Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix pyspark docs #48

Merged
merged 3 commits into from
Dec 23, 2024
Merged

Fix pyspark docs #48

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
32b9baa
Fix pyspark docs
ohad-definity Dec 10, 2024
a9cd5b9
revert blog
ohad-definity Dec 10, 2024
bb898fc
update readme version
ohad-definity Dec 10, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 7 additions & 12 deletions datafu-spark/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,19 +34,17 @@ In order to call the datafu-spark API's from Pyspark, you can do the following (
First, call pyspark with the following parameters

```bash
export PYTHONPATH=datafu-spark_2.11-1.8.0.jar
export PYTHONPATH=datafu-spark_2.12-2.0.0.jar

pyspark --jars datafu-spark_2.11-1.8.0.jar --conf spark.executorEnv.PYTHONPATH=datafu-spark_2.11-1.8.0.jar
pyspark --jars datafu-spark_2.12-2.0.0.jar --conf spark.executorEnv.PYTHONPATH=datafu-spark_2.12-2.0.0.jar
```

The following is an example of calling the Spark version of the datafu _dedup_ method

```python
from pyspark_utils.df_utils import PySparkDFUtils
from pyspark_utils import df_utils

df_utils = PySparkDFUtils()

df_people = sqlContext.createDataFrame([
df_people = spark.createDataFrame([
("a", "Alice", 34),
("a", "Sara", 33),
("b", "Bob", 36),
Expand All @@ -57,12 +55,9 @@ df_people = sqlContext.createDataFrame([
("c", "Zoey", 36)],
["id", "name", "age"])

func_dedup_res = df_utils.dedup_with_order(dataFrame=df_people, groupCol=df_people.id,
orderCols=[df_people.age.desc(), df_people.name.desc()])

func_dedup_res.registerTempTable("dedup")

func_dedup_res.show()
df_dedup = df_utils.dedup_with_order(df=df_people, group_col=df_people.id,
order_cols=[df_people.age.desc(), df_people.name.desc()])
df_dedup.show()
```

This should produce the following output
Expand Down
19 changes: 10 additions & 9 deletions site/source/docs/spark/guide.html.markdown.erb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,11 +43,9 @@ pyspark --jars datafu-spark_2.12-<%= current_page.data.version %>-SNAPSHOT.jar -
The following is an example of calling the Spark version of the datafu _dedup_ method

```python
from pyspark_utils.df_utils import PySparkDFUtils
from pyspark_utils import df_utils

df_utils = PySparkDFUtils()

df_people = sqlContext.createDataFrame([
df_people = spark.createDataFrame([
("a", "Alice", 34),
("a", "Sara", 33),
("b", "Bob", 36),
Expand All @@ -58,12 +56,15 @@ df_people = sqlContext.createDataFrame([
("c", "Zoey", 36)],
["id", "name", "age"])

func_dedup_res = df_utils.dedup_with_order(dataFrame=df_people, groupCol=df_people.id,
orderCols=[df_people.age.desc(), df_people.name.desc()])

func_dedup_res.registerTempTable("dedup")
df_dedup = df_utils.dedup_with_order(df=df_people, group_col=df_people.id,
order_cols=[df_people.age.desc(), df_people.name.desc()])
df_dedup.show()

func_dedup_res.show()
# or with activate()
df_utils.activate()
df_dedup_top_n = df_people.dedup_top_n(n=2, group_col=df_people.id,
order_cols=[df_people.age.desc(), df_people.name.desc()])
df_dedup_top_n.show()
```

This should produce the following output
Expand Down
Loading