Implementing efficient Spark application with the goal of having maximal performance often requires knowledge that goes beyond official documentation. Understanding Spark’s internal processes and features may help to design the queries in alignment with internal optimizations and thus achieve high efficiency during execution. In this talk we will focus on some internal features of Spark SQL which are not well described in official documentation with a strong emphasis on explaining these features on some basic examples while sharing some performance tips along the way.

1. Spark SQL Beyond Official
Documentation
David Vrba Ph.D.
Senior ML Engineer

2. About Myself
▪ Senior ML Engineer at Socialbakers
▪ developing and optimizing Spark jobs
▪ productionalizing Spark applications and deploying ML
models
▪ Spark Trainer
▪ 1-day, 2-days trainings
▪ reach out to me at https://www.linkedin.com/in/vrba-
david/
▪ Writer
▪ publishing articles at medium
▪ follow me at https://medium.com/@vrba.dave

3. Goal
▪ Knowledge sharing
▪ Free continuation of my previous talk
▪ Physical Plans in Spark SQL
▪ https://databricks.com/session_eu19/physical-plans-in-spark-sql
▪ Describe the non-obvious behavior of some Spark features
▪ Go beyond the documentation
▪ Focus on practical aspects of Spark SQL

4. Topics
▪ Statistics
▪ Saving data in sorted state to a file format

5. Statistics
▪ How to see them
▪ How they are computed
▪ Where they are used
▪ What to be careful about

6. Statistics - how to see them
▪ Table level:
▪ DESCRIBE EXTENDED
▪ DESCRIBE FORMATTED
spark.sql(“DESCRIBE EXTENDED table_name”).show(n=50)
spark.sql(“ANALYZE TABLE table_name COMPUTE
STATISTICS”).show(n=50)

7. Statistics - how to see them
▪ Column level:
spark.sql(“DESCRIBE EXTENDED table_name column_name”).show()

8. Statistics - how to see them
▪ From the plan - since Spark 3.0
spark.table(table_name).explain(mode=“cost”)

9. Statistics - how they are propagated
Relation
Filter
Project
Aggregate
Leaf Node - Responsible for
computing the statistics
Statistics are propagated
through the tree and adjusted
along the way

10. Statistics - how they are propagated
▪ Simple way
▪ propagates only sizeInBytes
▪ propagation through the plan is very basic (Filter is
not adjusted at all)
(
spark.table(table_name)
.filter(col(“user_id”) < 0)
.explain(mode=”cost”)
)

11. spark.conf.set(“spark.sql.cbo.enabled”, True)
Statistics - how they are propagated
▪ More advanced
▪ propagates sizeInBytes and rowCount + column level
▪ since Spark 2.2
▪ better propagation through plan (selectivity for Filter)
▪ CBO has to be enabled (by default OFF)
▪ works with metastore
No change in Filter statistics - it
requires column stats to be computed

12. Statistics - how they are propagated
▪ Selectivity requires having column level stats
spark.sql(“ANALYZE TABLE table_name COMPUTE STATISTICS
FOR COLUMNS user_id”)

13. Statistics - how they are computed
Relation
Filter
Project
Aggregate
Leaf Node - Responsible for
computing the statistics
1. Taken from metastore
2. Computed using Hadoop API (only sizeInBytes)
3. Default value sizeInBytes = 8EB
spark.sql.defaultSizeInBytes

14. Statistics - how they are computed
CBO ON
Analyze table ON
Table partitionedAll Stats from M
CatalogFileIndex InMemoryFI
T
T
T F
F
Analyze table ON
Stats from M
TF
F
CatalogTable
InMemoryFI
FT
Using Hadoop API - only
sizeInBytes
Using Hadoop API - only
sizeInBytes
Only sizeInBytes -
taken directly
All stats except for size which
is computed from rowCount
Maximum value (8 EB)
spark.table(...)
spark.sql.defaultSizeInBytes
spark.sql.cbo.enabled

15. Statistics - how they are computed
Partitioned table - ANALYZE TABLE haven’t run yet:
Not partitioned table - ANALYZE TABLE haven’t run yet:

16. Statistics - where they are used
▪ joinReorder - in case you join more than two tables
▪ finds most optimal configuration for multiple joins
▪ by default it is OFF
spark.conf.set(“spark.sql.cbo.joinReorder.enabled”, True)
▪ join selection - decide whether to use BroadcastHashJoin
▪ spark.sql.autoBroadcastJoinThreshold - 10MB default

17. Saving data in a sorted state to a file format
▪ Functions for sorting
▪ How to save in sorted state

18. Sorting in Spark SQL
▪ orderBy / sort
▪ DataFrame transformation
▪ samples data in separate job
▪ creates a shuffle to achieve global sort
▪ sortWithinPartitions
▪ DataFrame transformation
▪ sorts each partition
▪ sortBy
▪ called on DataFrameWriter after calling write
▪ used together with bucketing - sorts each bucket
▪ requires using saveAsTable

19. Example - save in sorted state
▪ Partition your data by the column: year
▪ Have each partition sorted by the column: user_id
▪ Have one file per partition (this file should be sorted by user_id)

20. Example - save in sorted state
(
df.repartition(‘year’)
.sortWithinPartitions(‘user_id’)
.write
.mode(‘overwrite’)
.partitionBy(‘year’)
.option(‘path’, output_path)
.saveAsTable(table_name)
)
This will not save the data sorted!
When saving the data to a file format Spark requires this
ordering:
(partitionColumns + bucketingIdExpression + sortColumns)
If this requirement is not satisfied Spark will forget the
sort and will sort it again with this ordering

21. Example - save in sorted state
(
df.repartition(‘year’)
.sortWithinPartitions(‘user_id’)
.write
.mode(‘overwrite’)
.partitionBy(‘year’)
.option(‘path’, output_path)
.saveAsTable(table_name)
)
requiredOrdering = (partitionColumns) = (year)
actualOrdering = (user_id)
The requirement is not satisfied.

22. Example - save in sorted state
(
df.repartition(‘year’)
.sortWithinPartitions(‘year’, ‘user_id’)
.write
.mode(‘overwrite’)
.partitionBy(‘year’)
.option(‘path’, output_path)
.saveAsTable(table_name)
)
requiredOrdering = (partitionColumns) = (year)
actualOrdering = (year, user_id)
The requirement is satisfied - Spark will keep the order
Instead call it as follows:

23. Conclusion
▪ Using statistics can improve performance of your joins
▪ Don’t forget to call ANALYZE TABLE especially if your table is partitioned
▪ Saving sorted data requires caution
▪ Don’t forget to sort by partition columns

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