RussellSpitzer edited a comment on issue #1894:
URL: https://github.com/apache/iceberg/issues/1894#issuecomment-774303754
All Iceberg tables exist in the context of a Catalog. If you haven't
specified one and just provided a path it is using the implied "HadoopCatalog".
The table will have It's own Metadata.json file which will specify the schema
of the table and partition spec. Once created, that is the schema and partition
spec until it is specifically altered.
So in your example "./data-iceberg" will be treated as a HadoopTable and
look in that directory for a Metadata.json with a specified schema and
partitioning. If such a file did not exist you would get an error like
```scala
spark.read.parquet("/Users/russellspitzer/Temp/parquet_data").write.format("iceberg").save("/Users/russellspitzer/Temp/iceberg_table")
org.apache.iceberg.exceptions.NoSuchTableException: Table does not exist at
location: /Users/russellspitzer/Temp/iceberg_table
```
Since you didn't get such an error means that a table does exist there and
it has a schema.
When attempting attempting to write Spark saw "partitionBy" "a", "b", and
preformed a shuffle on the input data
So all spark partitions are now like
```
SparkPartition 1 - > (1,1,1,1) , (1,1,2,1), (1,1,1,3) ...
SparkPartition 2 -> (1,2,1,1), (1,2,2,1), (1,2,1,2) ...
SparkPartition 3 -> (2,4,1,1), (2,4,2,1), (2,4,1,2) ...
```
The first two values are always the same within the spark partition, but the
last two are in a random order.
Then the Iceberg writer gets one of these partitions and it creates a writer
based on it's Metadata.json which let's say is not
the same as the `partitionedBy` argument. For this example let's say the
table is actually partitioned by 'c'
By default in Spark Iceberg's writer opens a file for each partition assumes
it will write all the values for that partition, then close the file. If it
tries to write partition 1 it ends up doing the following
```
(1,1,1,1) -> Open partition c = 1 and start new file
(1,1,2,1) -> Close partition c = 1 file; Open partition c =2 and start new
file
(1,1,1,3) -> Open partition c = 1 /// Error already closed file for
Partition c=1
```
Since this is a runtime check you won't see this error with a single row
since that will always be able to open and close the file without incident, but
a dataset with multiple rows can potentially trigger this. Most of the time you
want this Spark partitioning to match the Iceberg partitioning because this
minimizes the number of files you need to actually write.
If you really don't want to have the Spark Partitioning match for some
reason you can use the sortWithinPartitions to order the data within the
partitions by their Iceberg partition values.
The other possibility is to use the new spark fanout writer which keeps all
of the files open until they entire task has been completed.
Neither of these solutions is really as good as having your data partitioned
like the data is expecting since that minimizes the number of files being
written since all the files for a given partition will be done by the same
Spark Task.
An example
```scala
scala> spark.sql("CREATE table ice.default.part (x Int, y Int) USING iceberg
partitioned by (x) location '/Users/russellspitzer/Temp/iceberg_table'");
val df =
spark.createDataFrame(Seq((1,1),(1,2),(1,3),(2,1),(2,2),(2,3),(3,1),(3,2),(3,3))).toDF("x","y")
scala>
df.write.format("iceberg").partitionBy("x").mode("overwrite").saveAsTable("ice.default.part")
// No Error
scala>
df.write.format("iceberg").partitionBy("y").mode("overwrite").saveAsTable("ice.default.part")
// Many errors
// Caused by: java.lang.IllegalStateException: Already closed files for
partition: y=1
```
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