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From Luca Canali <Luca.Can...@cern.ch>
Subject RE: Spark 3.0 using S3 taking long time for some set of TPC DS Queries
Date Mon, 24 Aug 2020 13:47:34 GMT
Hi Abhishek,

Just a few ideas/comments on the topic:

When benchmarking/testing I find it useful to  collect a more complete view of resources usage
and Spark metrics, beyond just measuring query elapsed time. Something like this:
https://github.com/cerndb/spark-dashboard

I'd rather not use dynamic allocation when benchmarking if possible, as it adds a layer of
complexity when examining results.

If you suspect that reading from S3 vs. HDFS may play an important role on the performance
you observe, you may want to drill down on that with a simple micro-benchmark, for example
something like this (for Spark 3.0):

val df=spark.read.parquet("/TPCDS/tpcds_1500/store_sales")
df.write.format("noop").mode("overwrite").save

Best,
Luca

From: Rao, Abhishek (Nokia - IN/Bangalore) <abhishek.rao@nokia.com>
Sent: Monday, August 24, 2020 13:50
To: user@spark.apache.org
Subject: Spark 3.0 using S3 taking long time for some set of TPC DS Queries

Hi All,

We're doing some performance comparisons between Spark querying data on HDFS vs Spark querying
data on S3 (Ceph Object Store used for S3 storage) using standard TPC DS Queries. We are observing
that Spark 3.0 with S3 is consuming significantly larger duration for some set of queries
when compared with HDFS.
We also ran similar queries with Spark 2.4.5 querying data from S3 and we see that for these
set of queries, time taken by Spark 2.4.5 is lesser compared to Spark 3.0 looks to be very
strange.
Below are the details of 9 queries where Spark 3.0 is taking >5 times the duration for
running queries on S3 when compared to Hadoop.

Environment Details:

  *   Spark running on Kubernetes
  *   TPC DS Scale Factor: 500 GB
  *   Hadoop 3.x
  *   Same CPU and memory used for all executions

Query

Spark 3.0 with S3 (Time in seconds)

Spark 3.0 with Hadoop (Time in seconds)



Spark 2.4.5 with S3
(Time in seconds)

Spark 3.0 HDFS vs S3 (Factor)

Spark 2.4.5 S3 vs Spark 3.0 S3 (Factor)

Table involved

9

880.129

106.109

147.65

8.294574

5.960914

store_sales

44

129.618

23.747

103.916

5.458289

1.247334

store_sales

58

142.113

20.996

33.936

6.768575

4.187677

store_sales

62

32.519

5.425

14.809

5.994286

2.195894

web_sales

76

138.765

20.73

49.892

6.693922

2.781308

store_sales

88

475.824

48.2

94.382

9.871867

5.04147

store_sales

90

53.896

6.804

18.11

7.921223

2.976035

web_sales

94

241.172

43.49

81.181

5.545459

2.970794

web_sales

96

67.059

10.396

15.993

6.450462

4.193022

store_sales


When we analysed it further, we see that all these queries are performing operations either
on store_sales or web_sales tables and Spark 3 with S3 seems to be downloading much more data
from storage when compared to Spark 3 with Hadoop or Spark 2.4.5 with S3 and this is resulting
in more time for query completion. I'm attaching the screen shots of Driver UI for one such
instance (Query 9) for reference.
Also attached the spark configurations (Spark 3.0) used for these tests.

We're not sure why Spark 3.0 on S3 is having this behaviour. Any inputs on what we're missing?

Thanks and Regards,
Abhishek


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