Difference between revisions of "Tabular Data Service Model and Operations"

Revision as of 19:19, 17 December 2013

This page is dedicated to provide the reader with detailed information on the Tabular Data Service component, in particular on the data model and the set of operations it supports.

Data Model

Tables in the Tabular Data system are entities made of two separate elements:

• Raw data: This can be imagined as the data contained in user provided CSV files
• Metadata
  • Data structure: this metadata describes how data is structured (e.g.: columns number or column data type) and how raw data can be reached
  • Enriching Metadata: This metadata adds information on top of raw data and provides some context or additional information on top of it.

Raw data is managed directly by leveraging relational database services (PostgreSQL with Postgis extension). Metadata is managed and represented through a metadata model library called tabular-model. Tabular Model provides

• a description for tables entities covering the minimum table structure description requirements
• elements that helps in enriching tables with additional metadata (column labels, descriptions, table version, etc.)

Tabular Model is GWT friendly, which means that it can be used in GWT Web application on client side, since it's java beans are translatable into javascript code.

Tabular Data Dataset

Tabular Data Metadata

Tabular Data Template

Operations

Operation modules are a group of Java classes that provide, each one, a single functionality to the system. Functionality provided by operation modules may fall under one of these categories:

• Data Import: a set of operations supporting the ingestion of datasets in the Tabular Data service;
• Data Validation: a set of operations supporting ...
• Data Transformation: a set of operations supporting data manipulation, e.g. filtering, aggregation, etc.
• Data Export: a set of operations supporting the transfer of Tabular Data products in a format that can be used by other systems;

Each Operation takes an input, which is a set of parameters. These parameters may include a tabular data table or a column of a tabular data table or none of them (like in the import case). Along with additional parameters, each operation must belong to one of these categories:

• Void scoped: does not require a table to compute
• Table scoped: requires a target table to compute
• Column scope: requires a target table column to compute

Each operation produce, as a result of its computation, a table and zero or more collateral tables. The create tables are always a new table probably created by first cloning the input table, if any is provided.

Operation modules leverages Cube Manager capabilities in order to create new tables, clone existing ones or modify the structure or additional metadata of tables. Operation modules can work with raw data directly on the DB, therefore data experts can rely on their SQL knowledge.

Data Import

Data Validation

Data Transformation

Data Export

Expressions

Tabular-model provides a simple model for describing conditions on table data. Conditions can be expressed as a set of minimal constructs chained together with logical connectors. The data model used to compose expression relies on the composite design pattern which allows to build tree of expressions. Logical connectors are OR and AND and can take any number of child expression. Leaf expression are expression that describe a particular conditions. Each Leaf Expression can take an arbitrary set of parameters in order to be defined.

Starting from release 3.0.0 of tabular model a set of leaf expressions have been defined:

• IsNull, Value is null;
• ValueIsIn: Value contained in another column of another table;
• Conditions on comparable values: Equals, Greater than, Lesser than, Not Equals;
• Conditions on text values: contains text, text equals, text mathes SQL regexp.

An expression can be evaluated by an Evaluator, which is simply an object that, by processing a given expression, performs some action and returns a custom result. Two evaluators have been provided:

• evaluator-sql: produces a SQL WHERE clause given an expression;
• evaluator-description: produces a human readable description of the expression.

A client can obtain an instance of a Evaluator using its related EvaluatorFactory. Usage of expressions and evaluators is explained in the following code snippet taken from a test case of the evaluator-description project:

	DescriptionExpressionEvaluatorFactory evaluatorFactory = getDescriptionEvaluatorFactory();
 
	ColumnReference targetColumnReference = createColumnReference();
	ColumnReference anotherTargetColumnReference = createColumnReference();
 
	// Other expression
	Expression isNull = new IsNull(targetColumnReference);
	Expression columnIsIn = new ValueIsIn(targetColumnReference, anotherTargetColumnReference);
 
	// Text expression
	Expression textContains = new TextContains(createColumnReference(), new TDText("test"));
	Expression textEquals = new TextEquals(createColumnReference(), new TDText("test"));
	Expression textMatchRegexp = new TextMatchSQLRegexp(targetColumnReference, new TDText("[a-b]*"));
 
	// Comparable
	Expression equals = new Equals(targetColumnReference, new TDDate(new Date()));
	Expression greaterThan = new Equals(targetColumnReference, new TDInteger(5));
	Expression lessThan = new LessThan(targetColumnReference, new TDNumeric(5.1f));
	Expression notEquals = new NotEquals(targetColumnReference, new TDBoolean(false));
 
	// Composite
	Expression and = new And(textContains, isNull, columnIsIn, textContains);
	Expression or = new Or(and, textEquals, textMatchRegexp, equals, greaterThan, lessThan, notEquals);
 
	String description = evaluatorFactory.getEvaluator(or).evaluate();

LeafExpression are usually parameterized in terms of column references or typed values. Column references are objects that allow to reference a column of a tabular data table. Typed values must comply with allowed data types for table columns, therefore boxed custom types are used for the representation of those values.