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| <h1>Apache Avro™ 1.10.0 Getting Started (Python)</h1> |
| <div id="front-matter"> |
| <div id="minitoc-area"> |
| <ul class="minitoc"> |
| <li> |
| <a href="#notice_python3">Notice for Python 3 users</a> |
| </li> |
| <li> |
| <a href="#download_install">Download</a> |
| </li> |
| <li> |
| <a href="#Defining+a+schema">Defining a schema</a> |
| </li> |
| <li> |
| <a href="#Serializing+and+deserializing+without+code+generation">Serializing and deserializing without code generation</a> |
| </li> |
| </ul> |
| </div> |
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| |
| <p> |
| This is a short guide for getting started with Apache Avro™ using |
| Python. This guide only covers using Avro for data serialization; see |
| Patrick Hunt's <a href="https://github.com/phunt/avro-rpc-quickstart">Avro |
| RPC Quick Start</a> for a good introduction to using Avro for RPC. |
| </p> |
| |
| |
| <a name="notice_python3"></a> |
| <h2 class="h3">Notice for Python 3 users</h2> |
| <div class="section"> |
| <p> |
| A package called "avro-python3" had been provided to support |
| Python 3 previously, but the codebase was consolidated into |
| the "avro" package and that supports both Python 2 and 3 now. |
| |
| The avro-python3 package will be removed in the near future, |
| so users should use the "avro" package instead. |
| They are mostly API compatible, but there's a few minor difference |
| (e.g., function name capitalization, |
| such as avro.schema.Parse vs avro.schema.parse). |
| </p> |
| </div> |
| |
| |
| <a name="download_install"></a> |
| <h2 class="h3">Download</h2> |
| <div class="section"> |
| <p> |
| For Python, the easiest way to get started is to install it from PyPI. |
| Python's Avro API is available over <a href="https://pypi.org/project/avro/">PyPi</a>. |
| </p> |
| <pre class="code"> |
| $ python3 -m pip install avro |
| </pre> |
| <p> |
| The official releases of the Avro implementations for C, C++, C#, Java, |
| PHP, Python, and Ruby can be downloaded from the <a href="https://avro.apache.org/releases.html">Apache Avro™ |
| Releases</a> page. This guide uses Avro 1.10.0, the latest |
| version at the time of writing. Download and unzip |
| <em>avro-1.10.0.tar.gz</em>, and install via <span class="codefrag">python |
| setup.py</span> (this will probably require root privileges). Ensure |
| that you can <span class="codefrag">import avro</span> from a Python prompt. |
| </p> |
| <pre class="code"> |
| $ tar xvf avro-1.10.0.tar.gz |
| $ cd avro-1.10.0 |
| $ python setup.py install |
| $ python |
| >>> import avro # should not raise ImportError |
| </pre> |
| <p> |
| Alternatively, you may build the Avro Python library from source. From |
| your the root Avro directory, run the commands |
| </p> |
| <pre class="code"> |
| $ cd lang/py/ |
| $ python3 -m pip install -e . |
| $ python |
| </pre> |
| </div> |
| |
| |
| <a name="Defining+a+schema"></a> |
| <h2 class="h3">Defining a schema</h2> |
| <div class="section"> |
| <p> |
| Avro schemas are defined using JSON. Schemas are composed of <a href="spec.html#schema_primitive">primitive types</a> |
| (<span class="codefrag">null</span>, <span class="codefrag">boolean</span>, <span class="codefrag">int</span>, |
| <span class="codefrag">long</span>, <span class="codefrag">float</span>, <span class="codefrag">double</span>, |
| <span class="codefrag">bytes</span>, and <span class="codefrag">string</span>) and <a href="spec.html#schema_complex">complex types</a> (<span class="codefrag">record</span>, |
| <span class="codefrag">enum</span>, <span class="codefrag">array</span>, <span class="codefrag">map</span>, |
| <span class="codefrag">union</span>, and <span class="codefrag">fixed</span>). You can learn more about |
| Avro schemas and types from the specification, but for now let's start |
| with a simple schema example, <em>user.avsc</em>: |
| </p> |
| <pre class="code"> |
| {"namespace": "example.avro", |
| "type": "record", |
| "name": "User", |
| "fields": [ |
| {"name": "name", "type": "string"}, |
| {"name": "favorite_number", "type": ["int", "null"]}, |
| {"name": "favorite_color", "type": ["string", "null"]} |
| ] |
| } |
| </pre> |
| <p> |
| This schema defines a record representing a hypothetical user. (Note |
| that a schema file can only contain a single schema definition.) At |
| minimum, a record definition must include its type (<span class="codefrag">"type": |
| "record"</span>), a name (<span class="codefrag">"name": "User"</span>), and fields, in |
| this case <span class="codefrag">name</span>, <span class="codefrag">favorite_number</span>, and |
| <span class="codefrag">favorite_color</span>. We also define a namespace |
| (<span class="codefrag">"namespace": "example.avro"</span>), which together with the name |
| attribute defines the "full name" of the schema |
| (<span class="codefrag">example.avro.User</span> in this case). |
| |
| </p> |
| <p> |
| Fields are defined via an array of objects, each of which defines a name |
| and type (other attributes are optional, see the <a href="spec.html#schema_record">record specification</a> for more |
| details). The type attribute of a field is another schema object, which |
| can be either a primitive or complex type. For example, the |
| <span class="codefrag">name</span> field of our User schema is the primitive type |
| <span class="codefrag">string</span>, whereas the <span class="codefrag">favorite_number</span> and |
| <span class="codefrag">favorite_color</span> fields are both <span class="codefrag">union</span>s, |
| represented by JSON arrays. <span class="codefrag">union</span>s are a complex type that |
| can be any of the types listed in the array; e.g., |
| <span class="codefrag">favorite_number</span> can either be an <span class="codefrag">int</span> or |
| <span class="codefrag">null</span>, essentially making it an optional field. |
| </p> |
| </div> |
| |
| |
| <a name="Serializing+and+deserializing+without+code+generation"></a> |
| <h2 class="h3">Serializing and deserializing without code generation</h2> |
| <div class="section"> |
| <p> |
| Data in Avro is always stored with its corresponding schema, meaning we |
| can always read a serialized item, regardless of whether we know the |
| schema ahead of time. This allows us to perform serialization and |
| deserialization without code generation. Note that the Avro Python |
| library does not support code generation. |
| </p> |
| <p> |
| Try running the following code snippet, which serializes two users to a |
| data file on disk, and then reads back and deserializes the data file: |
| </p> |
| <pre class="code"> |
| import avro.schema |
| from avro.datafile import DataFileReader, DataFileWriter |
| from avro.io import DatumReader, DatumWriter |
| |
| schema = avro.schema.parse(open("user.avsc", "rb").read()) |
| |
| writer = DataFileWriter(open("users.avro", "wb"), DatumWriter(), schema) |
| writer.append({"name": "Alyssa", "favorite_number": 256}) |
| writer.append({"name": "Ben", "favorite_number": 7, "favorite_color": "red"}) |
| writer.close() |
| |
| reader = DataFileReader(open("users.avro", "rb"), DatumReader()) |
| for user in reader: |
| print user |
| reader.close() |
| </pre> |
| <p>This outputs:</p> |
| <pre class="code"> |
| {u'favorite_color': None, u'favorite_number': 256, u'name': u'Alyssa'} |
| {u'favorite_color': u'red', u'favorite_number': 7, u'name': u'Ben'} |
| </pre> |
| <p> |
| Do make sure that you open your files in binary mode (i.e. using the modes |
| <span class="codefrag">wb</span> or <span class="codefrag">rb</span> respectively). Otherwise you might |
| generate corrupt files due to |
| <a href="https://docs.python.org/library/functions.html#open"> |
| automatic replacement</a> of newline characters with the |
| platform-specific representations. |
| </p> |
| <p> |
| Let's take a closer look at what's going on here. |
| </p> |
| <pre class="code"> |
| schema = avro.schema.parse(open("user.avsc", "rb").read()) |
| </pre> |
| <p> |
| |
| <span class="codefrag">avro.schema.parse</span> takes a string containing a JSON schema |
| definition as input and outputs a <span class="codefrag">avro.schema.Schema</span> object |
| (specifically a subclass of <span class="codefrag">Schema</span>, in this case |
| <span class="codefrag">RecordSchema</span>). We're passing in the contents of our |
| user.avsc schema file here. |
| </p> |
| <pre class="code"> |
| writer = DataFileWriter(open("users.avro", "wb"), DatumWriter(), schema) |
| </pre> |
| <p> |
| We create a <span class="codefrag">DataFileWriter</span>, which we'll use to write |
| serialized items to a data file on disk. The |
| <span class="codefrag">DataFileWriter</span> constructor takes three arguments: |
| </p> |
| <ul> |
| |
| <li>The file we'll serialize to</li> |
| |
| <li>A <span class="codefrag">DatumWriter</span>, which is responsible for actually |
| serializing the items to Avro's binary format |
| (<span class="codefrag">DatumWriter</span>s can be used separately from |
| <span class="codefrag">DataFileWriter</span>s, e.g., to perform IPC with Avro).</li> |
| |
| <li>The schema we're using. The <span class="codefrag">DataFileWriter</span> needs the |
| schema both to write the schema to the data file, and to verify that |
| the items we write are valid items and write the appropriate |
| fields.</li> |
| |
| </ul> |
| <pre class="code"> |
| writer.append({"name": "Alyssa", "favorite_number": 256}) |
| writer.append({"name": "Ben", "favorite_number": 7, "favorite_color": "red"}) |
| </pre> |
| <p> |
| We use <span class="codefrag">DataFileWriter.append</span> to add items to our data |
| file. Avro records are represented as Python <span class="codefrag">dict</span>s. |
| Since the field <span class="codefrag">favorite_color</span> has type <span class="codefrag">["int", |
| "null"]</span>, we are not required to specify this field, as shown in |
| the first append. Were we to omit the required <span class="codefrag">name</span> |
| field, an exception would be raised. Any extra entries not |
| corresponding to a field are present in the <span class="codefrag">dict</span> are |
| ignored. |
| </p> |
| <pre class="code"> |
| reader = DataFileReader(open("users.avro", "rb"), DatumReader()) |
| </pre> |
| <p> |
| We open the file again, this time for reading back from disk. We use |
| a <span class="codefrag">DataFileReader</span> and <span class="codefrag">DatumReader</span> analagous |
| to the <span class="codefrag">DataFileWriter</span> and <span class="codefrag">DatumWriter</span> above. |
| </p> |
| <pre class="code"> |
| for user in reader: |
| print user |
| </pre> |
| <p> |
| The <span class="codefrag">DataFileReader</span> is an iterator that returns |
| <span class="codefrag">dict</span>s corresponding to the serialized items. |
| </p> |
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