3 ms·

In one project I was able to achieve an identical "ideal ddd aggregate store" using MSSQL. - The aggregates are XML serializable entities persisted into tables

In one project I was able to achieve an identical "ideal ddd aggregate store" using MSSQL. - The aggregates are XML serializable entities persisted into tables using the XML datatype (fairly comparable to PostreSQL's JSON datatype). - MSSQL has numerous methods for querying, updating, and reading from XML typed columns. They're certainly clunky, but they do work. - To simplify querying and indexing, we created computed columns with a user defined function to query from the XML column. By marking these columns as persisted, the UDF is only executed and the column updated when the row is written. And as persisted, the computed columns can be included in normal indexes to improve performance. A lot of folks may scoff at using XML, and especially MSSQL's XML datatype. But I will say that it works. And after you've built a simple foundation, developing your aggregates and working with them almost becomes a breeze. And as far as XML versus JSON... it almost doesn't matter in this case.

jchrisa 12y ago
This sounds like the approach used by Couch-style incremental map reduce. For each JSON document, run a JavaScript function to determine the document's contribution to the map reduce index. (Basically a user-defined column with optional custom aggregation.) So you can easily normalize multiple incoming JSON types to a single index (eg mix JSON data from different APIs into a single "column", etc.) And that's not even getting into the reduce side. From [1] "if documents represent your company’s financial transactions, a view can answer the question of what the spending was in the last week, month, or year." [1] http://guide.couchdb.org/draft/views.html http://guide.couchdb.org/draft/views.html
- daigoba66 12y ago
  It's very much the same pattern. Also check out RavenDB (http://ravendb.net/ http://ravendb.net/). It's another JSON document database with JavaScript powered map/reduce indexes. One caveat is that RavenDB updates its indexes async. It's a type of eventual consistency. That means that after a write, an index may not immediately have the updated data. Depending on the application, this may or may not be acceptable.
  - jchrisa 12y ago
    There are as many sync/async options for Couch map reduce as you could possibly need. The key is for some of the patterns to work, you need to be able to know your index queries come from the same database snapshot as your event feed, which is easier to do when you aren't in clustered mode.

jchrisa 12y ago

This sounds like the approach used by Couch-style incremental map reduce. For each JSON document, run a JavaScript function to determine the document's contribution to the map reduce index. (Basically a user-defined column with optional custom aggregation.) So you can easily normalize multiple incoming JSON types to a single index (eg mix JSON data from different APIs into a single "column", etc.) And that's not even getting into the reduce side. From [1] "if documents represent your company’s financial transactions, a view can answer the question of what the spending was in the last week, month, or year." [1] http://guide.couchdb.org/draft/views.html http://guide.couchdb.org/draft/views.html

daigoba66 12y ago
It's very much the same pattern. Also check out RavenDB (http://ravendb.net/ http://ravendb.net/). It's another JSON document database with JavaScript powered map/reduce indexes. One caveat is that RavenDB updates its indexes async. It's a type of eventual consistency. That means that after a write, an index may not immediately have the updated data. Depending on the application, this may or may not be acceptable.
- jchrisa 12y ago
  There are as many sync/async options for Couch map reduce as you could possibly need. The key is for some of the patterns to work, you need to be able to know your index queries come from the same database snapshot as your event feed, which is easier to do when you aren't in clustered mode.