Loaded data of spark data frame in hive table.
Before loading df.show(10) shows date column in proper format and data, but while querying hive table date column shows null.
//Spark Code
df.show(10)
+----------+
| bus_dt|
+----------+
|2021-12-01|
+----------+
//Hive shell
select distinct bus_dt from stg.dateTable;
NULL
//Table
create table stg.dateTable (
`bus_dt` date comment 'from deserializer')
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '/u0003'
STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
TBLPROPERTIES (
'serialization.null.format'=''
);
Can you try changing the DDL to use serde rather than TxtInputFormat.
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.MultiDelimitSerDe'
WITH SERDEPROPERTIES ('field.delim'='/u0003')
TBLPROPERTIES('serialization.null.format'='');
The issue seems to be with storage and retrival at hive side rather than at spark processing.
Related
I have created below table create using spark sql and inserted value using spark.sql
create_table=""" create table tbl1 (tran int,count int) partitioned by (year string) """
spark.sql(create_table)
insert_query="insert into tbl1 partition(year='2022') values (101,500)"
spark.sql(insert_query)
But I want to insert values into timestamp column using spark sql
create_table="create table tbl2 (tran int,trandate timestamp) partitioned by (year string)"
spark.sql(create_table)
But below insert statement is not working and throws error
insert_query="insert into tbl2 partition(year='2022') values (101,to_timestamp('2019-06-13 13:22:30.521000000', 'yyyy-mm-dd hh24:mi:ss.ff'))"
spark.sql(insert_query)
how to insert timestamp value into table using spark sql. Please help
Try below:
create_table="create table tbl5 (tran int,trandate timestamp) partitioned by (year string)"
spark.sql(create_table)
insert_query="insert into tbl5 partition(year='2022') values (101,cast(date_format('2019-06-13 13:22:30.521000000', 'yyyy-MM-dd HH:mm:ss.SSS') as timestamp))"
spark.sql(insert_query)
spark.sql("select * from tbl5").show(100,False)
+----+-----------------------+----+
|tran|trandate |year|
+----+-----------------------+----+
|101 |2019-06-13 13:22:30.521|2022|
+----+-----------------------+----+
I am trying to create a hive paritioned table from pyspark dataframe using spark sql. Below is the command I am executing, but getting an error. Error message below.
df.createOrReplaceTempView(df_view)
spark.sql("create table if not exists tablename PARTITION (date) AS select * from df_view")
Error: pyspark.sql.utils.ParseException:u"\nmismatched input 'PARTITION' expecting <EOF>
When I try to run without PARTITION (date) in the above line it works fine. However I am unable to create with partition.
How to create table with partition and insert date from.pyspark dataframe to hive.
To address this I created the table first
spark.sql("create table if not exists table_name (name STRING,age INT) partitioned by (date_column STRING)")
Then set dynamic partition to nonstrict using below.
spark.sql("SET hive.exec.dynamic.partition = true")
spark.sql("SET hive.exec.dynamic.partition.mode = nonstrict")
spark.sql("insert into table table_name PARTITION (date_column) select *,'%s from df_view" % current_date))
Where current date is a variable with today's date.
Hive 2.3.6-mapr
Spark v2.3.1
I am running same query:
select count(*)
from TABLE_A a
left join TABLE_B b
on a.key = c.key
and b.date > '2021-01-01'
and date_add(last_day(add_months(a.create_date, -1)),1) < '2021-03-01'
where cast(a.TIMESTAMP as date) >= '2021-01-20'
and cast(a.TIMESTAMP as date) < '2021-03-01'
But getting 1B rows as output in hive, while 1.01B in spark-sql.
By some initial analysis, it seems like all the extra rows in spark are having timestamp column as 2021-02-28 00:00:00.000000.
Both the TIMESTAMP and create_date columns have data type string.
What could be the reason behind this?
I will give you one possibility, but I need more information.
If you drop an external table, the data remains and spark can read it, but the metadata in Hive says it doesn't exist and doesn't read it.
That's why you have a difference.
I am learning Spark. I have a dataframe ts of below structure.
ts.show()
+--------------------+--------------------+
| UTC| PST|
+--------------------+--------------------+
|2020-11-04 02:24:...|2020-11-03 18:24:...|
+--------------------+--------------------+
I need to insert ts into Partitioned table in Hive with below structure,
spark.sql(""" create table db.ts_part
(
UTC timestamp,
PST timestamp
)
PARTITIONED BY( bkup_dt DATE )
STORED AS ORC""")
How do i dynamically pass system run date in the insert statement so that it gets partitioned on bkup_dt in table based on date.
I tried something like this code. But it didn't work
ts.write.partitionBy(current_date()).insertInto("db.ts_part",overwrite=False)
How should I do it? Can someone please help!
Try by creating new column with current_date() and then write as partitioned by hive table.
Example:
df.\
withColumn("bkup_dt",current_date()).\
write.\
partitionBy("bkup_dt").\
insertInto("db.ts_part",overwrite=False)
UPDATE:
try by creating temp view then run insert statement.
df.createOrReplaceTempView("tmp")
sql("insert into table <table_name> partition (bkup_dt) select *,current_date bkup_dt from tmp")
I am using spark 2.2.1 and hive2.1. I am trying to insert overwrite multiple partitions into existing partitioned hive/parquet table.
Table was created using sparkSession.
I have a table 'mytable' with partitions P1 and P2.
I have following set on sparkSession object:
"hive.exec.dynamic.partition"=true
"hive.exec.dynamic.partition.mode"="nonstrict"
Code:
val df = spark.read.csv(pathToNewData)
df.createOrReplaceTempView("updateTable") //here 'df' may contains data from multiple partitions. i.e. multiple values for P1 and P2 in data.
spark.sql("insert overwrite table mytable PARTITION(P1, P2) select c1, c2,..cn, P1, P2 from updateTable") // I made sure that partition columns P1 and P2 are at the end of projection list.
I am getting following error:
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.Table.ValidationFailureSemanticException: Partition spec {p1=, p2=, P1=1085, P2=164590861} contains non-partition columns;
dataframe 'df' have records for P1=1085, P2=164590861 . It looks like issue with casing (lower vs upper). I tried both cases in my query but it's still not working.
EDIT:
Insert statement works with static partitioning but that is not what I am looking for:
e.g. following works
spark.sql("insert overwrite table mytable PARTITION(P1=1085, P2=164590861) select c1, c2,..cn, P1, P2 from updateTable where P1=1085 and P2=164590861")
Create table stmt:
`CREATE TABLE `my_table`(
`c1` int,
`c2` int,
`c3` string,
`p1` int,
`p2` int)
PARTITIONED BY (
`p1` int,
`p2` int)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
'maprfs:/mds/hive/warehouse/my.db/xc_bonus'
TBLPROPERTIES (
'spark.sql.partitionProvider'='catalog',
'spark.sql.sources.schema.numPartCols'='2',
'spark.sql.sources.schema.numParts'='1',
'spark.sql.sources.schema.part.0'='{.spark struct metadata here.......}';
'spark.sql.sources.schema.partCol.0'='P1', //Spark is using Capital Names for Partitions; while hive is using lowercase
'spark.sql.sources.schema.partCol.1'='P2',
'transient_lastDdlTime'='1533665272')`
In above, spark.sql.sources.schema.partCol.0 uses all uppercase while PARTITIONED BY statement uses all lowercase for partitions columns
Based on the Exception and also assuming that the table 'mytable' was created as a partitioned table with P1 and P2 as partitions. One way to overcome this exception would be to force a dummy partition manually before executing the command. Try doing
spark.sql("alter table mytable add partition (p1=default, p2=default)").
Once successful, execute your insert overwrite statement. Hope this helps?
As I mentioned in EDIT section issue was in fact with difference in partition columns casing (lower vs upper) between hive and spark! I created hive table with all Upper cases but hive still internally stored it as lowercases but spark metadata kept is as Upper cases as intended by me. Fixing create statement with all lower case partition columns fixed the issue with subsequent updates!
If you are using hive 2.1 and spark 2.2 make sure following properties in create statement have same casing.
PARTITIONED BY (
p1int,
p2int)
'spark.sql.sources.schema.partCol.0'='p1',
'spark.sql.sources.schema.partCol.1'='p2',