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LaTiS https:// github/dlindhol/LaTiS

LaTiS https:// github.com/dlindhol/LaTiS. Doug Lindholm Laboratory for Atmospheric and Space Physics University of Colorado Boulder ESIP – July 8, 2014. Motivation - Get Data Into Analysis Code/Tools. Disparate Data. Unified Interface. LaTiS Server Architecture. Client Applications.

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LaTiS https:// github/dlindhol/LaTiS

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  1. LaTiShttps://github.com/dlindhol/LaTiS Doug Lindholm Laboratory for Atmospheric and Space Physics University of Colorado Boulder ESIP – July 8, 2014

  2. Motivation - Get Data Into Analysis Code/Tools Disparate Data Unified Interface

  3. LaTiS Server Architecture Client Applications Adapters Filters Descriptors Writers Native Data • LaTiS Data Model Web Browser ASCII Subset CSV TSML Excel Constrain (sst > 20) JSON Binary TSML JDBC Convert Units DAP2 Analysis Tools TSML FITS Missing Values Image Programs TSML Web Service Derived Products code snippet Web Service TSML Custom Custom Custom

  4. LaTiS Client Options • Any OPeNDAP client. Available for most programming languages (python, IDL, Matlab,...). • Analysis/visualization tools with built in OPeNDAP support. • Web browser: Directly enter http URL query. • wget, curl: command line tools for making an HTTP request. • Custom Web Applications (Open Source coming soon) that make AJAX requests to LaTiS to get JSON output and make interactive plots. • Custom programming APIs that wrap a LaTiS call.

  5. Related Technology Comparisons • OPeNDAP • Both implement DAP2 protocol (standard service API) • OPeNDAP servers tend to be file centric • LaTiS presents “virtual” dataset via aggregation • LaTiS aims to be easier to install, configure, and extend • NetCDF Common Data Model (CDM) • Multidimensional array centric • Coupled to NetCDF file format • Climate and forecast model (simulation) emphasis • THREDDS Data Server • Built around NetCDF CDM • Provides OPeNDAP and other service interfaces • TSDS • First generation of LaTiS built on NetCDF CDM • VisAD • Essentially the same logical data model as LaTiS with a clunkier implementation based on old Java capabilities • LaTiS is implemented around modern paradigms like Functional Programming

  6. What do I mean by Data Model • NOT a simulation or forecast (climate model) • NOT a metadata model (ISO 19115) • NOT a file format (NetCDF) • NOT how the data are stored (RDBMS) • NOT the representation in computer memory (data structure) • Logical model • What the data represent, conceptually • How the data are used

  7. Data Abstractions bits 10110101000001001111001100110011111110 bytes 00105e0 e6b0 343b 9c74 0804 e7bc 0804 e7d5 0804 int, long, float, double, scientific notation (Number) 1, -506376193, 13.52, 0.177483826523, 1.02e-14 array

  8. Scientific Data Abstractions • Multi-dimensional Arrays • Key Features: • Single data type • - Access by index

  9. Relational Data Relational Database Table = Relation Row = Tuple of Attributes e.g. (0, 3.5, B) Key Features: - Supports different data types - Well suited for access by value e.g. time>2, class=A But the relation is limited to a sequence of tuples:

  10. LaTiS Unified Data Model • Extends the Relational Model to add Functional relationships. • Represents multi-dimensional domain of data grids. • Access by value or index. Independent Variable (domain) Independent Variable Dependent Variables (range) Example: time series of gridded surface winds Time -> ((Lon, Lat) -> (U,V))

  11. LaTiS Data Model Only Three Variable Types: Scalar: single Variable Tuple: group of Variables Function: mapping from one Variable to another Extend to capture higher level, domain specific abstractions

  12. Discipline Agnostic Data Access with LaTiS Philosophy: Leave data in their native form Expose via a common interface Software: • Reusable adapters (software modules) to read common formats, extension points for custom formats • XML dataset descriptors, map native data model to the LaTiS data model • Open Source, community Web services: • Standard service interfaces, currently OPeNDAP • Server side processing and output format options

  13. Implementing the Data Model • The LaTiS Data Model is an abstract representation • Can be represented several ways • UML • VisAD grammar • Java Interface (no implementation) • Need an implementation in code • Scientific data Domain Specific Language (DSL) • Expose an API that fits the application domain • Scala programming language • http://www.scala-lang.org/

  14. Why Scala • Evolution of Java • Use with existing Java code • Runs on the Java Virtual Machine (JVM) • Command line (REPL), script, or compiled • Statically typed (safer than dynamic languages) • Industrial strength (Twitter, LinkedIn, …) • Object-Oriented • Encapsulation, polymorphism, … • Traits: interfaces with implementation, multiple inheritance, mix-ins • Functional Programming • Immutable data structures • Functions with no side effects • Provable, parallelizable • Syntactic sugar for Domain Specific Languages • Operator “overloading”, natural math language for Variables • Parallel collections

  15. Scala Implementation • Dataset as a Scala collection • Functional Programming Paradigms: • Function composition over object manipulation • Functions as first class citizens • a LaTiS Function can be used like a programming function • Immutable data structures • No side-effects: parallelizable, provable • Lazy evaluation: scalable • Math and resampling mixed in • e.g. dataset3 = (dataset1 + dataset2) / 2 • Metadata encapsulated • enforce data consistency: unit conversions ... • track provenance

  16. LaTiS Server Implementation • RESTful web service API (OPeNDAP +) • Java Servlet, build and deploy war file • XML dataset descriptor (TSML) for each dataset • Specify Adapter to use • Map native data source to LaTiS data model • Define transformations as Processing Instructions • Catalog to map dataset names to TSML • Plugins: implement the Adapter, Filter or Writer interfaces or extend existing ones • Properties file to map filter and writer names to implementing classes

  17. Example – Serving an ASCII File Sunspot data for October 2003 TSML Dataset descriptor 2003 10 01 75 2003 10 02 72 2003 10 03 59 2003 10 04 60 2003 10 05 53 2003 10 06 51 2003 10 07 50 2003 10 08 56 2003 10 09 58 2003 10 10 50 2003 10 11 44 2003 10 12 22 2003 10 13 12 2003 10 14 4 2003 10 15 17 2003 10 16 24 2003 10 17 37 2003 10 18 43 2003 10 19 43 2003 10 20 64 2003 10 21 66 2003 10 22 72 2003 10 23 68 2003 10 24 81 2003 10 25 89 2003 10 26 102 2003 10 27 141 2003 10 28 161 2003 10 29 167 2003 10 30 171 2003 10 31 156 <?xml version="1.0" encoding="UTF-8"?> <tsml> <dataset name="Sunspot_Number" history="Read by LaTiS"> <adapter class="latis.reader.tsml.AsciiAdapter" url="file:/data/latis/ssn.txt" /> <time units="yyyy MM dd” /> <integer name="ssn” /> </dataset> </tsml>

  18. Example – Serving an ASCII File

  19. Current Applications • LASP Interactive Solar Irradiance Data Center (LISIRD) • Uses LaTiS to read, subset, reformat data, metadata • http://lasp.colorado.edu/lisird/ • Time Series Data Server (TSDS) • Common RESTful interface to NASA Heliophysics data • http://tsds.net/ Other LASP projects: MMS, MAVEN, database statistics, log files External users?

  20. Capabilities – Data Reader Modules • Operational: • ASCII (file, web service, system call), binary, NetCDF, Relational database, data “generators” • Time Series of scalars, vectors, and spectra • Arbitrarily long time series • Prototyped: • HDF, CDF, FITS, GRIB, OPeNDAP (e.g. other LaTiS servers), NoSQL (MongoDB) • Nested 2D (gridded) data structures • Planned: • Arbitrarily complex data structures

  21. Capabilities – Data Writer Modules • Operational: • OPeNDAP, ASCII (e.g. csv), binary, JSON, Image (PNG), IDL code, HTML dataset landing page • Prototyped: • NetCDF, HDF, IDL save file, interactive plot • Planned: • GeoTIFF, …

  22. Capabilities – Data Filter Modules • Operational: • Subset, aggregate, stride, thin, replace, integrate, bin average • Prototyped: • FFT, min, max, unique, resampling, unit conversion • Planned: • Coordinate system transformations • Make it easier to plug in custom computations • Track provenance

  23. Capabilities – Service Interface • Operational: • OPeNDAP • Java Servlet, simply deploy war file (Tomcat, Glassfish) • Prototyped: • Authentication • Single executable (jetty) • THREDDS Data Server (TDS) integration • Planned: • Open Geospatial Consortium (OGC) standards • Web Map Server • Web Coverage Server

  24. Capabilities - Metadata • Operational: • THREDDS catalog, static XML, browse • Prototyped: • Semantic Web triple store (RDF, SPARQL) • Text search (Solr) • Modeling RDF triples (subject, predicate, object) • Track provenance, record Dataset modifications • Planned: • Serve metadata in various schema (e.g. ISO 19115, SPASE) • Unique IDs, Digital Object Identifiers (DOI) for publishing

  25. Other Capabilities • Operational: • Time API with formatting • Time conversions with leap seconds • Prototyped: • Caching, improve performance • Parallel processing, multi-core • Planned: • Big Data, Hadoop, Map Reduce • Workflow integration

  26. Source Code Management – Open Source • Time Series Server (a.k.a. TSS1) • Core of Time Series Data Server (TSDS, tsds.net) • Built around Unidata Common Data Model • SourceForge: https://sourceforge.net/projects/tsds/ • LaTiS (a.k.a. TSS2) • New LaTiS data model, scala implementation • GitHub: https://github.com/dlindhol/LaTiS • LASP internal development branch • Plug-ins as separate projects (e.g. data collections, math, custom readers/writers,…), keep core small

  27. My Background (i.e. bias) • Astrophysicist by degree, software engineer by profession • Data user and provider • Scientific data applications developer: • astrophysics, atmospheric science, space science • Holy Grail: common data model • Favorite scientific data models: • VisAD(http://www.ssec.wisc.edu/~billh/visad.html) • Unidata Common Data Model (http://www.unidata.ucar.edu/software/netcdf-java/CDM/) • OPeNDAP(http://www.opendap.org/)

  28. Motivation – Stove Pipes

  29. Single Data Access Interface

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