Introduction to LSST Data Management

Jeffrey KantorData Management Project Manager

LSST Data Management Principal Responsibilities

• Archive Raw Data: Receive the incoming stream of images that the Camera system generates to archive the raw images.

• Process to Data Products: Detect and alert on transient events within one minute of visit acquisition. Approximately once per year create and archive a Data Release, a static self-consistent collection of data products generated from all survey data taken from the date of survey initiation to the cutoff date for the Data Release.

• Publish: Make all LSST data available through an interface that uses community-accepted standards, and facilitate user data analysis and production of user-defined data products at Data Access Centers (DACs) and external sites.

LSST From the User’s Perspective

• A stream of ~10 million time-domain events per night, detected and transmitted to event distribution networks within 60 seconds of observation.

• A catalog of orbits for ~6 million bodies in the Solar System.

• A catalog of ~37 billion objects (20B galaxies, 17B stars), ~7 trillion observations (“sources”), and ~30 trillion measurements (“forced sources”), produced annually, accessible through online databases.

• Deep co-added images.

• Services and computing resources at the Data Access Centers to enable user-specified custom processing and analysis.

• Software and APIs enabling development of analysis codes.

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02C.06.02Data Access Services

02C.07.01, 02C.06.03Processing Middleware

02C.07.02Infrastructure Services

(System Administration, Operations, Security)

02C.08.03Long-Haul

Communications

Physical Plant (included in above)

02C.07.04.02Base Site

Application Layer (LDM-151)• Scientific Layer• Pipelines constructed from reusable,

standard “parts”, i.e. Application Framework• Data Products representations standardized• Metadata extendable without schema change• Object-oriented, python, C++ Custom Software

Middleware Layer (LDM-152)• Portability to clusters, grid, other• Provide standard services so applications

behave consistently (e.g. provenance)• Preserve performance (<1% overhead) • Custom Software on top of Open Source, Off-

the-shelf Software

Infrastructure Layer (LDM-129)• Distributed Platform• Different sites specialized for real-time

alerting, data release production, peta-scale data access• Off-the-shelf, Commercial Hardware &

Software, Custom Integration

02C.06.01Science Data Archive

(Images, Alerts, Catalogs)

02C.01.02.01, 02C.02.01.04,02C.03, 02C.04

Alert, SDQA, Calibration,Data Release

Productions/Pipelines

02C.03.05, 02C.04.07Application Framework

02C.05Science User Interface

and Analysis Tools

02C.07.04.01Archive Site

Data Management System Design (LDM-148)

02C.01.02.02 - 03SDQA and

Science Pipeline Toolkits

Data Management System Architecture

Mapping Data Products into Pipelines

• 02C.01.02.01/02. Data Quality Assessment Pipelines

• 02C.01.02.04. Calibration Products Production Pipelines

• 02C.03.01. Instrumental Signature Removal Pipeline

• 02C.03.01. Single-Frame Processing Pipeline

• 02C.03.04. Image Differencing Pipeline

• 02C.03.03. Alert Generation Pipeline

• 02C.03.06. Moving Object Pipeline

• 02C.04.04. Coaddition Pipeline

• 02C.04.04/.05 Association and Detection Pipelines

• 02C.04.06. Object Characterization Pipeline

• 02C.04.03. PSF Estimation

• 02C.01.02.03. Science Pipeline Toolkit

• 02C.03.05/04.07 Common Application Framework

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Data Management Applications Design (LDM-151)

Infrastructure: Petascale Computing, Gbps Networks

The computing cluster at the LSST Archive at NCSA will

run the processing pipelines.

• Single-user, single-application data center

• Commodity computing clusters.• Distributed file system for scaling and

hierarchical storage• Local-attached, shared-nothing storage

when high bandwidth needed

Long Haul Networks to transport data from Chile to the U.S.

• 2x100 Gbps from Summit to La Serena (new fiber)• 2x40 Gbps for La Serena to Champaign, IL (path

diverse, existing fiber)

Archive Site and U.S. Data Access Center

NCSA, Champaign, IL

Base Site and Chilean Data Access Center

La Serena, Chile

Middleware Layer: Isolating Hardware, Orchestrating Software

Enabling execution of science pipelines on hundreds of thousands of cores.

• Frameworks to construct pipelines out of basic algorithmic components

• Orchestration of execution on thousands of cores• Control and monitoring of the whole DM System

Isolating the science pipelines from details of underlying hardware

• Services used by applications to access/produce data and communicate

• "Common denominator" interfaces handle changing underlying technologies

Data Management Middleware Design (LDM-152)

Database and Science UI: Delivering to Users

Massively parallel, distributed, fault-tolerant

relational database.

• To be built on existing, robust, well-understood, technologies (MySQL and xrootd)

• Commodity hardware, open source• Advanced prototype in existence (qserv)

Science User Interface to enable the access to and analysis of LSST data

• Web and machine interfaces to LSST databases• Visualization and analysis capabilities

More: Talks by Becla, Van Dyk

Critical Prototypes:Algorithms and Technologies

Petascale Database Design• Conducted parallel database tests up to 300

nodes, 100 TB of data, 100% of scale for operations year 1

Petascale Computing Design• Executed in parallel on up to 10k cores

(TeraGrid/XSEDE and NCSA Blue Waters hardware) with scalable results

Algorithm Design• Approximately 60% of the software

functional capability has been prototyped• Over 350,000 lines of c++, python coded,

unit tested, integrated, run in production mode

• Have released three terabyte-scale datasets, including single frame measurements, point source and galaxy photometry

• Pre-cursors leveraged• Pan-STARRS, SDSS, HSC

Gigascale Network Design• Currently testing at up to 1 Gbps• Agreements in principle are in hand with key

infrastructure providers (NCSA, FIU/AmPath, REUNA, IN2P3)

Data Management Scope is Defined and Requirements are Established

• Data Product requirements have been vetted with Science Collaborations multiple times and have successfully passed review (Jul ‘13)

• Data quality and algorithmic assessments are far advanced and we understand the risks, successfully passed review (Sep ‘13)

• Hardware sizing has been refreshed based on latest scientific and engineering requirements, system design, technology trends, software performance profiles, acquisition strategy

• Interfaces are defined to Phase 2 level• Requirements and Final Design have been baselined (Data Management Technical Control Team)• Traceability from OSS to DMSR has been verified• All WBS elements have been estimated and scheduled in PMCS with scope and basis of estimate documented

Data Management ICDs needed for Construction start are at Phase 2 Level

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ICDs on Confluence:http://ls.st/mmmDocushare:http://ls.st/col-1033

Going Where the Talent is: Distributed Team

Infrastructure

Middleware

Science Pipelines

Database

User Interfaces

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QA

Science User Interface & Tools

X. WuD. Ciardi IPAC

Project ManagerJ. Kantor

Project ScientistM. Juric

System Architecture

K-T. LimG. Dubois-Felsmann

SLAC

Survey Science Group

SSG Lead Scientist TBD

F. Economou LSST

Alert Production

A. Connolly UW/OPEN

International Comms/Base Site

R. Lambert NOAO

Processing Services & Site Infrastructure

D. Petravick NCSA

Science Database & Data Acc Services

J. Becla SLAC

Data Release Production

R. LuptonJ. Swinbank Princeton

Data Management Organization document-139

Data Management Organization

LSST DM Leadership

• DM Lead institutions are integrated into one project and are performing in their construction roles/responsibilities

Leveraging national and international investments

• NSF/OCI Funded

– Formal relationships continue with the IRNC-funded AmLight project and they are the lead entity in securing Chile - US network capacity for LSST

– We have leveraged significant XSEDE and Blue Waters Service Unit and storage allocations for critical R&D phase prototypes and productions

– Our LSST Archive Center and US Data Access Center will hosted in the National PetascaleComputing Facility at NCSA

– A strong relationship has been established with the Condor Group at the University of Wisconsin and HTCondor is now in our processing middleware baseline

– We have reused a wide range of open source software libraries and tools, many of which received seed funding from the NSF

• Other National/International Funded

– We have participated in joint development of astronomical software with Pan-STARRS and HSC

– We have fostered collaborative development of scientific database technology via the eXtremelyLarge Data Base (XLDB) conferences and collaborations with database developers (e.g. SciDB, MySQL, MonetDB)

– We have a deep process of community engagement to deliver products that are needed, and an architecture to allow the community to deliver their own tools

Data Management isConstruction Ready

• The Data Management System is scoped and credibly estimated– Requirements have been baselined and are achievable (LSE-61)

– Final Design baselined (LDM-148, -151, 152, -129, -135)

– Approximately 60% of the software functional capability has been prototyped

– Data and algorithmic assessments are far advanced and we understand the risks

– Hardware sizing has been done based on scientific and engineering requirements, system design, technology trends, software performance profiles, acquisition strategy

– All lowest level WBS elements have been estimated and scheduled in PMCS with scope and basis of estimate documented

• All lead institutions are demonstrably integrated into one project and are performing in their construction roles/responsibilities– Core lead technical personnel are on board at all institutions

– Agreements in principle are in hand with key technology and center providers (NCSA, NOAO, FIU/AmPath, REUNA)

• The software development process has been exercised fully– Have successfully executed eight software and data releases

– Standard/formal processes, tools, environment exercised repeatedly and refined

– Automated build, test environment is configured and exercised nightly/weekly

• Data Management PMCS plans current and complete