While it includes a defined structure, the extension borrows the looks and feels of the Access version you are using. Clean looks and intuitive functionalityĪs its name suggests, this is a template for Access's database, so it goes without saying that you need to have Microsoft Office installed on your computer. Microsoft Access Family Tree Genealogy History Templates Database is an extension that can help you record the data you learn while going through documents and records about your family's history.

More often than not, it is easier to collect more data when starting with a date of birth or the marriage of a relative than attempting to trace down a person you do not know too much about. 5.If you are just getting started with your family history and do not have too much information at your disposal, then one idea is to work methodically backwards in time. However, this might not be an issue depending on our exact requirements. The best we could do was to list the entire tree and work out the ancestors ourselves from the returned data. We also can’t efficiently list all of the ancestors of a given node. Unfortunately, we can’t use this to select subtrees. We can then put together the tree structure in memory due to this query. However, it means that we can now select every node within the same tree with a single query instead of needing many: SELECT * FROM tree WHERE tree_id = 1 It also means that inserting records into the table is slightly more complicated – because we need to provide both the immediate parent and the root of the tree. This means that our table gains an extra column, so we’re storing more data. This could be the ID of the root node, or some other identifier for the entire tree. For example, MySQL supports this in 8.0 but not in earlier versions.Īn alternative method to allow us to retrieve the entire tree is to have an extra column that identifies the tree to which each node belongs. However, not all database engines support this form of query, so it might not be an option for us. This is the most efficient way to work with this kind of tree structure since we can cheaply insert records into the database and query the entire tree structure comparatively cheaply. Similar queries can be written to traverse in the opposite direction if we need to list all ancestors instead of all child nodes. This will start with selecting the node with node_id = 1, recurse down the tree finding rows where t.parent_id = rectree.node_id, until no more rows are returned. If our database engine supports Recursive Common Table Expressions, it’s possible to use the WITH RECURSIVE clause to query a structure such as this in one go.ĭoing this involves writing a query that essentially joins the table back onto itself and informs the database engine that it should recursively do this until it runs out of rows.įor example, to query the above table, we could do: WITH RECURSIVE rectree AS ( If we want to write to the middle of the tree structure, though, or if we want to make updates across the entire structure, this gets very complicated very quickly and can risk breaking the integrity of the records. And worse, it might be an arbitrary number of levels, so we can’t just plan this into our query.Ĭonversely, if we want to write a new node at the bottom of a tree structure, then this is a simple write. If we want to read an entire structure, though, we need to be able to traverse many levels of the tree. If we want to read a single level of a nested tree structure, this is the same as any normal query. And this gets even more complicated if we want to have a deeply nested structure instead of just a single level of relationship. Both in terms of reading the data back out again correctly, but also in terms of writing it and maintaining integrity. This is possible when the relationship points back to the same table, but it gets more complicated. We can very easily store data in two different tables which relate to each other. Relational databases are fantastic at storing tabular data, where some data relates to other data.

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