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Database Query API

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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,
          start=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:

db.select("Person", 
          ["gramps_id"], 
          where=["AND", [("primary_name.surname_list.0.surname", "=", "Smith"), 
                         ("primary_name.first_name", "LIKE", "J%")]],
          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).

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:

  • total - total number of records (in the case of start or limit is given)
  • time - the time in seconds it too to collect the data
  • expanded - whether the data needed to be expanded (unpickled and primary objects created). BSDDB selects alway are expanded
  • query - the actual SQL query, if one

The goal is to always have a query, and to always have expanded be False. Those will be the fastest queries.

Implementation

There are now two database backends: Berkeley DB (BSDDB), and 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 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.surname_list[0].surname
  • person.primary_name.first_name

respectively. We could make special fields for these, and special indexes. Gramps 5.0 creates "secondary" fields and indexes in SQL for every str, int, or bool data on a primary object. These secondary fields are known from the primary object's schema.

The schema idea has been augmented with additional methods based on the idea of "fields". Now, you can ask a person object:

>> person.get_field("primary_name.first_name")
"Sarah"

and with some additional syntax:

>> person.get_field("primary_name.surname_list.0.surname")
"Johnson"

or even:

>> person.get_field("primary_name.surname_list.surname")
["Johnson", "Johansen", "Johnston"]

In the last example, the "surname" field was applied to each of the surname_list items.

Speed Tests

Using he dotted-field path string field-based Select API, we can write code as follows. Consider that we want to select the handle of all people whose surname is "Smith", given name starts with a "J", and ordered by gramps_id:

db.select("Person", 
          ["handle", "gramps_id"], 
          where=["AND", [("primary_name.surname_list.0.surname", "=", "Smith"), 
                          ("primary_name.first_name", "LIKE", "J%")]],
          order_by=[("gramps_id", "ASC")])

This code works on BSDDB as well as DB-API. Let's see the difference in timing on databases that have 187,294 people (created from GenFan, this is 20 full generations).

Here is a summary:

       | Filter |  Select All   | Sort All
-------|--------|---------------|----------
BSDDB  | 20.6s  |       9.2s    | 18.1s
DB-API |   .3s  |        .5s    |   .6s

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().