[
https://issues.apache.org/jira/browse/SPARK-39474?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Chris Mahoney updated SPARK-39474:
----------------------------------
Description:
Hi Team!
I'd like to set up a much easier way to optimize my {{delta}} tables.
Specifically, I am referring to the {{sql}} command {{{}OPTIMIZE <table>{}}}.
Let me show you the differences:
*Current:*
First, run:
{code:java}
import pandas as pd
df = spark.createDataFrame(
pd.DataFrame(
{'a': [1,2,3,4],
'b': ['a','b','c','d']}
)
)
df.write.mode('overwrite').format('delta').save('./folder'){code}
Then, once it's saved, run:
{code:java}
CREATE TABLE df USING DELTA LOCATION './folder' {code}
Then, once the table is loaded, run:
{code:java}
OPTIMIZE df
--or
OPTIMIZE df ZORDER BY (b) {code}
As you can see, there are many steps needed.
*Future:*
I'd like to be able to do something like this:
{code:java}
import pandas as pd
df = spark.createDataFrame(
pd.DataFrame(
{'a':[1,2,3,4],
'b':['a','b','c','d']}
)
)
df.write.mode('overwrite').format('delta').options(optimize=True).save('./folder')
#or
df.write.mode('overwrite').format('delta').options(optimize=True,('b')).save('./folder')
{code}
As you can see, it's much more streamlined, and keeps the code to a
higher-level.
Thank you.
References: *
https://docs.azuredatabricks.net/_static/notebooks/delta/optimize-python.html
*
https://medium.com/@debusinha2009/cheatsheet-on-understanding-zorder-and-optimize-for-your-delta-tables-1556282221d3
*
https://www.cloudiqtech.com/partition-optimize-and-zorder-delta-tables-in-azure-databricks/
* https://docs.databricks.com/delta/optimizations/file-mgmt.html
*
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-optimize.html
*
https://stackoverflow.com/questions/65320949/parquet-vs-delta-format-in-azure-data-lake-gen-2-store?_sm_au_=iVV4WjsV0q7WQktrJfsTkK7RqJB10
*
https://www.i-programmer.info/news/197-data-mining/12582-databricks-delta-adds-faster-parquet-import.html#:~:text=Databricks%20says%20Delta%20is%2010,data%20management%2C%20and%20query%20serving.
was:
Hi Team!
I'd like to set up a much easier way to optimize my {{delta}} tables.
Specifically, I am referring to the {{sql}} command {{{}OPTIMIZE <table>{}}}.
Let me show you the differences:
*Current:*
First, run:
{code:java}
import pandas as pd
df = spark.createDataFrame(
pd.DataFrame(
{'a': [1,2,3,4],
'b': ['a','b','c','d']}
)
)
df.write.mode('overwrite').format('delta').save('./folder'){code}
Then, once it's saved, run:
{code:java}
CREATE TABLE df USING DELTA LOCATION './folder' {code}
Then, once the table is loaded, run:
{code:java}
OPTIMIZE df
--or
OPTIMIZE df ZORDER BY (b) {code}
As you can see, there are many steps needed.
*Future:*
I'd like to be able to do something like this:
{code:java}
import pandas as pd
df = spark.createDataFrame(
pd.DataFrame(
{'a':[1,2,3,4],
'b':['a','b','c','d']}
)
)
df.write.mode('overwrite').format('delta').options(optimize=True).save('./folder')
#or
df.write.mode('overwrite').format('delta').options(optimize=True,('b')).save('./folder')
{code}
As you can see, it's much more streamlined, and keeps the code to a
higher-level.
Thank you.
References:
*
[https://docs.azuredatabricks.net/_static/notebooks/delta/optimize-python.html]
*
[https://medium.com/@debusinha2009/cheatsheet-on-understanding-zorder-and-optimize-for-your-delta-tables-1556282221d3]
*
[https://www.cloudiqtech.com/partition-optimize-and-zorder-delta-tables-in-azure-databricks/]
* [https://docs.databricks.com/delta/optimizations/file-mgmt.html]
*
[https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-optimize.html]
*
[https://stackoverflow.com/questions/65320949/parquet-vs-delta-format-in-azure-data-lake-gen-2-store?_sm_au_=iVV4WjsV0q7WQktrJfsTkK7RqJB10]
*
[https://www.i-programmer.info/news/197-data-mining/12582-databricks-delta-adds-faster-parquet-import.html#:~:text=Databricks%20says%20Delta%20is%2010,data%20management%2C%20and%20query%20serving]
> Streamline the options for the `.write` method of a Spark DataFrame
> -------------------------------------------------------------------
>
> Key: SPARK-39474
> URL: https://issues.apache.org/jira/browse/SPARK-39474
> Project: Spark
> Issue Type: Wish
> Components: PySpark
> Affects Versions: 3.2.1
> Reporter: Chris Mahoney
> Priority: Minor
> Fix For: 3.2.1
>
>
> Hi Team!
> I'd like to set up a much easier way to optimize my {{delta}} tables.
> Specifically, I am referring to the {{sql}} command {{{}OPTIMIZE <table>{}}}.
> Let me show you the differences:
> *Current:*
> First, run:
> {code:java}
> import pandas as pd
> df = spark.createDataFrame(
> pd.DataFrame(
> {'a': [1,2,3,4],
> 'b': ['a','b','c','d']}
> )
> )
> df.write.mode('overwrite').format('delta').save('./folder'){code}
> Then, once it's saved, run:
> {code:java}
> CREATE TABLE df USING DELTA LOCATION './folder' {code}
> Then, once the table is loaded, run:
> {code:java}
> OPTIMIZE df
> --or
> OPTIMIZE df ZORDER BY (b) {code}
> As you can see, there are many steps needed.
> *Future:*
> I'd like to be able to do something like this:
> {code:java}
> import pandas as pd
> df = spark.createDataFrame(
> pd.DataFrame(
> {'a':[1,2,3,4],
> 'b':['a','b','c','d']}
> )
> )
> df.write.mode('overwrite').format('delta').options(optimize=True).save('./folder')
> #or
> df.write.mode('overwrite').format('delta').options(optimize=True,('b')).save('./folder')
> {code}
> As you can see, it's much more streamlined, and keeps the code to a
> higher-level.
> Thank you.
>
> References: *
> https://docs.azuredatabricks.net/_static/notebooks/delta/optimize-python.html
> *
> https://medium.com/@debusinha2009/cheatsheet-on-understanding-zorder-and-optimize-for-your-delta-tables-1556282221d3
> *
> https://www.cloudiqtech.com/partition-optimize-and-zorder-delta-tables-in-azure-databricks/
> * https://docs.databricks.com/delta/optimizations/file-mgmt.html
> *
> https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-optimize.html
> *
> https://stackoverflow.com/questions/65320949/parquet-vs-delta-format-in-azure-data-lake-gen-2-store?_sm_au_=iVV4WjsV0q7WQktrJfsTkK7RqJB10
> *
> https://www.i-programmer.info/news/197-data-mining/12582-databricks-delta-adds-faster-parquet-import.html#:~:text=Databricks%20says%20Delta%20is%2010,data%20management%2C%20and%20query%20serving.
>
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