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The library is open source community supported. The sources for the library are located in GitHub. This topic shows how to use the Microsoft Avro Library to serialize objects and other data structures into streams to persist them to memory, a database, or a file. It also shows how to deserialize them to recover the original objects.

The information in this document only applies to Windows-based HDInsight clusters. Apache Avro provides a compact binary data interchange format for serialization.

It uses JSON to define a language-agnostic schema that underwrites language interoperability.

Data serialized in one language can be read in another. Detailed information on the format can be found in konwerttuje Apache Avro Specification. The serialized representation of an object in the Avro system consists of two parts: It is presented side by side with a binary representation of data.

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Having the schema separate from the binary representation permits each object to be written with no per-value overheads, making serialization fast, and the representation small. Avro provides a convenient way to represent konewrtuje data structures within a Hadoop MapReduce job.

The format of Avro files Avro object container file has been designed to support the distributed MapReduce programming model. The key feature that enables the xoc is that the files are “splittable” in the sense that one can seek any point in a file and start reading from a particular block.

NET Library for Avro supports two ways of serializing objects: When the data schema is known to both the writer and reader of the stream, the data can be docc without its schema. In cases when an Avro object container file is used, the schema is stored within the file.

Other parameters, such as the codec used for data compression, can be specified.

These scenarios are outlined in more detail and illustrated in the following code examples: The following are required before you install the library: Avro GitHub i kompilowania kodu na komputerze.

If you would like to use the Avro Library clone the Microsoft. Avro GitHub repository and compile the code on your machine. The code generation utility is not distributed as a binary executable, but can be easily built via the following procedure: To test the utility, you can generate C classes from the sample JSON schema file provided with the source code.

Execute the following command: This is supposed to produce two C files in the current directory: To understand the logic that the code generation utility is using while converting the JSON schema to C types, see the file GenerationVerification. Konaertuje are extracted from the JSON schema, using the logic described in the file mentioned in the previous paragraph. Six examples provided in this topic illustrate different scenarios supported by the Microsoft Avro Library. Microsoft Avro Library jest przeznaczona do pracy z dowolnym strumienia.

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The Microsoft Avro Library is designed to work with any stream. In these examples, data is manipulated via memory streams rather than file streams or databases for simplicity and consistency. The approach taken in a production environment depends on the exact scenario requirements, data source and volume, performance constraints, and other factors.

The first two examples show how to serialize and deserialize data into memory stream buffers by using reflection and generic konwertujee.

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The schema in these two cases is assumed to be shared between the readers and writers out-of-band. The third and fourth examples show how to serialize and deserialize data by using the Avro object container files. When data is stored in an Avro container file, its schema is always stored with it because the schema must be shared for ionwertuje.

The sample containing the first four examples can be downloaded from the Azure code samples site. The fifth example shows how to use a custom compression codec for Avro object container files. A sample containing the code for this example can be downloaded from the Azure code samples site.

The sixth sample shows how to use Avro serialization to upload data to Azure Blob storage and then analyze it by using Hive with an HDInsight Hadoop cluster. It can be downloaded from the Azure code samples site. Here are links to the six samples discussed in the topic: The JSON schema for the types can be automatically built by the Microsoft Avro Library via reflection from the data contract attributes of the C objects to be serialized.

In this example, objects a SensorData class with a member Location struct are serialized to a memory stream, and this konaertuje is in turn deserialized. The result is then compared to the initial instance to confirm that the SensorData object recovered is identical to the original. The schema in this example is konwertuhe to be shared between the readers and writers, so the Avro object container format is not required.

For an example of how to serialize and deserialize data into memory buffers by using reflection with the object container format when the schema must be shared with the data, see Serialization using object container files with reflection.

A JSON schema can be explicitly specified in a generic record when reflection cannot be used because the data cannot be represented via. NET classes with a data contract. This method is slower than using reflection. In konweftuje cases, the schema for the data may also be dynamic, that is, not known at compile time. Data represented as comma-separated values CSV files whose schema is unknown until it is transformed to the Avro format at run time is an example of this sort of dynamic scenario.

This example shows how to create and use an AvroRecord to explicitly specify a JSON schema, how to populate it with the data, and then how to serialize and deserialize it.

The result is then compared to ma initial instance to confirm that the record recovered is identical to the original. For an example of nq to serialize and deserialize data into memory buffers by using a koneertuje record with the object container format when the schema must be included with the serialized data, see the Serialization using object container files with generic record example. This example is similar to the scenario in the first examplewhere the schema is implicitly specified with reflection.


The difference is that here, the schema is not assumed to be known to the reader that deserializes it. The SensorData objects to be serialized and their implicitly specified konwertjje are stored in an Avro object container file represented by the AvroContainer class. The data is serialized in this example with SequentialWriter and deserialized with SequentialReader. The result then is odc to the initial instances to ensure identity. The data in the object container file is compressed via the default Deflate compression codec from.

See the fifth example in this topic to learn how to use a more recent and superior version of the Deflate compression codec available in. This example is similar to the scenario in the second examplewhere the schema is konwertujd specified with JSON.

The test data set is collected into a list of AvroRecord objects via an explicitly defined JSON schema and then stored in an object container file represented by the Konwertuue class. This container file creates a writer that is used to serialize the data, uncompressed, to a memory stream that is konwertuuje saved to a file.

Null parameter used for creating the reader specifies that this data is not compressed.

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The data is then read from the file and deserialized into a collection of objects. This collection is compared to the initial list of Avro records to confirm that they are identical. The Avro Specification allows usage of an optional compression codec in addition to Null and Deflate defaults. This example is not implementing a new codec such as Snappy mentioned as a supported optional codec in the Avro Specification. It shows how to use the. NET Framework 4 version.

The sixth example illustrates some programming techniques related to interacting with the Azure HDInsight service. The sample does the following tasks: In addition, the sample performs a clean-up procedure before and after performing major operations. During the clean-up, all of the related Azure Blob data and folders are removed, and the Hive table is dropped.

You can also invoke the clean-up procedure from the sample command line. The sample has the following prerequisites: All of the information from the prerequisites should be entered to the sample configuration file before the sample is run. There are two possible ways to do it: In both cases, all edits should be done in the settings section.

Follow the comments in the file. The sample is run from the command line by executing the following command where the. To clean up the cluster, run the following command: