1. History Server & API Christopher Larrieu Jefferson Laboratory

2. Goals Simple interface to archived data Efficient access to distributed data sources Useful data manipulation capabilities Portable integration with existing EPICS tools

3. CADataStore: abstract interface to read CA data from file, db, etc. archive –Provides channel listing, creates data range objects range –Describes data for one channel on time range channel_state –Complete information for one channel associated with one point in time. range_iterator –Access sequential channel states by updating channel_state

4. Server Specialization Server accesses CA archive via CADataStore object Different derived classes for different archives –LANL_DataStore wraps around Kay’s archive API –JLAB_DataStore wraps around JLAB archive API –RDB_DataStore ? –*_DataStore Derived classes compiled as shared object code, loaded at run-time. –Narrow interface eases specialization, but captures essential functionality.

5. Server Schematic LANL_DataStore Channel Archiver API History Server History Data Client

6. Server Components Data access plug-in (derived from CADataStore) Exported network interface –Archive description (name, acquisition mode, etc.) –Data manipulation (transform library) –Data production (data stream, iterator) Network communication component

7. Client Components Consumer code (e.g. data mining, visualization) Library API –Directory Services –Data manipulation (transform library) –Data consumption (stream from server) Network communication component

8. DataStoreDirectory Provides hierarchical structure to signal names Enumerates registered history servers –Name –Location –Data quality Maps signal names to history servers

9. DataSequence Describes a stream of sequenced data Every element contains –Indexing attribute (scalar, numeric) (e.g. time) –Primary attribute (e.g. value) –Ancillary attributes (e.g. status, severity, units) –Attribute dependencies (e.g. alarm depends upon value) Elements are iterated over via the indexing attribute, and return primary attribute as default value.

10. DataTransform Reads input sequence, generates output sequence. Generic transforms operate upon primary attribute. Specialized transforms operate on known attributes.

11. Example 1: Binning Transform Parameters: bin width Input: (index,value) pairs (e.g. time, value) –All other attributes discarded Output: (count, mean, variance, min, max) tuples, indexed by bin number, with mean as primary attribute.

12. Example 2: Discrete Fourier Transform Input (index, value) pairs (e.g. time, value) –All other attributes discarded Output (frequency, amplitude) pairs, index by frequency, with amplitude as primary attribute.

13. Standard Transforms Binning Interpolating Decimating DFT, IDFT Digital Filter Averaging Rasterizing Attribute Mapping Attribute Filtering

14. Network Communication CORBA –Transparent binary compatibility –Easy communication between distributed objects –Integrate with other CORBA-enabled tools MICO –open source implementation –branded CORBA 2.3 compliant by OMG –Portable: Unix (hp-ux, linux, solaris, aix, etc.), Win32 –C++, Java bindings

15. Uses in EPICS Portable CA Server interface can supply history data to ca clients (e.g. medm). Adaptor library supporting Kay’s API: current applications become network capable. New applications: java tools, etc.

16. Intended Use at JLAB StripTool history plug-in Replacement for current XARR interface Operator / user scripting. DARTH (vaporous data-mining tool)