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commit ac8f407c74e32171d5c8537faf2db06ec1c4214e
Author: Stephen Sisk <s...@google.com>
AuthorDate: Tue Jul 18 18:15:18 2017 -0700
Add initial text for I/O Integration Test documentation
---
src/documentation/io/testing.md | 456 +++++++++++++++++++++++++++++++++++++++-
1 file changed, 455 insertions(+), 1 deletion(-)
diff --git a/src/documentation/io/testing.md b/src/documentation/io/testing.md
index adfde04..8846dc1 100644
--- a/src/documentation/io/testing.md
+++ b/src/documentation/io/testing.md
@@ -108,6 +108,461 @@ This allows the I/O transform's unit tests to act as if
they have a perfect netw
If your I/O transform allows batching of reads/writes, you must force the
batching to occur in your test. Having configurable batch size options on your
I/O transform allows that to happen easily. These must be marked as test only.
+
+## I/O Transform Integration Tests {#i-o-transform-integration-tests}
+
+
+### Goals {#goals}
+
+* Allow end to end testing of interactions between data stores, I/O
transforms, and runners, simulating real world conditions.
+* Allow both small scale and large scale testing.
+* Self contained: require the least possible initial setup or existing
outside state, besides the existence of a data store that the test can modify.
+* Anyone can run the same set of I/O transform integration tests that Beam
runs on its continuous integration servers.
+
+
+### Integration tests, data stores, and Kubernetes
{#integration-tests-data-stores-and-kubernetes}
+
+In order to test I/O transforms in real world conditions, you must connect to
a data store instance.
+
+The Beam community hosts the data stores used for integration tests in
Kubernetes. In order for an integration test to be run in Beam's continuous
integration environment, it must have Kubernetes scripts that set up an
instance of the data store.
+
+However, when working locally, there is no requirement to use Kubernetes. All
of the test infrastructure allows passing in connection info, so developers can
use their preferred hosting infrastructure for local development.
+
+
+### Running integration tests {#running-integration-tests}
+
+The high level steps for running an integration test are:
+1. Set up the data store corresponding to the test being run
+1. Run the test, passing it connection info from the just created data store
+1. Clean up the data store
+
+Since setting up data stores and running the tests involves a number of steps,
and we wish to time these tests when running performance benchmarks, we use
PerfKit Benchmarker (PKB) to manage the process end to end. With a single
command, you can go from an empty Kubernetes cluster to a running integration
test.
+
+However, **PerfKit Benchmarker is not required for running integration
tests**. Therefore, we have listed the steps for both using PerfKit, and
manually running the tests below.
+
+
+#### Using PerfKit Benchmarker {#using-perfkit-benchmarker}
+
+Prerequisites:
+1. [Install
PerfKit](https://github.com/GoogleCloudPlatform/PerfKitBenchmarker)
+1. Have a running Kubernetes cluster you can connect to locally using kubectl
+
+You won't need to invoke PerfKit directly. Run mvn verify in the directory of
the I/O module you'd like to test, with the parameter io-it-suite.
+
+Example run with the direct runner:
+```
+mvn verify -Dio-it-suite -Dio-it-suite-local -pl
sdks/java/io/jdbc,sdks/java/io/jdbc
-DpkbLocation="/Users/me/dev/PerfKitBenchmarker/pkb.py" -DforceDirectRunner
-DintegrationTestPipelineOptions=["--myTestParam=val"]
+```
+
+
+Example run with the Cloud Dataflow runner:
+```
+mvn verify -Dio-it-suite -pl sdks/java/io/jdbc
-DintegrationTestPipelineOptions=["--project=PROJECT","--gcpTempLocation=GSBUCKET"]
-DintegrationTestRunner=dataflow
-DpkbLocation="/Users/me/dev/PerfKitBenchmarker/pkb.py"
+```
+
+
+Parameter descriptions:
+
+
+<table class="table">
+ <thead>
+ <tr>
+ <td>
+ <strong>Option</strong>
+ </td>
+ <td>
+ <strong>Function</strong>
+ </td>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>-Dio-it-suite
+ </td>
+ <td>Invokes the call to PerfKit.
+ </td>
+ </tr>
+ <tr>
+ <td>-Dio-it-suite-local
+ </td>
+ <td>Modifies the call to PerfKit so that it exposes the postgres service
via LoadBalancer, making it available to users not on the immediate network of
the kubernetes cluster. This is useful if you are running on a remote
kubernetes cluster.
+ </td>
+ </tr>
+ <tr>
+ <td>-pl sdks/java/io/jdbc
+ </td>
+ <td>Specifies the maven project of the I/O to test.
+ </td>
+ </tr>
+ <tr>
+ <td>-Dkubectl="path-to-kubectl" -Dkubeconfig="path-to-kubeconfig"
+ </td>
+ <td>Options for specifying non-standard kubectl configurations. Optional.
Defaults to "kubectl" and "~/.kube/config".
+ </td>
+ </tr>
+ <tr>
+ <td>integrationTestPipelineOptions
+ </td>
+ <td>Passes pipeline options directly to the test being run.
+ </td>
+ </tr>
+ <tr>
+ <td>-DforceDirectRunner
+ </td>
+ <td>Runs the test with the direct runner.
+ </td>
+ </tr>
+ </tbody>
+</table>
+
+
+
+#### Without PerfKit Benchmarker {#without-perfkit-benchmarker}
+
+If you're using Kubernetes, make sure you can connect to your cluster locally
using kubectl. Otherwise, skip to step 3 below.
+
+1. Set up the data store corresponding to the test you wish to run. You can
find Kubernetes scripts for all currently supported data stores in
[.test-infra/kubernetes](https://github.com/apache/beam/tree/master/.test-infra/kubernetes).
+ 1. In some cases, there is a setup script (*.sh). In other cases, you can
just run ``kubectl create -f [scriptname]`` to create the data store.
+ 1. Convention dictates there will be:
+ 1. A core yml script for the data store itself, plus a `NodePort`
service. The `NodePort` service opens a port to the data store for anyone who
connects to the Kubernetes cluster's machines.
+ 1. A separate script, called for-local-dev, which sets up a
LoadBalancer service.
+ 1. Examples:
+ 1. For JDBC, you can set up Postgres: `kubectl create -f
./test-infra/kubernetes/postgres/postgres.yml`
+ 1. For Elasticsearch, you can run the setup script: `bash
./test-infra/kubernetes/elasticsearch/setup.sh`
+1. Determine the IP address of the service:
+ 1. NodePort service: `kubectl get pods -l 'component=elasticsearch' -o
jsonpath={.items[0].status.podIP}`
+ 1. LoadBalancer service:` kubectl get svc elasticsearch-external -o
jsonpath='{.status.loadBalancer.ingress[0].ip}'`
+1. Run the test using the instructions in the class (e.g. see the
instructions in JdbcIOIT.java)
+1. Tell Kubernetes to delete the resources specified in the Kubernetes
scripts:
+ 1. JDBC: `kubectl delete -f ./test-infra/kubernetes/postgres/postgres.yml`
+ 1. Elasticsearch: `bash ./test-infra/kubernetes/elasticsearch/teardown.sh`
+
+
+### Implementing Integration Tests {#implementing-integration-tests}
+
+There are three components necessary to implement an integration test:
+* **Test code**: the code that does the actual testing: interacting with the
I/O transform, reading and writing data, and verifying the data.
+* **Kubernetes scripts**: a Kubernetes script that sets up the data store
that will be used by the test code.
+* **Integrate with PerfKit Benchmarker using io-it-suite**: this allows
users to easily invoke perfkit, creating the Kubernetes resources and running
the test code.
+
+These three pieces are discussed in detail below.
+
+
+#### Test Code {#test-code}
+
+These are the conventions used by integration testing code:
+* **Your test should use pipeline options to receive connection
information.**
+ * For Java, there is a shared pipeline options object in the io/common
directory. This means that if there are two tests for the same data store (e.g.
for `Elasticsearch` and the `HadoopInputFormatIO` tests), those tests share the
same pipeline options.
+* **Generate test data programmatically and parameterize the amount of data
used for testing.**
+ * For Java, `CountingInput` + `TestRow` can be combined to generate
deterministic test data at any scale.
+* **Use a write then read style for your tests.**
+ * In a single `Test`, run a pipeline to do a write using your I/O
transform, then run another pipeline to do a read using your I/O transform.
+ * The only verification of the data should be the result from the read.
Don't validate the data written to the database in any other way.
+ * Validate the actual contents of all rows in an efficient manner. An
easy way to do this is by taking a hash of the rows and combining them.
`HashingFn` can help make this simple, and `TestRow` has pre-computed hashes.
+ * For easy debugging, use `PAssert`'s `containsInAnyOrder` to validate
the contents of a subset of all rows.
+* **Tests should assume they may be run multiple times and/or simultaneously
on the same database instance.**
+ * Clean up test data: do this in an `@afterClass` to ensure it runs.
+ * Use unique table names per run (timestamps are an easy way to do this)
and per-method where appropriate.
+
+An end to end example of these principles can be found in
[JdbcIOIT](https://github.com/ssisk/beam/blob/jdbc-it-perf/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/JdbcIOIT.java).
+
+If you'd like to implement Python I/O integration tests or integration tests
for unbounded or eventually consistent data stores, please contact the Beam
dev@ mailing list for more information.
+
+
+#### Kubernetes scripts {#kubernetes-scripts}
+
+As discussed in [Integration tests, data stores, and
Kubernetes](#integration-tests-data-stores-and-kubernetes), to have your tests
run on Beam's continuous integration server, you'll need to implement a
Kubernetes script that creates an instance of your data store.
+
+If you would like help with this or have other questions, contact the Beam
dev@ mailing list and the community may be able to assist you.
+
+Guidelines for creating a Beam data store Kubernetes script:
+1. **You must only provide access to the data store instance via a `NodePort`
service.**
+ * This is a requirement for security, since it means that only the local
network has access to the data store. This is particularly important since many
data stores don't have security on by default, and even if they do, their
passwords will be checked in to our public Github repo.
+1. **Convention: define two Kubernetes scripts.**
+ * This is the best known way to implement item #1.
+ * The first script will be the core script run on the Beam CI servers
and should contain the main datastore instance script (`StatefulSet`) plus a
`NodePort` service exposing the data store.
+ * The second script will define a `LoadBalancer` service, used for local
development if the Kubernetes cluster is on another network. This file's name
is usually suffixed with '-for-local-dev'.
+1. **You must ensure that pods are recreated after crashes.**
+ * If you use a `pod` directly, it will not be recreated if the pod
crashes or something causes the cluster to move the container for your pod.
+ * In most cases, you'll want to use `StatefulSet` as it supports
persistent disks that last between restarts, and having a stable network
identifier associated with the pod using a particular persistent disk.
`Deployment`s/`ReplicaSet`s are also possibly useful, but likely in fewer
scenarios since they do not have those features.
+1. **Convention: create separate scripts for small and large instances of
your data store.**
+ * This seems to be the best way to support having both a small and large
data store available for integration testing, as discussed in [Small Scale and
Large Scale Integration Tests](#small-scale-and-large-scale-integration-tests).
+1. **You must use a Docker image from a trusted source and pin the version of
the Docker image.**
+ * You should prefer images in this order:
+ 1. An image provided by the creator of the data source/sink (if they
officially maintain it). For Apache projects, this would be the official Apache
repository.
+ 1. Official Docker images, because they have security fixes and
guaranteed maintenance.
+ 1. Non-official Docker images, or images from other providers that
have good maintainers (e.g. [quay.io](http://quay.io/)).
+
+
+#### Integrate with PerfKit Benchmarker {#integrate-with-perfkit-benchmarker}
+
+To allow developers to easily invoke your I/O integration test, perform the
following steps:
+1. Create a PerfKit benchmark configuration file for the data store. Each
pipeline option needed by the integration test should have a configuration
entry. See [Defining I/O test
configuration](#defining-the-benchmark-configuration-file) for information
about what to include.
+1. Modify the pom of the I/O to set the [correct PerfKit
parameters](#per-i-o-mvn-pom-configuration).
+
+The goal is that a checked in config has defaults such that other developers
can run the test without changing the configuration.
+
+
+#### Defining the benchmark configuration file
{#defining-the-benchmark-configuration-file}
+
+The benchmark configuration file is a yaml file that defines the set of
pipeline options for a specific data store. Some of these pipeline options are
**static** - they are known ahead of time, before the data store is created
(e.g. username/password). Others options are **dynamic** - they are only known
once the data store is created (or after we query the Kubernetes cluster for
current status).
+
+All known cases of dynamic pipeline options are for extracting the IP address
that the test needs to connect to. For I/O integration tests, we must allow
users to specify:
+
+
+
+* The type of the IP address to get (load balancer? node address?)
+* The pipeline option to pass that IP address to
+* How to find the Kubernetes resource with that value (ie. what load
balancer service name? what node selector?)
+
+The style of dynamic pipeline options used here should support a variety of
other types of values derived from Kubernetes, but we do not have specific
examples.
+
+The dynamic pipeline options are:
+
+
+<table class="table">
+ <thead>
+ <tr>
+ <td>
+ <strong>Type name</strong>
+ </td>
+ <td>
+ <strong>Meaning</strong>
+ </td>
+ <td>
+ <strong>Selector field name</strong>
+ </td>
+ <td>
+ <strong>Selector field value</strong>
+ </td>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>NodePortIp
+ </td>
+ <td>We will be using the IP address of a k8s NodePort service, the value
will be an IP address of a Pod
+ </td>
+ <td>podLabel
+ </td>
+ <td>A kubernetes label selector for a pod whose IP address can be used to
connect to
+ </td>
+ </tr>
+ <tr>
+ <td>LoadBalancerIp
+ </td>
+ <td>We will be using the IP address of a k8s LoadBalancer, the value will
be an IP address of the load balancer
+ </td>
+ <td>serviceName
+ </td>
+ <td>The name of the LoadBalancer kubernetes service.
+ </td>
+ </tr>
+ </tbody>
+</table>
+
+#### Benchmark configuration files: full example configuration file
{#benchmark-configuration-files-full-example-configuration-file}
+
+A configuration file will look like this:
+```
+static_pipeline_options:
+ -postgresUser: postgres
+ -postgresPassword: postgres
+dynamic_pipeline_options:
+ - paramName: PostgresIp
+ type: NodePortIp
+ podLabel: app=postgres
+```
+
+
+and may contain the following elements:
+
+
+<table class="table">
+ <thead>
+ <tr>
+ <td><strong>Configuration element</strong>
+ </td>
+ <td><strong>Description and how to change when adding a new test</strong>
+ </td>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>static_pipeline_options
+ </td>
+ <td>The set of preconfigured mvn pipeline options.
+ </td>
+ </tr>
+ <tr>
+ <td>dynamic_pipeline_options
+ </td>
+ <td>The set of mvn pipeline options that PerfKit will determine at
runtime.
+ </td>
+ </tr>
+ <tr>
+ <td>dynamic_pipeline_options.name
+ </td>
+ <td>The name of the parameter to be passed to mvn's invocation of the I/O
integration test.
+ </td>
+ </tr>
+ <tr>
+ <td>dynamic_pipeline_options.type
+ </td>
+ <td>The method of determining the value of the pipeline options.
+ </td>
+ </tr>
+ <tr>
+ <td>dynamic_pipeline_options - other attributes
+ </td>
+ <td>These vary depending on the type of the dynamic pipeline option - see
the table of dynamic pipeline options for a description.
+ </td>
+ </tr>
+ </tbody>
+</table>
+
+
+
+#### Per-I/O mvn pom configuration {#per-i-o-mvn-pom-configuration}
+
+Each I/O is responsible for adding a section to its pom with a profile that
invokes PerfKit with the proper parameters during the verify phase. Below are
the set of PerfKit parameters and how to configure them.
+
+The [JdbcIO
pom](https://github.com/apache/beam/blob/master/sdks/java/io/jdbc/pom.xml) has
an example of how to put these options together into a profile and invoke
Python+PerfKit with them.
+
+
+<table class="table">
+ <thead>
+ <tr>
+ <td><strong>PerfKit Parameter</strong>
+ </td>
+ <td><strong>Description</strong>
+ </td>
+ <td><strong>Example value</strong>
+ </td>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>benchmarks
+ </td>
+ <td>Defines the PerfKit benchmark to run. This is same for all I/O
integration tests.
+ </td>
+ <td>beam_integration_benchmark
+ </td>
+ </tr>
+ <tr>
+ <td>beam_location
+ </td>
+ <td>The location where PerfKit can find the Beam repository.
+ </td>
+ <td>${beamRootProjectDir} - this is a variable you'll need to define for
each maven pom. See example pom for an example.
+ </td>
+ </tr>
+ <tr>
+ <td>beam_prebuilt
+ </td>
+ <td>Whether or not to rebuild the Beam repository before invoking the I/O
integration test command.
+ </td>
+ <td>true
+ </td>
+ </tr>
+ <tr>
+ <td>beam_sdk
+ </td>
+ <td>Whether PerfKit will run the Beam SDK for Java or Python.
+ </td>
+ <td>java
+ </td>
+ </tr>
+ <tr>
+ <td>beam_runner_profile
+ </td>
+ <td>Optional command line parameter used to override the runner, allowing
us to use the direct runner.
+ </td>
+ <td>Always use the predefined variable instead of specifying this
parameter ${pkbBeamRunnerProfile}
+ </td>
+ </tr>
+ <tr>
+ <td>beam_runner_option
+ </td>
+ <td>Optional command line parameter used to override the runner, allowing
us to use the direct runner.
+ </td>
+ <td>Always use the predefined variable instead of specifying this
parameter ${pkbBeamRunnerOption}
+ </td>
+ </tr>
+ <tr>
+ <td>beam_it_module
+ </td>
+ <td>The path to the pom that contains the test (needed for invoking the
test with PerfKit).
+ </td>
+ <td>sdks/java/io/jdbc
+ </td>
+ </tr>
+ <tr>
+ <td>beam_it_class
+ </td>
+ <td>The test to run.
+ </td>
+ <td>JdbcIOIT
+ </td>
+ </tr>
+ <tr>
+ <td>beam_it_options
+ </td>
+ <td>Pipeline options for the beam job - meant to be a way to pass
pipeline options the user specifies on the commandline when invoking io-it-suite
+ </td>
+ <td>Always use ${integrationTestPipelineOptions}, which allows the user
to pass in parameters.
+ </td>
+ </tr>
+ <tr>
+ <td>kubeconfig
+ </td>
+ <td>The standard PerfKit parameter `kubeconfig`, which specifies where
the Kubernetes config file lives.
+ </td>
+ <td>Always use ${kubeconfig}
+ </td>
+ </tr>
+ <tr>
+ <td>kubectl
+ </td>
+ <td>The standard PerfKit parameter `kubectl`, which specifies where the
kubectl binary lives.
+ </td>
+ <td>Always use ${kubectl}
+ </td>
+ </tr>
+ <tr>
+ <td>beam_kubernetes_scripts
+ </td>
+ <td>The Kubernetes script files to create and teardown via create/delete.
Specify absolute path.
+ </td>
+ <td>${beamRootProjectDir}/.test-infra/kubernetes/postgres/pkb-config.yml
+ </td>
+ </tr>
+ </tbody>
+</table>
+
+
+There is also a set of Maven properties which are useful when invoking
PerfKit. These properties are configured in the I/O parent pom, and some are
only available when the io-it-suite profile is active in Maven.
+
+
+#### Small Scale and Large Scale Integration Tests
{#small-scale-and-large-scale-integration-tests}
+
+Apache Beam expects that it can run integration tests in multiple
configurations:
+* Small scale
+ * Execute on a single worker on the runner (it should be *possible* but
is not required).
+ * The data store should be configured to use a single node.
+ * The dataset can be very small (1000 rows).
+* Large scale
+ * Execute on multiple workers on the runner.
+ * The datastore should be configured to use multiple nodes.
+ * The data set used in this case is larger (10s of GBs).
+
+You can do this by:
+1. Creating two Kubernetes scripts: one for a small instance of the data
store, and one for a large instance.
+1. Having your test take a pipeline option that decides whether to generate a
small or large amount of test data (where small and large are sizes appropriate
to your data store)
+
+An example of this is `HadoopInputFormatIO`'s tests.
+
<!--
# Next steps
@@ -115,4 +570,3 @@ If you have a well tested I/O transform, why not contribute
it to Apache Beam? R
[Contributing I/O Transforms]({{site.baseurl }}/documentation/io/contributing/)
-->
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