Is any way to start go_binary before java_test? - bazel

Our project has a few GRPC servers defined as go_binary targets. We develop client SDKs for Java and Python applications and we would like to use java_test and py_test. Is any way to start a specific go_binary target before java_test or py_test?

You can create a test harness that starts the gRPC server before running the tests. For example, you could add the binary to the data attribute of the test, and then started it beforehand:
go_binary(
name = "my_grpc_server",
[...]
)
py_test(
name = "my_test",
[...]
data = [":my_grpc_server"],
)
and then inside the test file:
class ClientTestCase(unittest.TestCase):
def setUp(self):
r = runfiles.Create()
self.server = subprocess.Popen([r.Rlocation("path/to/my_grpc_server")])
def tearDown(self):
self.server.terminate()
self.server.wait()
This example is very simple, you'll probably run into issues regarding the availability of the port the server listens on, or waiting for the server to start up. You could add flags to your gRPC server to allow communication over a domain socket, or make it listen on an unused port and have the test parse the port number from the server's log output.
For details on finding the server with runfiles: https://github.com/bazelbuild/bazel/blob/a7a0d48fbeb059ee60e77580e5d05baeefdd5699/tools/python/runfiles/runfiles.py#L16-L58
If you find yourself copy-pasting this pattern a lot, or having to implement it in multiple languages, you could try using an sh_test() rule to wrap the underlying py_test or java_test, and to start the server, then start the test with an environment variable telling it how to reach the server (eg MY_GRPC_SERVER_ADDRESS=localhost:${test_port}.

Related

How to set the django-channels ChannelsLiveServerTestCase server port?

I am trying to write tests with selenium and I am using ChannelsLiveServerTestCase.
I need to set the port the server listens to.
I suppose this should be a rare situation where someone needs to set the port as no one answered the question.
Anyway, I had to dig into the source code of daphne.
In testing.py file look for the line
endpoints = build_endpoint_description_strings(host=self.host, port=0)
in my case it was line 139 and change it into
endpoints = build_endpoint_description_strings(host=self.host, port=WHICHEVER_PORT_YOU_WANT)

TFF: Remote Executor

We are setting up a federated scenario with Server and Client on different physical machines.
On the server, we have used the docker container to kickstart:
The above has been borrowed from Kubernetes tutorial. We believe this creates a 'local executor' [Ref 1] which helps create a gRPC server [Ref 2].
Ref 1:
Ref 2:
Next on the client 1, we are calling tff.framework.RemoteExecutor that connects to the gRPC server.
Our understanding based on the above is that the Remote Executor runs on the client which connects to the gRPC server.
Assuming the above is correct, how can we send a
tff.tf_computation
from the server to the client and print the output on the client side to ensure the whole setup works well.
Your understanding is definitely correct.
If you construct an ExecutorFactory directly, as seems to be the case in the code above, passing it to tff.framework.set_default_context will install your remote stack as the default mechanism for executing computations in the TFF runtime. You should additionally be able to pass the appropriate channels to tff.backends.native.set_remote_execution_context to handle the remote executor construction and context installation if desired, but the way you are doing it certainly works, and allows for greater customization.
Once you have set this up, running an example end-to-end should be fairly simple. We will set up a computation which takes a set of federated integers, prints on the clients, and sums the integers up. Let:
#tff.tf_computation(tf.int32)
def print_and_return(x):
# We must use tf.print here, as this logic will be
# serialized and run on the clients as TensorFlow.
tf.print('hello world')
return x
#tff.federated_computation(tff.FederatedType(tf.int32, tff.CLIENTS))
def print_and_sum(federated_arg):
same_ints = tff.federated_map(print_and_return, federated_arg)
return tff.federated_sum(same_ints)
Suppose we have N clients; we simply instantiate the set of federated integers, and invoke our computation.
federated_ints = [1] * N
total = print_and_sum(federated_ints)
assert total == N
This should cause the tf.prints defined above to run on the remote machine; as long as tf.print is directed to an output stream which you can monitor, you should be able to see it.
PS: you may note that the federated sum above is unnecessary; it certainly is. The same effect can be had by simply mapping the identity function with the serialized print.

Launching composed task built by DSL from stream application

Every example I've seen (task-launcher sink and triggertask source ) shows how to launch the task defined by uri attribute.
My tasks definitions look like this :
sampleTask <t2: timestamp || t1: timestamp>
sampleTask-t1 timestamp
sampleTask-t2 timestamp
sampleTaskRunner composed-task-runner --graph=sampleTask
My question is how do I launch the composed task runner (sampleTaskRunner, defined by DSL) from stream application.
Thanks
UPDATE
I ended up with the below solution that triggers task using SCDF REST API :
composedTask definition :
<timestamp || mySampleTask>
Stream definition :
http | httpclient | log
Deployment properties :
app.http.port=81
app.httpclient.body=name=composedTask&arguments=--increment-instance-enabled=true
app.httpclient.http-method=POST
app.httpclient.url=http://localhost:9393/tasks/executions
app.httpclient.headers-expression={'Content-Type':'application/x-www-form-urlencoded'}
Though it's easy to implement http sink component, would be great if stream application starters will provide one out of the box.
Another concern I have is about discovering the SCDF REST URL when deployed in distributed environment.
Here's a quick take from one of the SCDF's R&D team members (Glenn Renfro).
stream create foozer --definition "trigger --fixed-delay=5 | tasklaunchrequest-transform --uri=maven://org.springframework.cloud.task.app:composedtaskrunner-task:1.1.0.BUILD-SNAPSHOT --command-line-arguments='--graph=sampleTask-t1||sampleTask-t2 --increment-instance-enabled=true --spring.datasource.url=jdbc:mariadb://localhost:3306/test --spring.datasource.username=root --spring.datasource.password=password --spring.datasource.driverClassName=org.mariadb.jdbc.Driver' | task-launcher-local" --deploy
In the foozer stream definition,
1) "trigger" source happens to trigger an upstream event every 5s
2) "tasklaunchrequest-transform" processor takes a few arguments; more specifically, it uses "composedtaskrunner-task:1.1.0.BUILD-SNAPSHOT" to launch a composed-task graph (i.e., sampleTask-t1||sampleTask-t2)
3) Pay attention to --increment-instance-enabled. This was recently added to CTR application and this provides the ability to re-launch a composed-task in a recurring cadence
4) Since the CTR and SCDF must share the same database, we are also passing datasource properties as command-line args. (SCDF-server is already started with the same datasource credentials)
Hope this helps.
Lastly, we will add a sample to the reference guide via: spring-cloud/spring-cloud-dataflow#1780

Network Tables C++

I am quite new to C++ socket programming. Since I am in an FRC team, I need to communicate between my application and the Compact RIO via an interface known as "Network Tables". I need to communicate from my C++ vision application to our robot code in Java. How do I implement NetworkTables in regular C++?
So here is what I did in python but the concept is the same. The goal would be to move motors based on values (sensor data) from what you receive in your driver station? so, how do I accomplish this... data transfers will be done through network tables
first, initlize...
from networktables import NetworkTables
# As a client to connect to a robot
NetworkTables.initialize(server='roborio-XXX-frc.local')
creating the instance you will be able to access NetworkTables conections, configure settings, listeners and create table objects which is what is actually being used to send data
next,
sd = NetworkTables.getTable('SmartDashboard')
sd.putNumber('someNumber', 1234)
otherNumber = sd.getNumber('otherNumber')
Here, we're interacting with the SmartDashboard and calling two methods, to send and recieve values.
another example, from API docs
#!/usr/bin/env python3
#
# This is a NetworkTables server (eg, the robot or simulator side).
#
# On a real robot, you probably would create an instance of the
# wpilib.SmartDashboard object and use that instead -- but it's really
# just a passthru to the underlying NetworkTable object.
#
# When running, this will continue incrementing the value 'robotTime',
# and the value should be visible to networktables clients such as
# SmartDashboard. To view using the SmartDashboard, you can launch it
# like so:
#
# SmartDashboard.jar ip 127.0.0.1
#
import time
from networktables import NetworkTables
# To see messages from networktables, you must setup logging
import logging
logging.basicConfig(level=logging.DEBUG)
NetworkTables.initialize()
sd = NetworkTables.getTable("SmartDashboard")
i = 0
while True:
print("dsTime:", sd.getNumber("dsTime", -1))
sd.putNumber("robotTime", i)
time.sleep(1)
i += 1

Windows Service Starts then Stops

I have a Windows Service that I inherited from a departed developer. The Windows Service is running just fine in the QA environment. When I install the service and run it locally, I receive this error:
Service cannot be started. System.InvalidOperationException: The requested Performance Counter is not a custom counter, it has to be initialized as ReadOnly.
Here is the code:
ExternalDataExchangeService exchangeService = new ExternalDataExchangeService();
workflowRuntime.AddService(exchangeService);
workflowRuntime.AddService(new SqlTrackingService(AppContext.SqlConnectionImportLog));
ChallengerWorkflowService challengerWorkflowService = new ChallengerWorkflowService();
challengerWorkflowService.SendDataEvent += new EventHandler<SendDataEventArgs>(challengerWorkflowService_SendDataEvent);
workflowRuntime.AddService(challengerWorkflowService);
workflowRuntime.StartRuntime(); <---- Exception is thrown here.
Check for installer code. Often you will find counters are created within an installation (which is going to of been run under admin privledges on client site) and the code then uses them as though they exist - but will not try create them because they do not expect to have the permissions.
If you just get the source and then try run it, the counters / counter classes do not exist so you fall over immediately. (Alternatively check whether the counter exists / you have local admin if they wrote the code to create it in the service.)
Seen it before so mentioned it.
Attach Debugger and break on InvalidOperationException (first-chance, i.e. when thrown)?

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