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Writing DataFrame to MemSQL Table in Spark


Writing DataFrame to MemSQL Table in Spark

By : Garb
Date : November 22 2020, 02:42 PM
it should still fix some issue Try using createMemSQLTableAs instead of saveToMemSQL.
saveToMemSQL loads a dataframe into an existing table, where as createMemSQLTableAs creates the table and then loads it. It also returns a handy dataframe wrapping that MemSQL table :).
code :


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MemSQL Spark connector inserting nulls from Spark to MemSQL

MemSQL Spark connector inserting nulls from Spark to MemSQL


By : Maria Alee
Date : March 29 2020, 07:55 AM
wish of those help You are creating a table with a SHARD key, and then setting useKeylessShardingOptimization = true, which will give undefined behavior. Set this to false, and it should be good to go.
Also, I'm not sure what df.select().saveToMemSQL... does. Try just df.saveToMemSQL ...
Error writing spark dataframe to mysql table

Error writing spark dataframe to mysql table


By : grandhi grandhi
Date : March 29 2020, 07:55 AM
Any of those help A correct driver class for MySQL is com.mysql.jdbc.Driver. Assuming the rest of you settings is correct:
code :
prop.setProperty("driver", "com.mysql.jdbc.Driver")
Writing Custom Spark functions on Spark columns/ Dataframe

Writing Custom Spark functions on Spark columns/ Dataframe


By : OP00
Date : March 29 2020, 07:55 AM
Hope that helps I want to normalize Names of authors by removing the accents , It requires an udf:
code :
val stringNormalizer = udf((s: String) => StringUtils.stripAccents(s))

df_article.select(stringNormalizer(col("authors")))
Memory allocation issue in writing Spark DataFrame to Hive table

Memory allocation issue in writing Spark DataFrame to Hive table


By : Kevin McGrail
Date : March 29 2020, 07:55 AM
help you fix your problem It turns out I was partitioning with a nullable field that was throwing the .saveAsTable() off. When I was converting the RDD to a Spark DataFrame, the schema I was providing was generated like this:
code :
from pyspark.sql.types import *

# Define schema
my_schema = StructType(
                    [StructField('col_a_str', StringType(), False),
                     StructField('col_b_num', DoubleType(), True),
                     StructField('col_c_num', DoubleType(), True),
                     StructField('partition_d_str', StringType(), False),
                     StructField('partition_e_str', StringType(), True)])

# Convert RDD to Spark DataFrame
sdf = sqlContext.createDataFrame(my_rdd, schema=my_schema)
# Define schema
my_schema = StructType(
                    [StructField('col_a_str', StringType(), False),
                     StructField('col_b_num', DoubleType(), True),
                     StructField('col_c_num', DoubleType(), True),
                     StructField('partition_d_str', StringType(), False),
                     StructField('partition_e_str', StringType(), False)])
Spark Dataframe : Missing files when writing a dataframe into CSV after partitionning over a window

Spark Dataframe : Missing files when writing a dataframe into CSV after partitionning over a window


By : Fakher Mousawi
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Finally the issue was not on the partionning neither on the writing part. But instead it was due to the algorithm (Bucketed Random LSH) which was building the dataframe (similarity_join); That algorithm was non deterministic so, the number of results was different depending on the random chosen parameters.
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