avro

Pyspark 2.4.0, read avro from kafka with read stream - Python

由 二次信任 提交于 2019-11-27 08:08:47
问题 I am trying to read avro messages from Kafka, using PySpark 2.4.0. The spark-avro external module can provide this solution for reading avro files: df = spark.read.format("avro").load("examples/src/main/resources/users.avro") df.select("name", "favorite_color").write.format("avro").save("namesAndFavColors.avro") However, I need to read streamed avro messages. The library documentation suggests using the from_avro() function, which is only available for Scala and Java. Are there any other

How to fix Expected start-union. Got VALUE_NUMBER_INT when converting JSON to Avro on the command line?

由 ♀尐吖头ヾ 提交于 2019-11-27 07:52:17
I'm trying to validate a JSON file using an Avro schema and write the corresponding Avro file. First, I've defined the following Avro schema named user.avsc : {"namespace": "example.avro", "type": "record", "name": "user", "fields": [ {"name": "name", "type": "string"}, {"name": "favorite_number", "type": ["int", "null"]}, {"name": "favorite_color", "type": ["string", "null"]} ] } Then created a user.json file: {"name": "Alyssa", "favorite_number": 256, "favorite_color": null} And then tried to run: java -jar ~/bin/avro-tools-1.7.7.jar fromjson --schema-file user.avsc user.json > user.avro But

Integrating Spark Structured Streaming with the Confluent Schema Registry

由 半世苍凉 提交于 2019-11-27 04:51:43
I'm using a Kafka Source in Spark Structured Streaming to receive Confluent encoded Avro records. I intend to use Confluent Schema Registry, but the integration with spark structured streaming seems to be impossible. I have seen this question, but unable to get it working with the Confluent Schema Registry. Reading Avro messages from Kafka with Spark 2.0.2 (structured streaming) tstites It took me a couple months of reading source code and testing things out. In a nutshell, Spark can only handle String and Binary serialization. You must manually deserialize the data. In spark, create the

Avro vs. Parquet

由 ⅰ亾dé卋堺 提交于 2019-11-26 23:57:44
问题 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? 回答1: If you haven't already decided, I'd go ahead and write Avro schemas for your data. Once that's done, choosing

What are the pros and cons of parquet format compared to other formats?

由 时光总嘲笑我的痴心妄想 提交于 2019-11-26 23:46:15
问题 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? 回答1: I think the main difference I can describe relates to record

Deserialize an Avro file with C#

由 好久不见. 提交于 2019-11-26 23:14:53
问题 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

How to covert JSON string to Avro in Python?

由 旧时模样 提交于 2019-11-26 21:51:19
问题 Is there a way to convert a JSON string to an Avro without a schema definition in Python? Or is this something only Java can handle? 回答1: Apache Avro™ 1.7.6 Getting Started (Python): import avro.schema avro.schema.parse(json_schema_string) 回答2: I recently had the same problem, and I ended up developing a python package that can take any python data structure, including parsed JSON and store it in Avro without a need for a dedicated schema. I tested it for python 3. You can install it as pip3

Create Hive table to read parquet files from parquet/avro schema

由 时光怂恿深爱的人放手 提交于 2019-11-26 21:09:03
问题 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 :) 回答1: 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

Apache Avro: map uses CharSequence as key

由 橙三吉。 提交于 2019-11-26 20:28:14
问题 I am using Apache Avro. My schema has map type: {"name": "MyData", "type" : {"type": "map", "values":{ "type": "record", "name": "Person", "fields":[ {"name": "name", "type": "string"}, {"name": "age", "type": "int"}, ] } } } After compile the schema, the genated Java class use CharSequence as the key for the Map MyData . It is very inconvenient to use CharSequence in Map as key, is there a way to generate String type key for Map in Apache Avro? P.S. Problem is that, for example dataMap

Oozie: Launch Map-Reduce from Oozie <java> action?

由 一世执手 提交于 2019-11-26 07:46:49
问题 I am trying to execute a Map-Reduce task in an Oozie workflow using a <java> action. O\'Reilley\'s Apache Oozie (Islam and Srinivasan 2015) notes that: While it’s not recommended, Java action can be used to run Hadoop MapReduce jobs because MapReduce jobs are nothing but Java programs after all. The main class invoked can be a Hadoop MapReduce driver and can call Hadoop APIs to run a MapReduce job. In that mode, Hadoop spawns more mappers and reducers as required and runs them on the cluster.
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