chessdatamanagement

Chess Data Management service

Introduction

The CHESS data flow has been discussed in this document.

Here we propose a possible architecture for CHESS data management based on gradual enchancement of existing infrastructure:

In particular, we propose to introduce the following components:

• MetaData DB based on MongoDB or similar document-oriented database. Such solution should provide the following features:

  • be able to handle free-structured text documents
  • provide reach QueryLanguage (QL)

• Files DB based on any relation database, e.g. MySQL or free alternative MariaDB. The purpose of this database is provide data bookkeeping capabilities and organize meta-data in the following form:

  • a dataset is a collection of files (or blocks)
  • each dataset name may carry on an Experiment name and additional meta-data information
  • organize files in specific data-tiers, e.g. RAW for raw data, AOD for processed data, etc.
  • as such each dataset will have a form of a path: /Experiment/Processing/Tier

Both databases may reside in their own data-service called MetaData Service. Such service can provide RESTful APIs for end-users, such as

• inject data to DBs
• fetch results
• update data in DBs
• delete data in DBs

In addition, we suggest to introduce Input Data Service which can take care of standardization of user inputs, e.g. key-value pairs, tagging, etc. It is not required originally, but will help in a long run to provide uniform data representation for Meta Data Service.

Finally, the data access can be organized via XrootD service.

Insert data into MetaData DB:

We provide a chess_parser.py script to parse input Microsoft Word documents, extract and inject its content into MongoDB. Here is an example of such operation

# prepare files.db (so far we use SqliteDB)
rm files.db; sqlite3 files.db < doc/schema.sql

# start MongoDB

# prepare parameter file which will contain DB, experiment parameters
# so far I choose JSON data-format, but it can be replaced with
# YAML data-format which is much more simpler and intuitive for end-users
cat doc/params.json
{
    "fname": "doc/miller-774-1_beamtime_notes.docx",
    "path": "files",
    "dburi": "mongodb://localhost:8230",
    "dbname": "chess",
    "dbcoll": "meta",
    "filesdb": "files.db",
    "experiment": "Titanium",
    "processing": "FirstPass",
    "tier": "RAW"
}

# inject data into MongoDB (MetaDataDB) and Sqlite (FilesDB)
./chess_parser.py --params=doc/params.json --verbose

Find documents in MetaData DB

We provide basic implementation of finder script chess_finder.py which should be able to find required meta-data in MongoDB via provide free-text query:

# find meta-data information
./chess_finder.py --params=doc/params.json --query="scan 74-77"
# find corredponding files
./chess_finder.py --params=doc/params.json --query="scan 74-77" --list-files --verbose

Please note, to perform free text search queries we need to define text index, e.g.

db.meta.createIndex( { description: "text" } )

References

1. MongoDB
2. MySQL
3. MariaDB
4. MongoDB free-text search