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Auto increment entity ids from given range in datastore appengine?

We are developing an application in which entity ids for tables must be in incremental order starting from 1 to so on, for each namespace.
We came across allocateIdRange, allocateIds methods in DatastoreService interface but these ids must be assigned manually and will not be assigned by DatastoreService itself. Assigning ids manually may leads to synchronization problems with multiple instances.
Can anyone provide me suggestions to overcome this problem?
We are using objectify 3.0 for DatastoreService operations.

I agree with Tim Hoffman and tx802 when they say you should reconsider your design regarding sequential ids. However a while ago i had to implement something very similar because the customer forced us to use sequential and uninterrupted numbers for order numbers (for unclear reasons). Regardless we complied with the customers wishes by using sharding counters(link contains full code sample) for the order numbers. Sharding counters work like this:
You create a couple of entities of the same kind in your datastore which are just counter values
The actual value is calculated by querying all entities of that kind and summarizing their values
When you wish to increase the value, one of the entities is randomly chosen and incremented
The current counter value may be cached in memcache for improved performance
Why does this work:
As you may know you have a restriction/limitation of 1 transaction per second and entity group in the datastore. Therefor you shard the counter into multiple entities and avoid this limitation. The more traffic you expect, the more shards you're going to need. Luckily you can increase the count of shards at any time.
We also know that writes are slow in comparison to reads. Therefor building the sum of all shards is a fast operation while increasing a single shard value (write) is slow, which doesn't bother us when using sharding counters because we have sufficient time.
Summarized:
You can use sharding counters for sequential ids. If you can avoid the whole sequential id dilemma it would be a better solution though.

How to generate a unique reference ID of at most 10 characters for a financial transaction in an Android app?

I want to create a unique key for a transaction done by a particular user of my Android app at a particular time. I have read about two ways of doing this:
Concatenating the current timestamp with the user id or user's device id
Using Java's UUID class for generating a unique string for each transaction
I have a couple of concerns with the resultant strings from these methods:
The result of first method could probably be too obvious for users to guess and access others' transactions
The results of both methods appear to be too long to communicate to the users
Does anyone know of a better way of doing this?

Use what you are suggesting as your transaction identifiers when making transactions between your app and your server (machines talking to machines). 29 characters isn't that much for a transaction key, especially with the kind of connections smartphones have today.
I would salt those values though just to add a little obfuscation.
After that, for your transaction ids that need to be sent from/to humans, you are going to want something smaller (more tolerable and human-readable). I would use smaller ids (which can collide) but make them work only for a certain amount of time.
See my comments for more information.
Hope I helped.

You could use ISBN13. Easy to calculate and the code space is about 2^40. If you concat to ISBNs you can reach a code space above 2^80.

Using hashmap or H2 database?

I am developing a web application in which I need to store session, user messages etc. I am thinking of using HashMap or H2 database.
Please let me know which is better approach in terms of performance and memory utilization. The web site has to support 10,000 users.
Thanks.

As usual with these questions, I would worry about performance as/when you know it's an issue.
10000 users is not a lot of data to hold in memory. I would likely start off with a standard Java collection, and look at performance when you predict it's going to cause you grief.
Abstract out the access to this Java collection such that when you substitute it, the refactoring required is localised (and perhaps make it configurable, such that you can easily perform before/after performance tests with your different solutions -H2, Derby, Oracle, etc. etc.)

If your session objects aren't too big (which should be the case), there is no need to persist them in a database.
Using a database for this would add a lot of complexity in a case when you can start with a few lines of code. So don't use a database, simply store them in a ligth memory structure (HashMap for example).
You may need to implement a way to clean your HashMap if you don't want to keep sessions in memory when the user left from a long time. Many solutions are available (the easiest is simply to have a background thread removing from time to time the too old sessions). Note that it's usually easier to clean a hashmap than a database.

Both H2 and Hash Map are gonna keep the data in memory (So from space point of view they are almost the same).
If look ups are simple like KEY VALUE then looking up in the Hash Map will be quicker.
If you have to do comparisons like KEY < 100 etc use H2.
In fact 10K user info is not that high a number.

If you don't need to save user messages - use the collections. But if the message is should be saved, be sure to use a database. Because after restart you lost all data.

The problem with using a HashMap for storing objects is that you would run into issues when your site becomes too big for one server and would need to be clustered in order to scale with demand. Then you would face problems with how to synchronise the HashMap instances on different servers.
A possible alternative would be to use a key-value store like Redis as you won't need the structure of a database or even use the distributed cache abilities of something like EHCache

How to retrieve huge (>2000) amount of entities from GAE datastore in under 1 second?

We have some part of our application that need to load a large set of data (>2000 entities) and perform computation on this set. The size of each entity is approximately 5 KB.
On our initial, naïve, implementation, the bottleneck seems to be the time required to load all the entities (~40 seconds for 2000 entities), while the time required to perform the computation itself is very small (<1 second).
We had tried several strategies to speed up the entities retrieval:
Splitting the retrieval request into several parallel instances and then merging the result: ~20 seconds for 2000 entities.
Storing the entities at an in-memory cache placed on a resident backend: ~5 seconds for 2000 entities.
The computation needs to be dynamically computed, so doing a precomputation at write time and storing the result does not work in our case.
We are hoping to be able to retrieve ~2000 entities in just under one second. Is this within the capability of GAE/J? Any other strategies that we might be able to implement for this kind of retrieval?
UPDATE: Supplying additional information about our use case and parallelization result:
We have more than 200.000 entities of the same kind in the datastore and the operation is retrieval-only.
We experimented with 10 parallel worker instances, and a typical result that we obtained could be seen in this pastebin. It seems that the serialization and deserialization required when transferring the entities back to the master instance hampers the performance.
UPDATE 2: Giving an example of what we are trying to do:
Let's say that we have a StockDerivative entity that need to be analyzed to know whether it's a good investment or not.
The analysis performed requires complex computations based on many factors both external (e.g. user's preference, market condition) and internal (i.e. from the entity's properties), and would output a single "investment score" value.
The user could request the derivatives to be sorted based on its investment score and ask to be presented with N-number of highest-scored derivatives.

200.000 by 5kb is 1GB. You could keep all this in memory on the largest backend instance or have multiple instances. This would be the fastest solution - nothing beats memory.
Do you need the whole 5kb of each entity for computation?
Do you need all 200k entities when querying before computation? Do queries touch all entities?
Also, check out BigQuery. It might suit your needs.

Use Memcache. I cannot guarantee that it will be sufficient, but if it isn't you probably have to move to another platform.

This is very interesting, but yes, its possible & Iv seen some mind boggling results.
I would have done the same; map-reduce concept
It would be great if you would provide us more metrics on how many parallel instances do you use & what are the results of each instance?
Also, our process includes retrieval alone or retrieval & storing ?
How many elements do you have in your data store? 4000? 10000? Reason is because you could cache it up from the previous request.
regards

In the end, it does not appear that we could retrieve >2000 entities from a single instance in under one second, so we are forced to use in-memory caching placed on our backend instance, as described in the original question. If someone comes up with a better answer, or if we found a better strategy/implementation for this problem, I would change or update the accepted answer.

Our solution involves periodically reading entities in a background task and storing the result in a json blob. That way we can quickly return more than 100k rows. All filtering and sorting is done in javascript using SlickGrid's DataView model.
As someone has already commented, MapReduce is the way to go on GAE. Unfortunately the Java library for MapReduce is broken for me so we're using non optimal task to do all the reading but we're planning to get MapReduce going in the near future (and/or the Pipeline API).
Mind that, last time I checked, the Blobstore wasn't returning gzipped entities > 1MB so at the moment we're loading the content from a compressed entity and expanding it into memory, that way the final payload gets gzipped. I don't like that, it introduces latency, I hope they fix issues with GZIP soon!

Google app engine: Poor Performance with JDO + Datastore

I have a simple data model that includes
USERS: store basic information (key, name, phone # etc)
RELATIONS: describe, e.g. a friendship between two users (supplying a relationship_type + two user keys)
COMMENTS: posted by users (key, comment text, user_id)
I'm getting very poor performance, for instance, if I try to print the first names of all of a user's friends. Say the user has 500 friends: I can fetch the list of friend user_ids very easily in a single query. But then, to pull out first names, I have to do 500 back-and-forth trips to the Datastore, each of which seems to take on the order of 30 ms. If this were SQL, I'd just do a JOIN and get the answer out fast.
I understand there are rudimentary facilities for performing two-way joins across un-owned relations in a relaxed implementation of JDO (as described at http://gae-java-persistence.blogspot.com) but they sound experimental and non-standard (e.g. my code won't work in any other JDO implementation).
Worse yet, what if I want to pull out all the comments posted by a user's friends. Then I need to get from User --> Relation --> Comments, i.e. a three-way join, which isn't even supported experimentally. The overhead of 500 back-and-forths to get a friend list + another 500 trips to see if there are any comments from a user's friends is already enough to push runtime >30 seconds.
How do people deal with these problems in real-world datastore-backed JDO applications? (Or do they?)
Has anyone managed to extract satisfactory performance from JDO/Datastore in this kind of (very common) situation?
-Bosh

First of all, for objects that are frequently accessed (like users), I rely on the memcache. This should speedup your application quite a bit.
If you have to go to the datastore, the right way to do this should be through getObjectsById(). Unfortunately, it looks like GAE doesn't optimize this call. However, a contains() query on keys is optimized to fetch all the objects in one trip to the datastore, so that's what you should use:
List myFriendKeys = fetchFriendKeys();
Query query = pm.newQuery(User.class, ":p.contains(key)");
query.execute(myFriendKeys);
You could also rely on the low-level API get() that accept multiple keys, or do like me and use objectify.
A totally different approach would be to use an equality filter on a list property. This will match if any item in the list matches. So if you have a friendOf list property in your user entity, you can issue a single Query friendOf == theUser. You might want to check this: http://www.scribd.com/doc/16952419/Building-scalable-complex-apps-on-App-Engine

You have to minimize DB reads. That must be a huge focus for any GAE project - anything else will cost you. To do that, pre-calculate as much as you can, especially oft-read information. To solve the issue of reading 500 friends' names, consider that you'll likely be changing the friend list far less than reading it, so on each change, store all names in a structure you can read with one get.
If you absolutely cannot then you have to tweak each case by hand, e.g. use the low-level API to do a batch get.
Also, rather optimize for speed and not data size. Use extra structures as indexes, save objects in multiple ways so you can read it as quickly as possible. Data is cheap, CPU time is not.

Unfortunately Phillipe's suggestion
Query query = pm.newQuery(User.class, ":p.contains(key)");
is only optimized to make a single query when searching by primary key. Passing in a list of ten non-primary-key values, for instance, gives the following trace
alt text http://img293.imageshack.us/img293/7227/slowquery.png
I'd like to be able to bulk-fetch comments, for example, from all a user's friends. If I do store a List on each user, this list can't be longer than 1000 elements long (if it's an indexed property of the user) as described at: http://code.google.com/appengine/docs/java/datastore/overview.html .
Seems increasingly like I'm using the wrong toolset here.
-B

Facebook has 28 Terabytes of memory cache... However, making 500 trips to memcached isn't very cheap either. It can't be used to store a gazillion pieces of small items. "Denomalization" is the key. Such applications do not need to support ad-hoc queries. Compute and store the results directly for the few supported queries.
in your case, you probably have just 1 type of query - return data of this, that and the others that should be displayed on a user page. You can precompute this big ball of mess, so later one query based on userId can fetch it all.
when userA makes a comment to userB, you retrieve userB's big ball of mess, insert userA's comment in it, and save it.
Of course, there are a lot of problems with this approach. For giant internet companies, they probably don't have a choice, generic query engines just don't cut it. But for others? Wouldn't you be happier if you can just use the good old RDBMS?

If it is a frequently used query, you can consider preparing indexes for the same.
http://code.google.com/appengine/articles/index_building.html

The indexed property limit is now raised to 5000.
However you can go even higher than that by using the method described in http://www.scribd.com/doc/16952419/Building-scalable-complex-apps-on-App-Engine
Basically just have a bunch of child entities for the User called UserFriends, thus splitting the big list and raising the limit to n*5000, where n is the number of UserFriends entities.