Metadata-Version: 1.1
Name: avro-gen
Version: 0.2.2
Summary: Avro record class and specific record reader generator
Home-page: https://github.com/rbystrit/avro_gen
Author: Roman Bystritskiy
Author-email: rbystrit@gmail.com
License: License :: OSI Approved :: Apache Software License
Description: AVRO-GEN

        ========

        

        [![Build Status](https://travis-ci.org/rbystrit/avro_gen.svg?branch=master)](https://travis-ci.org/rbystrit/avro_gen)

        [![codecov](https://codecov.io/gh/rbystrit/avro_gen/branch/master/graph/badge.svg)](https://codecov.io/gh/rbystrit/avro_gen)

        ##### Avro record class and specific record reader generator.

        

        Current Avro implementation in Python is completely typelss and operates on dicts. 

        While in many cases this is convenient and pythonic, not being able to discover the schema

        by looking at the code, not enforcing schema during record constructions, and not having any 

        context help from the IDE could hamper developer performance and introduce bugs. 

        

        This project aims to rectify this situation by providing a generator for constructing concrete

        record classes and constructing a reader which wraps Avro DatumReader and returns concrete classes

        instead of dicts. In order not to violate Avro internals, this functionality is built strictly

        on top of the DatumReader and all the specific record classes dict wrappers which define accessor

        properties with proper type hints for each field in the schema. For this exact reason the 

        generator does not provide an overloaded DictWriter; each specific record appears just to be a 

        regular dictionary.

         

        ##### Usage:

            schema_json = "....."

            output_directory = "....."

            from avrogen import write_schema_files

            

            write_schema_files(schema_json, output_directory)

            

        The generator will create output directory if it does not exist and put generated files there. 

        The generated files will be:

        

        >  OUTPUT_DIR

        >  + \_\_init\_\_.py   

        >  + schema_classes.py 

        >  + submodules*

         

        In order to deal with Avro namespaces, since python doesn't support circular imports, the generator

         will emit all records into schema_classes.py as nested classes. The top level class there will be

         SchemaClasses, whose children will be classes representing namespaces. Each namespace class will 

         in turn contain classes for records belonging to that namespace. 

         

         Consider following schema:

         

             {"type": "record", "name": "tweet", "namespace": "com.twitter.avro", "fields": [{"name": "ID", "type": "long" }

         

         Then schema_classes.py would contain:

         

            class SchemaClasses(object):

                class com(object):

                    class twitter(object):

                        class acro(object):

                            class tweetClass(DictWrapper):

                                def __init__(self, inner_dict=None):

                                    ....

                                @property

                                def ID(self):

                                    """

                                    :rtype: long

                                    """

                                    return self._inner_dict.get('ID', None)

                                

                                @ID.setter

                                def ID(self, value):

                                    #"""

                                    #:param long value:

                                    #"""

                                    self._inner_dict['ID'] = value                        

            

         In order to map specific record types and namespaces to modules, so that proper importing can

         be supported, there generator will create a sub-module under the output directory for each namespace

         which will export names of all types contained in that namespace. Types declared with empty 

         namespace will be exported from the root module. 

         

         So for the example above, output directory will look as follows:

         

         >  OUTPUT_DIR

         >  + \_\_init\_\_.py

         >  + schema_classes.py

         >  + com

         >   + twitter

         >     + avro

         >       + \_\_init\_\_.py  

        

        The contents of OUTPUT_DIR/com/twitter/avro/\_\_init\_\_.py will be:

            

            from ....schema_classes import SchemaClasses

            tweet = SchemaClasses.com.twitter.avro.tweet

            

        So in your code you will be able to say:

            

            from OUTPUT_DIR.com.twitter.avro import tweet

            from OUTPUT_DIR import SpecificDatumReader as TweetReader, SCHEMA as your_schema

            from avro import datafile, io

            my_tweet = tweet()

            

            my_tweet.ID = 1

            with open('somefile', 'w+b') as f:

                writer = datafile.DataFileWriter(f,io.DatumWriter(), your_schema)

                writer.append(my_tweet)

                writer.close()

            

            with open('somefile', 'rb') as f:

                reader = datafile.DataFileReader(f,TweetReader(readers_schema=your_schema))

                my_tweet1 = reader.next()

                reader.close()

                

               

        ### Avro protocol support

        

        Avro protocol support is implemented the same way as schema support. To generate classes 

        for a protocol:

        

            protocol_json = "....."

            output_directory = "....."

            from avrogen import write_protocol_files

            

            write_protocol_files(protocol_json, output_directory)

            

        The structure of the generated code will be exactly same as for schema, but in addition to

        regular types, *Request types will be generated in the root namespace of the protocol for each 

        each message defined.

        

        ### Logical types support

        

        Avrogen implements logical types on top of standard avro package and supports generation of 

        classes thus typed. To enable logical types support, pass **use_logical_types=True** to schema 

        and protocol generators. If custom logical types are implemented and such types map to types 

        other than simple types or datetime.* or decimal.* then pass **custom_imports** parameter to 

        generator functions so that your types are imported. Types implemented out of the box are:

        

        - decimal (using string representation only)

        - date

        - time-millis

        - time-micros

        - timestamp-millis

        - timestamp-micros

        

        To register your custom logical type, inherit from avrogen.logical.LogicalTypeProcessor, implement

        abstract methods, and add an instance to avrogen.logical.DEFAULT_LOGICAL_TYPES dictionary under the 

        name of your logical type. A sample implementation looks as follows:

        

            class DateLogicalTypeProcessor(LogicalTypeProcessor):

                _matching_types = {'int', 'long', 'float', 'double'}

            

                def can_convert(self, writers_schema):

                    return isinstance(writers_schema, schema.PrimitiveSchema) and writers_schema.type == 'int'

            

                def convert(self, value):

                    if not isinstance(value, datetime.date):

                        raise Exception("Wrong type for date conversion")

                    return (value - EPOCH_DATE).total_seconds() // SECONDS_IN_DAY

            

                def convert_back(self, writers_schema, readers_schema, value):

                    return EPOCH_DATE + datetime.timedelta(days=int(value))

            

                def does_match(self, writers_schema, readers_schema):

                    if isinstance(writers_schema, schema.PrimitiveSchema):

                        if writers_schema.type in DateLogicalTypeProcessor._matching_types:

                            return True

                    return False

            

                def typename(self):

                    return 'datetime.date'

            

                def initializer(self, value=None):

                    return ((

                                'logical.DateLogicalTypeProcessor().convert_back(None, None, %s)' % value) if value is not None

                            else 'datetime.datetime.today().date()')

        

        To read/write data with logical type support, use generated SpecificDatumReader 

        and a LogicalDatumWriter from avro.logical.

         

        

        

        

            
Keywords: avro class generator
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.7
