Refresh metadata for Dataframe while reading parquet file - apache-spark

Question

I am trying to read a parquet file as a dataframe which will be updated periodically(path is /folder_name. whenever a new data comes the old parquet file path(/folder_name) will be renamed to a temp path and then we union both new data and old data and will store in the old path(/folder_name)
What happens is suppose we have a parquet file as hdfs://folder_name/part-xxxx-xxx.snappy.parquet before updation and then after updation it is changed to hdfs://folder_name/part-00000-yyyy-yyy.snappy.parquet
The issue is happening is when I try to read the parquet file while the update is being done
sparksession.read.parquet("filename") => it takes the old path hdfs://folder_name/part-xxxx-xxx.snappy.parquet(path exists)
when an action is called on the dataframe it is trying to read the data from hdfs://folder_name/part-xxxx-xxx.snappy.parquet but because of updation the filename changed and I am getting the below issue
java.io.FileNotFoundException: File does not exist: hdfs://folder_name/part-xxxx-xxx.snappy.parquet
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
I am using Spark 2.2
Can anyone help me how to refresh the metadata?

Answer 1

That error occurs when you are trying to read a file that doesn't exists.
Correct me if I'm wrong but I suspect you are overwriting all the files when you save the new dataframe (using .mode("overwrite")). While this process is running you are trying to read a file that was deleted and that exception is thrown - this makes the table unavailable for a period of time (during the update).
As far as I know there is no direct way of "refreshing the metadata" as you want.
Two (of several possible) ways of solving this:
1 - Use append mode
If you just want to append the new dataframe to the old one there is no need of creating a temporary folder and overwriting the old one. You can just change the save mode from overwrite to append. This way you can add partitions to an existing Parquet file without having to rewrite existing ones.
df.write
.mode("append")
.parquet("/temp_table")
This is by far the simplest solution and there is no need to read the data that was already stored. This, however, won't work if you have to update the old data (ex: if you are doing an upsert). For that you have option 2:
2 - Use a Hive view
You can create hive tables and use a view to point to the most recent (and available) one.
Here is an example on the logic behind this approach:
Part 1
If the view <table_name> does not exist we create a new table called
<table_name>_alpha0 to store the new data
After creating the table
we create a view <table_name> as select * from
<table_name>_alpha0
Part 2
If the view <table_name> exists we need to see to which table it is pointing (<table_name>_alphaN)
You do all the operations you want with the new data save it as a table named <table_name>_alpha(N+1)
After creating the table we alter the view <table_name> to select * from <table_name>_alpha(N+1)
And a code example:
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.apache.spark.sql.types._
import spark.implicits._
//This method verifies if the view exists and returns the table it is pointing to (using the query 'describe formatted')
def getCurrentTable(spark: SparkSession, databaseName:String, tableName: String): Option[String] = {
if(spark.catalog.tableExists(s"${databaseName}.${tableName}")) {
val rdd_desc = spark.sql(s"describe formatted ${databaseName}.${tableName}")
.filter("col_name == 'View Text'")
.rdd
if(rdd_desc.isEmpty()) {
None
}
else {
Option(
rdd_desc.first()
.get(1)
.toString
.toLowerCase
.stripPrefix("select * from ")
)
}
}
else
None
}
//This method saves a dataframe in the next "alpha table" and updates the view. It maintains 'rounds' tables (default=3). I.e. if the current table is alpha2, the next one will be alpha0 again.
def saveDataframe(spark: SparkSession, databaseName:String, tableName: String, new_df: DataFrame, rounds: Int = 3): Unit ={
val currentTable = getCurrentTable(spark, databaseName, tableName).getOrElse(s"${databaseName}.${tableName}_alpha${rounds-1}")
val nextAlphaTable = currentTable.replace(s"_alpha${currentTable.last}",s"_alpha${(currentTable.last.toInt + 1) % rounds}")
new_df.write
.mode("overwrite")
.format("parquet")
.option("compression","snappy")
.saveAsTable(nextAlphaTable)
spark.sql(s"create or replace view ${databaseName}.${tableName} as select * from ${nextAlphaTable}")
}
//An example on how to use this:
//SparkSession: spark
val df = Seq((1,"I"),(2,"am"),(3,"a"),(4,"dataframe")).toDF("id","text")
val new_data = Seq((5,"with"),(6,"new"),(7,"data")).toDF("id","text")
val dbName = "test_db"
val tableName = "alpha_test_table"
println(s"Current table: ${getCurrentTable(spark, dbName, tableName).getOrElse("Table does not exist")}")
println("Saving dataframe")
saveDataframe(spark, dbName, tableName, df)
println("Dataframe saved")
println(s"Current table: ${getCurrentTable(spark, dbName, tableName).getOrElse("Table does not exist")}")
spark.read.table(s"${dbName}.${tableName}").show
val processed_df = df.unionByName(new_data) //Or other operations you want to do
println("Saving new dataframe")
saveDataframe(spark, dbName, tableName, processed_df)
println("Dataframe saved")
println(s"Current table: ${getCurrentTable(spark, dbName, tableName).getOrElse("Table does not exist")}")
spark.read.table(s"${dbName}.${tableName}").show
Result:
Current table: Table does not exist
Saving dataframe
Dataframe saved
Current table: test_db.alpha_test_table_alpha0
+---+---------+
| id| text|
+---+---------+
| 3| a|
| 4|dataframe|
| 1| I|
| 2| am|
+---+---------+
Saving new dataframe
Dataframe saved
Current table: test_db.alpha_test_table_alpha1
+---+---------+
| id| text|
+---+---------+
| 3| a|
| 4|dataframe|
| 5| with|
| 6| new|
| 7| data|
| 1| I|
| 2| am|
+---+---------+
By doing this you can guarantee that a version of the view <table_name> will always be available. This also has the advantage (or not, depending on your case) of maintaining the previous versions of the table. i.e. the previous version of <table_name_alpha1> will be <table_name_alpha0>
3 - A bonus
If upgrading your Spark version is an option, take a look at Delta Lake (minimum Spark version: 2.4.2)
Hope this helps :)

Answer 2

Cache the parquet first, then do overwrite.
var tmp = sparkSession.read.parquet("path/to/parquet_1").cache()
tmp.write.mode(SaveMode.Overwrite).parquet("path/to/parquet_1") // same path
Error is thrown because spark does lazy evaluation. When the DAG is executed on "write" command, it starts to read the parquet and write/overwrite at the same time.

Answer 3

Spark doesn't have a transaction manager like Zookeeper to do locks on files hence doing concurrent read/writes is a challenge which needs to be take care of separately.
To refresh the catalog you can do the following:-
spark.catalog.refreshTable("my_table")
OR
spark.sql(s"REFRESH TABLE $tableName")

Answer 4

A simple solution would be to use df.cache.count to bring in memory first, then do union with new data and write to /folder_name with mode overwrite. You won't have to use temp path in this case.
You mentioned that you are renaming the /folder_name to some temp path. So you should read the old data from that temp path rather than hdfs://folder_name/part-xxxx-xxx.snappy.parquet.

Answer 5

Example
From reading your question, I think this might be your issue if so you should be able to run your code without using DeltaLake. In the below use-case Spark will run the code as such: (1) load the inputDF a store locally the file names of the folder location [in this case the explicit part file names] ; (2a) reach line 2 and overwrite the files within the tempLocation; (2b) load the contents from the inputDF and output it to the tempLocation; (3) follow the same steps as 1 but on the tempLocation; (4a) delete the files within the inputLocation folder; and (4b) try to load the part files cached in 1 to load the data from the inputDF to run the union and break because the file does not exist.
val inputDF = spark.read.format("parquet").load(inputLocation)
inputDF.write.format("parquet").mode("overwrite").save(tempLocation)
val tempDF = spark.read.foramt("parquet").load(tempLocation)
val outputDF = inputDF.unionAll(tempDF)
outputDF.write.format("parquet").mode("overwrite").save(inputLocation)
From my experience you can follow two pathways persistence or temporarily output everything used for the overwrite.
Persistence
In the below use case we are going to load the inputDF and immediately save it as another element and persist it. When following with the action the persist will be on the data and not the file paths within the folder.
Else you can do the persistence on the outputDF, which will have, relatively, the same effect. Because the persistence is tethered to the data and not the file paths, the destruction of the inputs will not, cause the file paths to be missing during overwrite.
val inputDF = spark.read.format("parquet").load(inputLocation)
val inputDF2 = inputDF.persist
inputDF2.count
inputDF2.write.format("parquet").mode("overwrite").save(tempLocation)
val tempDF = spark.read.foramt("parquet").load(tempLocation)
val outputDF = inputDF2.unionAll(tempDF) outputDF.write.format("parquet").mode("overwrite").save(inputLocation)
Temporary load
Instead of loading the temporary output for the union input, if you instead entirely load the outputDF to a temporary file and reload that file for the output then you shouldn't see the file not found error.