While trying to load data from a dataset into Hive table getting the error:
org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid
call to dataType on unresolved object, tree: 'ipl_appl_signed_date
My dataset contains same columns as the Hive table and the column for which am getting the error has Date datatype in my code(Java) as well as in Hive.
java code:
Date IPL_APPL_SIGNED_DATE =rs.getDate("DTL.IPL_APPL_SIGNED_DATE"); //using jdbc to get record.
Encoder<DimPolicy> encoder = Encoders.bean(Foo.class);
Dataset<DimPolicy> test=spark.createDataset(allRows,encoder); //spark is the spark session
test.write().mode("append").insertInto("someSchema.someTable"); //
I think the issue is due to a bug in Spark i.e. [SPARK-26379] Use dummy TimeZoneId for CurrentTimestamp to avoid UnresolvedException in CurrentBatchTimestamp, that got fixed in 2.3.3, 2.4.1, 3.0.0.
A solution is to downgrade to the version of Spark that is unaffected by the bug (or wait for a new version).
Related
We want to move from spark 3.0.1 to 3.1.2. According to migration guide varchar data types are now supported in table schema. Unfortunately data onboarded with new version cant be queried by old spark versions which considered varchar as a string in table schema. According to migration guide applying spark.sql.legacy.charVarcharAsString to true in Spark Session configuration should do the trick but we still get varchar datatype instead of string in hive table schema.
As is:
To be:
What are we missing here?
You should upgrade spark version according to this https://issues.apache.org/jira/browse/SPARK-37452. There is a bug which affect versions 3.1.2, 3.2.0. And it was fixed in versions 3.1.3, 3.2.1, 3.3.0
I'm creating a delta table on a EMR (6.2) using the following code:
try:
self.spark.sql(f'''
CREATE TABLE default.features_scd
(`{entity_key}` {entity_value_type}, `{CURRENT}` BOOLEAN,
`{EFFECTIVE_TIMESTAMP}` TIMESTAMP, `{END_TIMESTAMP}` TIMESTAMP, `date` DATE)
USING DELTA
PARTITIONED BY (DATE)
LOCATION 's3://mybucket/some/path'
''')
except IllegalArgumentException as e:
self.logger.error('got an illegal argument exception')
pass
I have enableHiveSupport() on the spark session.
I'm getting the warning:
WARN HiveExternalCatalog: Couldn't find corresponding Hive SerDe for data source provider delta. Persisting data source table default.features_scd into Hive metastore in Spark SQL specific format, which is NOT compatible with Hive.
And the exception:
IllegalArgumentException: Can not create a Path from an empty string
Basically the table is being created well and I'm able to get my goal, but I wish to not have to try except and pass on that error.
If I run the same code without the enableHiveSupport() it runs smoothly. But I need the hive support in the same session for creating/updating a hive table.
Is there a way to prevent this exception?
I have a Hive Orc table with a definition similar to the following definition
CREATE EXTERNAL TABLE `example.example_table`(
...
)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
WITH SERDEPROPERTIES (
'path'='s3a://path/to/table')
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'
LOCATION
's3a://path/to/table'
TBLPROPERTIES (
...
)
I am attempting to use PySpark to append a dataframe to this table using "df.write.insertInto("example.example_table")". When running this, I get the following error:
org.apache.spark.sql.AnalysisException: Can only write data to relations with a single path.;
at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:188)
at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134)
...
When looking at the underlying Scala code, the condition that throws this error is checking to see if the table location has multiple "rootPaths". Obviously, my table is defined with a single location. What else could cause this?
It is that path that you are defining that causes the error. I just ran into this same problem myself. Hive generates a location path based on the hive.metastore.warehouse.dir property, so you have that default location plus the path you specified, which is causing that linked code to fail.
If you want to pick a specific path other than the default, then try using LOCATION.
Try running a describe extended example.example_table query to see more detailed information on the table. One of the output rows will be a Detailed Table Information which contains a bunch of useful information:
Table(
tableName:
dbName:
owner:
createTime:1548335003
lastAccessTime:0
retention:0
sd:StorageDescriptor(cols:
location:[*path_to_table*]
inputFormat:org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat
outputFormat:org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat
compressed:false
numBuckets:-1
serdeInfo:SerDeInfo(
name:null
serializationLib:org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe
parameters:{
serialization.format=1
path=[*path_to_table*]
}
)
bucketCols:[]
sortCols:[]
parameters:{}
skewedInfo:SkewedInfo(skewedColNames:[]
skewedColValues:[]
skewedColValueLocationMaps:{})
storedAsSubDirectories:false
)
partitionKeys:[]
parameters:{transient_lastDdlTime=1548335003}
viewOriginalText:null
viewExpandedText:null
tableType:MANAGED_TABLE
rewriteEnabled:false
)
We had the same problem in a project when migrating from Spark 1.x and HDFS to Spark 3.x and S3. We solve this issue setting the next Spark property to false:
spark.sql.hive.convertMetastoreParquet
You can just run
spark.sql("SET spark.sql.hive.convertMetastoreParquet=false")
Or maybe
spark.conf("spark.sql.hive.convertMetastoreParquet", False)
Being spark the SparkSession object. The explanaition of this is currently in Spark documentation.
The below command was successfully converting external tables to managed tables in Spark 2.0.0:
ALTER TABLE {table_name} SET TBLPROPERTIES(EXTERNAL=FLASE);
However the above command is failing in Spark 2.2.0 with the below error:
Error in query: Cannot set or change the preserved property key:
'EXTERNAL';
As #AndyBrown pointed our in a comment you have the option of dropping to the console and invoking the Hive statement there. In Scala this worked for me:
import sys.process._
val exitCode = Seq("hive", "-e", "ALTER TABLE {table_name} SET TBLPROPERTIES(\"EXTERNAL\"=\"FALSE\")").!
I faced this problem using Spark 2.1.1 where #Joha's answer does not work because spark.sessionState is not accessible due to being declared lazy.
In Spark 2.2.0 you can do the following:
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.catalog.CatalogTable
import org.apache.spark.sql.catalyst.catalog.CatalogTableType
val identifier = TableIdentifier("table", Some("database"))
val oldTable = spark.sessionState.catalog.getTableMetadata(identifier)
val newTableType = CatalogTableType.MANAGED
val alteredTable = oldTable.copy(tableType = newTableType)
spark.sessionState.catalog.alterTable(alteredTable)
The issue is case-sensitivity on spark-2.1 and above.
Please try setting TBLPROPERTIES in lower case -
ALTER TABLE <TABLE NAME> SET TBLPROPERTIES('external'='false')
I had the same issue while using a hive external table. I solved the problem by directly setting the propery external to false in hive metastore using a hive metastore client
Table table = hiveMetaStoreClient.getTable("db", "table");
table.putToParameters("EXTERNAL","FALSE");
hiveMetaStoreClient.alter_table("db", "table", table,true);
I tried the above option from scala databricks notebook, and the
external table was converted to MANAGED table and the good part is
that the desc formatted option from spark on the new table is still
showing the location to be on my ADLS. This was one limitation that
spark was having, that we cannot specify the location for a managed
table.
As of now i am able to do a truncate table for this. hopefully there
was a more direct option for creating a managed table with location
specified from spark sql.
We have an External Hive Table which is stored as Parquet. I am not the owner of the schema in which this hive-parquet table is so don't have much info.
The Problem here is when in try to Query that table from spark-sql>(Shell prompt) Not by using scala like spark.read.parquet("path"), I am getting 0 records stating "Unable to infer schema". But when i created a Managed Table by using CTAS in my personal schema just for testing i was able to query it from the spark-sql>(Shell prompt)
When i try it from spark-shell> via spark.read.parquet("../../00000_0").show(10) , I was able to see the data.
So this clears that something is wrong between
External Hive table - Parquet - Spark-SQL(shell)
If locating Schema would be the issue then it should behave same while accessing through spark session (spark.read.parquet(""))
I am using MapR 5.2, Spark version 2.1.0
Please suggest what can be the issue