Generate sample json from avro schema

This guide uses Avro 1.

generate sample json from avro schema

Download and unzip avro Ensure that you can import avro from a Python prompt. Alternatively, you may build the Avro Python library from source. From your the root Avro directory, run the commands.

Avro schemas are defined using JSON. Schemas are composed of primitive types nullbooleanintlongfloatdoublebytesand string and complex types recordenumarraymapunionand fixed.

You can learn more about Avro schemas and types from the specification, but for now let's start with a simple schema example, user.

This schema defines a record representing a hypothetical user. Note that a schema file can only contain a single schema definition. We also define a namespace "namespace": "example. User in this case. Fields are defined via an array of objects, each of which defines a name and type other attributes are optional, see the record specification for more details.

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The type attribute of a field is another schema object, which can be either a primitive or complex type. 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. 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:. Do make sure that you open your files in binary mode i. Otherwise you might generate corrupt files due to automatic replacement of newline characters with the platform-specific representations.

Schema object specifically a subclass of Schemain this case RecordSchema. We're passing in the contents of our user. We create a DataFileWriterwhich we'll use to write serialized items to a data file on disk.

AVRO - Schemas

The DataFileWriter constructor takes three arguments:. We use DataFileWriter. Avro records are represented as Python dict s. Were we to omit the required name field, an exception would be raised. Any extra entries not corresponding to a field are present in the dict are ignored. We open the file again, this time for reading back from disk.

The DataFileReader is an iterator that returns dict s corresponding to the serialized items. Download Defining a schema Serializing and deserializing without code generation.

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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: import avro. Let's take a closer look at what's going on here.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Arg reads a schema through either stdin or a CLI-specified file and generates random data to fit it.

Arg can output data in either JSON or binary format, and when outputting in JSON, can either print in compact format one instance of spoofed data per line or pretty format. Arg can output data either to stdout or a file.

After outputting all of its spoofed data, Arg prints a single newline. Arg also allows for special annotations in the Avro schema it spoofs that narrow down the kind of data produced. For example, when spoofing a string, you can currently either specify a length that the string should be or one or both of a minimum and maximum that the length should bea list of possible strings that the string should come from, or a regular expression that the string should adhere to.

These annotations are specified inside the schema that Arg spoofs, as parts of a JSON object with an attribute name of "arg. They should not collide with any existing properties, or cause any issues if present when the schema is used with other programs.

A non-annotated schema. The resulting output will just be a random enum chosen from the symbols list. An annotated record schema, with a variety of string fields. Each field has its own way of preventing the specified string from becoming too long, either via the length annotation or the regex annotation. A record schema that draws its content from two files, 'nouns-list. The script must be run from the repository base directory in order for this schema to work with it properly due, to the relative paths of the files.

A schema where every field is annotated with an example usage of the options annotation, as well as an example of the keys annotation. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Used to generate mock Avro data. Java Shell.

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Java Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. What does it do? The boring stuff Arg reads a schema through either stdin or a CLI-specified file and generates random data to fit it. The number of instances of spoofed data can also be specified; the default is currently 1.

The cool stuff Arg also allows for special annotations in the Avro schema it spoofs that narrow down the kind of data produced. If given as an object, a list of data will be read from the file after decoding with the specified format currently "json" and "binary" are the only supported values, and "binary" may be somewhat buggy.A fluent interface for building Schema instances.

All Rights Reserved. Object org. TypeBuilderSchemaBuilder. FieldTypeBuilderor SchemaBuilder. UnionFieldTypeBuilderdepending on the context. These types all share a similar API for selecting and building types. Primitive Types All Avro primitive types are trivial to configure.

The first line above is a shortcut for the second, analogous to the JSON case. Named Types Avro named types have names, namespace, aliases, and doc.

The builders for named types require a name to be constructed, and optional configuration via: SchemaBuilder. After configuration of optional properties, an array or map builds or selects its nested type with SchemaBuilder.

Fields SchemaBuilder. FieldAssembler for defining the fields of the record and completing it. Each field must have a name, specified via SchemaBuilder. FieldBuilder for defining aliases, custom properties, and documentation of the field. After configuring these optional values for a field, the type is selected or built with SchemaBuilder. Fields have default values that must be specified to complete the field. FieldAssembler for primitive types.

These shortcuts create required, optional, and nullable fields, but do not support field aliases, doc, or custom properties. Unions Union types are built via SchemaBuilder. This chains together multiple types, in union order. For example:. Unions have two shortcuts for common cases. In a field type context, optional is available and creates a union of null and a type, with a null default. The below two are equivalent:. A namespace will propagate as a default to child fields, nested types, or later defined types in a union.

To specify a name that has no namespace and ignore the inherited namespace, set the namespace to "".This guide uses Avro 1. For the examples in this guide, download avro From the Jackson download pagedownload the core-asl and mapper-asl jars. Add avro Alternatively, if you are using Maven, add the following dependency to your POM:. You may also build the required Avro jars from source.

Building Avro is beyond the scope of this guide; see the Build Documentation page in the wiki for more information. Avro schemas are defined using JSON. Schemas are composed of primitive types nullbooleanintlongfloatdoublebytesand string and complex types recordenumarraymapunionand fixed.

Avro Introduction

You can learn more about Avro schemas and types from the specification, but for now let's start with a simple schema example, user.

This schema defines a record representing a hypothetical user. Note that a schema file can only contain a single schema definition. We also define a namespace "namespace": "example.

User in this case. Fields are defined via an array of objects, each of which defines a name and type other attributes are optional, see the record specification for more details. The type attribute of a field is another schema object, which can be either a primitive or complex type. Code generation allows us to automatically create classes based on our previously-defined schema. Once we have defined the relevant classes, there is no need to use the schema directly in our programs. We use the avro-tools jar to generate code as follows:.

This will generate the appropriate source files in a package based on the schema's namespace in the provided destination folder. For instance, to generate a User class in package example. Note that if you using the Avro Maven plugin, there is no need to manually invoke the schema compiler; the plugin automatically performs code generation on any. Now that we've completed the code generation, let's create some User s, serialize them to a data file on disk, and then read back the file and deserialize the User objects.

As shown in this example, Avro objects can be created either by invoking a constructor directly or by using a builder. Unlike constructors, builders will automatically set any default values specified in the schema.

generate sample json from avro schema

Additionally, builders validate the data as it set, whereas objects constructed directly will not cause an error until the object is serialized. However, using constructors directly generally offers better performance, as builders create a copy of the datastructure before it is written. Note that we do not set user1 's favorite color. Since that record is of type ["string", "null"]we can either set it to a string or leave it null ; it is essentially optional.

Similarly, we set user3 's favorite number to null using a builder requires setting all fields, even if they are null. We create a DatumWriterwhich converts Java objects into an in-memory serialized format. The SpecificDatumWriter class is used with generated classes and extracts the schema from the specified generated type. Next we create a DataFileWriterwhich writes the serialized records, as well as the schema, to the file specified in the dataFileWriter.

We write our users to the file via calls to the dataFileWriter. When we are done writing, we close the data file. Deserializing is very similar to serializing. We create a SpecificDatumReaderanalogous to the SpecificDatumWriter we used in serialization, which converts in-memory serialized items into instances of our generated class, in this case User.Mar 06, 11 min read.

Benjamin Fagin. The pursuit of efficient object serialization in Java has recently received a leg up from the Apache Avro project. Avro is a binary marshalling framework, supporting both schema and introspection based format specification.

Avro is similar to Thrift or Google's Protocol Buffers in that the output format is a byte stream. The performance gains from working with binary data make these cross-platform frameworks highly appealing. To get the most from Avro, a schema should be created to describe each object or 'datum' in Avro-speak in your application.

While the schema specification uses JSON, there is currently a lack of tools designed to create schemas in Avro's format. On the other hand, there are currently many tools in existence for creating and editing XSD schema files [1,2].

Creating one schema for XML as well as Avro is therefore quite appealing, since it would require less work to maintain one set of XSD files, which are probably already being maintained for other purposes. The tool was designed to be somewhat extensible, and includes support for plugins written in Java however, there aren't many plugins available, and the documentation for the process is somewhat lacking.

Plugins are given access to the generated code model and allowed to make changes or otherwise utilize the information.

Apache Avro™ 1.8.1 Getting Started (Java)

There are several smaller plugins in the wild, addressing use cases ranging from printing an index of generated classes [3], to modifying the generated code to be simpler to use [4], to adding interfaces and methods supporting the visitor pattern [5]. Kubernetes and Minikube for DevOps. This cheat sheet shows how adding Minikube makes DevOps' lives easier when working with Kubernetes.

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Generating Avro Schemas from XML Schemas Using JAXB

In this article, I am using a new plugin which works alongside the JAXB code generation process to create Avro schemas which closely parallel the generated JavaBeans classes. This has the main advantage of automating the Avro schema creation process, as well as keeping the Avro bindings looking as close as possible to the JAXB bindings. No handmade Avro schema is required, which means one less mapping to maintain in your application code.

The plugin continues past the schema generation phase to create Java class bindings from the schemas, but the schema files could instead be processed by one of the other compilers for another language currently supported by the Avro project. Each plugin starts by extending the Plugin class provided by XJC. However, getting XJC to actually work with its plugins is just not as simple as it should be. There are several issues involving class paths and order of execution which are likely to cause some headache the JAXB-Basics project includes an Ant task, which is currently the preferred way to execute XJC with plugins [6,7].

Once you are integrated, your plugin will be called by XJC after it has created an outline of the Java code it intends to generate. The outline object stores basic information about the bean classes and their properties. Sufficient information about the properties and their types is provided, and we are able to inspect the output to create Avro schemas accordingly.

Operational definition

There are several levels of models available at runtime, including the Outline a coarse outline of beans and their fieldsand the JCodeModel a Java representation of Java code.

Creating comparable Avro schemas requires more type information than the Outline can provide, and so I decided to use the code model for the majority of the processing. The two high level constructs JAXB creates are 'enums' and 'beans'. The enums translate very well to Avro's enum type, and the beans can become Record types. Constructing the records for the bean classes is at times straightforward and other times not so simple.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Is there e. You can create random data using trevni dependency and test scope. Here you have a sample code. Learn more. Ask Question.

Asked 1 year, 11 months ago. Active 4 months ago. Viewed 4k times. Got a fairly complicated Avro schema which I can not modify. AvroTypeException: Expected start-union. Active Oldest Votes. Here you have a sample code import org. Schema; import org. RandomData; import java. Where were you all my life? I had one issue with enum types. It looks like it generated nicely based on input avro schema but when tried creating genericRecord - it complained so after putting null for it - all worked out good.

Is the Parser the recommended way to get a schema? You need to import below packages compile "org. Juvin James Juvin James 1. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.

generate sample json from avro schema

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Stack Overflow works best with JavaScript enabled.Comment 0. Today, I'll explain how you can use the Schema Generator to automatically create Avro schemas. We'll use our old friend, the Taxi tutorial pipeline, as a basis, modifying it to write Avro-formatted data rather than a delimited data format. We'll look at an initial naive implementation — just dropping the Schema Generator into the pipeline — then see how, with a little more work, we get a much better result.

I'm starting with the basic Taxi tutorial pipeline. If you have not yet completed the SDC tutorial, I urge you to do so. It really is the quickest, easiest way to get up-to-speed with creating dataflow pipelines. You'll notice that we need to specify the Avro schema somehow:. Let's insert the Schema Generator processor just before the Local FS destination and give the schema a suitable name:. Notice that the Schema Generator processor puts the schema in a header attribute named avroSchema.

We can now configure the Local FS destination to use this generated schema:. We can use Preview to get some insight into what will happen when the pipeline runs.

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Preview will read the first few records from the origin and process them in the pipeline but not, by default, write them to the destination. Selecting the Schema Generator and drilling into the first record, we can see the Avro schema:. Let's reformat the Avro schema so it's more readable.

I've removed most of the fields so we can focus on the key points:. The Schema Generator has created an Avro schema, but it's likely not going to be very useful. Delimited input data for example, data from CSV files doesn't have any type information, so all the fields are strings. Previewing again, the schema looks much better, but we still have a little work to do. Notice that the Field Type Converter "guesses" the precision for the decimal fields based on the values in each individual record:.

The precision attributes of the generated schemas will vary from record to record, but the schema needs to be uniform across all of the data. We can use an Expression Evaluator to set the field headers to override the generated precision attribute with sensible values for the entire dataset:. Let's run the pipeline and take a look at the output.

generate sample json from avro schema

I used Avro Tools to verify the schema and records in the output file from the command line here's a useful primer on Avro Tools. As expected, that matches what we saw in the pipeline preview. Let's take a look at the data:.

The strings and integers look fine, but what's happened to the datetime and amount fields? Avro defines Logical Types for timestamp-millisdecimaland other derived types, specifying the underlying Avro type for serialization and additional attributes.

Timestamps are represented as a long number of milliseconds from the Unix epoch, 1 January The decimal fields, in particular, look a bit strange in their JSON representation, but rest assured that the data is stored in full fidelity in the actual Avro encoding!


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