I'm trying to insert some data in a table that will have 1500 dynamic partitions and I receive this error:
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException:
Number of dynamic partitions created is 1500, which is more than 1000.
To solve this try to set hive.exec.max.dynamic.partitions to at least 1500.
So, I try to: SET hive.exec.max.dynamic.partitions=2048 but I still get the same error.
How can I change this value from Spark?
Code:
this.spark.sql("SET hive.exec.dynamic.partition=true")
this.spark.sql("set hive.exec.dynamic.partition.mode=nonstrict")
this.spark.sql("SET hive.exec.max.dynamic.partitions=2048")
this.spark.sql(
"""
|INSERT INTO processed_data
|PARTITION(event, date)
|SELECT c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,event,date FROM csv_data DISTRIBUTE BY event, date
""".stripMargin
).show()
Using Spark 2.0.0 standalone mode.
Thank you!
From spark 2.x version, adding hive set properties in Spark CLI may not work. Please add your hive set properties in hive-site.xml of your both spark and hive conf directories.
adding below property in hive-site.xml file should resolve your issue.
<name>hive.exec.max.dynamic.partitions</name>
<value>2048</value>
<description></description>
Note: restart hiveserver2 and spark history server if it didn't work.
Related
Conf
spark.conf.set('spark.sql.hive.convertMetastoreParquet', "true")
Hive table
spark.sql("create table table_name (ip string, user string) PARTITIONED BY (date date) STORED AS PARQUET")
InsertInto
df.write.insertInto("table_name", overwrite=True)
Error
Caused by: java.lang.ClassNotFoundException: org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1
Btw insert into ORC table is good. Running on cluster with client mode.
Is your hive-site.xml file present in the Spark config folder?
Edit:
Can you try with:
df.write.mode("overwrite").partitionBy("date").saveAsTable("db.table_name")
It should not be necessary to set any configuration beforehand and to run the SQL create statement.
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.
There is an external table in hive pointing to s3 location that is not partitioned. The table points to a folder in s3 but the data is in multiple subfolders inside that folder.
This table can be queried even though the table is not partitioned by setting few properties in hive like below,
set hive.input.dir.recursive=true;
set hive.mapred.supports.subdirectories=true;
set hive.supports.subdirectories=true;
set mapred.input.dir.recursive=true;
However, when the same table is used in spark to load the data into a dataframe using a sql statement like df = sqlContext.sql("select * from table_name"), the action fails saying 'The subfolders in the external s3 location is not a file'.
I tried setting above hive properties in spark using sc.hadoopConfiguration.set("mapred.input.dir.recursive","true") method, but it did not help. Looks like this would help only for sc.textFile kind of loading.
This can be achieved by setting the following property in spark,
sqlContext.setConf("mapreduce.input.fileinputformat.input.dir.recursive","true")
Note here that the property is set usign sqlContext instead of sparkContext.
And I tested this in spark 1.6.2
I'd like to save data in a Spark (v 1.3.0) dataframe to a Hive table using PySpark.
The documentation states:
"spark.sql.hive.convertMetastoreParquet: When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in support."
Looking at the Spark tutorial, is seems that this property can be set:
from pyspark.sql import HiveContext
sqlContext = HiveContext(sc)
sqlContext.sql("SET spark.sql.hive.convertMetastoreParquet=false")
# code to create dataframe
my_dataframe.saveAsTable("my_dataframe")
However, when I try to query the saved table in Hive it returns:
hive> select * from my_dataframe;
OK
Failed with exception java.io.IOException:java.io.IOException:
hdfs://hadoop01.woolford.io:8020/user/hive/warehouse/my_dataframe/part-r-00001.parquet
not a SequenceFile
How do I save the table so that it's immediately readable in Hive?
I've been there...
The API is kinda misleading on this one.
DataFrame.saveAsTable does not create a Hive table, but an internal Spark table source.
It also stores something into Hive metastore, but not what you intend.
This remark was made by spark-user mailing list regarding Spark 1.3.
If you wish to create a Hive table from Spark, you can use this approach:
1. Use Create Table ... via SparkSQL for Hive metastore.
2. Use DataFrame.insertInto(tableName, overwriteMode) for the actual data (Spark 1.3)
I hit this issue last week and was able to find a workaround
Here's the story:
I can see the table in Hive if I created the table without partitionBy:
spark-shell>someDF.write.mode(SaveMode.Overwrite)
.format("parquet")
.saveAsTable("TBL_HIVE_IS_HAPPY")
hive> desc TBL_HIVE_IS_HAPPY;
OK
user_id string
email string
ts string
But Hive can't understand the table schema(schema is empty...) if I do this:
spark-shell>someDF.write.mode(SaveMode.Overwrite)
.format("parquet")
.saveAsTable("TBL_HIVE_IS_NOT_HAPPY")
hive> desc TBL_HIVE_IS_NOT_HAPPY;
# col_name data_type from_deserializer
[Solution]:
spark-shell>sqlContext.sql("SET spark.sql.hive.convertMetastoreParquet=false")
spark-shell>df.write
.partitionBy("ts")
.mode(SaveMode.Overwrite)
.saveAsTable("Happy_HIVE")//Suppose this table is saved at /apps/hive/warehouse/Happy_HIVE
hive> DROP TABLE IF EXISTS Happy_HIVE;
hive> CREATE EXTERNAL TABLE Happy_HIVE (user_id string,email string,ts string)
PARTITIONED BY(day STRING)
STORED AS PARQUET
LOCATION '/apps/hive/warehouse/Happy_HIVE';
hive> MSCK REPAIR TABLE Happy_HIVE;
The problem is that the datasource table created through Dataframe API(partitionBy+saveAsTable) is not compatible with Hive.(see this link). By setting spark.sql.hive.convertMetastoreParquet to false as suggested in the doc, Spark only puts data onto HDFS,but won't create table on Hive. And then you can manually go into hive shell to create an external table with proper schema&partition definition pointing to the data location.
I've tested this in Spark 1.6.1 and it worked for me. I hope this helps!
I have done in pyspark, spark version 2.3.0 :
create empty table where we need to save/overwrite data like:
create table databaseName.NewTableName like databaseName.OldTableName;
then run below command:
df1.write.mode("overwrite").partitionBy("year","month","day").format("parquet").saveAsTable("databaseName.NewTableName");
The issue is you can't read this table with hive but you can read with spark.
metadata doesn't already exist. In other words, it will add any partitions that exist on HDFS but not in metastore, to the hive metastore.
Running CDH4 cluster with Impala, I created parquet table and after adding parquet jar files to hive, I can query the table using hive.
Added same set of jars to /opt/presto/lib and restarted coordinator and workers.
parquet-avro-1.2.4.jar
parquet-cascading-1.2.4.jar
parquet-column-1.2.4.jar
parquet-common-1.2.4.jar
parquet-encoding-1.2.4.jar
parquet-format-1.0.0.jar
parquet-generator-1.2.4.jar
parquet-hadoop-1.2.4.jar
parquet-hive-1.2.4.jar
parquet-pig-1.2.4.jar
parquet-scrooge-1.2.4.jar
parquet-test-hadoop2-1.2.4.jar
parquet-thrift-1.2.4.jar
Still getting this error when running parquet select query from Presto:
> select * from test_pq limit 2;
Query 20131116_144258_00002_d3sbt failed : org/apache/hadoop/hive/serde2/SerDe
Presto now supports Parquet automatically.
Try to add the jars in presto plugin dir instead of presto lib dir.
Presto auto loads jars from plugins dirs.