, all phone numbers containing 5. Editing. In a simple key- value store, the document content is opaque. A document- oriented database provides APIs or a query/update language that exposes the ability to query or update based on the internal structure in the document. This difference may be moot for users that do not need richer query, retrieval, or editing APIs that are typically provided by document databases. Modern key- value stores often include features for working with metadata, blurring the lines between document stores. Relationship to search engines. The tables define the data within each record's fields, meaning that every record in the table has the same overall form. The administrator also defines the relationships between the tables, and selects certain fields that they believe will be most commonly used for searching and defines indexes on them. A key concept in the relational design is that any data that may be repeated is normally placed in its own table, and if these instances are related to each other, a column is selected to group them together, the foreign key. This design is known as database normalization. In a canonical relational database, tables would be created for each of these rows with predefined fields for each bit of data: the CONTACT table might include FIRST. In order to recreate the original contact, the database engine uses the foreign keys to look for the related items across the group of tables and reconstruct the original data. In contrast, in a document- oriented database there may be no internal structure that maps directly onto the concept of a table, and the fields and relationships generally don't exist as predefined concepts. Instead, all of the data for an object is placed in a single document, and stored in the database as a single entry. In the address book example, the document would contain the contact's name, image, and any contact info, all in a single record. That entry is accessed through its key, which allows the database to retrieve and return the document to the application. Cross-platform C library that implements a self-contained, embeddable, zero-configuration SQL database engine. The site offers user and developer documentation. This kit includes Version 3.3.2.0 of the Oracle ODBC Driver for Rdb. This version is considerably different internally from the version 2 Oracle ODBC Driver for Rdb. 11/17/2015 - Originally written for DreamFactory 1.X, this post has been updated to reflect the current version, DreamFactory 2.0. Have you got SQL data that you need. No additional work is needed to retrieve the related data; all of this is returned in a single object. A key difference between the document- oriented and relational models is that the data formats are not predefined in the document case. In most cases, any sort of document can be stored in any database, and those documents can change in type and form at any time. If one wishes to add a COUNTRY. To aid retrieval of information from the database, document- oriented systems generally allow the administrator to provide hints to the database to look for certain types of information. These work in a similar fashion to indexes in the relational case. Most also offer the ability to add additional metadata outside of the content of the document itself, for instance, tagging entries as being part of an address book, which allows the programmer to retrieve related types of information, like . This provides functionality similar to a table, but separates the concept (categories of data) from its physical implementation (tables). In the classic normalized relational model, objects in the database are represented as separate rows of data with no inherent structure beyond that given to them as they are retrieved. This leads to problems when trying to translate programming objects to and from their associated database rows, a problem known as object- relational impedance mismatch. These are often marketed using the term No. SQL. Implementations. Uses JSON model. Yes. Clusterpoint Database. Clusterpoint Ltd. Proprietary with free download. Java. Script, SQL, PHP, . NET, Java, Python, Node. C, C++,Distributed document- oriented XML / JSON database platform with ACID- compliant transactions; high- availabilitydata replication and sharding; built- in full text search engine with relevanceranking; JS/SQL query language; GIS; Available as pay- per- use database as a service or as an on- premise free software download. Uses map and reduce for views and queries. Based on Lucene / Elasticsearch ecosystem with built- in support for binary objects (BLOBs). Yes. Being replaced/supplanted by Cosmos DB. Yes. Elasticsearch. Shay Banon. Apache License. Java. JSON, Search engine. Yese. Xiste. Xist. LGPLXQuery, Java. XML over REST/HTTP, Web. DAV, Lucene Fulltext search, binary data support, validation, versioning, clustering, triggers, URL rewriting, collections, ACLS, XQuery Update. Yes. Built- in Full text search, ACID transactions, High availability and Disaster recovery, certified security. Yes. Mongo. DBMongo. DB, Inc. GNU AGPL v. DBMS, Apache 2 License for the client drivers. Add a REST API to any SQL DB in Minutes. Originally written for Dream. Factory 1. X, this post has been updated to reflect the current version, Dream. Factory 2. 0. Have you got SQL data that you need to access from your mobile or web apps? If so, Dream. Factory provides an easy and secure way to add a REST API to any SQL database in minutes. Supported SQL databases include SQLite, My. SQL, SQL Server, Oracle, IBM DB2, Postgres, and SAP SQL Anywhere. With the free, open source Dream. Factory REST API backend all you have to do is create a service for your database, then use the auto- generated REST API to access that service. Each Dream. Factory instance comes with its own SQLite database that's accessible via the default 'db' service. Bitnami installs also include pre- configured My. SQL and Mongo. DB (No. SQL) services. See the wiki for supported database types on the various host operating systems. In this blog post we'll show how to add additional services to REST- enable any SQL database. Then we'll show some simple examples of how to use the REST API to manage your SQL schema and data. If you're a video kind of person, we have some screencasts available. If you haven't already signed up for a free, cloud- hosted instance of Dream. Factory, sign up here. You can also download and install Dream. Factory to run it on any server, cloud, or even your local Windows, Mac, or Linux desktop. In this blog post I'll be using a Bitnami Dream. Factory setup on OS X, but the process is similar for other installation types. Setting Up Your Database Service. The first step is to go to your instance and set up a service for the SQL database you want to connect to. To do that, go to the admin console for your instance and click on the Services tab. Click the Create button in the upper left then complete the Info tab. Set the service type to 'SQL DB' and enter a name and a label. It's best to keep the name short and lower case since it's part of the URL for the REST API. We'll use 'sql' for this example. Click the Config tab to enter connection information. This is where you enter the username and password for your database. Database credentials are stored securely on the instance and never exposed to the client- side app. Lastly you must enter the connection string. The connection string will be pre- populated based on the SQL type, then you can customize it to work for your database. Some setups require additional drivers to be installed. See the wiki for supported database types on the various host operating systems. Click the Create Service button at the bottom of the page. The service you just created is for managing both SQL data and schema. To access the table records via the REST API you would use /api/v. To access the schema you would use /api/v. For example you might want user A to have permission to perform CRUD operations on table X, but not modify the schema for table X in any way. User B, who is an admin, could have full access to the data and the schema. Access Control. If you're using an instance that is hosted by Dream. Factory, you may need to provision your remote SQL database to allow access from our hosted system. The IP address for that system is 5. Testing the Services. Now we've created the new service so let's make sure it's working. To try it out click the API Docs tab in the admin console. Here you can experiment with the REST API for the service. To retrieve a list of available tables, find and click the GET button for 'sql/. Scroll down and click the 'Try it out!' button. It shows the full URL for the request which ishttp: //localhost: 8. If you get an error go back and check the settings on your service. If your database had a single table named 'contact' you would see . In that case the URL ishttp: //localhost: 8. Now let's add a new table named 'todo' to the database. To create a table you POST the schema to /api/v. You can use any REST client for this or you can use the Schema tab in the admin console. We'll be using the API Docs, so go there and click the button for 'POST /sql/. Now that your table exists you can perform other schema operations such as creating, updating, and deleting fields or deleting the table. This can be done through the API or from the Schema tab in the admin console. The API Docs show the syntax and options for these operations. Adding Records to a Table. To add a record to the new table you can POST the following JSON data to /api/v. If the 'table. Retrieving Records from a Table. To retrieve records from the table click on GET sql/. Update and delete are very similar, except you must specify the id(s) of the records. These are simple CRUD operations, but we also support relationship queries where you can perform CRUD operations on parent and child tables in a single API call. See the API Docs for examples of how to do this. Changing Databases. It's easy to change to an entirely different type of database such as Mongo. DB just by changing the service name from 'sql' to another service. Dream. Factory takes care of translating the data to a consistent JSON format allowing the client code to be essentially the same for many different database types. Pretty cool! Customizing APIs with Server- Side Scripting. In addition to providing the REST API Dream. Factory allows you to customize your APIs with server- side scripting. For each database table there's a pre- process and a post- process script for each HTTP verb. These scripts are created and edited from the Scripts tab in the admin console. They can be written in Java. Script (V8js or Node. PHP. They can be used for things like field validation, business logic, and service orchestration. See the wiki for some examples. Hopefully this has given you a good introduction to using Dream. Factory to REST- enable your own SQL databases. It's easy and secure, and open source. Please add any questions or comments below or contact our support team if you need help! Firebird 2. 5 Release Notes(CORE- 3. If a request was cancelled for some reason, such. Khorsun ~ ~ ~ (CORE- 3. On disconnect a “Fatal lock manager. Khorsun ~ ~ ~ (CORE- 3. Various “Cannot initialize the shared. Khorsun ~ ~ ~ (CORE- 3. New checks for the existence of a SUSPEND. SELECT statement were working properly: if the procedure. SELECT statement and SUSPEND was not present, an error would be thrown, e. Procedure .. Yemanov ~ ~ ~ (CORE- 2. A single error in the status vector. Khorsun ~ ~ ~ (CORE- 2. On a highly loaded system, the fatal. Invalid lock id (NNN)” could occur while working with monitoring. 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Set the fixed and documented evaluation order. WHERE clause and other predicates. Yemanov ~ ~ ~ (CORE- 1. LPAD() and RPAD() functions hit. Khorsun ~ ~ ~ (CORE- 2. Incorrect handling of LOWER/UPPER when. Peshkov ~ ~ ~ (CORE- 2. The “Invalid SQLDA” error. Khorsun ~ ~ ~ (CORE- 2. More database corruption problems showed up. Khorsun ~ ~ ~ (CORE- 2. Bugcheck 2. 58 (page slot not empty) could. Khorsun ~ ~ ~ (CORE- 2. A complex recursive query did not. Khorsun ~ ~ ~ (CORE- 2. INF. Valderrama ~ ~ ~ (CORE- 2. A WITH RECURSIVE query could cause. Khorsun ~ ~ ~ (CORE- 2. The second evaluation of SUBSTRING(). Khorsun ~ ~ ~ (CORE- 2. The engine was incorrectly populating integer. BPB) with integers in machine- local format, causing. Big Endian platforms. Peshkov ~ ~ ~ (CORE- 2. If an ALTER TABLE ALTER COLUMN. Khorsun ~ ~ ~ (CORE- 2. Input parameters for EXECUTE BLOCK were. Yemanov ~ ~ ~ (CORE- 2. A “request synchronization. Khorsun ~ ~ ~ (CORE- 2. Indexed retrieval could not be chosen. Yemanov ~ ~ ~ (CORE- 2. Incorrect ROW. Yemanov ~ ~ ~ (CORE- 2. A Bugcheck 2. 49 (pointer page. PSQL cursor. Yemanov ~ ~ ~ (CORE- 2. It was not possible to create a view. Khorsun ~ ~ ~ (CORE- 2. If selective non- indexed predicates were. Yemanov ~ ~ ~ (CORE- 2. Parts of the RDB$DB. Yemanov ~ ~ ~ (CORE- 2. Incorrect view expansion when RDB$DB. Yemanov ~ ~ ~ (CORE- 2. A v. 2. 1 regression was picked up. RETURNING OLD and non- nullable columns. A. Yemanov ~ ~ ~ (CORE- 2. NULL in the first record in a. RDB$DB. Khorsun ~ ~ ~ (CORE- 2. NOT NULL flag for procedure parameters. A. Valderrama ~ ~ ~ (CORE- 2. When trying to show a conversion error. Valderrama ~ ~ ~ (CORE- 2. The Lock Manager could report false. Khorsun ~ ~ ~ (CORE- 1. Altering the name of a domain was causing. Khorsun ~ ~ ~ (CORE- 1. Sweeper could consume 1. CPU. indefinitely. Khorsun ~ ~ ~ (CORE- 1. Lock conversion denied (2. Khorsun ~ ~ ~ (CORE- 1. The function EXTRACT. MILLISECONDS FROM a. Time. Stamp. Or. Time) was returning incorrect results. Yemanov ~ ~ ~ (CORE- 1. Long access control lists (ACLs) were. Peshkov ~ ~ ~ (CORE- 1. A statement that aggregated on a RAND(). Yemanov ~ ~ ~ (CORE- 1. The built- in function LOG(base. NAN values for out- of- range input. Peshkov ~ ~ ~ (CORE- 1. For some date/time expressions in. Yemanov ~ ~ ~ (CORE- 1. An improbable case was demonstrated whereby. SELECT GEN. Yemanov ~ ~ ~ (CORE- 1. Ability to insert child record if parent. A. Potapchenko, V. Khorsun ~ ~ ~ (CORE- 1. Multiple updates to a table in a single. Yemanov ~ ~ ~ (CORE- 1. When a user application created. RDB$PROCEDURES. RDB$PROCEDURE. A similar fix was applied to RDB$GENERATORS and RDB$EXCEPTIONS as well. Yemanov ~ ~ ~ (CORE- 1. Simple case and a subquery. A. Yemanov ~ ~ ~ (CORE- 5. COALESCE exhibited an optimization. Peshkov ~ ~ ~ (CORE- 1. Super. Server could not shut down immediately if the. Peshkov ~ ~ ~ (CORE- 1. Dropping and adding a domain constraint in the same. Valderrama ~ ~ ~ (CORE- 1. Newly created databases had wrong access rights. Peshkov ~ ~ ~ (CORE- 1. Roles granting/revoking logic differed between v. Peshkov ~ ~ ~ (CORE- 1. If some VIEW used derived tables and long table. RDB$VIEW. Khorsun ~ ~ ~ (CORE- 1. Each DDL execution would cause a small memory leak. Yemanov ~ ~ ~ (CORE- 1. SET STATISTICS INDEX on an index for a GTT could. Khorsun ~ ~ ~ (CORE- 1. Possible index corruption with multiple updates of the. Khorsun ~ ~ ~ (CORE- 1. The Relaxed. Alias. Checking parameter was having no effect. RDB$DB. Khorsun ~ ~ ~ (CORE- 1. Parser reacted incorrectly to the unquoted usage of. VALUE”. Yemanov ~ ~ ~ (CORE- 1. RDB$DB. Valderrama ~ ~ ~ (CORE- 1. Security checking was performing poorly during. Khorsun ~ ~ ~ (CORE- 1. Bugcheck 2. 91 in DDL. Peshkov ~ ~ ~ (CORE- 1. Behavour problem with SET DEFAULT action argument in. Khorsun ~ ~ ~ (CORE- 1. Problems arose if one of the directories specified in. Temp. Directories config setting was not available. Yemanov ~ ~ ~ (CORE- 1. Common table expressions could not be used in computed. IN / ANY / ALL). Khorsun ~ ~ ~ (CORE- 1. Bug in CREATE/ALTER database trigger, where comments. Russian. Yemanov ~ ~ ~ (CORE- 1. There are < n> dependencies” error. C. Valderrama ~ ~ ~ (CORE- 1. Dummy. Packet. Interval mechanism was broken. Yemanov ~ ~ ~ (CORE- 1. Switch - s of fb. Peshkov ~ ~ ~ (CORE- 4. Grants would overwrite previous entries in. RDB$SECURITY. Peshkov ~ ~ ~ (CORE- 3. The server could hang or crash while monitoring. Yemanov ~ ~ ~ (CORE- 2. The engine could crash or raise. ODS 8. x database. Khorsun ~ ~ ~ (CORE- 2. A source of memory corruption could. Peshkov ~ ~ ~ (CORE- 2. The server could crash on executing an. UPDATE OR INSERT statement. Peshkov ~ ~ ~ (CORE- 2. Superserver could terminate abnormally. Peshkov ~ ~ ~ (CORE- 2. An access violation would follow the call. Khorsun ~ ~ ~ (CORE- 2. 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