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== Overview API ==
Starting with Gramps 5.0, there is a new method on the database object called "select" that works as follows:
db.select(TABLE-NAME,
SELECT-LIST,
where=WHERE-EXPRESSION, order_by=ORDER-BY-LIST, order_bystart=START-ROW, limit=LIMIT-ROW-COUNT) The following are required: * TABLE-NAME - the name of the table. That would be "Person", Family", "Media", "Repository", "Place", "Note", "Source", "Citation", or "Tag"* SELECT-LIST - a list of dot-separated field path strings from this object (eg, "gramps_id", "primary_name.first_name", etc) Optional arguments: * WHERE-EXPRESSION - a matching expression, such as ("gramps_id", "=", "I0001"). These can be nested (see below)* ORDER-BY-LIST- a list of dot-separated field path strings, each paired with a sorting direction, for example [("gramps_id", "ASC")]* START-ROW - the row number on which to start. Default is 0, meaning start at beginning* LIMIT-ROW-COUNT - the limit of how many rows to return. Default is -1, meaning no limit
As an example, consider selecting the gramps_id from all people who have a surname of "Smith" and whose name begins with a "J", ordered by the gramps_id:
["gramps_id"],
where=["AND", [("primary_name.surname_list.0.surname", "=", "Smith"),
order_by=[("gramps_id", "ASC")])
The parameters "start" and "limit" are used for paged selects. These will also return the total of the selection as if start or limit had not been given (see Result below). == API =WHERE-EXPRESSION === The where expression must be in one of these four forms (tuples or lists allowed): * None - no filter applied to data* (dot-separated field path string, COMPARISON-OPERATOR, value)* ["AND" | "OR", [WHERE-EXPRESSION, WHERE-EXPRESSION, ...]]* ["NOT", WHERE-EXPRESSION] COMPARISON-OPERATOR is one of: * "LIKE" - use with "%" wildcard* "="* "!=", or "<>"* "<* "<="* ">"* ">="* "IS"* "IS NOT"* "IN" Examples: * ("primary_name.first_name", "=", "Mary")* ["OR", [("primary_name.first_name", "=", "Mary"), ("primary_name.first_name", "LIKE", "Eliza%")]]* ["NOT", ("primary_name.first_name", "=", "Mary")] === ORDER-BY-LIST === The ORDER-BY-LIST is either None or is a list of dotted-field path strings paired with "ASC" or "DESC". Example: * [("gramps_id", "DESC")]* [("gramps_id", "DESC"), ("primary_name.first_name", "ASC")] == Result == The database.select() method will always return a Result. A result is a collection of all of the data (ie, it is not a generator). Results are a subclass of the Python list object, with additional properties:
== Implementation ==
There are now two database backends: Berkeley DB (BSDDB), and [https://www.python.org/dev/peps/pep-0249/ Python's DB-API]. BSDDB is a data store with much of the database code written in Python, and DB-API is a common interface to the popular SQL engines. We have used BSDDB in Gramps for many years, but are now transitioning to DB-API.
With BSDDB, Gramps has a pipeline design when it comes to accessing the data. For example, consider getting the People for the flat view. First we get a cursor that iterates over the data. Then we sort it, on whatever criteria we have requested. Finally, we filter the data. The select method will always perform a linear search on fully expanded data.
In order to make this the select operation faster for DB-API, we need to know the filter information, and sort order when we ask for the data. With SQL we can simply add WHERE clauses and ORDER BY clauses to the basic SELECT statement. But these are only useful if we can have indexes on the relevant data.
This is made more difficult because Gramps uses a hierarchical representation of data. For example, we might wish to have the People data sorted by "surname, given" of the primary_name. But that information is actually in:
* person.primary_name.first_name
respectively. We could make special fields for these, and special indexes. But it would be much more flexible if we could create a variety of ad hoc queries on the flyGramps 5. The BSDDB datastore doesn't have any schema, which means that it has no idea of 0 creates "gramps_idsecondary" fields and indexes in SQL for every str, int, or "primary_name" or any fieldbool data on a primary object. An idea of These secondary fields are known from the primary object's schema has been developed over the last few years. This makes possible the Database Differences Report, without having to write any field-specific code: the data knows its own structure.
The schema idea has been augmented with additional methods based on the idea of "fields". Now, you can ask a person object:
"Johnson"
== Speed Tests ==
db.select("Person",
So, where we can access the data via SQL, we can get a speedup, the biggest will always be in the filter as it makes it so we don't have to load into Python many objects. We have linear code in many places that could benefit from using db.select().
