GoodData FlexFunctions host template

This repository serves as a template that you can use to create your own Flight RPC server that hosts your custom-made FlexFunctions.

This template lets you focus on building and testing the FlexFunctions themselves; the infrastructure and boilerplate related to hosting and exposing your functions is handled by GoodData code.

What is a FlexFunction

A FlexFunction is custom code that generates tabular data and can be integrated with GoodData Cloud.

In many ways, the FlexFunction resembles 'table functions' (tabular user-defined-functions) that are available in many database and data warehouse systems.

The FlexFunctions are intended for integration into the GoodData Cloud's semantic model and are then used for report computation. These use cases influence how the interface of FlexFunction looks like:

The schema of tabular data returned by a function must be known up-front
The function arguments are prescribed by GoodData Cloud and describe computation context in which the function is called

Technical perspective

A FlexFunction is a class that implements the FlexFun interface. The concrete implementations provide:

Name of the function
Schema of the returned tabular data
Implementation of call method to compute and return the data

FlexFunctions are implemented on top of the Apache Arrow. The schema describing the tabular data is expected to be an Apache Arrow Schema. The returned tabular data is either an Apache Arrow Table (fully materialized data) or a RecordBatchReader (stream of data).

FlexFunction hosting

The FlexFunction implementations are supposed to be hosted on a Flight RPC server which is then added as a data source to GoodData Cloud or Cloud Native: thus, the FlexFunctions become available in the semantic model and can be used in report computations.

This template project (backed by infrastructure available in GoodData Python SDK) solves and simplifies the task of creating a production-ready Flight RPC server where the FlexFunctions run.

The artifact produced by the template is a Docker image containing Flight RPC server with your FlexFunctions plugged into it.

You then have to deploy and run this Docker image 'somewhere' - for example on some AWS EC2 instance and then add that location as data source to GoodData Cloud or Cloud Native.

Getting Started

To get started with custom functions is easy. The only prerequisite is a working installation of Python 3.12 and access to internet:

Clone this template: git clone git@github.com:gooddata/gooddata-flexfun-template.git <your-directory>
Navigate to your directory cd <your-directory>
Bootstrap development environment make dev

This will create Python Virtual Environment in .venv directory.
[Optional] The project is set up with .envrc - if you use direnv, then do direnv allow

HINT: If you have trouble installing Python 3.12, we recommend using pyenv. First, correctly install pyenv using its installation guide. Then after step #2 above, do pyenv install and pyenv local. Then you can continue with make dev

If you use PyCharm or IDEA, you can now open the project and:

Add existing virtual environment (bootstrapped in previous steps and available in .venv directory)
Mark src as sources root, mark tests as test sources root

Developing FlexFunctions

The template sets initial convention where the FlexFunctions are located in src/flexfun directory. You can find the sample_function.py there. The easiest way to get poking around is to modify this function to do your bidding.

Adding new function

Adding the code

The only requirement is that your new FlexFunction must be a file inside the src directory and within that you need to have a class implementing the FlexFun interface. See the sample_function.py for inspiration.

How your organize the implementations is completely up to you:

If you have many small functions, then it may be enough to create file-per-function inside the existing src/flexfun directory.
If, however, you expect that the implementation of the function may grow large and be split into multiple smaller files, then we recommend that you create a new Python package somewhere under src directory.

For example, you can go with layout such as:
- src/flexfun/function_name1/
  - api.py < this is where you have class implementing the FlexFun interface
  - any other files and sub-packages that the api.py depends on
This way, each possibly complex function is separated in its own package, there is a well-defined entry point (the api.py) and then bunch of other files needed for the implementation - as you see fit.

IMPORTANT: if you create your own packages and sub-packages, make sure to include the __init__.py files - otherwise you run risk of Import/ModuleLoad errors.

Registering the function

In order for the function to be loaded and exposed via Flight RPC, you need to register it to the server. This is purely a configuration step:

Open the config/flexfun.config.toml configuration file
Within that file, there is a functions setting. This is a list of Python modules that are expected to contain the FlexFun implementations.

You add name of Python module that contains the FlexFun. You code this the same way as when doing imports in Python.

For example if you build your FlexFun in src/flexfun/function_name1/api.py, then you need to register flexfun.function_name1.api.

HINT: you can have multiple FlexFun implementations within a single Python source file. All those implementations will be discovered and registered. This is fine to do if you have many small, trivial functions. However, as the functions grow in complexity, the single file may grow too big and harder to manage code-wise.

FlexFunctions recommendations

Your concrete FlexFunction implementation is integrated into GoodData Flight server which handles all the technicalities and boilerplate related to server operations and function invocations.

For every invocation of FlexFunction via the Flight RPC, the server will create a new instance of your class. It is strongly recommended that this is as fast as possible and does not perform any expensive initialization.

HINT: expensive one-time initialization can be done by overloading on_load method.

The server comes with built-in call queuing and separate thread pool that services the invocations. See server configuration and the comments in it to find out more.

IMPORTANT: Your code must not make any assumptions that a thread that creates the FlexFunction instance will be the same thread that invokes the call.

Adding third party dependencies

If your function implementation requires some 3rd party libraries, you should add them to the requirements.txt file. After you add the dependency, re-run make dev.

This will modify existing .venv and reinstall the dependencies. Usually, you do not have to remove the existing .venv.

However, especially when adding more dependencies, removing dependencies or running into issues, we recommend to re-bootstrap the environment: rm -rf .venv && make dev.

Logging

This template comes with the structlog dependency installed by default. The structlog is used and configured so that it uses Python stdlib logging backend. The structlog pipeline is set up so that:

In dev mode, the logs are pretty-printed into console (achieved by --dev-log option of the server)
In production deployment, the logs are serialized into JSON (using orjson) which is then written out. This is ideal for consumption in log aggregators.

The stdlib loggers are configured using the config/default.logging.ini file. In the default setup, all INFO-level logs are emitted. You can tweak the config file as you see fit - see Python logging documentation to learn more. This document will not go into those details.

NOTE: you typically do not want to touch the formatter settings inside the logging ini file - the structlog library creates the entire log lines accordingly to deployment mode.

The use of structlog and loggers is fairly straightforward:

import structlog

_LOGGER = structlog.get_logger("sample_flex_function")
_LOGGER.info("event-name", some_event_key="value_to_log")

Recommendations

Here are few assorted recommendations based on our production experience with structlog:

You can log complex objects such as lists, tuples, dicts and data classes no problem
- Be conservative though. What can be serialized into dev-log may not always serialize using orjson into production logs
Always log exceptions using the special exc_info event key.
Mind the cardinality of the logger instances. If you have a class of which you may have thousands of instances, then it is not a good idea to create a logger instance for each instance of your class - even if the logger name is the same; this is because each logger instance comes with memory overhead.

Prometheus Metrics

GoodData's Flight RPC server can be configured to start HTTP endpoint that exposes values of Prometheus metrics. This is disabled by default.

To get started with Prometheus metrics you need to:

Set metrics_host and metrics_port
- Check out the config file comments to learn more about these settings.
- What you have to remember is that the Prometheus scraper is an external process that needs to reach the HTTP endpoint via network.
Install prometheus_client
- Uncomment the prometheus_client line requirements.txt and run make dev
- Note: as is, the gooddata-flight-server already installs this package so the make dev step above will actually not install anything new. However, this may change in the future; not to mention that it is generally not a good idea to rely on transitive dependencies.

From then on, you can start using the Prometheus client to create various types of metrics. For example:

from prometheus_client import Counter

# instantiate counter
MY_COUNTER = Counter(
    "my_counter",
    "Fitting description of `my_counter`.",
)

def some_function():
    # ...
    MY_COUNTER.inc()

Recommendations

Here are a few assorted recommendations based on our production experience:

You must avoid double-declaration of metrics. If you try to define metric with same identifier twice, the registration will fail.
It is nice to declare all/most metrics in single place. For example create my_metrics.py file and in that have MyMetrics class with one static field per metric.

This approach leads to better 'discoverability' of available metrics just by looking at code. Using class with static field per-metric in turn makes imports and autocomplete more convenient.

OpenTelemetry Tracing

GoodData's Flight RPC server can be configured to integrate with OpenTelemetry and start and auto-configure OpenTelemetry exporters. It will also auto-fill the ResourceAttributes by doing discovery where possible.

See the otel_* options in the configuration files to learn more. In a nutshell it goes as follows:

Configure which exporter to use using otel_exporter_type setting.

Nowadays, the otlp-grpc or otlp-http is the usual choice.

Depending on the exporter you use, you may/must specify additional, exporter-specific environment variables to configure the exporter. The supported environment variables are documented in the respective OpenTelemetry exporter package; e.g. they are not something special to GoodData's Flight Server.

See official exporter documentation.
Install opentelemetry-api and the respective exporter package.

See the requirements.txt. The packages are already listed there but are commented out. Once you uncomment them, do make dev to ensure they are installed.
Tweak the other otel_* settings: you must at minimum set the otel_service_name

The settings apart from otel_service_name will fall back to defaults.

To start tracing, you need to initialize a tracer. You can do so as follows:

from opentelemetry import trace

MY_TRACER: trace.Tracer = trace.ProxyTracer("my_tracer")

Typically, you want to create one instance of tracer for your entire project and then import that instance and use it wherever needed to create spans:

from your_module_with_tracer import MY_TRACER

def some_function():
    # ... code
    with MY_TRACER.start_as_current_span("do_some_work") as span:
        # ... code
        pass

Note: there are many ways to instrument your code with spans. See OpenTelemetry documentation to find out more.

Recommendations

Here are a few assorted recommendations based on our production experience:

Always use the ProxyTracer. The underlying initialization code in GoodData's Flight server will correctly set the actual tracer which the proxy will call.

This way, if you turn off OpenTelemetry (by commenting out the otel_export_type setting or setting it to 'none'), the NoOpTracer will be injected under the covers and all the tracing code will be no-op as well.
Do not skim on the contextual detail / essential info for troubleshooting:
- Add span attributes to provide additional contextual information.
- Log events and errors to span to propagate significant return values / errors

With tracing, it is always a dance between adding too much information (and thus possibly overwhelming your underlying tracing infrastructure) vs only having basic span information which is useful to identify control flow but does not provide necessary detail to help debugging problems.

Health Checks

The GoodData Flight Server comes with a basic health-checking infrastructure - this is especially useful when deploying to environments (such as k8s) that monitor health of your server and can automatically restart it in case of problems.

When you configure the health_check_host (and optionally also health_check_port) setting, the Flight Server will expose two HTTP endpoints:

/ready - indicates whether the server is up and ready to serve requests

The endpoint will respond with status 500 if the server is not ready. Otherwise will respond with 202. The server is deemed ready when all its modules (which includes your FlexFunctions) are up and the Flight RPC server is 'unlocked' to handle requests.
/live - indicates whether the server is still alive and can be used. The liveness is determined from the status of the modules.

Each of the server's modules can report its status to a central health checking service. If any of the modules is unhealthy, the whole server is unhealthy.

Similar to the readiness, the server will respond with status 500 when not healthy. Otherwise, it will respond with status 202.

Creating health-checks for your FlexFunctions is fairly straightforward:

There is the on_load() static method that you can implement on your FlexFunction; it receives an instance of ServerContext
- The ServerContext contains health property - which returns an instance of ServerHealthMonitor
- At this occasion, your code should hold onto / propagate the health monitor to any mission-critical modules / components that are used by your FlexFunction
The ServerHealthMonitor has set_module_status(module, status) method - you can use this to indicate status
- The module name argument to this method can be anything you see fit
- The status is either ModuleHealthStatus.OK or ModuleHealthStatus.NOT_OK
- When your module is NOT_OK, the entire server is NOT_OK
- Usually, there is a grace period for which the server can be NOT_OK; after the time is up, environment will restart the server
- If you return your module back to OK status, the server returns to OK status as well - thus avoiding the automatic restarts.

Here is an example component using health monitoring:

import gooddata_flight_server as gf

class YourMissionCriticalComponent:
    """
    Let's say this component is used from your FlexFunctions to perform
    some heavy lifting / important job.

    The component is created / initialized during `on_load` of FlexFunction and you
    propagate the `health` monitor that comes in the server context.
    """
    def __init__(self, health: gf.ServerHealthMonitor) -> None:
        self._health = health

    def some_important_method(self):
        try:
            # this does some important work
            return
        except OSError:
            # it runs into some kind of unrecoverable error (OSError here is purely example);
            # by setting the status to NOT_OK, your component indicates that it is unhealthy
            # and the /live endpoint will report the entire server as unhealthy.
            #
            # usually, the liveness checks have a grace period. if you set the module back
            # to `gf.ModuleHealthStatus.NOT_OK` everything turns healthy again. If the grace
            # period elapses, the server will usually be restarted by the environment.
            self._health.set_module_status("YourMissionCriticalComponent", gf.ModuleHealthStatus.NOT_OK)
            raise

Dev-testing

A FlexFunction is a piece of code like any other. The fact that it is exposed via Flight RPC is a technical detail.

This template project comes with pytest pre-installed. You can test your FlexFunction implementations as you see fit using the standard techniques. You can run the tests either from IDE or use the make test target.

Typically, if your function does not do any crazy / non-standard stuff, then there is a very solid guarantee that a function passing tests will run fine once it is running inside the Flight RPC server.

Additionally, the template comes with fixtures for automated end-to-end testing. See the test_e2e.py. In a nutshell, the testing_flexfun_server fixture will start a server running your functions; you can then make calls to the server using the standard FlightClient.

HINT: if you need additional third-party dependencies for dev/test, then add those to the requirements-dev.txt.

Template dev infrastructure

This project is set up with:

ruff for linting, formating, import sorting
mypy for type checking
pre-commit hooks

The pre-commit hooks are enabled by default and run ruff and other basic hooks. The use of mypy is optional - but we strongly encourage it especially if your functions grow large.

There are a few Makefile targets for the usual tasks:

make mypy - run type checker
make fix-format - run ruff format and ruff check in auto-fix mode on all files
make fix-all - runs configured pre-commit hooks on all files in the repository. Note: ruff format and check with auto-fix is part of the pre-commit.

IMPORTANT: Keep in mind that pre-commit will not auto-stage files that were modified while it is running the configured hooks. What this means is that commit will fail if the pre-commit hooks get involved and auto-fix the files. You typically then have to re-drive the commit.

Secrets for dev and dev testing

During the bootstrap, the make dev will create .secrets file. This file is included in .gitignore and will not be committed to git. It is intended to hold any env variables that contain secrets.

If you use direnv, then the contents of this file will be automatically sourced when you navigate to the directory. Otherwise, you may want to source the file manually (or finally get around and install direnv).

As the name indicates, the .secrets file is useful for holding any sensitive information:

GDCN_LICENSE_KEY - the license key to use if you are doing end-to-end testing using docker-compose (see below)
GOODDATA_FLIGHT_ENUMERATED_TOKENS__TOKENS - can be used to specify list of secret tokens used for authentication.

This one is a bit tricky, you have to specify an array of tokens: '["...", "..."]'

Running and testing locally using Docker Compose

The template comes with docker-compose.yaml which consists of two services:

The GoodData Cloud Native Container Edition (gooddata-cn-ce). This runs the entire Cloud Native in a single container.
The server hosting your FlexFunctions (gooddata-flexfun-server)

To start it all up, you do the usual: docker compose up -d --build. At the time of startup, the Docker image with server hosting your functions will be built (see section above).

Right now, you have to add your server as a data source to GoodData CN manually. Do POST the following payload to http://localhost:3000/api/v1/entities/dataSources:

curl http://localhost:3000/api/v1/entities/dataSources \
-H "Authorization: Bearer YWRtaW46Ym9vdHN0cmFwOmFkbWluMTIz" \
-s -H "Content-Type: application/vnd.gooddata.api+json" \
-X POST \
-d '{
  "data": {
    "id": "flexfun-server",
    "type": "dataSource",
    "attributes": {
      "url": "grpc://gooddata-flexfun-server:17001",
      "name": "flexfun-server",
      "type": "FLIGHTRPC",
      "token": "none",
      "schema": "",
      "cacheStrategy": "NEVER"
    }
  }
}'

If you make some changes and want to rebuild & restart just the FlexFun server run: docker compose up -d --build gooddata-flexfun-server.

Rebuild and restart of your server is usually very quick so this should allow you to iterate very fast. Still, even faster and more natural is to use the typical tests that you can write in tests directory.

We recommend doing most of the testing using programmatic tests and then use the end-to-end testing using docker compose for final verification.

Getting ready for deployment

Configuration

The underlying server infrastructure uses Dynaconf to manage configuration. This template comes with two configuration files:

dev.server.toml is tailored for dev-testing on localhost
prod.server.toml is a stub of configuration for production server deployment.

The 'prod' configuration file is preset to listen on all addresses, use TLS and token authentication. But it naturally omits configuration of TLS (certificate, private key) and the details of token authentication. You have to supply those or (and we do not recommend this) turn them off.

IMPORTANT: depending on your production host, you may further tweak the configuration - especially the advertise_host. Check out the following sections to learn more.

Environment Variables

You can use environment variables to override any setting from the configuration files.

This is typically useful when handling secrets: you don't want to hammer those into the configuration files that make it to a VCS. Instead, the values of secrets are injected at runtime using environment variables.

For list of environment variables, see for example the run-server.sh. The environment variables are derived from setting names:

Always start with GOODDATA_FLIGHT
The section name (e.g. [server]) comes next, so for example GOODDATA_FLIGHT_SERVER
Then comes the setting name itself (e.g. listen_host), separated with two underscores; so you end up with GOODDATA_FLIGHT_SERVER__LISTEN_HOST

All settings that can be specified in the TOML file can be also set via environment variable. The value from environment variable always wins.

Furthermore, we recommend checking the Dynaconf documentation - it has a lot of additional features which you can take advantage of. It even has its own templating.

Setting up hostnames

This part can get somewhat tricky if you do not read the documentation. We recommend to check out the comments in the prod.server.toml.

For production, go as follows:

Set listen_host to 0.0.0.0
Set advertise_host to either public IP or public hostname of your server.

Essentially, this is the value that you would also use when adding the server as data source to the GoodData Cloud.

NOTE: Setting correct advertise_host is critical because the primary flow to invoke functions is the GetFlightInfo -> DoGet flow. Where GetFlightInfo call returns information where and how to consume the function result. The server infrastructure needs your input (e.g. the advertise_host) so that it can fill the 'where' part.

Authentication

At the moment, the server infrastructure supports two authentication methods:

No authentication at all (this is default that the template starts with)
Token based authentication, where the client has to present a secret token

For deployment where your server is reachable from the public networks, you almost always want to set up authentication.

Set the authentication_method option to token

Alternatively, you can use the GOODDATA_FLIGHT_SERVER__AUTHENTICATION_METHOD environment variable.
Define the secret tokens. This is done either using configuration or environment variable.
```
[enumerated_tokens]
tokens = []
```
The tokens setting should contain one or more tokens. These are really some values that you keep secret and distribute them to clients. They can be whatever you see fit.

The same can be achieved using the GOODDATA_FLIGHT_ENUMERATED_TOKENS__TOKENS. The value of this environment variable has to be coded as an array:

GOODDATA_FLIGHT_ENUMERATED_TOKENS__TOKENS='["...", "..."]'

IMPORTANT: Never commit the secrets to VCS. Keep the secret token configuration outside of this project.

TLS

Using TLS (Transport Level Security) is highly recommended. To do so, you have to obtain certificate and private key for your server - a topic that is not covered in this manual.

When you have private key and the certificate (typically in .pem files), you can configure these server settings (server.config.toml):

use_tls = true to enable TLS
tls_certificate - configure server's certificate
tls_private_key - configure server's private key

For further information, see the comments for these settings in the prod.server.toml file.

IMPORTANT: you can opt to generate and self-sign the server certificate. It is a viable option. If you do that, you must distribute the CA certificate the clients. So for example you have to configure the data source in GoodData so that it includes the CA certificate (this is covered in followup sections.)

IMPORTANT: do not commit server certificate and private key to VCS. Keep them outside of this project (e.g. only keep them on / distribute them to the host where your server runs).

Deployment without Docker

Deploying and running your server without use of Docker is a viable approach and in many cases can be the simplest or most straightforward.

You have to get the whole template project to a host
- You can do this by creating tarball of essential content using make archive
- Then get the tarball to the host using your favorite method and extract it there
HINT: Another approach is to have a git repository where you push and manage the contents of this project. Then check out the contents of this repository from the host.
Install production dependencies:
- Your host must have Python 3.12 installed
- On the host, run make prod - this will install production dependencies into its own virtual environment.
Start the server

Easiest is to use ./run-server.sh prod
This will start server using the prod.server.toml configuration stored in config directory
Typically, you will need to specify host-specific settings, you can do so using environment variables or further tweak the configuration.
If you have not already, see the 'Configuration' section above for additional detail.

Dockerized deployment

This template comes with a Dockerfile and an example script to start the dockerized server hosting your FlexFunctions.

As everything in the template, both the Dockerfile and the example are just one way to proceed with Dockerized deployment; they are yours to change or even throw away and do them your way from scratch to suit your deployment. You may be using Docker Swarm, Nomad, k8s or some managed service - naturally, the example script is of little use in such contexts.

Docker Image

The make docker target is available and builds Docker image flexfun-server. You may want to modify the image name and specify your own custom one.

The Docker image build will:

Install all dependencies from requirements.txt file

IMPORTANT: dependencies from requirements-dev.txt are intentionally not installed.
Copy content of the entire src folder and make sure it is added on PYTHONPATH

IMPORTANT: contents of tests directory is not copied to the image.
Copy content of the entire config folder

Important considerations when running in Docker

The most important aspect to re-iterate here is the setup of listen_host and advertise_host:

You most often want to set listen_host to 0.0.0.0 so that the server listens on all addresses inside the Docker container.
The advertise_host must be then set to a host name that can be correctly resolved by your server's clients; this is usually different from any hostname that is set inside the Docker container. In most cases, you have to provide the hostname from outside.

Multiple server replicas and load balancing

To foster resiliency and horizontal scalability, you can run multiple replicas of the server running your Flex Functions. You can register all these replicas to the Flight RPC data source and then set the load balancing strategy. Check out the following section to find an example.

It is critical to remember, that any infrastructure-level load balancing will most likely break things. That is because the FlexFunction servers are stateful:

FlexFunction invocation happens in two steps.
In the first step, the Flight RPC GetFlightInfo is called (as per spec) to tell the server to invoke the function and prepare the data. A response to GetFlightInfo contains, among other things, the advertise_host location of the server where to pick up the result.
In the second step, the Flight RPC DoGet is called to actually pick up the data from the advertised location returned in the previous step.

Now, if you have a setup (common in k8s with gRPC load balancing) where there is a single hostname that transparently balances to multiple replicas, things will break. The GetFlightInfo request lands on one replica, computes results and returns pointers to access that result. The subsequent DoGet call may however, be routed to completely different replica where the result is not present.

Adding Data Source to GoodData Cloud

Now that you have the Flight RPC server with your FlexFunctions up and running, you can add it as data source to GoodData.

At the moment, this is only possible using the REST API.

You need to do a POST request on the /api/v1/entities/dataSources resource. The payload should look like this:

curl https://<gooddatacloud>/api/v1/entities/dataSources \
-H "Authorization: Bearer <gooddata-token>" \
-s -H "Content-Type: application/vnd.gooddata.api+json" \
-X POST \
-d '{
  "data": {
    "id": "flexfun-server",
    "type": "dataSource",
    "attributes": {
      "url": "grpc+tls://<your-hostname>:<port>",
      "name": "flexfun-server",
      "type": "FLIGHTRPC",
      "token": "<secret authentication token for the Flight RPC server>",
      "schema": "",
      "cacheStrategy": "NEVER"
    }
  }
}'

TLS and custom certificate

If your server has TLS enabled and uses self-signed certificates, then you also have to provide this certificate as a parameter of the data source:

curl https://<gooddatacloud>/api/v1/entities/dataSources \
-H "Authorization: Bearer <gooddata-token>" \
-s -H "Content-Type: application/vnd.gooddata.api+json" \
-X POST \
-d '{
  "data": {
    "id": "flexfun-server",
    "type": "dataSource",
    "attributes": {
      "url": "grpc+tls://<your-hostname>:<port>",
      "name": "flexfun-server",
      "type": "FLIGHTRPC",
      "token": "<secret authentication token for the Flight RPC server>",
      "schema": "",
      "cacheStrategy": "NEVER",
      "parameters": [
        {
          "name": "tlsRootCertificate",
          "value": "..."
        }
      ]
    }
  }
}'

The value of the tlsRootCertificate is Base64 encoded content of the CA certificate .pem file. You can do the base64 encoding for example using something like this:

import base64

with open("ca-cert.pem", "rb") as file:
    print(base64.b64encode(file.read()))

Load Balancing

If you have multiple replicas of the server running your FlexFunctions, you can code them in the url, delimited using semicolon.

You can then specify the loadBalancing parameter to so the load balancing strategy to use:

none - there will be no load balancing; the first server from the list will be used all the time; when the server is down, GoodData will contact next server in the list. This is the default.
round-robin - requests will be distributed among servers using round-robin strategy.
random - requests will be distributed among servers randomly

curl https://<gooddatacloud>/api/v1/entities/dataSources \
-H "Authorization: Bearer <gooddata-token>" \
-s -H "Content-Type: application/vnd.gooddata.api+json" \
-X POST \
-d '{
  "data": {
    "id": "flexfun-server",
    "type": "dataSource",
    "attributes": {
      "url": "grpc+tls://<your-hostname1>:<port>;grpc+tls://<your-hostname2>:<port>",
      "name": "flexfun-server",
      "type": "FLIGHTRPC",
      "token": "<secret authentication token for the Flight RPC server>",
      "schema": "",
      "cacheStrategy": "NEVER",
      "parameters": [
        {
          "name": "loadBalancing",
          "value": "round-robin"
        }
      ]
    }
  }
}'

Cache strategy

The cacheStrategy set to 'NEVER' means that GoodData Cloud will never cache any results obtained when invoking FlexFunctions on your server.

You can set this to ALWAYS if you want to add caching layer on top of your server - just keep in mind that then you have to do POST on /api/v1/actions/dataSources/my-flexfun/uploadNotification every time you want to invalidate the caches.

The use of caching on top of server running FlexFunctions is at your discretion - in many cases it depends on what your FlexFunctions do. For example if the function does complex computations on top of mostly static data - then using the caching is a no-brainer especially in production deployments.

Name		Name	Last commit message	Last commit date
Latest commit History 68 Commits
config		config
src/flexfun		src/flexfun
tests		tests
.dockerignore		.dockerignore
.editorconfig		.editorconfig
.envrc		.envrc
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.python-version		.python-version
Dockerfile		Dockerfile
LICENSE.txt		LICENSE.txt
Makefile		Makefile
README.md		README.md
docker-compose.yaml		docker-compose.yaml
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
run-docker.sh		run-docker.sh
run-server.sh		run-server.sh

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gooddata/gooddata-flexfun-template

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GoodData FlexFunctions host template

What is a FlexFunction

Technical perspective

FlexFunction hosting

Getting Started

Developing FlexFunctions

Adding new function

Adding the code

Registering the function

FlexFunctions recommendations

Adding third party dependencies

Logging

Recommendations

Prometheus Metrics

Recommendations

OpenTelemetry Tracing

Recommendations

Health Checks

Dev-testing

Template dev infrastructure

Secrets for dev and dev testing

Running and testing locally using Docker Compose

Getting ready for deployment

Configuration

Environment Variables

Setting up hostnames

Authentication

TLS

Deployment without Docker

Dockerized deployment

Docker Image

Important considerations when running in Docker

Multiple server replicas and load balancing

Adding Data Source to GoodData Cloud

TLS and custom certificate

Load Balancing

Cache strategy

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