In my example directories were distinct.
So If I would like to have to distinct directories ex.
/tmp/data/year=2012
/tmp/data/year=2013
It does not work with
val df = Seq((2012, "Batman")).toDF("year","title")
df.write.partitionBy("year").avro("/tmp/data")
val df2 = Seq((2013, "Batman")).toDF("year","title")
df2.write.partitionBy("year").avro("/tmp/data")
As you can see, it complains about the target directory (/tmp/data) and not
about the partitioning keys.
org.apache.spark.sql.AnalysisException: path hdfs://nameservice1/tmp/data
already exists.;
at
org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:76)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
On 22 Dec 2015, at 15:44, Yash Sharma
<yash...@gmail.com<mailto:yash...@gmail.com>> wrote:
Well this will indeed hit the error if the next run has similar year and months
and writing would not be possible.
You can try working around by introducing a runCount in partition or in the
output path.
Something like-
/tmp/data/year/month/01
/tmp/data/year/month/02
Or,
/tmp/data/01/year/month
/tmp/data/02/year/month
This is a work around.
Am sure other better approaches would follow.
- Thanks, via mobile, excuse brevity.
On Dec 22, 2015 7:01 PM, "Jan Holmberg"
<jan.holmb...@perigeum.fi<mailto:jan.holmb...@perigeum.fi>> wrote:
Hi Yash,
the error is caused by the fact that first run creates the base directory ie.
"/tmp/data" and the second batch stumbles to the existing base directory. I
understand that the existing base directory is a challenge but I do not
understand how to make this work with streaming example where each batch would
create a new distinct directory.
Granularity has no impact. No matter how data is partitioned, second 'batch'
always fails with existing base dir.
scala> df2.write.partitionBy("year").avro("/tmp/data")
org.apache.spark.sql.AnalysisException: path hdfs://nameservice1/tmp/data
already exists.;
at
org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:76)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
at
org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933)
at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933)
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137)
at
com.databricks.spark.avro.package$AvroDataFrameWriter$$anonfun$avro$1.apply(package.scala:37)
at
com.databricks.spark.avro.package$AvroDataFrameWriter$$anonfun$avro$1.apply(package.scala:37)
at
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:38)
On 22 Dec 2015, at 14:06, Yash Sharma
<yash...@gmail.com<mailto:yash...@gmail.com>> wrote:
Hi Jan,
Is the error because a past run of the job has already written to the location?
In that case you can add more granularity with 'time' along with year and
month. That should give you a distinct path for every run.
Let us know if it helps or if i missed anything.
Goodluck
- Thanks, via mobile, excuse brevity.
On Dec 22, 2015 2:31 PM, "Jan Holmberg"
<jan.holmb...@perigeum.fi<mailto:jan.holmb...@perigeum.fi>> wrote:
Hi,
I'm stuck with writing partitioned data to hdfs. Example below ends up with
'already exists' -error.
I'm wondering how to handle streaming use case.
What is the intended way to write streaming data to hdfs? What am I missing?
cheers,
-jan
import com.databricks.spark.avro._
import org.apache.spark.sql.SQLContext
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
val df = Seq(
(2012, 8, "Batman", 9.8),
(2012, 8, "Hero", 8.7),
(2012, 7, "Robot", 5.5),
(2011, 7, "Git", 2.0)).toDF("year", "month", "title", "rating")
df.write.partitionBy("year", "month").avro("/tmp/data")
val df2 = Seq(
(2012, 10, "Batman", 9.8),
(2012, 10, "Hero", 8.7),
(2012, 9, "Robot", 5.5),
(2011, 9, "Git", 2.0)).toDF("year", "month", "title", "rating")
df2.write.partitionBy("year", "month").avro("/tmp/data")
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