I have created a HIVE table through pyspark in ORC format and everything is working as per the requirement.
However, when I observed the details o fthe HIVE table, I see below
describe formatted <tbl_name>;
I get below output
Table Parameters:
COLUMN_STATS_ACCURATE false
EXTERNAL FALSE
numFiles 99
numRows -1
rawDataSize -1
How can I change the value of "COLUMN_STATS_ACCURATE" while writing the code in pyspark? IS there any way to do that? If no, then is there a way to change it after the table has been created?
You can call ANALYZE TABLE:
spark.sql("ANALYZE TABLE foo COMPUTE STATISTICS")
but please remember, that Spark output in general, provides only partial Hive compatibility.
Related
Running into an issue on Spark 2.4 on EMR 5.20 in AWS.
I have a string column as a partition, which has date values. My goal is to have the max value of this column be referenced as a filter. The values look like this 2019-01-01 for January 1st, 2019.
In this query, I am trying to filter to a certain date value (which is a string data type), and Spark ends up reading all directories, not just the resulting max(value).
spark.sql("select mypartitioncolumn, column1, column2 from mydatabase.mytable where mypartitioncolumn= (select max(mypartitioncolumn) from myothertable) group by 1,2,3 ").show
However, in this instance, If I hardcode the value, it only reads the proper directory.
spark.sql("select mypartitioncolumn, column1, column2 from mydatabase.mytable where mypartitioncolumn= '2019-01-01' group by 1,2,3 ").show
Why is Spark not recognizing both methods in the same way? I made sure that if I run the select max(mypartitioncolumn) from myothertable query, it shows the exact same value as my hardcoded method (as well as the same datatype).
I can't find anything in the documentation that differentiates partition querying other than data type differences. I checked to make sure that my schema in both the source table as well as value are string types, and also tried to cast my value as a string as well cast( (select max(mypartitioncolumn) from myothertable) as string), it doesn't make any difference.
Workaround by changing configuration
sql("set spark.sql.hive.convertMetastoreParquet = false")
Spark docs
"When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the spark.sql.hive.convertMetastoreParquet configuration, and is turned on by default."
I'm having an issue writing a Hive table from Spark. The following code works just fine; I can write the table (which defaults to the Parquet format) and read it back in Hive:
df.write.mode('overwrite').saveAsTable("db.table")
hive> describe table;
OK
val string
Time taken: 0.021 seconds, Fetched: 1 row(s)
However, if I specify the format should be csv:
df.write.mode('overwrite').format('csv').saveAsTable("db.table")
then I can save the table, but Hive doesn't recognize the schema:
hive> describe table;
OK
col array<string> from deserializer
Time taken: 0.02 seconds, Fetched: 1 row(s)
It's also worth noting that I can create a Hive table manually and then insertInto it:
spark.sql("create table db.table(val string)")
df.select('val').write.mode("overwrite").insertInto("db.table")
Doing so, Hive seems to recognize the schema. But that's clunky and I can't figure a way to automate the schema string anyway.
That is because Hive SerDe do not support csv by default.
If you insist on using csv format, creating table as below:
CREATE TABLE my_table(a string, b string, ...)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
WITH SERDEPROPERTIES (
"separatorChar" = "\t",
"quoteChar" = "'",
"escapeChar" = "\\"
)
STORED AS TEXTFILE;
And insert data through df.write.insertInto
For more info:
https://cwiki.apache.org/confluence/display/Hive/CSV+Serde
You are creating a table with text format and trying to insert CSV data into it, which may run in to problems. So as suggested in the answer by Zhang Tong, create the hive table using hive OpenCSVSerde.
After that, if you are more comfortable with Hive query language than dataframes, you can try this.
df.registerTempTable("temp")
spark.sql("insert overwrite db.table select * from temp")
This happens because HiveSerde is different for csv than what is used by Spark. Hive by default use TEXTFORMAT and the delimiter has to be specified while creating the table.
One Option is to use the insertInto API instead of saveAsTable while writing from spark. While using insertInto, Spark writes the contents of the Dataframe to the specified table. But it requires the schema of the dataframe to be same as the schema of the table. Position of the columns is important here as it ignores the column names.
Seq((5, 6)).toDF("a", "b").write.insertInto("t1")
is there a way, how to convert an input string field into orc table with column specified as varchar(xx) in sparksql select query? Or I have to use some workaroud? I'm using Spark 1.6.
I found on Cloudera forum, Spark does not care about length, it saves the value as string with no size limit.
The table is inserted into Hive OK, but I'm little bit worried about data quality.
temp_table = sqlContext.table(ext)
df = temp_table.select(temp_dable.day.cast('string'))
I would like to see something like that :)))
df = temp_table.select(temp_dable.day.cast('varchar(100)'))
Edit:
df.write.partitionBy(part).mode('overwrite').insertInto(int)
Table I'm inserting into is saved as an ORC file (the line above probably should have .format('orc')).
I found here, that If I specify a column as a varchar(xx) type, than the input string will be cutoff to the xx length.
Thx
I have a dataframe with n parameter of columns more than 100 and i want to insert them to hbase.
What's the best way to do that?
HBase connector can be used, more details here:
https://github.com/hortonworks-spark/shc
The Hive table was created using 4 partitions.
CREATE TABLE IF NOT EXISTS hourlysuspect ( cells int, sms_in int) partitioned by (traffic_date_hour string) stored as ORC into 4 buckets
The following lines in the spark code insert data into this table
hourlies.write.partitionBy("traffic_date_hour").insertInto("hourly_suspect")
and in the spark-defaults.conf, the number of parallel processes is 128
spark.default.parallelism=128
The problem is that when the inserts happen in the hive table, it has 128 partitions instead of 4 buckets.
The defaultParallelism cannot be reduced to 4 as that leads to a very very slow system. Also, I have tried the DataFrame.coalesce method but that makes the inserts too slow.
Is there any other way to force the number of buckets to be 4 when the data is inserted into the table?
As of today {spark 2.2.0} Spark does not support writing to bucketed hive tables natively using spark-sql. While creating the bucketed table, there should be a clusteredBy clause on one of the columns form the table schema. I don't see that in the specified CreateTable statement. Assuming, that it does exist and you know the clustering column, you could add the
.bucketBy([colName])
API while using DataFrameWriter API.
More details for Spark2.0+: [Link] (https://spark.apache.org/docs/2.0.0/api/java/org/apache/spark/sql/DataFrameWriter.html)