I have some orc files produced by spark job.
Is there some easy way to create an external table directly from those files?
The way I have done this is to first register a temp table in Spark job itself and then leverage the sql method of the HiveContext to create a new table in hive using the data from the temp table. For example if I have a dataframe df and HiveContext hc the general process is:
df.registerTempTable("my_temp_table")
hc.sql("CREATE TABLE new_table_name STORED AS ORC AS SELECT * from my_temp_table")
Related
I have spark conf as:
sparkConf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
sparkConf.set("hive.exec.dynamic.partition", "true")
sparkConf.set("hive.exec.dynamic.partition.mode", "nonstrict")
I am using the spark context to write the parquet files into hdfs location as:
df.write.partitionBy('asofdate').mode('append').parquet('parquet_path')
In hdfs location, the parquet files are stored as 'asofdate' but in hive table I have to do 'MSCK REPAIR TABLE <tbl_name>' everyday. I am looking for a solution on how I can do recover table for every new partitions using spark script (or at the time of partition creation itself).
It's better if you integrate hive with spark to make your job easier.
After the hive-spark integration setup, you can enable hive support while creating SparkSession.
spark = SparkSession.builder.enableHiveSupport().getOrCreate()
Now you can access hive tables from spark.
You can run repair command from spark itself.
spark.sql("MSCK REPAIR TABLE <tbl_name>")
I would suggest to write dataframe directly as a hive table instead of writing it to parquet and do repair table.
df.write.partitionBy("<partition_column>").mode("append").format("parquet").saveAsTable("<table>")
I am trying to understand the spark DataFrame API method called saveAsTable.
I have following question
If I simply write a dataframe using saveAsTable API
df7.write.saveAsTable("t1"), (assuming t1 did not exist earlier), will the newly created table be a hive table which can be read outside spark using Hive QL ?
Does spark also create some non-hive table (which are created using saveAsTable API but can not be read outside spark using HiveQL)?
How can check if a table is Hive Table or Non-Hive table ?
(I am new to big data processing, so pardon me if question is not phrased properly)
Yes. Newly created table will be hive table and can be queried from Hive CLI(Only if the DataFrame is created from single input HDFS path i.e. from non-partitioned single input HDFS path).
Below is the documentation comment in DataFrameWriter.scala class. Documentation link
When the DataFrame is created from a non-partitioned
HadoopFsRelation with a single input path, and the data source
provider can be mapped to an existing Hive builtin SerDe (i.e. ORC and
Parquet), the table is persisted in a Hive compatible format, which
means other systems like Hive will be able to read this table.
Otherwise, the table is persisted in a Spark SQL specific format.
Yes, you can do. You table can be partitioned by a column, but can not use bucketing (its a problem between spark and hive).
I have a simple Hive-External table which is created on top of S3 (Files are in CSV format). When I run the hive query it shows all records and partitions.
However when I use the same table in Spark ( where the Spark SQL has a where condition on the partition column) it does not show that a partition filter is applied. However for a Hive Managed table , Spark is able to use the information of partitions and apply the partition filter.
Is there any flag or setting that can help me make use of partitions of Hive external tables in Spark ? Thanks.
Update :
For some reason, only the spark plan is not showing the Partition Filters. However, when you look at the data loaded its only loading the data needed from the partitions.
Ex: Where rating=0 , loads only one file of 1 MB, when I don't have filter its reads all 3 partition for 3 MB
tl; dr set the following before the running sql for external table
spark.sql("set spark.sql.hive.convertMetastoreOrc=true")
The difference in behaviour is not because of extenal/managed table.
The behaviour depends on two factors
1. Where the table was created(Hive or Spark)
2. File format (I believe it is ORC in this case, from the screen capture)
Where the table was created(Hive or Spark)
If the table was create using Spark APIs, it is considered as Datasource table.
If the table was created usng HiveQL, it is considered as Hive native table.
The metadata of both these tables are store in Hive metastore, the only difference is in the provider field of TBLPROPERTIES of the tables(describe extended <tblName>). The value of the property is orcor empty in Spark table and hive for a Hive.
How spark uses this information
When provider is not hive(datasource table), Spark uses its native way of processing the data.
If provider is hive, Spark uses Hive code to process the data.
Fileformat
Spark gives config flag to instruct the engine to use Datasource way of processing the data for the floowing file formats = Orc and Parquet
Flags:
Orc
val CONVERT_METASTORE_ORC = buildConf("spark.sql.hive.convertMetastoreOrc")
.doc("When set to true, the built-in ORC reader and writer are used to process " +
"ORC tables created by using the HiveQL syntax, instead of Hive serde.")
.booleanConf
.createWithDefault(true)
Parquet
val CONVERT_METASTORE_PARQUET = buildConf("spark.sql.hive.convertMetastoreParquet")
.doc("When set to true, the built-in Parquet reader and writer are used to process " +
"parquet tables created by using the HiveQL syntax, instead of Hive serde.")
.booleanConf
.createWithDefault(true)
I also ran into this kind of problem having multiple joins of internal and external tables.
None of the tricks work including：
spark.sql("set spark.sql.hive.convertMetastoreParquet=false")
spark.sql("set spark.sql.hive.metastorePartitionPruning=true")
spark.sql("set spark.sql.hive.caseSensitiveInferenceMode=NEVER_INFER")
anyone who knows how to solve this problem.
I am trying to create a spark dataframe on a existing HBase Table(HBase is secured via Kerberos). I need to perform some spark Sql operations on this table.
I have tried creating a RDD on a Hbase table but unable to convert it into dataframe.
You can create hive external table with HBase storage handler and then use that table to run your spark-sql queries.
Creating the hive external table:
CREATE TABLE foo(rowkey STRING, a STRING, b STRING)
STORED BY ‘org.apache.hadoop.hive.hbase.HBaseStorageHandler’
WITH SERDEPROPERTIES (‘hbase.columns.mapping’ = ‘:key,f:c1,f:c2’)
TBLPROPERTIES (‘hbase.table.name’ = ‘bar’);
Spark-sql:
val df=spark.sql("SELECT * FROM foo WHERE …")
Note: Here spark is a SparkSession
I need to load some data from HDFS to Hive. But I need to some aggregations between the files that I've in HDFS. I read that Sqoop can do that but only using MySQL. Which another choices I've to do this?
Thanks!
Your best option would be to create an external table in Hive that sources from your files in HDFS. Then you can create a Hive table to store your aggregated data and some Hive SQL to do the insert into that table.