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Matthew Powers, Prognos Health
Optimizing Delta / Parquet
Data Lakes
#UnifiedDataAnalytics #SparkAISummit
Agenda
• Why Delta?
• Delta basics and transaction log
• Compacting Delta lake
• Vacuuming old files
• Partitioning Delta lakes
• Deleting rows
• Persisting transformations in columns
3
About
4
MungingData
• Time travel
• Compacting
• Vacuuming
• Update columns
Contact me
• GitHub: MrPowers
• Email: matthewkevinpowers@gmail.com
• Delta Slack channel
• Open source hacking
5
What is Delta lake?
• Parquet + transaction log
• Provides awesome features for free!
6
Delta Lake =!= Databricks Delta
7
https://github.com/delta-io/delta/issues/49
#UnifiedDataAnalytics #SparkAISummit
TL;DR
• 1 GB files
• No nested directories
8
#UnifiedDataAnalytics #SparkAISummit 9
Delta Lake Slack says 1GB files
Databricks Delta autoOptimize
10
Why does compaction speed up
lakes?
• Parquet: files need to be listed before they are
read. Listing is expensive in object stores.
• Delta: Data is read via the transaction log.
• Easier for Spark to read partitioned lakes into
memory partitions.
11
Sample Data
12
Create Delta Data Lake
13
Delta Lake on Disk
14
_delta_log/00000000000000000000.json
15
Code examples
16
Compact Delta Data Lake
17
Files post-compaction
18
_delta_log/00000000000000000001.json
19
Compacting Delta lakes without breaking
downstream apps
20
https://github.com/delta-io/delta/issues/146
21
Delta Lake Vacuum
• Files marked for removal older than the retention
period
• Default retention period is 7 days
• Not going to improve performance
22
Vacuum Delta Data Lake
23
Files post-vacuum
24
Optimal number of partitions
(delta)
25
spark-daria helps!
26
spark-daria on GitHub
27
Optimal number of partitions (parquet)
28
https://github.com/MrPowers/spark-daria/blob/master/src/main/scala/com/github/
mrpowers/spark/daria/utils/DirHelpers.scala
Why partition data lakes?
• Data skipping
• Massively improve query performance
• I’ve seen queries run 50-100 times faster on
partitioned lakes
29
Sample data
30
Filtering unpartitioned lake
31
== Physical Plan ==
Project [first_name#12, last_name#13, country#14]
+- Filter (((isnotnull(country#14) && isnotnull(first_name#12)) && (country#14 = Russia)) &&
StartsWith(first_name#12, M))
+- FileScan csv [first_name#12,last_name#13,country#14]
Batched: false,
Format: CSV,
Location: InMemoryFileIndex[file:/Users/powers/Documents/tmp/blog_data/people.csv],
PartitionFilters: [],
PushedFilters: [IsNotNull(country), IsNotNull(first_name), EqualTo(country,Russia),
StringStartsWith(first_name,M)],
ReadSchema: struct
Partitioning the data lake
32
Partitioned lake on disk
33
_delta_log/00000000000000000000.json
34
Filtering partitioned lake
35
== Physical Plan ==
*(1) Project [first_name#662, last_name#663, country#664]
+- *(1) Filter (isnotnull(first_name#662) && StartsWith(first_name#662, M))
+- *(1) FileScan parquet [first_name#662,last_name#663,country#664]
Batched: true,
Format: Parquet,
Location: TahoeLogFileIndex[file:/…/tmp/europe_partitioned1],
PartitionCount: 1,
PartitionFilters: [isnotnull(country#664), (country#664 = Russia)],
PushedFilters: [IsNotNull(first_name), StringStartsWith(first_name,M)],
ReadSchema: struct<first_name:string,last_name:string>
Comparing physical plans
36
Unpartitioned
Project [first_name#12, last_name#13, country#14]
+- Filter (((isnotnull(country#14) && isnotnull(first_name#12))
&& (country#14 = Russia)) && StartsWith(first_name#12, M))
+- FileScan csv [first_name#12,last_name#13,country#14]
Batched: false,
Format: CSV,
Location: InMemoryFileIndex[….],
PartitionFilters: [],
PushedFilters: [IsNotNull(country), IsNotNull(first_name),
EqualTo(country,Russia), StringStartsWith(first_name,M)],
ReadSchema: struct
Partitioned
Project [first_name#662, last_name#663, country#664]
+- Filter (isnotnull(first_name#662) && StartsWith(first_name#662, M))
+- FileScan parquet [first_name#662,last_name#663,country#664]
Batched: true,
Format: Parquet,
Location: TahoeLogFileIndex[file:/…/tmp/europe_partitioned1],
PartitionCount: 1,
PartitionFilters: [isnotnull(country#664), (country#664 =
Russia)],
PushedFilters: [IsNotNull(first_name),
StringStartsWith(first_name,M)],
ReadSchema: struct<first_name:string,last_name:string>
Directly grabbing the partitions is
faster for Parquet lakes…
37
Directly grabbing partitions was 83 times faster than relying on partition
filters for a simple query
Real partitioned data lake
• Updates every 3 hours
• Has 5 million files
• 15,000 files are being added every day
• Still great for a lot of queries
38
Creating partitioned lake (2/3)
39
Partitioned lake on disk (2/3)
40
Creating partitioned lake (3/3)
41
Incrementally updating
partitioned lakes
• Small file problem grows quickly
• Compaction is hard
42
Filtering data from a lake
43
We can delete rows in Delta lakes
44
Deleting under the hood
45
Append a column on the fly
46
Resulting DataFrame
47
Append a column in Delta
48
Delta lake downsides… not many
49
Contact me
• GitHub: MrPowers
• Email: matthewkevinpowers@gmail.com
• Delta Slack channel
• Open source hacking
50
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

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