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View Code? Open in Web Editor NEWCascading Scheme for the Apache Avro data serialization format
Cascading Scheme for the Apache Avro data serialization format
=============================== Introduction =============================== cascading.avro is a Cascading Scheme for the Apache Avro serialization format, which has been publicly released by Bixo Labs under the Apache license. This means you can use Avro as the source of tuples for a Cascading flow, and as a sink for saving results. This is particularly useful when you need to exchange data with other programming languages, as Avro is both efficient and cross-language. Information about Avro is available from http://hadoop.apache.org/avro/ and also http://wiki.apache.org/hadoop/Avro =============================== Design =============================== When you create an AvroSchema, you specify the fields and types (classes) of each field. This lets the schema auto-generate a corresponding Avro scheme, for both reading and writing data. The set of supported types, with the corresponding Avro type, is: Integer.class, Schema.Type.INT Long.class, Schema.Type.LONG Boolean.class, Schema.Type.BOOLEAN Double.class, Schema.Type.DOUBLE Float.class, Schema.Type.FLOAT String.class, Schema.Type.STRING BytesWritable.class, Schema.Type.BYTES List.class, Schema.Type.ARRAY Map.class, Schema.Type.MAP Enum.class, Schema.Type.ENUM See below for how the List, Map and Enum types are actually represented in a Tuple as a nested Tuple. =============================== Example =============================== final Fields cascadingFields = new Fields("id", "timestamp", "statusMsg", "content"); final Class<?>[] schemeTypes = {Integer.class, Long.class, String.class, BytesWritable.class}; Tap avroSource = new Hfs(new AvroScheme(cascadingFields, schemeTypes), "outputdir"); ...hook up your Cascading workflow here... =============================== Limitations =============================== There's currently no way to set the metadata for the generated Avro file, nor is there any way to read that data back in. This is now supported in Avro, we just need to hook it up in the Scheme. The support for lists and maps is rudimentary and kludgy. Since these aren't natively supported by Cascading yet (as of 1.1), we imitate this by nesting Tuples into the Tuple. So a list item is a Tuple of 0...n primitive values, and a map is a Tuple that contains alternative key/value pairs. Note that Avro maps always use a String as the key, so that same constraint exists on the Cascading side. The support for enums assumes that the Tuple has stored the enum value as the string returned by enum.toString(), such that enum.valueOf(string) will return the original enum. There's no support for using anything other than primitive types as values, so you can't leverage Avro support for nesting records/maps/arrays as fields in the Avro record. While Cascading does support using arbitrary types for fields, as long as they can be serialized, we do not and probably will not attempt to synthesize mappings of arbitrarily complex data types between Cascading and Avro. =============================== Known Issues =============================== Avro doesn't work properly with standard Hadoop installations prior to 0.20, since Hadoop includes an older version (1.0.1) of the Jackson jar that is required by Avro, and Avro uses some newer APIs. So you'll have to sculpt the Hadoop setup to exclude that older Jackson jar, or play games with the classloader to ensure the newer jar gets used with your job. =============================== Building =============================== You need Apache Ant 1.7 or higher, and a git client. 1. Download source from GitHub % git clone git://github.com/bixolabs/cascading.avro.git % cd cascading.avro 2. Build the jar % ant clean jar or to build and install the jar in your local Maven repo: % ant clean install 3. Create Eclipse project files % ant eclipse Then, from Eclipse follow the standard procedure to import an existing Java project into your Workspace.
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