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How to integrate BIRT with Python Django Project by using Py4j

Hi is there anyone who is help me to Integrate BIRT report with Django Projects? or any suggestion for connect third party reporting tools with Django like Crystal or Crystal Clear Report.

Some of the 3rd-party Crystal Reports viewers listed here provide a full command line API, so your python code can preview/export/print reports via subprocess.call()
The resulting process can span anything between an interactive Crystal Report viewer session (user can login, set/change parameters, print, export) and an automated (no user interaction) report printing/exporting.
While this would simplify your code, it would restrict deployment to Windows.

For prototyping, or if you don't mind performance, you can call from BIRT from the command line.
For example, download the POJO runtime and use the script genReport.bat (IIRC) to generate a report to a file (eg. PDF format). You can specify the output options and the report parameters on the command line.
However, the BIRT startup is heavy overhead (several seconds).
For achieving reasonable performance, it is much better to perform this only once.
To achieve this goal, there are at least two possible ways:
You can use the BIRT viewer servlet (which is included as a WAR file with the POJO runtime). So you start the servlet with a web server, then you use HTTP requests to generate reports.
This looks technically old-fashioned (eg. no JSON Requests), but it should work. However, I never used this approach.
The other option is to write your own BIRT server.
In our product, we followed this approach.
You can take the viewer servlet as a template for seeing how this could work.
The basic idea is:
You start one (or possibly more than one) Java process.
The Java process initializes the BIRT runtime (this is what takes some seconds).
After that, the Java process listens for requests somehow (we used a plain socket listener, but of course you could use HTTP or some REST server framework as well).
A request would contain the following information:
which module to run
which output format
report parameters (specific to the module)
possibly other data/metadata, e.g. for authentication
This would create a RunAndRenderTask or separate RunTask and RenderTasks.
Depending on your reports, you might consider returning the resulting output (e.g. PDF) directly as a response, or using an asynchronous approach.
Note that BIRT will happily create several reports at the same time - multi-threading is no problem (except for the initialization), given enough RAM.
Be warned, however, that you will need at least a few days to build a POC for this "create your own server" approach, and probably some weeks for prodction quality.
So if you just want to build something fast to see if the right tool for you, you should start with the command line approach, then the servlet approach and only then, and only if you find that the servlet approach is not quite good enough, you should go the "create your own server" way.
It's a pity that currently there doesn't seem to exist an open-source, production-quality, modern BIRT REST service.
That would make a really good contribution to the BIRT open-source project... (https://github.com/eclipse/birt)

Python Interprocess w/REST Interface options

Is there any tool/library for Python that will aid in interprocess communication, while keeping the API/client code easy to maintain?
I'm writing an application wherein I own both the server and the client portion, but I'd like to make it expandable to others via a REST interface (or something similarly accessible). Is my only options to write the boilerplate connective tissue for REST communication?

The REST interface should be implemented with small functions that call the actual Python API, which you will implement any way.
If you search here, on SO, the most frequent recommendation will be to use Flask to expose the REST interface.
There are libraries around that will try to turn the methods of a class into REST paths, and such, and those may save you a couple of hours on the onset, but cost you many hours down the road.
This morning I coded a backend service that way. The Requests calls to the external service are hidden by a module so the business logic doesn't know where the objects come from (ORM?), and the business logic produces objects that a simple Flask layer consumes to produce the JSON required by each matched URL.
#app.route("/api/users")
def users():
return json_response(
api.users(limit=request.args.get('limit', None)),
)
A one-liner.

Keeping partly-offline sqlite db in sync with postgresql

This question is more on architecture and libs, than on implementation.
I am currently working at project, which requires a local long-term cache storage (updated once a day) at client kept in sync with a remote db at server. For client side sqlite has been chosen as a lightweight approach and postgresql as feature rich db at server. Native replication mechanisms of postgres are no-opt cause I need to keep client really lightweight and free of relying on external components like db servers.
The implementation language would be Python. Now I'm looking at ORMs like SQLAlchemy, but haven't worked with any before.
Does SQLAlchemy have any tools to keep sqlite and postgres dbs in sync?
If not, are there any other Python libraries which have such tools?
Any ideas about how should the architecture look like, if the task must be solved "by hand"?
Added:
It's like telemetry, cause client would have internet connection only for approximately 20 minutes a day
So, the main question is about architecure of such a system

It doesn't usually fall within the tasks of an ORM to sync data between databases, so you will likely have to implement it yourself. I don't know of any solution that will handle syncing for you given your choice of databases.
There are a couple important design choices to consider:
how do you figure out what data changed ( i.e. inserted, updated or deleted )
what is the most efficient way to package the change-log
will you have to deal with conflicts ? and how will you do that.
The most efficient way to figure out what changed is to have the database tell you that directly. Bottled water can offer some inspiration in this regard. The idea is to tap into the event log postgres would use for replication. You will need something like Kafka to keep track of what each of your clients already knows. This will allow you to optimize your server for writes, as you won't have clients querying trying to figure out what changed since they were last online.
The same can be achieved on the sqlight end with event callbacks, you'll just have to trade some storage space on the client to retain the changes to be sent to the server. If that sounds like too much infrastructure for your needs, it's something that you can easily implement with SQL and pooling as well, but I would still think of it as an event log, and consider how it's implemented a detail - possibly allowing for a more efficient implementation lather on.
The best way to structure and package your change log will depend on your applications requirements, available band-with, etc. You could use standard formats such as json, compress and encrypt if needed.
It will be much simpler to design your application as such to avoid conflicts, and possibly flow data in a single direction, or partition your data so that it always flows in a single direction for a specific partition.
One final taught is that with such an architecture you would be getting incremental updates, some of which might be missed for unplanned reasons ( system failure, bugs, dropped messages, etc ). You could have some built in heuristic to check that your data matches, like at least checking the number of records on each side, with some way to recover such a fault, at a minimal a way to manually re-fetch the data from the authoritative source, i.e. if the server is authoritative, the client should be able to discard it's data and re-fetch it. You might need such a mechanism anyway for cases wen the client is reinstalled, etc.

Durable architecture in Python distributed application

Wondering about durable architectures for distributed Python applications.
This question I asked before should provide a little guidance about the sort of application it is. We would like to have the ability to have several code servers and several database servers, and ideally some method of deployment that is manageable and not too much of a pain.
The question I mentioned provides an answer that I like, but I wonder how it could be made more durable, or if doing so requires using other technologies. In particular:
I would have my frontend endpoints be the WSGI (because you already have that written) and write the backend to be distributed via messages. Then you would have a pool of backend nodes that would pull messages off of the Celery queue and complete the required work. It would look sort of like:
Apache -> WSGI Containers -> Celery Message Queue -> Celery Workers.
The apache nodes would be behind a load balancer of some kind. This would be a fairly simple architecture to scale and is, if done correctly, fairly reliable. Code for failure in a system like this and you will be fine.
What is the best way to make durable applications? Any suggestions on how to either "code for failure" or design it differently so that we don't necessarily have to? If you think Python might not be suited for this, that is also a valid solution.

Well to continue on the previous answer I gave.
In my projects I code for failure, because I use AWS for a lot of my hosting needs.
I have implemented database backends that will make sure that the database, region, is accessible and if not it will choose another region from a specified list. This happens transparently to the rest of the system on that node. So, if the east-1a region goes down I have a few other regions that I also host in that it will failover into, such as the west coast. I keep track of currently going database transactions and send them over to the west coast and dump them to a file so I can import them into the old database region once it becomes available.
My front end servers sit behind a elastic load balancer that is distributed across multiple regions and this allows for durable recovery if a region fails. But, it cannot be relied upon so I am looking into solutions such as running a HAProxy and switching my DNS in the case that my ELB goes down. This is a work in progress and I cannot give specifics on my own solutions.
To make your data processing durable look into Celery and store the data in a distributed mongo server to keep your results safe. Using a durable data store to keep your results allows you to get them back in the event of a node crash. It comes at the cost of some performance, but it shouldn't be too terrible if you only rely on soft-realtime constraints.
http://www.mnxsolutions.com/amazon/designing-for-failure-with-amazon-web-services.html
The above article talks mostly about AWS but the ideas apply to any system that you need to keep high availability in and system durability. Just remember that downtime is ok as long as you minimize it for a subset of users.

Is it more efficient to parse external XML or to hit the database?

I was wondering when dealing with a web service API that returns XML, whether it's better (faster) to just call the external service each time and parse the XML (using ElementTree) for display on your site or to save the records into the database (after parsing it once or however many times you need to each day) and make database calls instead for that same information.

First off -- measure. Don't just assume that one is better or worse than the other.
Second, if you really don't want to measure, I'd guess the database is a bit faster (assuming the database is relatively local compared to the web service). Network latency usually is more than parse time unless we're talking a really complex database or really complex XML.

Everyone is being very polite in answering this question: "it depends"... "you should test"... and so forth.
True, the question does not go into great detail about the application and network topographies involved, but if the question is even being asked, then it's likely a) the DB is "local" to the application (on the same subnet, or the same machine, or in memory), and b) the webservice is not. After all, the OP uses the phrases "external service" and "display on your own site." The phrase "parsing it once or however many times you need to each day" also suggests a set of data that doesn't exactly change every second.
The classic SOA myth is that the network is always available; going a step further, I'd say it's a myth that the network is always available with low latency. Unless your own internal systems are crap, sending an HTTP query across the Internet will always be slower than a query to a local DB or DB cluster. There are any number of reasons for this: number of hops to the remote server, outage or degradation issues that you can't control on the remote end, and the internal processing time for the remote web service application to analyze your request, hit its own persistence backend (aka DB), and return a result.
Fire up your app. Do some latency and response times to your DB. Now do the same to a remote web service. Unless your DB is also across the Internet, you'll notice a huge difference.
It's not at all hard for a competent technologist to scale a DB, or for you to completely remove the DB from caching using memcached and other paradigms; the latency between servers sitting near each other in the datacentre is monumentally less than between machines over the Internet (and more secure, to boot). Even if achieving this scale requires some thought, it's under your control, unlike a remote web service whose scaling and latency are totally opaque to you. I, for one, would not be too happy with the idea that the availability and responsiveness of my site are based on someone else entirely.
Finally, what happens if the remote web service is unavailable? Imagine a world where every request to your site involves a request over the Internet to some other site. What happens if that other site is unavailable? Do your users watch a spinning cursor of death for several hours? Do they enjoy an Error 500 while your site borks on this unexpected external dependency?
If you find yourself adopting an architecture whose fundamental features depend on a remote Internet call for every request, think very carefully about your application before deciding if you can live with the consequences.

Consuming the webservices is more efficient because there are a lot more things you can do to scale your webservices and webserver (via caching, etc.). By consuming the middle layer, you also have the options to change the returned data format (e.g. you can decide to use JSON rather than XML). Scaling database is much harder (involving replication, etc.) so in general, reduce hits on DB if you can.

There is not enough information to be able to say for sure in the general case. Why don't you do some tests and find out? Since it sounds like you are using python you will probably want to use the timeit module.
Some things that could effect the result:
Performance of the web service you are using
Reliability of the web service you are using
Distance between servers
Amount of data being returned
I would guess that if it is cacheable, that a cached version of the data will be faster, but that does not necessarily mean using a local RDBMS, it might mean something like memcached or an in memory cache in your application.

It depends - who is calling the web service? Is the web service called every time the user hits the page? If that's the case I'd recommend introducing a caching layer of some sort - many web service API's throttle the amount of hits you can make per hour.
Whether you choose to parse the cached XML on the fly or call the data from a database probably won't matter (unless we are talking enterprise scaling here). Personally, I'd much rather make a simple SQL call than write a DOM Parser (which is much more prone to exceptional scenarios).

It depends from case to case, you'll have to measure (or at least make an educated guess).
You'll have to consider several things.
Web service
it might hit database itself
it can be cached
it will introduce network latency and might be unreliable
or it could be in local network and faster than accessing even local disk
DB
might be slow since it needs to access disk (although databases have internal caches, but those are usually not targeted)
should be reliable
Technology itself doesn't mean much in terms of speed - in one case database parses SQL, in other XML parser parses XML, and database is usually acessed via socket as well, so you have both parsing and network in either case.
Caching data in your application if applicable is probably a good idea.

As a few people have said, it depends, and you should test it.
Often external services are slow, and caching them locally (in a database in memory, e.g., with memcached) is faster. But perhaps not.
Fortunately, it's cheap and easy to test.

Test definitely. As a rule of thumb, XML is good for communicating between apps, but once you have the data inside of your app, everything should go into a database table. This may not apply in all cases, but 95% of the time it has for me. Anytime I ever tried to store data any other way (ex. XML in a content management system) I ended up wishing I would have just used good old sprocs and sql server.

It sounds like you essentially want to cache results, and are wondering if it's worth it. But if so, I would NOT use a database (I assume you are thinking of a relational DB): RDBMSs are not good for caching; even though many use them. You don't need persistence nor ACID.
If choice was between Oracle/MySQL and external web service, I would start with just using service.
Instead, consider real caching systems; local or not (memcache, simple in-memory caches etc).
Or if you must use a DB, use key/value store, BDB works well. Store response message in its serialized form (XML), try to fetch from cache, if not, from service, parse. Or if there's a convenient and more compact serialization, store and fetch that.