In the recent DFP API releases, we announced the addition of more tables to the PublisherQueryLanguageService, starting with Line_Item and Ad_Unit. These tables are an alternative to retrieving entities from their respective services’ get***ByStatement methods. They allow you to retrieve sparse entities containing only the fields you’re interested in. For example, the following select statement retrieves the first page of only the ID and name of line items that are missing creatives.
SELECT Id, Name from Line_Item WHERE IsMissingCreatives = true LIMIT 500 OFFSET 0
In this blog post, we’ll go over some situations where this feature can be utilized to speed up entity retrieval times from hours to minutes.

Entity synchronization

The first major use case that benefits from these new tables is entity synchronization. For example, if you’re synchronizing line items on your network into a local database, you’re most likely using LineItemService.getLineItemsByStatement and hopefully taking advantage of the LineItem.lastModifiedDateTime field to only filter out line items that have changed since the last time you synchronized. But even with lastModifiedDateTime, this synchronization can still take a while, depending on how many line items you have on your network, and how complex their targetings are. If you don’t need to synchronize all the fields in your line item objects, you may be able to use the Line_Item PQL table to perform this synchronization instead.

If you do need to synchronize fields not yet available in the Line_Item table, such as targeting, you can still take advantage of this table for computed fields that don’t affect lastModifiedDateTime, such as LineItem.status. What you can do is synchronize your line items as usual with getLineItemsByStatement filtering on lastModifiedDateTime. Then update your local statuses with selected line item statuses from the Line_Item table (a very quick process):
SELECT Id, Status from Line_Item LIMIT 500 OFFSET 0

Match tables for reports

Local copies of line item information can also be used as match tables to construct more detailed reports. Sometimes, you may want more information in your reports than what is currently available as a dimensionAttribute. For example, if you run a report by line item ID, you may also want other line item information like isMissingCreatives to show in the report. Because LineItem.isMissingCreatives is unavailable as a DimensionAttribute, you can create a local match table containing line item IDs and additional columns to be included in the report. Then you can merge this match table with the report by the line item ID to obtain a report with those additional columns.

For example, let’s say you run a report with the following configuration:
The report in CSV_DUMP format looks something like this:
Dimension.LINE_ITEM_ID, DimensionAttribute.LINE_ITEM_COST_TYPE,
1234567, CPM, 206
1234568, CPD, 45
1234569, CPD, 4
To also include LineItem.isMissingCreatives in the report, you would fetch a match table and save it (as a CSV file for example) by retrieving ID and isMissingCreatives from the Line_Item table.
SELECT Id, IsMissingCreatives from Line_Item LIMIT 500 OFFSET 0
Full examples of how to fetch match tables are available in all our client libraries. For instance, Python’s is here. Then using a script or a spreadsheet program, merge the match table with the report to produce something like this:
Dimension.LINE_ITEM_ID, DimensionAttribute.LINE_ITEM_COST_TYPE,
    Column.AD_SERVER_IMPRESSIONS, LineItem.isMissingCreatives
1234567, CPM, 206, true
1234568, CPD, 45, false
1234569, CPD, 4, false
If you have any questions on these new PQL tables, or suggestions on what PQL tables you want in the next release, please let us know on the API forum, or on our Google+ Developers page.