This document evaluates the performance of Apache Spark using TPC-H benchmarks, comparing Spark SQL and Spark Scala. Key findings indicate up to a 30% performance increase by switching APIs, highlighting that parquet with snappy compression offers the best performance. It also reveals that Hive metastore introduces some overhead without improving performance significantly.

1. AN EVALUATION OF TPC-H
ON SPARK & SPARK SQL IN ALOJA
M.SC. RAPHAEL RADOWITZ @DATAWORKS SUMMIT, BERLIN 19TH APRIL 2018
FRANKFURT BIG DATA LAB @GOETHE UNIVERSITY

2. AGENDA
 Motivation & Research Objectives
 Spark
 Ecosystem
 Data Access
 ALOJA & TPC-H
 Spark SQL with or without Hive Metastore
 File Formats
 Correlation Analysis
 Query Analysis
 Summary
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3. SPARK SCALA & SPARK SQL
Do you Want to improve your Apache Spark
performance?
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4. QUESTION'S ADDRESSED IN THIS SESSION
1. Should I use Spark Scala or Spark SQL?
2. Does Hive Metastore have an impact on the performance?
3. Should I consider a certain File Format?
 Master thesis: “Evaluation of TPC-H on Spark & Spark SQL in ALOJA”
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5. OUTCOME OF THE PERFORMANCE EVALUATION
1. Up to 30% of performance increase by switching between Spark Scala &
Spark SQL
2. Hive Metastore produces an overhead
3. File Format and compression increases performance
 Parquet with Snappy compression is the best choice
 Performance Evaluation conducted on Spark 2.1.1
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6. MOTIVATION & RESEARCH OBJECTIVES
 Absence of a comprehensive performance evaluation of
Spark SQL compared to Spark Scala
 Investigating the performance impact of Spark SQL and Spark Scala
 Investigating the influence of Hive’s Metastore on performance
 The attempt to detect possible bottlenecks in terms of runtime
 Impact of various alternate file formats with different applied compressions
 Implement a Spark Scala TPC-H benchmark within ALOJA
 Benchmark is publicly accessible on GitHub
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7. ALOJA
 Benchmark platform to characterize cost-effectiveness of Big Data
deployments
 https://aloja.bsc.es/
 https://github.com/Aloja/aloja
 Collaboration with the Barcelona Super Computer Center (BSC)
 Nicolas Poggi
 Alejandro Montero
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8. TPC-H BENCHMARK
 Popular decision support benchmark
 Composed of eight different sized tables
 22 complex business oriented ad-hoc queries
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9. SPARK ECOSYSTEM / INTERFACES
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https://pages.databricks.com/rs/094-YMS-629/images/SparkSQLSigmod2015.pdf

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 Data access from Spark on HDFS
 With or without Metastore
 Data File Formats: Text, ORC & Parquet
 Dataset API
DATA
ACCESS

11. FILE FORMATS
 Text
 ORC & Parquet with standard compression
 GZIP and ZLIB
 ORC with Snappy compression
 Parquet with Snappy compression
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12. FILE FORMATS Spark Scala file formats with Snappy compression on Cluster with 1TB
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13. FILE FORMATS
 Parquet is up to 50% faster than text
 Standard compressions – GZIP and ZLIB
 Parquet is up 16% faster than ORC
 Snappy compression (faster than standard
compression)
 On average Parquet with Snappy is 10% faster than ORC
with Snappy compression
 Only common compression
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14. TAKEAWAY
 File Formats and compression benefits the
performance of all queries and both benchmarks
equally
 ORC & Parquet perform overall best with Snappy
 Parquet with Snappy compression is the best
choice
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DATA
ACCESS

16. TPC-H
BENCHMARK
RESULTS
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17. TPC-H
BENCHMARK
RESULTS
Query Spark Scala (sec) Spark SQL (sec) Difference (%)
Q2 78 83 7%
Q4 73 100 26%
Q5 126 99 27%
Q7 111 94 18%
Q8 99 83 20%
Q11 83 68 21%
Q14 54 64 15%
Q15 69 80 14%
Q18 103 123 16%
Q19 60 80 25%
Q21 262 221 18%
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18. TAKEAWAY
 Spark Scala does not outperform Spark SQL
 Spark Scala and Spark SQL process queries
differently
 Are the applied optimization rules the same?
 Hive Metastore does not improve the performance,
but creates a minor overhead
 Possibility to improve performance by simply
switching API
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19. WHAT TO DO?
1. Is there a pattern?
 When to use Spark Scala?
 When to use Spark SQL?
2. What are the root causes?
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20. QUERY ANALYSIS
 2 approaches to investigate the performance differences identified:
1. Correlation analysis based on the Choke Point Analysis
2. Investigation of the Execution Plan
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21. CHOKE POINT
ANALYSIS
 Classifying each TPC-H benchmark query into 6
categories (Low/Medium/High):
 Aggregation Performance
 Join Performance
 Data Access Locality
 Expression Calculation
 Correlated Subqueries
 Parallel Execution
 The correlation analysis is based on this
classification
* P. Boncz, T. Neumann, and O. Erling, “TPC-H Analyzed: Hidden Messages and
Lessons Learned from an Influential Benchmark,” in Performance Characterization
and Benchmarking, 2013, pp. 61–76 Thursday, April 19,
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22. CORRELATION ANALYSIS
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23. SPARK SCALA – HIGH EXPRESSION CALCULATION
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24. SPARK SQL – DATA ACCESS LOCALITY & PARALLEL EXECUTION
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25. TAKEAWAY
 Spark Scala performs better in case of heavy
Expression Calculation
 Spark SQL is the better choice in case of
strong Data Access Locality in combination
with heavyweight Parallel Execution
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26. EXECUTION
PLAN ANALYSIS
 Execution Plan Analysis revealed different applied
optimizations
 Spark SQL and Spark Scala do have different physical
plans
 Query Q4, Q5, Q11, Q19 exemplify most substantial
Execution Plan variations:
 Different Joins
 Different Join order
 Different Join build side
 Missing filters
 Missing projection
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Not explicitly defined, but
applied for one API but not the
other.

27. QUERY ANALYSIS – Q11
 TPC-H query Q11 demonstrates bad performance for Spark Scala
 Performance differences can be tracked down to different applied joins
 Wrong build side for joins
QUERY 11
Spark Scala Spark SQL
1 x BroadCastHash
2 x SortMerge
1 x
BroadCastNestedLoop
4 x BroadCastHash
Bad performance Good performance
Join Type Complexity
BroadCastHash O(N)
SortMerge O(N Log N), if not
sorted
BoradCastNestedLoop O(N²)
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28. SUMMARY
 Up to 30% of performance increase by simply
switching API
 Parquet with Snappy is best
 Spark API’s can be intermixed seamlessly, but
 differences in the execution plan
 no guarantee for best performance
 Different optimization rules are applied
 Spark SQL uses the Catalyst Optimizer
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29. THANK YOU
RAPHAEL RADOWITZ @DATAWORKS SUMMIT, BERLIN 19TH APRIL 2018
M.SC. Raphael Radowitz
Contact Detail
Phone: +82 (0) 10 9174 3788
Email: rradowitz@outlook.de