1.
Julien Peloton, CNRS
Accelerating
Astronomical Discoveries
with Apache Spark
#UnifiedDataAnalytics #SparkAISummit
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2.
XXIst century astronomy
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3.
How we can get different data?
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~1/100,000 of sky
Large
butshallow
Hubble FoV
D
eep
butsm
all
#UnifiedDataAnalytics #SparkAISummit

4.
Large Synoptic Survey Telescope
2022-2032: Deep & large survey
Non-profit corporation
Site: Chile (Cerro Pachón)
US led, international
collaboration (1000+)
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5.
Million pieces puzzle
• LSST will deliver ~full sky map every 3 nights
– 3.2 Gigapixels camera (car size!)
– 15 TB/night of raw image data collected
– 1 TB/night of alerts streamed
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?

6.
We would like to be able to do at scale:
• Exploring large catalogs of data
• Cross-matching large catalogs
• Processing telescope images
• Classifying light-curves
• Processing telescope alerts
• ...
Apache Spark for astronomy?
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7.
FITS: astronomical data format
• First (last) release: 1981 (2016).
• Endorsed by NASA and the International
Astronomical Union.
• Multi-purposes: vectors, images, tables, ...
• Backward compatible
• Set of blocks.1 block: ASCII header+binary
data arrays of arbitrary dimension
• Support for C, C++, C#, Fortran, IDL, Java,
Julia, MATLAB, Perl, Python, R, and more…
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8.
spark-fits
• FITS data source for Spark SQL and DataFrames.
• Data Source V1 API.
• Images + tables available.
• Schema automatically inferred from the FITS header.
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9.
spark-fits in practice
• Spark 2.3.1 / Hadoop 2.8.4
• 1.1 billion rows, 153 cores
• Run it 100 times (no cache).
• Performances (IO throughput)
comparable to other built-in
Spark connectors (no attempt
to optimise anything
anywhere…)
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10.
Current limitations
Some limitations currently though…
• Need to migrate to Apache Spark DSv2.
• No column pruning, no filters at the level of the connector.
• (De)Compression is not handled yet.
• Scala FITS library lacks of many features.
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11.
We live in a 3D world
• Manipulating 2D data with Spark:
Geotrellis, Magellan, Geospark,
GeoMesa, …
• Very little about 3D!
• Need for e.g. astronomy, particle
physics, meteorology.
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12.
Manipulating 3D spatial data: spark3D
• 3D distributed partitioning
– KDTree, Octree, shells, ...
• Distributed spatial queries & data mining
– KNN, join, dbscan, …
– Typical usage on million/billion rows
• Visualisation
– Client/server architecture
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Student:
Mayur
Bhosale (now at Qubole)
#UnifiedDataAnalytics #SparkAISummit

13.
On the repartitioning...
Frequent as data comes unstructured, but
• Repartitioning implies heavy shuffle
between executors.
• Complex UDF in Spark are often
inefficient.
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14.
Need for (efficient) streaming
• We explored the static sky - namely what has been observed.
• But what about what is happening right now? E.g.
– Supernovae (star explosion)
– Black hole merger counterparts (multi-messenger astronomy)
– Micro-lensing (extrasolar planet search)
– Earth killers!
– Anomaly detection (unforeseen astronomical sources)
• Correlation past/present/future?
• Timescales range from seconds to months...
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15.
Desiderata & solution
We would like
• To work efficiently at scale
• Multi-modals analytics
capability (streaming & batch)
• Good integration with the
current ecosystem
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Structured
Streaming
#UnifiedDataAnalytics #SparkAISummit

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Introducing Fink
Fink is
• A broker system for sky alerts
• Based on Apache Spark
Fink does
• Collect, enrich & distribute sky
alerts
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01
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Distribute
Enrich
Collect

17.
On a quiet night...
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• 10,000 Avro alerts every 30 seconds
• 1TB alerts per night
• Parquet Database
Observation
Template
Difference
Credits: E. Bellm
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01
Distribute
Enrich
Collect
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18.
Who’s who
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Add values to the raw alerts
• Stream-static join
• Classification (BNN)
Structured
Streaming
Alert
stream
Internal
catalogs
Alert
database
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01
02
Distribute
Enrich
Collect
Alert database
Alert database
Structured
Streaming

19.
Joining external information
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Structured
Streaming
Neutrino
alert stream
Gamma ray
alert stream
Optical
alert stream
Gravitational
wave
alert stream
Join
output
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01
02
Distribute
Enrich
Collect
Spark does all the hard work
• Small delays
• Record throughput
• Stream position recovery
But it cannot do everything...
• Large delays
• False positives
Still need humans to take decisions

20.
The Hero’s Return
Processing based on Adaptive Learning (PoC)
• Ranking of promising candidates
• Improved classification over time
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New Candidates
Follow-up &
DiscoveryTraining
Streaming infrastructure by:
Abhishek Chauhan
(now at Morgan Stanley)
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02
Distribute
Enrich
Collect

21.
The fear of the shutdown!
What if we miss a
night?
• 14 million alerts, 830
GB of data
• Let Spark do the
hard work again
(offsets, updates...)
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Broker shutdown…. Collect & write
100 minutes on 3 machines
Collect alerts
(cache)
Limiting factors
• Number of machines
• Network

22.
Some lessons learned
Handling stream offsets
• Manual or not? Still not obvious...
Schema evolution
• User needs change often… Database choice is crucial
Dynamic filtering
• Need to adapt quickly to new situations
Handling watermarks
• How long shall we wait for data? Switch to post-processing.
Communication
• Using common communication protocols & data format...
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23.
Thanks!
You have a public/private project in
mind? You want to contribute to
astronomy?
Come talk to me!
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24.
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT
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