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If using the datastore API directly, a common pattern of usage is: // Get a handle on the datastore itself DatastoreService datastore = DatastoreServiceFactory.getDatastoreService(); // Lookup data by known key name Entity userEntity = datastore.get(KeyFactory.createKey("UserInfo", email)); // Or perform a query Query query = new Query("Task", userEntity); query.addFilter("dueDate", Query.FilterOperator.LESS_THAN, today); for (Entity taskEntity : datastore.prepare(query).asIterable()) { if ("done".equals(taskEntity.getProperty("status"))) { datastore.delete(taskEntity); } else { taskEntity.setProperty("status", "overdue"); datastore.put(taskEntity); } }

Queries involving keys use indexes just like queries involving properties. Queries on keys require custom indexes in the same cases as with properties, with a couple of exceptions: inequality filters or an ascending sort order on __key__ do not require a custom index, but a descending sort order on __key__ does. As with all queries, the development web server creates appropriate configuration entries in this file when a query that needs a custom index is tested.

protocol

ublic void doGet(HttpServletRequest req, HttpServletResponse resp)     throws IOException {    resp.setContentType("text/plain");    final DatastoreService datastore = DatastoreServiceFactory.getDatastoreService();    final Query query = new Query("Table/Entity Name");    //query.addSort(Entity.KEY_RESERVED_PROPERTY, Query.SortDirection.DESCENDING);    for (final Entity entity : datastore.prepare(query).asIterable()) {        resp.getWriter().println(entity.getKey().toString());        final Map<String, Object> properties = entity.getProperties();        final String[] propertyNames = properties.keySet().toArray(            new String[properties.size()]);        for(final String propertyName : propertyNames) {            resp.getWriter().println("-> " + propertyName + ": " + entity.getProperty(propertyName));        }    }}

A query like this will use the merge join strategy by default. If it's too slow for the merge join strategy, you could have to build a custom index for it, which would indeed be 'exploding'  - each User entity would have len(friends)*len(likes) index entries.

Merge join queries - queries with multiple equality filters but no inequalities or sort orders - are a special case. They can be satisfied using the built in indexes and a merge join strategy, but they can also use a custom index. The production environment will use the index if it's present, and otherwise will do a merge join. Generally, a merge join works well, but there are situations in which it doesn't - in which case you may need to add an explicit index.

Try this: SELECT * FROM PreparedTransaction WHERE __key__=KEY('agdwYXllbGV4cjkLEhNQcmVwYXJlZFRyYW5zYWN0aW9uIiAwMDAwMGNjMjMwYzg2MTFjZTFhOWZjZDJkZDEzMWMyNww') This is documented here: http://code.google.com/appengine/docs/python/datastore/gqlreference.html

Another way of solving the problem is to have a Horse object with a "farmKey" field like so: class Horse {  @PrimaryKey  Key mKey  Key farmKey } Then to get horses on a farm you'd do a query on Horse.kind with farmKey equal to your farm key.  (Keys only query would make this faster) What's nice about this approach is that you don't need Horse key at all, just create a Horse object, give it the farm key, and persist it without worrying about it.

> > so a Farm can have some horses. I'd like to create a new horse, then > > put it on a farm, in one transaction.

> This requires Horse to be a descendant of Farm to be in the same   > entity group.  Therefore a horse could not move farms unless you   > delete and recreate it with a new Key.

 Twig is an alternative to the standard   persistence interfaces JDO and JPA, specifically designed to make the   most of the underlying datastore's unique features.

Twig is the only interface to support direct unowned relationships so   your code is not dependent on low-level datastore classes.  It is the   only interface to support OR queries by merging together multiple   queries, sorting them and filtering out duplicates at the lowest level   for performance.

Async datastore calls are not yet part of the low-level API.  Twig   uses the underlying Protocol Buffer layer classes to call   ApiProxy.makeAsyncCall() instead of makeSyncCall.  All the code is   open source so you can check out how its done.

You can't in a query, the indexes only index values that exist,  you would have to explicitly set properties to None or some other sentinal value by default to find objects that haven't had a property set.

> How does one test for the presence or absence of a particular property > in an Entity?

About loading request/performance, there're lots of discussions that you can find in the groups, please just try google it. Here are some notes based on reading those. Latency causes by 1- time to start new JVM 2- time to load your application To reduce load time by 1) others star request to a) pay to reserve JVM b) request Google to load your app before start dispatch request to that instance c) accept the situation For 2) we try to a- try to use/replace frameworks with light-weight ones: datastore access framework, MVC framework,... b- try to limit calculation in your index page to alleviate the impact of loading request c- design your object model based on your need, so that you do calculation at insert time, not at query time. For example, in my app, if I want to report on year and quarter, then I have 5 summary "record" for those, instead of querying and computing those d- caching result. For example, If I know 1 one 5 piece of data above would be read frequently, then I will read those 5 all, and put into memcache for later use ... So it's application-specific, I don't know if each of above can help you. But only you who can know if which one of your code can be cached and how...

An end cursor allows you to specify when you want a query to stop. A cursor represents a query start position - an end cursor is another position. This lets you take, say, 10k entities, break them up into 100 "chunks", then do task queue or other background operations on them. Unlike using an offset (cursor +100), this method solves the case of new Entities being inserted, since you're not working with a cursor +100 Entities. You're working with all the Entities between startCursor and endCursor.

I believe that Twig is the only library that can store objects with entire collections embedded as components. So the Columns could actually be stored in the same entity as the Table. This means that querying or reading Tables is _much_ faster. If the tables are read more than written this would be ideal. Docs are a bit light but basically you just define an embedded collection like this: class Table { @Key String name; @Component Collection<Column> columns; } and thats it! The columns are then stored as a multi-valued property so you can even query properties them like "show all tables with a column named 'age'".

You can optimise it further, however: For each favorite, create a 'UserFavorite' entity as a child entity of the relevant Story entry (or equivalently, as a child entity of a UserInfo entry), with the key name set to the user's unique ID. This way, you can determine if a user has favorited a story with a simple get: UserFavorite.get_by_name(user_id, parent=a_story) get operations are 3 to 5 times faster than queries, so this is a substantial improvement.

A query like this will use the merge join strategy by default. If it's too slow for the merge join strategy, you could have to build a custom index for it, which would indeed be 'exploding'  - each User entity would have len(friends)*len(likes) index entries.