Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.

1. Hyperspace for
Delta Lake
Rahul Potharaju, Terry Kim, Eunjin Song
Microsoft
@RahulPotharaju

2. Who?

3. Rahul Potharaju
Principal Software Engineering Manager @Microsoft
Part of the Spark team at Microsoft
Azure Synapse Analytics
OSS: Hyperspace, .NET for Apache Spark
You can also find me publishing in VLDB, NSDI etc.
Terry Kim
Principal Software Engineer @Microsoft
Part of the Spark team at Microsoft
Azure Synapse Analytics
OSS: Hyperspace, .NET for Apache Spark,
Apache Spark
Eunjin Song
Senior Software Engineer @Microsoft
Part of the Spark team at Microsoft
Azure Synapse Analytics
OSS: Hyperspace

4. We work on
everything Apache SparkTM
Spark Runtime, Spark Service, HW Acc
in Synapse, Debugging & Diagnostics
Offer Apache
SparkTM
-as-a-service to
Microsoft customers
Runtimes for Synapse Spark, HDInsight
Spark, Spark on Cosmos
Contribute back to
Apache SparkTM
Spark SQL, Datasource v2, #47/1600
Spark contributor
We open source
our work!
Hyperspace, .NET for Spark

5. Agenda
▪ Rahul Potharaju
▪ Background, Concepts,
Conclusion
▪ Terry Kim
▪ Demo, Performance Deep-dive

6. What is Hyperspace?

7. Hyperspace in
a Nutshell
Simple
Usage API
// Index Maintenance
createIndex(df: DataFrame,
indexCfg: IndexConfig): Unit
deleteIndex(indexName: String): Unit
restoreIndex(indexName: String): Unit
vacuumIndex(indexName: String): Unit
refreshIndex(indexName: String): Unit
cancel(indexName: String): Unit
Language Choices
Scala
Python
.NET
New extensible indexing subsystem for
Apache Spark
Same technology that powers the indexing
engine inside Azure Synapse Analytics
Works out-of-box with open-source
Apache Spark
Accelerated performance on key workloads
Highlights

8. Hyperspace
Use Cases
High-Concurrency Interactive Analytics and Data Export
Indexing Privacy Attributes for GDPR Compliance
Time-series Analytics
Framework for Derived Dataset Maintenance
Needle-in-a-haystack Queries

9. https://aka.ms/HyperspaceIntroTalk https://aka.ms/Hyperspace
16
Project
Contributors
213
Pull Requests
Merged
263
GitHub
Stars
180
Issues
Reported
Up to 10x query performance
improvement
Open-Sourced
@Spark+AI
Summit 2020
https://aka.ms/Hyperspace-Blog https://github.com/microsoft/hyperspace

10. Top User Request from
Spark+AI Summit 2020 & Microsoft Customers
“Will Hyperspace work
for Delta Lake?”
Yes – giving back to the community like this makes me
believe Microsoft is a very different company than twenty
years ago. Definitely, a culture I would enjoy! Thank you!!! Great presentation. Just finished adding our
own secondary indexing schema for training
selection. Lots of common threads here.
Good to see Microsoft
contributing to the community
with such awesome work.
Kudos to the team.
Thank you, quite interesting
Great stuff. Thanks!
Awesome work!
Great stuff guys!
This is cool!
Very interesting presentation!

11. Hyperspace for Delta Lake
Index
Maintenance
Hybrid
Scan
ACID Data
Formats

12. 1 2 3 4 5
Data
1 2 3
Index
Query
Full Refresh
• Slowest refresh/fastest query
• Rebuilds the entire index
Incremental Refresh
• Slow refresh/fast query
• Builds index on newly added
files/partitions
• Drops rows from index
immediately 1 2 3 4 5
Data
1 2 3
Index
Query
.refresh(full)
4 5 6
6 7 8 9 10
6 7 8 9 10
4 5 6
.refresh(incremental)
Query
Query
Index
Maintenance
Quick Refresh
• Fastest refresh/fast query
• Captures meta-data for
appended and file/partition
predicates for deletes
• Leverage Hybrid Scan at
runtime 1 2 3 4 5
Data
1 2 3
Index
Query
6 7 8 9 10
.refresh(incremental) –
Only updates meta-data
Query
Model Assumptions:
Appends and Deletes are done at
file or partition level i.e., in-place
updates are not supported
Index was constructed with
lineage information

13. Query Processing:
Hybrid
Scan
Data 1 2 3 4 5 6 1 2 X X 5 6 7 8
Initial Dataset
Created at v1
1 2 X 4 5 6
Index
Created
I1
I1
Dataset
Updated to v2
time
Query on
Dataset at v2
What does Hybrid Scan entail?
Table A Table B Table B
Ս
σfile != 4
Index Scan using I1
as of Dataset v1
7 8
Shuffle
Step 1: Compute diff since indexed
[
{4, deleted},
{7, added},
{8, added}
]
Step 2: Rewrite Table Scan as Hybrid Scan

14. Indexing Support for
ACID
Data
Formats
Snapshot isolation
Readers use a consistent
snapshot of the table (no
locks). All table updates are
atomic.
Distributed planning
File pruning and predicate
push-down is distributed to
jobs, removing the metastore
as a bottleneck.
Version history, rollback
and time travel
Table snapshots are kept as
history and tables can roll
back if a job produces bad
data

15. Delta Lake
Time
Travel
Delta Lake
add
v1
add
v2
del
v3
add
v4
add
v5
add
v6
add
v7
1
2
3
1
2
3
4
1
2
3
4
1
2
3
4
5
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
Files in the
directory
Spark Code for
reading Delta
Lake Table
Current
default
version
val df = spark
.read
.format("delta")
.load(deltaTablePath)
df.show()
User
queries
v2
val df = spark
.read
.format("delta")
.option("versionAsOf", 2)
.load(deltaTablePath)
df.show()
Spark Code for
Delta Lake Time
Travel

16. Indexing Support for
Delta Lake
Time Travel
Hyperspace
create
v1
refresh
v2
refresh
v3
User queries
snapshot at v4
Hyperspace chooses hybrid scan over:
- Index(v1) + Scan(DeltaLake(v4-v3))
User queries
snapshot at v6
Delta Lake
add
v1
add
v2
del
v3
add
v4
add
v5
add
v6
add
v7
Hyperspace compares cost of hybrid scan over:
- Index(v2) + Scan(DeltaLake(v6-v5))
- Index(v3) + Scan(DeltaLake(v6-v7))
Hyperspace chooses:
- Index(v1)
User queries
snapshot at v3

17. Azure Synapse Analytics
offers the best offering of Hyperspace’s indexing yet!
• No additional JAR includes
• Fastest access to latest features
• Support for Scala | Python | .NET
• Seamless integration with the UI
• Meta-store integration
• Notebooks for faster iterations

18. Experience of Using
Hyperspace for Delta Lake
Notebook: https://aka.ms/hyperspace-for-delta-lake

19. Preliminary
Performance
Evaluation of
Hyperspace for
Delta Lake
Compute Configuration:
• VM Instance = Azure E8 V3
• Workers/Executors = 7
• Cores per executors = 8
• Executor memory = 47 GB
• Autoscale disabled
• ADLS Gen v2
Experimental Setting
TPC-DS:
store_sales
1 2 3 4 5 6 200
…
1 GB
TPC-DS:
items
1 2 3 4 5 6 200
…
200 KB
200 GB
40 MB
Performance of Hyperspace
using TPC-DS Q44
201 250
…
Append more data
Performance of Hyperspace
1. Without refreshing the index
2. After refreshing the index

20. Performance Implications of Using
Hyperspace for Delta Lake

21. Open Sourcing Hyperspace v0.1
Conclusion
New extensible indexing subsystem for
Apache Spark
Simply add on—no core changes needed
Same technology that powers the indexing
engine inside Azure Synapse Analytics
Works out-of-box with open-source
Apache Spark
Scala, Python, and .NET support
Accelerated performance on key workloads
Up to 10x query performance
improvement
https://github.com/microsoft/hyperspace
Open Sourced
It is not perfect… but fully open to contributions
towards being made perfect! ☺
@RahulPotharaju