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% Writing GDP applications

A typical application

The log interface acts as a proxy to interact with sensors and actuators. We describe how one would go about designing a simple application. Imagine an office building with some temperature sensors and some motor controlled window blinds. We would like to design an application that looks at the current temperature and some weather prediction feeds from the cloud to make a smart decision about the window blinds.

An example GDP application

As shown above, in order to do so, the application subscribes to all the relevant logs and writes the actuation values to the actuator log. A gateway working on behalf of the actuator subscribes to this actuation log and controls the motors for the window blinds. Note that instead of two separate logs, a composite log can be created for Sensor 1 and Sensor 2 in the diagram above, provided that the gateway implements the single-writer semantics.

Software Installation and Configuration

In this section, we talk about how to get access, install and use the GDP client side library. As mentioned earlier, we have a C library for clients with wrappers in other languages around this C-library.

The main GDP repository can be accessed in read-only mode by using

git clone

Or, in read-write mode either using HTTPS (requires username, password), or using SSH (requires key setup):

git clone
git clone

The main GDP repository contains the core GDP library (libgdp), client-side applications and language bindings, and the log-server (gdplogd). GDP-router is maintained in a separate repository (gdp_router_click.git). However, you should not need to worry about it if you are just playing around with GDP.

The repository gdp-if.git contains various interfaces to the GDP. It is not really part of the GDP itself, but it may prove instructive.


In summary, assuming you have the required dependencies installed, make install-dev-c should install the C include files, C libraries, and basic utility applications into system path. make install-python should install the Python bindings in the system path as well. These make targets do not create any necessary configuration files, however (see below). For more details, refer to in the main git tree.

Infrastructure information

Refer to in the main git tree.

Note that the software/infrastructure is still in very early experimental phase. We do not make any guarantees on data retention/backups at this moment. As the code stabilizes, we will make better effort to maintain data. In the meantime, contact us if you would like to use GDP for anything serious.


The GDP library, log-server, and various other utility programs consult a configuration file for the correct parameters to use. At the very minimum, your configuration file should contain the GDP router that your client should connect to (unless someone else is running a local router in the same subnet as you are in, in which case zeroconf should work). Refer to in the main git tree.

Creating logs

The main mechanism to create a log is using gdp-create (should be in your system path after make install-*). For example,

gdp-create -k none org.example.project.log17a

will create a log named org.example.project.log17a on one of the default log-servers at Berkeley.

Although you can create logs with any name, please stick to this convention (with "project" being the project name or the user name, as appropriate) so we can avoid name collisions. -k none means that gdp-create will not attempt to create a new signature key for signing appended data. Although crucial to the operation, key-management is better deferred to a stage when you are familiar with the basic operations of the GDP. Also, note that gdp-create has several other command-line options that will be useful later on.

Note that if you don't explicitly specify log-placement, a log-server at Berkeley is picked at random for hosting your log. This is especially important if you are running your own log-servers and want control over where data goes.

Writing applications in Python

Even though the GDP library is written in C, we provide a python package gdp that acts as a wrapper around the C-library. This python package enables quick prototyping using an object-oriented interface to GDP. What follows is a quick how-to on writing simple GDP programs in Python. Note that this document is just a starting point and is not intended to be a comprehensive guide to the complete interface. For a more thorough API documentation, refer to /lang/python/README.

Python package installation

The package gdp should be installed in your system path for python packages. Once you have the required dependencies for compilation installed, something like make install-python from the root of repository should do the trick (note that running with sudo may be required). Note that this also installs the client side C libraries and various utilities (such as gdp-create) in system path.

Appending data

Let's start with a simple Hello world program, that writes some data to a log and reads it back. Before we begin, we need to create the log; see Creating logs above. The tutorial uses the logname edu.berkeley.eecs.mor.01, but please replace it with the name of the log you create.

We need to import the package gdp to begin with.

>>> import gdp

Once imported, we need to initialize the GDP package by calling gdp_init(). An optional argument to gdp_init() is the address of a GDP-router. If no address provided, a default value of the parameter swarm.gdp.routers is used as configured by EP library. (See for details).

>>> # the following picks a router based on EP library configuration
>>> gdp.gdp_init()
>>> # For a specific router, use the following:
>>> # gdp.gdp_init('')

As mentioned earlier, we support human readable names for logs. The mechanism for translating a human readable name to a 256-bit name is probably going to change in the future, however, it is our hope that it should be a simple change. The Python wrapper uses instances of class gdp.GDP_NAME for a name, which could be initialized using a human readable name.

>>> # Create a GDP_NAME object from a human readable python string
>>> gin_name = gdp.GDP_NAME("edu.berkeley.eecs.mor.01")

Once we have a GDP_NAME, we can use this to open a handle to a log/GCL. A log handle works like a file handle in some ways. We need to tell whether we want to open the GCL in read-only mode (gdp.GDP_MODE_RO), or append-only mode (gdp.GDP_MODE_AO), or read-and-append mode (gdp.GDP_MODE_RA).

>>> # assume that this log already exists.
>>> gin_handle = gdp.GDP_GIN(gin_name, gdp.GDP_MODE_RA)

Next, let's append a few records to the log. The unit of read/write to a log is called a record--data with some automatically generated metadata--represented by a GDP_DATUM object. The GDP_DATUM object contains a GDP_BUF that holds the actual data. (Please see the C-api for more details on the behavior of buffer objects, and such).

>>> d = gdp.GDP_DATUM()
>>> for idx in xrange(10):
...   d["buf"].reset()
...   d["buf"].write("Hello world " + str(idx)}
...   gin_handle.append(d)

That's it. Ideally, it should finish without throwing any errors, resulting in 10 records append to the log specified.

Look at /lang/python/apps/ for a full example.

Reading data by record number

Next, let's read some data back and see if it matches what we wrote. Note that we need to tell what record number we want to read, and record numbers start from 1. To read data, we just use read_by_recno method of the GDP_GIN instance with the record number.

>>> for idx in xrange(1,11):
...   datum = gin_handle.read_by_recno(idx)
...   print datum["recno"], datum["buf"].peek()
(1, 'Hello world 0')
(2, 'Hello world 1')
(3, 'Hello world 2')
(4, 'Hello world 3')
(5, 'Hello world 4')
(6, 'Hello world 5')
(7, 'Hello world 6')
(8, 'Hello world 7')
(9, 'Hello world 8')
(10, 'Hello world 9')

So far, we saw how to read and write data by record number. However, most of the times, we are interested in the most recent record. For this, we support negative record numbers, i.e. -1 refers to the most recent record, -2 refers to the second most recent record, and so on.

Look at /lang/python/apps/ for a full example.


Next, let's see how can we subscribe to a log to get new data from a log as it gets appended. For this, we use subscribe_by_recno method of the gdp.GDP_GIN instance.

>>> # ignore the parameters for the moment
>>> gin_handle.subscribe_by_recno(0, 0, None)

This subscription returns events, that we need to process in order to get notified of the data as it appears.

>>> while True:
...   # this blocks until there is a new event
...   event = gin_handle.get_next_event(None)
...   # Events can be used to get the associated datum
...   if event["type"] == gdp.GDP_EVENT_DATA:
...     datum = event["datum"]
...     print datum["buf"].peek()
...   else: 
...     # we ignore other event types for simplicity
...     break

In the above code, event is an object of type GDP_EVENT, which can be used to get the associated GDP_DATUM (and then GDP_BUF). In order to see the above code in action, open another python console (while this is running), and append some new data to the log just the way you saw above.

Look at /lang/python/apps/ for a full example.

Reading multiple records at a time

Reading one record at a time can be very inefficient, especially when reading large amount of data. For this, we support asynchronous reads to read a range of records at a time. The interface is similar to subscribe_by_recno in some sense--events are returned as a result of an asynchronous call.

Look at /lang/python/apps/ for a full example.

Asynchronous write

Partially implemented. In the normal append call above, a client sends some data to the log-server and waits for an acknowledgement before returning control back to the application. In order to convert this blocking operation to a non-blocking operation, append_async could be used instead of regular append.

Refer to the API documentation at /lang/python/README for more details.