Essential sqlalchemy 2nd edition pdf download

Essential sqlalchemy 2nd edition pdf download

Essential SQLAlchemy,Related Booklists

Essential SQLAlchemy, 2nd Edition Sented by Shon Essential SQLAlchemy, 2nd Edition MAPPING PYTHON TO DATABASES Download book (pdf - MB) This link for 08/05/ · Essential SQLAlchemy, 2nd Edition. by eBook · May 8, eBook Details: Paperback: pages English; ISBN X; ISBN ; This new edition of Essential SQLAlchemy is the tool developers need to understand the technology. Rather than being a simple tutorial or API reference, this book builds an Download Free eBook:Essential SQLAlchemy, 2nd Edition - Free epub, mobi, pdf ebooks download, ebook torrents download 20/06/ · SQLAlchemy helps you map Python objects to database tables without substantially changing your existing Python code. If you’re an intermediate Python developer ... read more

Learning SPARQL, 2nd Edition. Pentaho Data Integration Cookbook, 2nd Edition. SQL Server AlwaysOn Revealed, 2nd Edition. Predictive Analytics with Microsoft Azure Machine Learning, 2nd Edition. Beginning Oracle Database 12c Administration, 2nd Edition. MySQL Pocket Reference, 2nd Edition. PostgreSQL 9 Administration Cookbook, Second Edition. PostgreSQL Replication, Second Edition. Beginning Database Design, 2nd Edition. Pro Oracle Database 12c Administration, 2nd Edition. Data Analysis Using SQL and Excel, 2nd Edition. PeopleSoft for the Oracle DBA, 2nd Edition.

R Graph Essentials. SQL Server Query Performance Tuning, 3rd Edition. The Definitive Guide to SQLite, 2nd Edition. The Definitive Guide to MySQL 5, 3rd Edition. Microsoft SQL Server Reporting Services, 4th Edition. Book Detail. Latest Downloads. permissions: if p. In SQLAlchemy, we could print the summary information exactly as shown, and we could detect membership in a group with a much simpler query. Suppose we wanted a count of how many users had each permission type. In the traditional object-oriented world, we would probably loop over each permission, then over each group, and finally count the users in the group without forgetting to remove duplicates! groups: for u in g. users: users. The SQLAlchemy home page puts it this way: SQLAlchemy Philosophy SQL databases behave less and less like object collections the more size and performance start to matter; object collections behave less and less like tables and rows the more abstraction starts to matter.

SQLAlchemy aims to accommodate both of these principles. org Using the object mapper pattern where plain Python objects are mapped to SQL tables via a mapper object, rather than requiring persistent objects to be derived from some Persistable class achieves much of this separation of concerns. There has also been a concerted effort in SQLAlchemy development to expose the full power of SQL, should you wish to use it. In SQLAlchemy, your objects are POPOs until you tell SQLAlchemy about them. For instance, consider an application that uses users, groups, and permissions, as shown. remove group Once your application moves beyond the prototype stage, you might expect to have to write code to manually load objects from the database or perhaps some other kind of persistent object store.

still work, even without modifying the class code. Instrumentation on Mapped Classes Mapped classes are actually fairly unmolested by the default SQLAlchemy mapper. In particular, the mapped class is given the following new attributes: c This attribute contains a collection of the columns in the table being mapped. This is useful when constructing SQL queries based on the mapped class, such as referring to User. This property generally should not be modified by the application programmer. SQLAlchemy Architecture SQLALchemy consists of several components, including the aforementioned databaseindependent SQL expression language object-relational mapper. In order to enable these components, SQLAlchemy also provides an Engine class, which manages connection pools and SQL dialects, a MetaData class, which manages your table information, and a flexible type system for mapping SQL types to Python types.

Engine The beginning of any SQLAlchemy application is the Engine. The engine manages the SQLAlchemy connection pool and the database-independent SQL dialect layer. Connection Pooling Thus far, we have glossed over the use of database connections. In order to execute queries against a database, a connection is required, and the establishment of a new connection is typically an expensive operation, involving a network connection, authentication of the user, and any database session setup required. In order to amortize the costs, the typical solution is to maintain a pool of database connections that are used over and over again in the application. The Engine object in SQLAlchemy is responsible for managing a pool of low-level DBAPI connections. In fact, both the engine and the low-level connection objects obey a Connectable protocol, allowing you to execute dynamic SQL queries either directly against a connection, or against the engine in which case the engine will automatically allocate a connection for the query.

In the thread-local strategy, a connection that is currently in use by a thread will be reused for other statements within that thread. This can reduce database server load, which is especially important when you could have several applications accessing the database simultaneously. The dialect object attempts to manage the idiosyncracies of each supported SQL dialect as well as manage the lowlevel DB-API modules implementing the connection. The dialect is mostly used as a transparent layer for your application programming. The main exception to this rule is when you want to access a data type that is supported only for particular database servers.

For instance, MySQL has BigInteger and Enum types. To use these types, you must import them directly from the appropriate module in the sqlalchemy. databases package: from sqlalchemy. We alluded to MetaData management before in describing how to create tables. A MetaData object must be created before any tables are defined, and each table must be associated with a MetaData object. execute As mentioned previously, you can also reflect your schema by setting the autoload parameter to True in your Table creation. Reflection, however, requires a database connection to function properly.

SQLAlchemy must query the database to determine the structure of the tables. Binding the MetaData to an engine is a convenient way to provide this connection. Note, however, that you are never required to bind the Meta Data object; any operation that you can perform with a bound MetaData or a table defined on it can also be performed by passing the engine or connection to the individual method. select Types System In many cases, SQLAlchemy can map SQL types to Python types in a straightforward way. In order to do this, SQLAlchemy provides a set of TypeEngine-derived classes that convert SQL data to Python data in the sqlalchemy. types module. TypeEngine subclasses are used to define the MetaData for tables. Sometimes, in keeping with the SQLAlchemy philosophy of letting your objects be objects, you may find that the provided TypeEngine classes do not express all of the data types you wish to store in your database.

In this case, you can write a custom TypeEn gine that converts data being saved to the database to a database-native type, and converts data being loaded from the database to a Python native type. Suppose, for instance, that we wished to have a column that stored images from the Python Imaging Library PIL. In this case, we might use the following TypeEngine definition: class ImageType sqlalchemy. save sfp, 'JPEG' return sfp. open sfp return image Once we have defined ImageType, we can use that type in our table definitions, and the corresponding PIL image will be automatically created when we select from the database or serialized when we insert or update the database. Using the SQLAlchemy SQL-generation layer has several advantages over hand-generating SQL strings: Security Application data including user-generated data is safely escaped via bind parameters, making SQL injection-style attacks extremely difficult.

For instance, if we wanted to select another user from the table, the SQL generated would be identical, and a different bind parameter would be sent. This allows the database server in some cases to re-use its execution plan from the first query for the second, increasing performance. Portability Although SQL is a standardized language, different database servers implement different parts of the standard, and to different degrees of faithfulness. SQLAlchemy provides you a way to write database-independent SQL in Python without tying you to a particular database server. With a little bit of planning, the same SQLAlchemy-based application can run on SQLite, Oracle, DB2, PostgreSQL, or any other SQLAlchemy-supported database without code changes. Most of the time, you will be using the SQL expression language by creating expressions involving the attributes of the table. c object. This is a special attribute that is added to Tables you have defined in the metadata, as well as any objects you have mapped to tables or other selectables.

filter User. To accomplish this, SQLAlchemy uses the data mapper pattern. In this pattern, you can define your tables or other selectables, such as joins in one module, your classes in another, and the mappers between them in yet another module. SQLAlchemy provides a great deal of flexibility in mapping tables, as well as a sensible set of default mappings. id' , The mapper, however, has a great deal more flexibility. new value. new password. password By providing an application-level override for the password property, we can ensure that only hashed passwords are ever stored to the database. Perhaps the most powerful feature of the ORM is the ability to use regular Python data structures to model relationships between tables. append user1 group2's "users" property will automatically be updated user2.

append group2 The ORM uses a Session object to keep track of objects loaded from the database and the changes made to them. Sessions are used to persist objects created by the application, and they provide a query interface to retrieve objects from the database. Rather than executing the database code to synchronize your objects with your tables every SQLAlchemy Architecture 17 time an object is modified, the Session simply tracks all changes until its flush method is called, at which point all the changes are sent to the database in a single unit of work.

A Session class is created using the sessionmaker function, and a Session object is created by instantiating the class returned from sessionmaker. save u tell SQLAlchemy to track the object session. This can be very powerful, but can also sometimes find the wrong column, particularly if you are querying based on a common column name, such as name, for instance. In this case, you can manually specify the joins that SQLAlchemy will perform in the query via the join method. filter Group. query User groups is a property of a User, permissions is a property of a filter Permission. oid SQLAlchemy Architecture 19 CHAPTER 2 Getting Started This chapter guides you through installing version 0. Installing SQLAlchemy In order to use SQLAlchemy, you need to install both the SQLAlchemy package as well as a Python database driver for your database. This section will guide you through installing both. Installing the SQLAlchemy Package Installing setup tools SQLAlchemy is distributed as an EGG file via the Python package index PyPI , also known as the CheeseShop.

Otherwise, you will need to install SetupTools, a package that enhances the Python standard library-provided distutils package. If you intend to take advantage of the rich library of free software available in the CheeseShop, or if you intend to take advantage of the benefits of distributing your own code through SetupTools, it is a good idea to become familiar with all its features. You will then need to run the script to download the rest of SetupTools. py, as both of these scripts modify your Python site-packages directory. In Windows, it is also generally a good idea to make sure that both Python and your Python scripts directories are on your path. Although this is convenient for distribution, it is often nice to see the actual source code. The -UZ options as shown specify that SQLAlchemy should be Updated if already installed and should not be Zipped. org if you wish to test the installation more extensively.

Installing Some Database Drivers The next step is installing the appropriate DB-API database drivers for the database you wish to use. If you are using a version of Python greater than or equal to 2. If you are using Python 2. Installing the SQLite driver on Python versions before 2. Even if your production database is not SQLite, it can be advantageous to install the driver for prototyping code and running the examples in this book. The SQLite database driver became part of the Python standard library in version 2. Installing SQLite is different depending on whether you are using Windows or another operating system. Installing SQLAlchemy 23 Other supported drivers If you wish to connect to other databases, you must install the appropriate DB-API driver module. This tutorial shows off some of the basic features of SQLAlchemy that you can use to become immediately productive. This tutorial is based on a stripped-down version of a user authentication module that might be used in a web application.

Connecting to the Database and Creating Some Tables Before doing anything, we need to import the modules we will use. We will also import the datetime class from the datetime package for use in defining default values for our tables. sqlite' The MetaData object we create is bound to a particular database Engine, in this case a SQLite engine connected to the database located in the file tutorial. If tutori al. sqlite does not already exist, it will be created automatically by SQLite. Once we have created our MetaData, we can define our tables. The columns are similarly defined with their SQL names, data types, and various optional constraints. In this case, since we defined an 'id' column as a primary key, SQLAlchemy will automatically create the column with an auto-increment default value.

Also note that we can specify uniqueness and nullability constraints on columns, provide literal defaults, or provide Python callables e. now as defaults. We also specified the data type of the foreign key columns as None. When a foreign key column is specified with this datatype, SQLAlchemy will examine the column on the related table e. id' to determine the data type for the foreign key column. Once the tables have been defined, we can create them in the database using the following code: metadata. SQLAlchemy will in any case create tables using the IF NOT EXISTS syntax, so a metadata. Performing Queries and Updates Once we have defined the tables in our schema, we can insert some data.

insert Once the insert statement has been created, it can be executed multiple times with different values: stmt. now when the insert was executed. execute for row in result: print row u'rick', u'secret1', u'Rick Copeland', datetime. datetime , 9, 7, 10, 6, 4, u'rick1', u'secret', u'Rick Copeland Clone', datetime. datetime , 9, 7, 10, 6, 4, u'rick2', u'secret', u'Rick Copeland Clone 2', datetime. created datetime. u'created', datetime. datetime , 9, 7, 10, 6, 4, ] To restrict the rows that are returned from the select method, we can supply a where clause. fetchall [ 1, u'rick', u'secret1', u'Rick Copeland', datetime , 9, 7, 10, 6, 4, ] The SQL expression language is covered in more detail in Chapter 5. except for 'rick' fetchall [ 1, u'rick', u'secret', u'Rick Copeland', Again, this is covered in more detail in Chapter 5. First off, though, we need to understand the unit of work UOW pattern.

The intent is that sessionmaker should be called once at the module level , with its return value used to create individual sessions. query User The simplest way to use the Query object is as an iterator for all the objects in the database. print user. id, user. c attribute of the User object. It was added by the mapper as a convenience to access the names of mapped columns. save newuser Due to the UOW pattern, the new user has not yet been saved to the database. count , INFO sqlalchemy. flush , INFO sqlalchemy. To flush the session and commit the transaction, we call session.

DELETE COMMIT , INFO sqlalchemy. The ORM is covered in more detail in Chapters 6, 7, 8. The Engine class provides database connectivity, including a connection pool with various strategies for acquiring connections from the pool. The MetaData class maintains information about your database schema, including any tables and indices defined. In this chapter, you will learn how to define a new database schema using MetaData as well as how to connect a MetaData instance to an existing schema. Engines and Connectables The SQLAlchemy-provided Engine class is responsible for managing the connection to the database. It does this by incorporating a database connection pool and a databasespecific Dialect layer to translate the SQL expression language chapter 5 into database-specific SQL. The default is {}. This can be useful, for instance, when dealing with a database server or schema that does not provide unicode support natively.

The default is False. creator A callable that returns a DB-API connection. The default is None. echo A flag that tells SQLAlchemy to echo all statements and bind parameter values to its logger. encoding Specifies the encoding to use in all translations between raw byte strings and Python unicode objects. module Specifies which module to use when a database implementation can use more than one such as PostgreSQL and Oracle. poolclass If the engine is creating its own connection pool, the class a subclass of sqlal chemy. Pool to use when constructing the pool object. If no pool class is specified, sqlalchemy. QueuePool will be used for all database drivers except for SQLite, which uses the sqlalchemy. The default is The default is 5. This is useful if the database server times out connections after a period of inactivity, as MySQL does. If this is not required, performance might be improved by setting this parameter to False.

Configuring SQLAlchemy Logging SQLAlchemy uses the Python standard library logging module to log various actions. Engines and Connectables 35 One useful debugging strategy is to add a logfile for a particular class of operations that SQLAlchemy is performing. FileHandler 'sqlalchemy. log' handler. DEBUG logging. getLogger 'sqlalchemy. addHandler handler The loggers used with SQLAlchemy are listed next. Note that several of these loggers deal with material covered in later chapters in particular, the sqlalchemy. engine -- control SQL echoing. INFO logs SQL query output, logging. DEBUG logs result sets as well. pool -- control connection pool logging. INFO logs checkins and checkouts.

orm -- control logging of ORM functions. INFO logs configurations and unit of work dumps. attributes -- Logs instrumented attribute operations. mapper -- Logs mapper configurations and operations. unitofwork -- Logs unit of work operations, including dependency graphs. strategies -- Logs relation loader operations lazy and eager loads. sync -- Logs synchronization of attributes from one object to another during a flush. close The Connection object is actually an instance of the sqlalchemy. Connection class, which serves as a proxy for the particular DB-API connection object. The result object is an instance of the sqlalchemy. ResultProxy class, which has many features in common with a database cursor. Most SQLAlchemy functions that therefore take an Engine as a parameter usually named bind can also take a Connection, and vice versa.

The connection Engines and Connectables 37 pool can, however, be used on its own to manage regular DB-API connections. manage call sets up a connection pool the exact object is an instance of sqlalchemy. When the connection proxy is garbage collected, the underlying DBAPI connection is returned to the connection pool. manage function. pool module are: AssertionPool Allows only one connection to be checked out at a time and raises an AssertionEr ror when this constraint is violated. This is the default connection pool class used for non-sqlite connections. SingletonThreadPool Maintains a single connection per thread. It is used with sqlite because this database driver does not handle using a single connection in multiple threads well. StaticPool Maintains a single connection that is returned for all connection requests. MetaData SQLAlchemy provides the MetaData class, which collects objects that describe tables, indices, and other schema-level objects.

Before using any of the higher-level features of SQLAlchemy, such as the SQL query language and the ORM, the schema of the database must be described using metadata. In some cases, you can reflect the structure of schema items into the MetaData from the database. In this case, you need only specify the name of the entity, and its structure will be loaded from the database directly. Getting Started with MetaData To create a new MetaData object, you simply call its constructor, possibly with information about how to connect to the database.

If the constructor is called with no arguments, it is considered to be unbound; if it is called with either an Engine or a SQL connection URI, it is considered bound. Shortcuts are available to bound MetaData and to objects within a bound MetaData to facilitate the execution of statements against the bound engine. Most of the time you will probably use a bound MetaData object. however, it is sometimes useful to use an unbound MetaData if you need to connect to multiple database servers, where each server contains the same database schema. db' MetaData 39 Note that you are never required to bind the MetaData object; all operations that rely on a database connection can also be executed by passing the Engine explicitly as the keyword parameter bind.

This is referred to as explicit execution. If a MetaData instance is bound, then the bind parameter can be omitted from method calls that rely on the database connection. This is referred to as implicit execution. create whereas a Table in an unbound MetaData would need to supply a bind parameter: table. sku'] To actually create a table, you can call the create method on it. Here, we will create the style table on an in-memory SQLite database and view the generated SQL. None , INFO sqlalchemy. COMMIT We see that the composite primary key and foreign key constraints are correctly generated.

Although the foreign key constraints are ignored by SQLite, it is still useful to generate them, as SQLAlchemy can use this information to perform joins automatically based on the foreign key relationships between tables. The Table constructor Table. metadata The MetaData object to which to attach this table. schema The schema name for this table, if required by the database. autoload Indicates whether to reflect the columns from the database. If None, all columns are reflected. If not None, any columns omitted from the list will not be represented on the reflected Table object. mustexist Indicates that the table must already be defined elsewhere in the Python application as part of this MetaData. An exception is raised if this is not true. useexisting Directs SQLAlchemy to use the previous Table definition for this table name if it exists elsewhere in the application. SQLAlchemy disregards the rest of the constructor arguments if this is True. owner Specifies the owning user of the table.

This is useful for some databases such as Oracle to help with table reflection. quote Forces the table name to be escaped and quoted before being sent to the database useful for table names that conflict with SQL keywords, for example. The Table constructor also supports database-specific keyword arguments. Table reflection Tables can also be defined using reflection from an existing database. tables['brand'] You can also use the reflect method of the MetaData to load the schema. Meta Data. schema Specifies an alternate schema from which to reflect tables. only Directs the MetaData to load only a subset of the available tables. This can be specified either as a sequence of the names to be loaded or as a boolean callable that will be called for each available table with the parameters only metadata, table name.

If the callable returns True, the table will be reflected. The MetaData constructor itself has the definition MetaData. Column Definitions The Column constructor Column. This can also be None if the column is a Foreign Key, in which case the type will be the same as the referenced column. key An alias for this column. If specified, the column will be identified everywhere in Python by this name rather than by its SQL-native name. Alternatively, the Table can have a PrimaryKeyConstraint defined. MetaData 43 nullable If set to False, this does not allow None as a value for the column. default A Python callable or a SQL expression language construct specifying a default value for this column.

Note that this is an active Python-generated default when a callable is specified; the SQL has the generated value inserted as a literal. index Indicates that the column is indexed with an autogenerated index name. Alternatively, use an Index object in the table declaration instead. unique Indicates that the column has a unique constraint. Alternatively, use an UniqueCon straint object in the table declation instead. onupdate Specifies an active default value generated by SQLAlchemy rather than the database server to be used when updating but not inserting a row in the table. autoincrement Indicates that integer-based primary keys should have autoincrementing behavior. This is applicable only if the column has no default value and is a type or subtype of Integer.

quote This forces the column name to be escaped and quoted before being sent to the database useful for column names that conflict with SQL keywords, for example. Constraints SQLAlchemy also supports a variety of constraints, both at the column level and at the table level. All constraints are derived from the Constraint class, and take an optional name parameter. If the name is not specified, SQLAlchemy auto-generates a suitable name if necessary. COMMIT Foreign keys Foreign keys are references from a row in one table to a row in another table. The usual way to specify simple non-complex foreign keys is by passing a ForeignKey object to the Column constructor. The ForeignKey constructor ForeignKey. columnname or schemaname. columnname, that specifies the referenced column. constraint The owning ForeignKeyConstraint, if any. If left unspecified, a new ForeignKeyCon straint will be created and added to the parent table. Otherwise, the constraint will be created in the CREATE TABLE statement.

MetaData 45 name The name of the constraint passed along to the owning ForeignKeyConstraint. onupdate Generates an ON UPDATE clause in the SQL for the constraint e. The default for this parameter is None. ondelete Generates an ON DELETE clause in the SQL for the constraint e. If you need to reference a compound primary key, SQLAlchemy provides the Foreign KeyConstraint class for increased flexibility. sku'] The ForeignKeyConstraint constructor ForeignKeyConstraint. columnname that specifies the referenced column in the local table the compound foreign key refcolumns Either a list of Column objects or a list of database-recognized strings such as tablename.

CheckConstraints are specified with a text constraint that will be passed directly through to the underlying database implementation, so care should be taken if you want to maintain database independence in the presence of CheckConstraints. MySQL and SQLite, in particular, do not actively support such constraints. Column 'original', Numeric 10,2 , CheckConstraint 'original Column 'discounted', Numeric 10,2 , These default values fall into one of two categories: active defaults or passive defaults. Active defaults Active defaults are values that are generated by SQLAlchemy and then sent to the database in a separate statement. Active defaults include constants, Python callables, SQL expressions including function calls to be executed before the insert or update, or a pre-executed sequence. In all of these cases, SQLAlchemy manages the generation of the default value and the statement that actually sends the default to the database. To specify an insert default, use the default parameter when creating the Column object.

default can be a constant, a Python callable, an SQL expression, or an SQL sequence. One is the standard library function datetime. MetaData 49 , INFO sqlalchemy. u' '] , INFO sqlalchemy. This is because these values were active defaults, as opposed to the passive defaults covered in the next section. Though we did not specify an update to the modified column, SQLAlchemy provides an update value based on the onupdate parameter of the column definition. Passive defaults Passive defaults are default values provided by the database itself. If a column is marked with a PassiveDefault instance, then the column will have a database-level default value and SQLAlchemy will make the Engine aware of the passive default. The Engine will, in turn, mark the ResultProxy as having passive default values. The ResultProxy is actually inspected by the object-relational mapping system to determine whether or not to re-fetch the row after an insert to get the default column values.

print 'Created at', row. PostgreSQL does not support passive defaults for primary keys. This is due to the fact that SQLAlchemy does not use the PostgreSQL OIDs to determine the identity of rows inserted OIDs are actually disabled by default in PostgreSQL version 8. lastrowid function only returns OIDs. Thus the only way to know the primary key of a row that is being inserted is to provide it as an active default. Defining Indexes Once your database grows to a certain size, you will probably need to consider adding indexes to your tables to speed up certain selects. if one is available in the Dialect being used, or if an auto-incrementing data type is not available as in the case of PostgreSQL and Oracle , implicitly generates a sequence and fetches values from that sequence.

SQLAlchemy also provides for the explicit use of a Sequence object to generate default values for columns not just primary keys. d0 CREATE TABLE brand id INTEGER, name VARCHAR NOT NULL, UNIQUE name , INFO sqlalchemy. COMMIT The parameters accepted by the Sequence constructor Sequence. This may be ignored, depending on the Dialect. increment The increment value of the sequence being created default None. optional If True, this specifies that the sequence should be used only if it is necessary e. quote This forces the sequence name to be escaped and quoted before being sent to the database useful for names that conflict with SQL keywords, for example.

The default False. MetaData Operations SQLAlchemy uses the MetaData object internally for several purposes, particularly inside the object relational mapper ORM , which is covered in Chapter 6. MetaData can also be used in connection with Engine and other Connectable instances to create or drop tables, indices, and sequences from the database. Binding MetaData As mentioned previously, MetaData can be bound to a database Engine. checkfirst Add an IF NOT EXISTS or IF EXISTS clause, whichever is appropriate to the SQL generated not supported for Indexes. The default None. checkfirst Add an IF NOT EXISTS or IF EXISTS clause whichever is appropriate to the SQL generated. This can be useful when working with the same schema in more than one Engine because it allows you to have bound MetaData and Tables for both engines. You can also use the MetaData. tometadata meta2 meta2. It covers the built-in types provided by SQLAlchemy, both database-independent types and database-specific types.

It then describes how to create your own custom types for use in mapping application data onto your database schema. These SQL data types are actually instances of SQLAlchemy-provided classes known as TypeEn gines. TypeEngine objects convert Python values to native database values and vice versa. For instance, String 40 is an instance of a TypeEngine that represents a VAR CHAR TypeEngines also supply SQL text for use when creating tables using metadata. SQLAlchemy provides three different ways of constructing types for use in your application. First, it provides a set of generic TypeEngines, which are fairly portable across different database engines.

Second, it provides database server-specific TypeEngines, which can be used to exploit particular types supported by certain databases. Built-in Types SQLAlchemy provides a fairly complete set of built-in TypeEngines for support of basic SQL column types. The SQLAlchemy-provided TypeEngines are broken into the generic types those portable across multiple database engines and the dialect-specific types, which work only on particular databases. In the SQLAlchemy tradition of not getting in your way, however, full support is provided for dialect-specific TypeEngines if you wish to exploit database server-specific types. Generic Types The generic TypeEngines provided by SQLAlchemy are found in the sqlalchemy. types package. These TypeEngines cover a fairly complete set of portable column types. The TypeEngines supported, their corresponding Python type, and their SQL representation, are listed in Table Note that there are several TypeEngines defined in all caps such as CLOB.

These are derived from other TypeEngines and may or may not be further specialized to allow finer-grained specification of the underlying database type. Table date time TIMESTAMP none Date datetime. date DATE none Time datetime. date time TIMESTAMP none CLOB String string TEXT length default is unbounded VARCHAR String string VARCHAR or TEXT length default is unbounded CHAR String string CHAR or TEXT length default is unbounded NCHAR Unicode string VARCHAR, NCHAR, or TEXT length default is unbounded BLOB Binary byte string BLOB length default is unbounded BOOLEAN Boolean bool BOOLEAN none When using TypeEngines to specify columns in Tables, you can use either an instance of the TypeEngine class or the class itself.

If you use the class, the default parameters will be used when constructing the SQL type. In some cases, in addition to implementing the generic types, a dialect may provide dialect-specific types such as IP address, etc. In this case, no conversion is done between the value supplied by the DB-API implementation and the application. Tables through list some of the types provided by particular database engines that are not automatically used by SQLAlchemy. Built-in Types 61 Table Oracle types Class name Python type SQL type Arguments OracleRaw byte string RAW length Table For instance, you may wish to emulate enumerations in a database engine that does not support enumerations by restricting the values that can be stored in a column. In SQLAlchemy, there are two ways to create an application-specific custom type. If you wish to implement a type that is similar to an existing TypeEngine, you would implement a TypeDecorator.

If your implementation is more involved, you can directly subclass TypeEngine. The implemented TypeEngine is specified in the impl attribute on the TypeDecorator. For instance, if you wish to implement a type for validating that a particular Integer column contains only the values 0, 1, 2, and 3 e. The TypeDecorator is used only when an existing TypeEngine provides the correct SQL type for the type you are implementing. TypeDecorator def processor value assert value in self. return result return value return processor join self. This language is based around the concept of an SQL statement, which represents some database-independent SQL syntax that may have one or more bind variables, and that can be executed on an SQL Engine or other Connectable.

This chapter introduces the various kinds of data manipulation supported by SQLAlchemy SQL INSERT, UPDATE, and DELETE and performed on the query interface SQL SELECT. Inserts, Updates, and Deletes Insert, Update, and Delete constructs are created in SQLAlchemy via the Table methods insert, update, and delete, or via the insert, update, and delete functions. The func- tionality of these data manipulation language DML constructs is equivalent, regardless of whether they are constructed via methods or functions; the distinction is a question of style more than substance. Although each DML construct has its own particulars regarding construction, they all end up generating a Statement. We can inspect the SQL text corresponding to the statement by printing it out. These parameters can either be supplied either directly to the execute method or in the statement construction phase. params ClauseParameters:{'col1': 'Initial value'} If parameters are supplied in both the statement construction and the execute call, the parameters supplied with the execute call override those supplied when creating the statement.

Insert Statements The Insert construct is perhaps the simplest. In order to create an Insert statement, you can use either the Table. insert method or the insert function. The method is actually just a wrapper for the function. The insert takes two arguments: the table into which a row is being inserted, and an optional dictionary of values to be inserted. Each key in the dictionary represents a column and may be either the metadata Col umn object or its string identifier. In this case, the value to be inserted is provided by a subquery. Like insert statements, update statements can be created by either the update function or the update method on the table being updated. The only parameters to the update function are the table being updated omitted if using the update method , the where clause, and the values to be set.

The where clause of the update query can be either a SQL clause object covered later in this chapter or a text string specifying the update condition. In order to update every row of a table, you can simply leave off the where clause. d0 Inserts, Updates, and Deletes Here, the where clause is bound when the statement is created, but the actual values to be updated are passed to the execute method. Note that prior to execution, the SQL has a bind parameter for the id column, but when the statement is executed, id is omitted because no value was provided for it.

Correlated update statements can also be generated using the SQL expression language. A correlated update is an update whose values are provided by a select statement. Suppose that we have a product catalog with the schema in the following listing, and the data in Tables through Contents of product table sku msrp "" Contents of store table id name 1 "Main Store" 2 "Secondary Store" Table execute , INFO sqlalchemy. msrp FROM product WHERE product. sku LIMIT 1 OFFSET 0 , INFO sqlalchemy. To create a Delete construct, you can use either the delete function or the delete method on the table from which you are deleting data. Unlike insert and update , delete takes no values parameter, only an optional where clause omitting the where clause will delete Inserts, Updates, and Deletes 71 all rows from the table. In that vein, you can always use the Text construct used previously in the UPDATE and DELETE examples to specify the exact SQL text you would like to use.

For most operations, however, the SQL expression language makes for a succinct, secure, and less error-prone way of expressing your queries. Basic Query Construction SQLAlchemy makes simple SQL queries easy to express, while also enabling the construction of quite complex queries in a straightforward manner. This section describes the basic building blocks of query construction in SQLAlchemy. The select function versus the select method Like the DML statements INSERT, UPDATE, and DELETE, SELECT statements can be generated using either a function or a Table method.

Unlike the DML statements, however, there is a minor difference in functionality between the select function and the Table. select method. The select function requires you to specify which columns you want in your result set. execute print row u'', Decimal " They are discussed in more detail later in the chapter. If this is not specified, the FROM clause is inferred from the tables referenced in other clauses. Some databases support other values for this parameter, such as MySQL, which supports "read" translating to LOCK IN SHARE MODE , or Oracle, which supports "nowait" translating to FOR UPDATE NOWAIT. Typically this uses the LIMIT clause, but SQLAlchemy provides some support for LIMIT even when the underlying database does not support it directly.

Typically this uses the OFFSET clause, but SQLAlchemy provides some support for OFFSET even when the underlying database does not support it directly. This is used for dialect-specific SQL extensions, to insert text between the SELECT keyword and the column list. Result set objects Thus far, we have glossed over the return value of the execute method on SQL statements, showing only that it is possible to iterate over this value and receive tuple-like objects. In fact, SQLAlchemy provides an object, defined in the ResultProxy class, to allow cursor-like access to the results of a query. Some of the useful methods and attributes available on the ResultProxy object are summarized next.

fetchone Fetch one result from the cursor. fetchall Fetch all results from the cursor. This method is called automatically when all result rows are exhausted. rowcount valid only for DML statements Return the number of rows updated, deleted, or inserted by the statement. As we have seen previously, it supports a tuplelike interface. items [ 'sku', u'' , 'msrp', values [u'', sku, product. fetchall , INFO sqlalchemy. SELECT product. This is an example of SQLAlchemy using a bind parameter. By using bind parameters, SQLAlchemy ensures that the entire SQL string passed to the database driver was constructed by SQLAlchemy, and that it is safe from SQL-injection attacks.

Of course, this can be subverted via the Text construct, which passes whatever the programmer specifies to the database driver. Here, SQLAlchemy provides the value of the bind parameter to the database driver directly. All SQLAlchemy-provided operators generate a ClauseElement-derived object as a result of the operation. ClauseElements provide the overloaded operators and other SQLconstructing features of the SQL expression language. This allows complex SQL expressions to be built up from complex Python expressions. SQLAlchemy provides overloading for most of the standard Python operators. msrp product. msrp SQLAlchemy also provides for use of the SQL boolean operators AND, OR, and NOT, as well as the LIKE operator for comparing strings. Special care must be taken when using the AND, OR, and NOT overloads because of Python operator precendence rules. AND product. sku LIKE? msrp SQLAlchemy also provides for the use of arbitrary SQL functions via the func variable, which generates functions using attribute access.

You can also use the special function func. msrp abs product. between cleft, cright Produces a BETWEEN clause like column BETWEEN cleft AND cright. distinct Adds a DISTINCT modifier like DISTINCT column. other can also be a subquery. like other Produces a LIKE clause like column LIKE other. op operator Produces an arbitrary operator like column operator. label name Produces an AS construct for the column a column alias like column AS name. Using custom bind parameters Up to this point, SQLAlchemy has been automatically creating bind parameters whenever we used a literal expression in the SQL query language. It is also possible to generate a custom bind parameter. This might be useful, for instance, if you wanted to generate a statement without knowing a priori what values would be used to bind the statement. You can also use this to speed up your queries when you have many statements that are identical except for the bind parameter values.

The Python overhead for executing each query is lower in such cases, and some database servers will cache the execution plan, making the server-side processing faster as well. fetchall [ scalar key Either a string representing the bind parameter name or a Column object which will be used to generate a bind parameter name. This name is used in the execute call to provide a value for the bind parameter. If no value is supplied here or in the execute call, an exception is raised. This can be useful if the key name is cumbersome, as when using a Column object. This can be useful for ensuring there are no unintended name collisions.

This is typically used along with the value parameter. Using literal text in queries We have already briefly seen the use of the text in constructing customized SQL strings. In fact, even when we want to use custom SQL strings, we rarely need to use the text function; SQLAlchemy can infer the need for it automatically in most cases. execute stmt. msrp' ], We can also bind the clause constructed to a particular engine using the bind parameter to the text function. msrp FROM product WHERE text The string with the SQL text to be constructed. Bind parameters can be used with the :parameter syntax.

Useful when constructing a statement entirely out of text objects. Used to convert result set values to Python objects. The Difference Between WHERE and HAVING Both the WHERE clause in SQL and the HAVING clause restrict results to those results matching a given SQL expression. The difference is that HAVING is always accompanied by grouping typically via the GROUP BY clause , and the HAVING clause filters the results after they are grouped, whereas the WHERE clause filters the rows before they are grouped. msrp FROM product ORDER BY product. fetchall [ u'', u'', fetchall [] We have already seen how we can use the distinct method on ClauseElements to specify that a column should be distinct in a result set. SQLAlchemy also provides support for selecting only distinct rows in a result set via the distinct parameter to select. This can be very useful, for instance, when displaying sortable data on a web form.

id, product. department, product. category, product. subclass FROM product WHERE product. class, Queries limit limit return query Although the two functions have the same functionality, the second one using the generative interface is more flexible. Suppose we wanted to refactor the original function into multiple parts, with each part potentially adding a different filtering criterion. The generative interface actually consists of a set of methods on the statement constructed by the select function or method. Those methods are summarized next. Note that none of these functions actually modify the query object in place; rather, they return a new query object with the new condition applied. where whereclause Add a constraint to the WHERE clause. All constraints added this way will be AND-ed together to create the whole WHERE clause. having having Generate a HAVING clause or add to an existing HAVING clause. limit limit Equivalent to the limit parameter in the select function or method.

offset offset Equivalent to the offset parameter in the select function or method. column column Add a column to the list of columns being selected. A prefix is inserted immediately after the SELECT keyword, as in the prefixes parameter to select. This can be useful when it is necessary to modify a query to use an alias when that query was originally written to use a reference to the actual table, for instance. label name Label the result of this query with name for use in the column list of an enclosing query. Joins and Set Operations In addition to the interface for selecting, filtering, sorting, and grouping on SELECT statements from single tables, SQLAlchemy provides full support for operations that combine multiple tables or other selectables JOINs , as well as set operations on selectables UNION, INTERSECT, and EXCEPT.

Joining selectables To join two selectablesin tables or other select statements together, SQLAlchemy provides the join implementing INNER JOIN and outerjoin implementing OUTER JOIN functions, as well as join and outerjoin methods on all selectables. For instance, to select all stores where the price of a product is different than its MSRP, you might write the following SQL: SELECT store. sku WHERE product.

English Pages xix, pages : illustrations ; 24 cm [] Year DOWNLOAD FILE. Dive into SQLAlchemy, the popular, open-source code library that helps Python programmers work with relational databases. Essential SQLAlchemy introduces a high-level open-source code library that makes it easier for Python programmers to acc. Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for. Python Essential Reference, 3rd Edition, is a comprehensive reference to the Python programming language. The focus of t. Learn the fundamentals of Python 3. Fully upda. Modern businesses depend on data for their very survival, creating a need for sophisticated databases and database techn. All rights reserved. Printed in the United States of America. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work.

Use of the information and instructions contained in this work is at your own risk. vii Introduction to SQLAlchemy. xiii Part I. SQLAlchemy Core 1. Schema and Types. Working with Data via SQLAlchemy Core. Exceptions and Transactions. Defining Schema with SQLAlchemy ORM. Working with Data via SQLAlchemy ORM. Understanding the Session and Exceptions. Testing with SQLAlchemy ORM. Reflection with SQLAlchemy ORM and Automap. Getting Started with Alembic. Building Migrations. Controlling Alembic. Where to Go from Here. Whether you are developing for the Web, the desktop, or other applications, you need fast and secure access to data.

Relational databases are still one of the most common places to put that data. You may have used string manipulation to generate queries to run over an ODBC interface, or used a DB API as a Python programmer. While those can be effective ways to handle data, they can make security and database changes very difficult. This book is about a very powerful and flexible Python library named SQLAlchemy that bridges the gap between relational databases and traditional programming. SQLAlchemy is powerful and flexible, but it can also be a little daunting. This book focuses on the 1.

It certainly works from 0. This book has been written in three major parts: SQLAlchemy Core, SQLAlchemy ORM, and an Alembic section. The first two parts are meant to mirror each other as vii closely as possible. We have taken care to perform the same examples in each part so that you can compare and contrast the two main ways of using SQLAlchemy. The book is also written so that you can read both the SQLAlchemy Core and ORM parts or just the one suits your needs at the moment. Who This Book Is For This book is intended for those who want to learn more about how to use relational databases in their Python programs, or have heard about SQLAlchemy and want more information on it. To get the most out of this book, the reader should have intermediate Python skills and at least moderate exposure to SQL databases.

If you are new to SQL and databases, check out Learning SQL by Alan Beaulieu. These will fill in any missing gaps as you work through this book. How to Use the Examples Most of the examples in this book are built to be run in a read-eval-print loop REPL. You can use the built-in Python REPL by typing python at the command prompt. The examples also work well in an ipython notebook. There are a few parts of the book, such as Chapter 4, that will direct you to create and use files instead of a REPL. You can learn more about IPython at its website. Assumptions This Book Makes This book assumes basic knowledge about Python syntax and semantics, particularly versions 2. In particular, the reader should be familiar with iteration and working with objects in Python, as these are used frequently throughout the book.

The second part of the book deals extensively with object-oriented programming and the SQLAlchemy ORM. The reader should also know basic SQL syntax and relational theory, as this book assumes familiarity with the SQL concepts of defining schema and tables along with creating SELECT, INSERT, UPDATE, and DELETE statements. viii Preface Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width bold Shows commands or other text that should be typed literally by the user. This element signifies a tip or suggestion. This element signifies a general note. This element indicates a warning or caution. Using Code Examples Supplemental material code examples, exercises, etc.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. For example, writing a program that uses several chunks of code from this Preface ix book does not require permission. Answering a question by citing this book and quoting example code does not require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. Copyright Jason Myers and Rick Copeland, Safari Books Online offers a range of plans and pricing for enterprise, government, education, and individuals. For more information about Safari Books Online, please visit us online. Without them, this book would have undoubtedly had many technical issues and been much harder to read.

My appreciation goes out to Mike Bayer, whose recommendation led to this book being written in the first place. I also would like to thank Brian Dailey for reading some of the roughest cuts of the book, providing great feedback, and laughing with me about it. I want to thank the Nashville development community for supporting me, especially Cal Evans, Jacques Woodcock, Luke Stokes, and William Golden. Thanks also to Justin at Mechanical Keyboards for keeping me supplied with everything I needed to keep my fingers typing. Preface xi Most importantly I want to thank my wife for putting up with me reading aloud to myself, disappearing to go write, and being my constant source of support and hope. I love you, Denise. xii Preface Introduction to SQLAlchemy SQLAlchemy is a library used to interact with a wide variety of databases. It enables you to create data models and queries in a manner that feels like normal Python classes and statements. It can be used to connect to most common databases such as Postgres, MySQL, SQLite, Oracle, and many others.

Why Use SQLAlchemy? The top reason to use SQLAlchemy is to abstract your code away from the underlying database and its associated SQL peculiarities. This makes it easy to migrate logic from Oracle to PostgreSQL or from an application database to a data warehouse. It also helps ensure that database input is sanitized and properly escaped prior to being submitted to the database. This prevents common issues like SQL injection attacks. SQLAlchemy also provides a lot of flexibility by supplying two major modes of usage: SQL Expression Language commonly referred to as Core and ORM. These modes can be used separately or together depending on your preference and the needs of your application. It is focused on the actual database schema; however, it is standardized in such a way that it provides a consistent language across a large number of backend databases. The SQL Expression Language also acts as the foundation for the SQLAlchemy ORM.

ORM The SQLAlchemy ORM is similar to many other object relational mappers ORMs you may have encountered in other languages. It is focused around the domain model of the application and leverages the Unit of Work pattern to maintain object state. It also provides a high-level abstraction on top of the SQL Expression Language that enables the user to work in a more idiomatic way. You can mix and match use of the ORM with the SQL Expression Language to create very powerful applications. The ORM leverages a declarative system that is similar to the active-record systems used by many other ORMs such as the one found in Ruby on Rails.

Choosing Between SQLAlchemy Core and ORM Before you begin building applications with SQLAlchemy, you will need to decide if you are going to primarly use the ORM or Core. The choice of using SQLAlchemy Core or ORM as the dominant data access layer for an application often comes down to a few factors and personal preference. The two modes use slightly different syntax, but the biggest difference between Core and ORM is the view of data as schema or business objects. SQLAlchemy Core has a schema-centric view, which like traditional SQL is focused around tables, keys, and index structures. However, if you intend to focus more on a domain-driven design, the ORM will encapsulate much of the underlying schema and structure in metadata and business objects.

This encapsulation can make it easy to make database interactions feel more like normal Python code.

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Contribute to guilhermedcorrea/pdfebook development by creating an account on GitHub Note that several of these loggers deal with material covered in later chapters (in particular, the blogger.com* loggers): • blogger.com -- control SQL echoing. blogger.com logs 20/06/ · SQLAlchemy helps you map Python objects to database tables without substantially changing your existing Python code. If you’re an intermediate Python developer Essential SQLAlchemy, 2nd Edition Sented by Shon Essential SQLAlchemy, 2nd Edition MAPPING PYTHON TO DATABASES Download book (pdf - MB) This link for DOWNLOAD FILE Polecaj historie Essential SQLAlchemy: mapping Python to databases [Second edition] , X Dive into SQLAlchemy, the popular, open 08/05/ · Essential SQLAlchemy, 2nd Edition. by eBook · May 8, eBook Details: Paperback: pages English; ISBN X; ISBN ; ... read more

It starts with a line that indicates the type of exception. Now we can perform queries that use this relationship. Predictive Analytics with Microsoft Azure Machine Learning, 2nd Edition. Access column by Column object. fetchone Returns one row, and leaves the cursor open for you to make additional fetch calls. INSERT INTO account balance VALUES?

An exception is raised if this is not true. return self. expire self, obj Mark the given object as no longer up-to-date. Using the ORM is as simple as writing your classes, defining your tables, and mapping your tables to your classes. py, as shown in Example The usual way to specify simple non-complex foreign keys is by passing a ForeignKey object to the Column constructor, essential sqlalchemy 2nd edition pdf download.