The document presents a detailed overview of using Spark DataFrames from a data analyst's perspective, covering aspects such as Spark execution models, data transformations, and query optimization techniques. It emphasizes the importance of efficient data handling to improve performance while ensuring compliance with data regulations. Additionally, the document provides practical examples and case studies demonstrating the application of Spark for data aggregation and processing.

1. Marcin Szymaniuk
DataFrames in Spark
Data Analysts’ perspective

2. www.tantusdata.com
About me
• Data Engineer @TantusData
• Have worked for: Spotify, Apple, telcos, startups
• Cluster installations, application architecture and
development, training, data team support
marcin@tantusdata.com
marcin.szymaniuk@gmail.com
@mszymani

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Agenda
• Spark for Data Analysts
• Spark execution model
• Case by case…
• Summary

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Data Analysts’/Scientists’ perspective
• Explain what happened and why
• Drive decisions to be made
• Build ML models

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Bring analysis to data
DATA
R / PYTHON
/ SAS
Sample
• Sample only (region, latest month…)
• Coarse aggregate eg. month vs hour (1:720)

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Bring analysis to data
• - Base station position
- Network technology
- Antenna specs
Photo credit: productcoalition.com

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Bring analysis to data
• Analyze all data
• Faster analysis
• No extra data copies (GDPR!)

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Data Analysts’/Scientists’ perspective
• Ad-hoc queries and reports
• Data cleanup

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Data Analysts’/Scientists’ perspective
• Ad-hoc queries and reports
• Data cleanup
Photo credit: CrowdFlower

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Spark API - DataFrame
eventsDf
.groupBy("userId")
.agg(
max("value").alias("maxVal"),
avg("value").alias("avgValue")
)
.join(usersDf, usersDf("id") === eventsDf("userId"))
.select("userId", "maxVal", "avgValue","name")

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Spark API - DataFrame
eventsDf
.groupBy("userId")
.agg(
max("value").alias("maxVal"),
avg("value").alias("avgValue")
)
.join(usersDf, usersDf("id") === eventsDf("userId"))
.select("userId", "maxVal", "avgValue","name")

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Data Analysts’ perspective
• Simple query fails?
• Simple query never finishes?
Photo credit: thedeliberatemusician.com

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Deep dive
Photo credit: amazon.ca

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DataFrame
DataFrame
Partition 1
Partition 2
Partition 3
Partition 4
…

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Transformation type
• Narrow - single partition at time
• Wide - exchange partition’s data

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed",sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)
Stage 1

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)
read Input Data
Stage 1

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)
read Input Data
do the magic
Stage 1

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Narrow transformation
dataFrame.
.withColumn("uIdHashed", sha2(col("userId"), 256))
.withColumn("strReversed", reverse(col(“str")))
.withColumn(“v1v2Sum”, col("v1") + col(“v2"))
.write
.save(output)
read Input Data
do the magic
write out the result
Stage 1

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Stage 1

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Wide transformations
Partition 1
Partition 2
Partition 3
DataFrame
2 | VIEW | … 1 | SEND | …1 | CLICK | …
3 | SEND | … 1 | CALL | …1 | CLICK | …
2 | VIEW | … 3 | CALL | …2 | SEND | …
1 | CALL | …3 | SEND | …
Partition 1
Partition 2
Partition 3
New DataFrame
groupBy

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Wide transformations
Partition 1
Partition 2
Partition 3
DataFrame
2 | VIEW | … 1 | SEND | …1 | CLICK | …
3 | SEND | … 1 | CALL | …1 | CLICK | …
2 | VIEW | … 3 | CALL | …2 | SEND | …
1 | CALL | …3 | SEND | …
Partition 1
Partition 2
Partition 3
New DataFrame
1 | SEND | … 1 | CLICK | …
1 | CALL | …
1 | CLICK | …
1 | CALL | …

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Wide transformations
Partition 1
Partition 2
Partition 3
DataFrame
2 | VIEW | … 1 | SEND | …1 | CLICK | …
3 | SEND | … 1 | CALL | …1 | CLICK | …
2 | VIEW | … 3 | CALL | …2 | SEND | …
1 | CALL | …3 | SEND | …
Partition 1
Partition 2
Partition 3
New DataFrame
1 | SEND | … 1 | CLICK | …
1 | CALL | …
1 | CLICK | …
1 | CALL | …
2 | VIEW | … 2 | SEND | … 2 | VIEW | …

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Wide transformations
Partition 1
Partition 2
Partition 3
DataFrame
2 | VIEW | … 1 | SEND | …1 | CLICK | …
3 | SEND | … 1 | CALL | …1 | CLICK | …
2 | VIEW | … 3 | CALL | …2 | SEND | …
1 | CALL | …3 | SEND | …
Partition 1
Partition 2
Partition 3
New DataFrame
1 | SEND | … 1 | CLICK | …
1 | CALL | …
1 | CLICK | …
1 | CALL | …
2 | VIEW | … 2 | SEND | … 2 | VIEW | …
3 | SEND | … 3 | CALL | … 3 | SEND | …

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Wide transformations
• join
• groupBy
• repartition
• coalesce
• distinct
• …

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Wide transformations
events
.join(users, users("id") === events("userId"))
//Extra logic
.write
.parquet(output)

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Wide transformation
Stage 1 Stage 2 Stage 3

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Wide transformation
Shuffle - exchange data between nodes
Stage 1 Stage 2 Stage 3

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Simplest scenario ever
val df = spark.read.parquet(“…”)
HDFS
TASK

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Simplest scenario ever
HDFS
TASK
ADDCOL ADDCOL
val df = spark.read.parquet(“…”)
df
.withColumn("year", year(col(“timestamp")))
.withColumn("month", month(col(“timestamp”)))
.withColumn("day", dayofmonth(col(“timestamp”)))

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Simplest scenario ever
HDFS
TASK
ADDCOL ADDCOL
HDFS
val df = spark.read.parquet(“…”)
df
.withColumn("year", year(col(“timestamp”)))
.withColumn("month", month(col("timestamp")))
.withColumn("day", dayofmonth(col("timestamp")))
.write.save(output)

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Simplest scenario ever
1TB of
events
8000 blocks

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Simplest scenario ever
1TB of
events
8000 blocks
8000 tasks

41. HDFS
TASK
ADDCOL ADDCOL
HDFS
HDFS
TASK
ADDCOL ADDCOL
HDFS
HDFS
TASK
ADDCOL ADDCOL
HDFS
HDFS
TASK
ADDCOL ADDCOL
HDFS
…
8000

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Case #1
• Faster queries with date condition?
• Organize your data by date!

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Case #1
/DATA

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Case #1
/DATA
.filter(
col("year")==="2018"
)

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Case #1
2015
/DATA

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Case #1
2016/DATA

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Case #1
2017
/DATA

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Case #1
2017
2016
2015
/DATA

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Case #1
/DATA 2016
2015
01
01
02
032017
…

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Case #1
val df = spark.read.parquet(“…”)
df
.withColumn("year", year(col(“timestamp”)))
.withColumn("month", month(col("timestamp")))
.withColumn("day", dayofmonth(col("timestamp")))
.partitionBy("year", "month", "day")
.write.save(output)

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Case #1

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Case #1

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Case #1

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Case #1

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Case #1

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Case #1

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Case #1
$ hdfs dfs -ls

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Case #1
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1

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Case #1
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l

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Case #1
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l
22

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Case #1
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l
22
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l
25

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Case #1
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l
22
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l
25
$ hdfs dfs -ls /user/marcin/out/year=2019/month=3/day=1 | wc -l
40

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val df = spark.read.parquet(“…”)
df
.withColumn("year", year(col(“timestamp”)))
.withColumn("month", month(col("timestamp")))
.withColumn("day", dayofmonth(col("timestamp")))
.partitionBy("year", "month", "day")
.write.save(output)
HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…
Case #1

64. HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…
HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…
HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…HDFS
TASK
READ ADDCOL ADDCOL
HDFS
Day 1
Day 2
Day 90
…
…

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Case #1
90 days of
data
8000 blocks

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Case #1
90 days of
data
8000 blocks
90*8000
files
produced

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Consequences
• Slow!
• Downstream jobs will struggle
• Memory pressure on NameNode
• Might kill the cluster!

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Consequences

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Case #1
• repartition before partitionBy in order to limit #files

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Repartition
df
.withColumn("year", year(col("eventTimestamp")))
.withColumn("month", month(col("eventTimestamp")))
.withColumn("day", dayofmonth(col("eventTimestamp")))
.repartition(col("year"), col("month"), col("day"))
.write
.partitionBy("year", "month", "day")
.save(output)

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73. HDFS
TASK
HDFS
TASK 1
STAGE 1

74. HDFS
TASK
HDFS
TASK 1
STAGE 1

75. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
STAGE 1

76. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
STAGE 1
HDFS
HDFS
HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
STAGE 1
HDFS
HDFS

77. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 1
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
STAGE 1 STAGE 2
HDFS
HDFS
HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 1
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
STAGE 1 STAGE 2
HDFS
HDFS

78. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 1
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 2
STAGE 1 STAGE 2
HDFS
HDFS

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Case #1 - summary
• each partitionBy task may create many files in HDFS
• repartition to the rescue

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Case #2
• It fails after repartitioning…

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Case #2

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Case #2

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Case #2

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Case #2
One day of
events is
100G

88. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 1
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 2
STAGE 1 STAGE 2
HDFS
HDFS

89. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
Day 1
Day 2
Day 3
LOCAL
TASK 1
TASK 2
Day 1
Day 2
Day 3
LOCAL
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 2
STAGE 1 STAGE 2
HDFS
HDFS
100G100G
100G

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Problems with shuffle
• Spill to disk
• Timeouts
• GC overhead limit exceeded
• OOM
• ExecutorLostFailure

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Case #2
df
.withColumn("year", year(col("eventTimestamp")))
.withColumn("month", month(col("eventTimestamp")))
.withColumn("day", dayofmonth(col("eventTimestamp")))
.repartition(col("year"), col("month"), col(“day”), col(”hour”))
.write
.partitionBy("year", "month", "day")
.save(output)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", "month",“day”)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", "month",“day”)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", "month",“day”)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", “month”,“day”,”hour”)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", “month”,“day”,”hour”)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", “month”,“day”,”hour”)

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day month year hour event …
1 1 2017 11 CALL
1 1 2017 1 TEXT
1 1 2017 4 CLICK
1 1 2017 7 SEND
2 1 2017 1 CLICK
2 1 2017 3 TEXT
2 1 2017 1 TEXT
2 1 2017 1 CALL
.repartition(“year", “month”,“day”,”hour”)

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Case #2

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Case #2 - recap
• Know your data distribution when repartitioning
• Keep all your tasks busy - repartition by more columns if
necessary

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Case #3
events
.join(users, users("id") === events("userId"))
//Extra logic
.write
.parquet(output)

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Case #3
events
.join(users, users("id") === events("userId"))
//Extra logic
.write
.parquet(output)

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Case #3
events
.join(users, users("id") === events("userId"))
//Extra logic
.write
.parquet(output)

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Case #3
events
.join(users,users(“id")===events("userId"))
//Extra logic
.write
.parquet(output)

105. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 2
users1
users2
users3
LOCAL
STAGE 1
HDFS

106. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2

107. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3

108. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3

109. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3

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Case #3
10TB of
events
uniform
distribution
1GB of users

114. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3

115. .config(
“spark.sql.shuffle.partitions",
“200”
)
HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3

116. .config(
“spark.sql.shuffle.partitions",
“200”
)
HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3
10TB/200 = 50GB/TASK

117. 10TB/200 = 50GB/TASK
HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3
50G
50G
.config(
“spark.sql.shuffle.partitions",
“200”
)

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Problems with shuffle
• Spill to disk
• Timeouts
• GC overhead limit exceeded
• OOM
• ExecutorLostFailure

119. www.tantusdata.com
What to do?
• Understand your data!
• Control the level of parallelism
• Choose number of partitions according to your data size
.config("spark.sql.shuffle.partitions", “2000")
.repartition(2000)

120. www.tantusdata.com
Case #3 - result

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Case #3 - result

122. www.tantusdata.com
Case #4 - Skewed join

123. www.tantusdata.com
Case #4
Photo credit: hiveminer.com

124. www.tantusdata.com
Case #4
Photo credit: hiveminer.com

125. www.tantusdata.com
Skewed join
Photo credit: hiveminer.com

126. www.tantusdata.com
Skewed join
Photo credit: hiveminer.com

127. www.tantusdata.com
Case #4
10TB of
events
One user with
1 TB of events
others are
uniformly
distributed

128. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3

129. HDFS
TASK
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 2
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
HDFS
TASK 1
users1
users2
users3
LOCAL
TASK 1
TASK 2
users1
users2
users3
LOCAL
TASK 1
users1
users2
users3
LOCAL
TASK 2
STAGE 1
HDFS
HDFS
HDFS
TASK X
Day 1
Day 2
Day 3
LOCAL
TASK 1
users1
users2
users3
LOCAL
HDFS
STAGE 2
STAGE 3
1TB

130. www.tantusdata.com
Skewed join
• Bad data?
• Wrong logic?
• Just ok?
Photo credit: hiveminer.com

131. www.tantusdata.com
Skewed Join
userId …
1
2
3
… …
eventId userId …
af8 1
bf9 1
ff1 1
881 1
91f 2
cc6 1
b22 1
ee4 1

132. www.tantusdata.com
userId …
1
2
3
… …
eventId userId …
af8 1
bf9 1
ff1 1
881 1
91f 2
cc6 1
b22 1
ee4 1
Skewed Join

133. www.tantusdata.com
userId …
1
2
3
… …
eventId userId … salt
af8 1 1
bf9 1 2
ff1 1 1
881 1 3
91f 2 2
cc6 1 3
b22 1 3
ee4 1 1
Skewed Join

134. www.tantusdata.com
userId … salt
1 1
1 2
1 3
2 1
2 2
2 3
3 1
3 2
3 3
… …
Skewed Join
eventId userId … salt
af8 1 1
bf9 1 2
ff1 1 1
881 1 3
91f 2 2
cc6 1 3
b22 1 3
ee4 1 1

135. www.tantusdata.com
userId … salt
1 1
1 2
1 3
2 1
2 2
2 3
3 1
3 2
3 3
… …
eventId userId … salt
af8 1 1
bf9 1 2
ff1 1 1
881 1 3
91f 2 2
cc6 1 3
b22 1 3
ee4 1 1
Skewed Join

136. www.tantusdata.com
eventId userId … salt
af8 1 1
bf9 1 2
ff1 1 1
881 1 3
91f 2 2
cc6 1 3
b22 1 3
ee4 1 1
Skewed Join
userId … salt
1 1
1 2
1 3
2 1
2 2
2 3
3 1
3 2
3 3
… …

137. www.tantusdata.com
userId … salt
1 1
1 2
1 3
2 1
2 2
2 3
3 1
3 2
3 3
… …
Skewed Join
eventId userId … salt
af8 1 1
bf9 1 2
ff1 1 1
881 1 3
91f 2 2
cc6 1 3
b22 1 3
ee4 1 1

138. www.tantusdata.com
Skewed Join
val eventsSalted = events
.withColumn("salt", toInt(rand() * 100))

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Skewed Join
val eventsSalted = events
.withColumn("salt", toInt(rand() * 100))

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Skewed Join
val eventsSalted = events
.withColumn("salt", toInt(rand() * 100))

141. www.tantusdata.com
Skewed Join
val allSalts = spark.sparkContext
.parallelize((1 to 100).toList)
.toDF("salt")
val usersSalted = users.join(allSalts)

142. www.tantusdata.com
Skewed Join
val allSalts = spark.sparkContext
.parallelize((1 to 100).toList)
.toDF("salt")
val usersSalted = users.join(allSalts)

143. www.tantusdata.com
Skewed Join
val allSalts = spark.sparkContext
.parallelize((1 to 100).toList)
.toDF("salt")
val usersSalted = users.join(allSalts)

144. www.tantusdata.com
Skewed Join
eventsSalted
.join(
usersSalted,
usersSalted("id") === eventsSalted("userId")
&& usersSalted("salt") === eventsSalted("salt")
)

145. www.tantusdata.com
Skewed Join
eventsSalted
.join(
usersSalted,
usersSalted("id") === eventsSalted("userId")
&& usersSalted(“salt") === eventsSalted("salt")
)

146. www.tantusdata.com
Skewed Join
eventsSalted
.join(
usersSalted,
usersSalted("id") === eventsSalted("userId")
&& usersSalted(“salt") === eventsSalted("salt")
)

147. www.tantusdata.com
Skewed Join
eventsSalted
.join(
usersSalted,
usersSalted("id") === eventsSalted("userId")
&&
usersSalted("salt")===eventsSalted("salt")
)

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Skewed Join

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• Know your data!
• Understand the transformations you are doing
• Keep your tasks busy
Use cases - recap
Photo credit: shamakern.com

150. www.tantusdata.com
Challenges ahead
• Broadcast
• Cache
• Locality
• File formats
• Sizing executors
• …

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Challenges ahead
OPS
DEVELOPERS
BUSINESS ANALYTICS
Common tools and knowledge

152. www.tantusdata.com
Q&A
• marcin@tantusdata.com
• marcin.szymaniuk@gmail.com
• @mszymani

153. www.tantusdata.com
Thank you!