avro

All of these provide binary serialization, RPC frameworks and IDL. I'm interested in key differences between them and characteristics (performance, ease of use, programming languages support). If you know any other similar technologies, please mention it in an answer. JUST MY correct OPINION ASN.1 is an ISO/ISE standard. It has a very readable source language and a variety of back-ends, both binary and human-readable. Being an international standard (and an old one at that!) the source language is a bit kitchen-sinkish (in about the same way that the Atlantic Ocean is a bit wet) but it is

I can't find a way to deserialize an Apache Avro file with C#. The Avro file is a file generated by the Archive feature in Microsoft Azure Event Hubs. With Java I can use Avro Tools from Apache to convert the file to JSON: java -jar avro-tools-1.8.1.jar tojson --pretty inputfile > output.json Using NuGet package Microsoft.Hadoop.Avro I am able to extract SequenceNumber , Offset and EnqueuedTimeUtc , but since I don't know what type to use for Body an exception is thrown. I've tried with Dictionary<string, object> and other types. static void Main(string[] args) { var fileName = "..."; using

We are looking for a solution in order to create an external hive table to read data from parquet files according to a parquet/avro schema. in other way, how to generate a hive table from a parquet/avro schema ? thanks :) Ram Manohar Try below using avro schema: CREATE TABLE avro_test ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe' STORED AS AVRO TBLPROPERTIES ('avro.schema.url'='myHost/myAvroSchema.avsc'); CREATE EXTERNAL TABLE parquet_test LIKE avro_test STORED AS PARQUET LOCATION 'hdfs://myParquetFilesPath'; Same query is asked in Dynamically create Hive external table with

Currently we are using Avro data format in production. Out of several good points using Avro, we know that it is good in schema evolution. Now we are evaluating Parquet format because of its efficiency while reading random columns. So before moving forward our concern is still schema evolution . Does anyone know if schema evolution is possible in parquet, if yes How is it possible, if no then Why not. Some resources claim that it is possible but it can only add columns at end . What does this mean? Schema evolution can be (very) expensive. In order to figure out schema, you basically have to

I'm trying to get Python to parse Avro schemas such as the following... from avro import schema mySchema = """ { "name": "person", "type": "record", "fields": [ {"name": "firstname", "type": "string"}, {"name": "lastname", "type": "string"}, { "name": "address", "type": "record", "fields": [ {"name": "streetaddress", "type": "string"}, {"name": "city", "type": "string"} ] } ] }""" parsedSchema = schema.parse(mySchema) ...and I get the following exception: avro.schema.SchemaParseException: Type property "record" not a valid Avro schema: Could not make an Avro Schema object from record. What am

I'm planning to use one of the hadoop file format for my hadoop related project. I understand parquet is efficient for column based query and avro for full scan or when we need all the columns data! Before I proceed and choose one of the file format, I want to understand what are the disadvantages/drawbacks of one over the other. Can anyone explain it to me in simple terms? If you haven't already decided, I'd go ahead and write Avro schemas for your data. Once that's done, choosing between Avro container files and Parquet files is about as simple as swapping out e.g., job.setOutputFormatClass

Characteristics of Apache Parquet are : Self-describing Columnar format Language-independent In comparison to Avro, Sequence Files, RC File etc. I want an overview of the formats. I have already read : How Impala Works with Hadoop File Formats , it gives some insights on the formats but I would like to know how the access to data & storage of data is done in each of these formats. How parquet has an advantage over the others? I think the main difference I can describe relates to record oriented vs. column oriented formats. Record oriented formats are what we're all used to -- text files,