I have a table in Databricks delta which is partitioned by transaction_date. I want to change the partition column to view_date. I tried to drop the table and then create it with a new partition column using PARTITIONED BY (view_date).
However my attempt failed since the actual files reside in S3 and even if I drop a hive table the partitions remain the same.
Is there any way to change the partition of an existing Delta table? Or the only solution will be to drop the actual data and reload it with a newly indicated partition column?
There's actually no need to drop tables or remove files. All you need to do is read the current table, overwrite the contents AND the schema, and change the partition column:
val input = spark.read.table("mytable")
input.write.format("delta")
.mode("overwrite")
.option("overwriteSchema", "true")
.partitionBy("colB") // different column
.saveAsTable("mytable")
UPDATE: There previously was a bug with time travel and changes in partitioning that has now been fixed.
As Silvio pointed out there is no need to drop the table. In fact the strongly recommended approach by databricks is to replace the table.
https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-create-table-using.html#parameters
in spark SQL, This can be done easily by
REPLACE TABLE <tablename>
USING DELTA
PARTITIONED BY (view_date)
AS
SELECT * FROM <tablename>
Modded example from:
https://docs.databricks.com/delta/best-practices.html#replace-the-content-or-schema-of-a-table
Python solution:
If you need more than one column in the partition
partitionBy(column, column_2, ...)
def change_partition_of(table_name, column):
df = spark.read.table(tn)
df.write.format("delta").mode("overwrite").option("overwriteSchema", "true").partitionBy(column).saveAsTable(table_name)
change_partition_of("i.love_python", "column_a")
Related
I have a partitioned parquet at the following path:
/path/to/partitioned/parq/
with partitions like:
/path/to/partitioned/parq/part_date=2021_01_01_01_01_01
/path/to/partitioned/parq/part_date=2021_01_02_01_01_01
/path/to/partitioned/parq/part_date=2021_01_03_01_01_01
When I run a Spark SQL CREATE TABLE statement like:
CREATE TABLE IF NOT EXISTS
my_db.my_table
USING PARQUET
LOCATION '/path/to/partitioned/parq'
The partition column part_date shows up in my dataset, but DESCRIBE EXTENDED indicates there are no PARTITIONS. SHOW PARTITIONS my_db.my_table shows no partition data.
This seems to happen intermittently, like sometimes spark infers the partitions, other times it doesn't. This is causing issues downstream where we add a partition and try to MSCK REPAIR TABLE my_db.my_table and it says you can't run that on non-partitioned tables.
I see that if you DO declare schema, you can FORCE the PARTITIONED BY part of the clause but we do not have the luxury of a schema, just the files from underneath.
Why is spark intermittently unable to determine partition columns from a parquet in this shape?
Unfortunately with Hive you need to specify the schema, even if parquet obviously has this itself.
You need to add partition by clause to DDL.
Use ALTER table statement to add each partition separately with location.
If am using df.write.partitionby(col1).parquet(path) .
the data will remove the partition column on the data.
how to avoid it ?
You can duplicate col1 before writing:
df.withColumn("partition_col", col("col1")).write.partitionBy("partition_col").parquet(path)
Note that this step is not really necessary, because whenever you read a Parquet file in a partitioned directory structure, Spark will automatically add that as a new column to the dataframe.
Actually spark does not remove the column but it uses that column in a way to organize the files so that when you read the files it adds that as a column and display that to you in a table format. If you check the schema of the table or the schema of the dataframe you would still see that as a column in the table.
Also you are partitioning your data so you know how that data from table is queried frequently and based on that information you might have decided to partition the data so that your reads becomes faster and more efficient.
I have external Hive Table which is filled by spark job and partitioned by(event_date date) now I have modified the spark code and added one extra column 'country'.In earlier written data country column will have null values as it is newly added. now I want to Alter 'partitioned by' clause as partition by(event_date date,country string) how can I achieve this.Thank you!!
Please try to alter the partition using below commnad-
ALTER TABLE table_name PARTITION part_spec SET LOCATION path
part_spec:
: (part_col_name1=val1, part_col_name2=val2, ...)
Try this databricks spark-sql language manual for alter command
i have 30 columns in a table i.e table_old
i want to use 29 columns in that table except one . that column is dynamic.
i am using string interpolation.
the below sparksql query i am using
drop_column=now_current_column
var table_new=spark.sql(s"""alter table table_old drop $drop_column""")
but its throwing error
mismatched input expecting 'partition'
i dont want to drop the column using dataframe. i requirement is to drop the column in a table using sparksql only
As mentioned in previous answer, DROP COLUMN is not supported by spark yet.
But, there is a workaround to achieve the same, without much overhead. This trick works for both EXTERNAL and InMemory tables. The code snippet below works for EXTERNAL table, you can easily modify it and use it for InMemory tables as well.
val dropColumnName = "column_name"
val tableIdentifier = "table_name"
val tablePath = "table_path"
val newSchema=StructType(spark.read.table(tableIdentifier).schema.filter(col => col.name != dropColumnName))
spark.sql(s"drop table ${tableIdentifier}")
spark.catalog.createTable(tableIdentifier, "orc", newSchema, Map("path" -> tablePath))
orc is the file format, it should be replaced with the required format. For InMemory tables, remove the tablePath and you are good to go. Hope this helps.
DROP COLUMN (and in general majority of ALTER TABLE commands) are not supported in Spark SQL.
If you want to drop column you should create a new table:
CREATE tmp_table AS
SELECT ... -- all columns without drop TABLE
FROM table_old
and then drop the old table or view, and reclaim the name.
Now drop columns is supported by Spark if you´re using v2 tables. You can check this link
https://spark.apache.org/docs/latest/sql-ref-syntax-ddl-alter-table.html
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)