I'm using the AvroKeyInputFormat to read avro files:
val records = sc.newAPIHadoopFile[AvroKey[T], NullWritable, AvroKeyInputFormat[T]](path)
.map(_._1.datum())
Because I need to reflect over the schema in my job, I get the Avro schema like…
I have a Spark job that failed at the COPY portion of the write. I have all the output already processed in S3, but am having trouble figuring out how to manually load it.
COPY table
FROM…
Hi i have developed an application where i have to store TB of data for the first time and then 20 GB monthly incremental like insert/update/delete in the form of xml that will be applied on top of this 5 TB of data .
And finally on request basis i…
We have an avro with partitioned like this:
table
--a=01
--a=02
We want to load the data from a single partition keeping the partition column a.
I found this stackoverflow question and I applied the suggested snippet:
DataFrame df =…
I am using Spark Shell v_1.6.1.5.
I have the following Spark Scala Dataframe:
val data = sqlContext.read.avro("/my/location/*.avro")
data.printSchema
root
|-- id: long (nullable = true)
|-- stuff: map (nullable = true)
| |-- key: string
| …
I'm trying to find the source of a bug on Spark 2.0.0, I have a map that holds table names as keys and the dataframe as the value, I loop through it and at the end use spark-avro (3.0.0-preview2) to write everything to S3 directories. It runs…
I am using Gobblin to periodically extract relational data from Oracle, convert it to avro and publish it to HDFS
My dfs directory structure looks like this
-tables
|
-t1
|
-2016080712345
|
-f1.avro
|
-2016070714345
|
…
Is there a pyspark function which could convert below _schema variable to an `avro schema?
df_schema = spark.read.format('parquet').load(input_directory)
_schema = df_schema.schema
After migrating to Spark 3.2.0 i had to upgrade the external package of spark-avro to spark-avro 2.12:3.2.0.
After this migration i was unable to read any avro file that contains spaces in their column names.
The errors occurs on the read method…
Via Concord, we can automatically spawn clusters with pyspark enabled dataproc clusters.
In these pyspark notebooks, spark version is 2.4.8
But, by default spark does not have .avro datasource extension. Without Avro extension, we can not read .avro…
I have streamed data in Avro format in Kafka storage and managed the schema of the data via the confluent schema registry.
I'd like to pull the data using pyspark and parse the Avro byte data using schema from schema registry but it kept raising…
There is some problem trying to deserialize data from .avro file. My process consists of these steps:
reading from Kafka
df = (
spark.read.format("kafka")
.option("kafka.security.protocol", "PLAINTEXT")
…
We are sending Avro data encoded with (azure.schemaregistry.encoder.avroencoder) to Event-Hub using a standalone python job and we can deserialize using the same decoder using another standalone python consumer. The schema registry is also supplied…
I am really struggling with this one. Spent a lot of time searching for an answer in Spark manual and stack-overflow posts. Really need help.
I've installed Apache Spark on my mac to build and debug PySpark code locally. However, in my PySpark code…