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API reference: <a href="api/kotlin/corda/index.html">Kotlin</a>/ <a href="api/javadoc/index.html">JavaDoc</a>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Load testing</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#configuration-of-the-load-testing-cluster">Configuration of the load testing cluster</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#running-the-load-tests">Running the load tests</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#configuration-of-individual-load-tests">Configuration of individual load tests</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#how-to-write-a-load-test">How to write a load test</a></li>
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<div class="section" id="load-testing">
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<h1>Load testing<a class="headerlink" href="#load-testing" title="Permalink to this headline">¶</a></h1>
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<p>This section explains how to apply random load to nodes to stress test them. It also allows the specification of disruptions that strain different resources, allowing us to inspect the nodes’ behaviour under extreme conditions.</p>
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<p>The load-testing framework is incomplete and is not part of CI currently, but the basic pieces are there.</p>
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<div class="section" id="configuration-of-the-load-testing-cluster">
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<h2>Configuration of the load testing cluster<a class="headerlink" href="#configuration-of-the-load-testing-cluster" title="Permalink to this headline">¶</a></h2>
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<p>The load-testing framework currently assumes the following about the node cluster:</p>
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<ul class="simple">
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<li>The nodes are managed as a systemd service</li>
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<li>The node directories are the same across the cluster</li>
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<li>The messaging ports are the same across the cluster</li>
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<li>The executing identity of the load-test has SSH access to all machines</li>
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<li>There is a single network map service node</li>
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<li>There is a single notary node</li>
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<li>Some disruptions also assume other tools (like openssl) to be present</li>
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</ul>
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<p>Note that these points could and should be relaxed as needed.</p>
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<p>The load test Main expects a single command line argument that points to a configuration file specifying the cluster hosts and optional overrides for the default configuration:</p>
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<div class="highlight-kotlin"><div class="highlight"><pre><span></span># nodeHosts = ["host1", "host2"]
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# sshUser = "someusername", by default it uses the System property "user.name"
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localCertificatesBaseDirectory = "build/load-test/certificates"
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localTunnelStartingPort = 10000
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remoteNodeDirectory = "/opt/r3cev"
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remoteMessagingPort = 31337
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remoteSystemdServiceName = "r3cev-node"
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</pre></div>
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</div>
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</div>
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<div class="section" id="running-the-load-tests">
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<h2>Running the load tests<a class="headerlink" href="#running-the-load-tests" title="Permalink to this headline">¶</a></h2>
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<p>In order to run the loadtests you need to have an active SSH-agent running with a single identity added that has SSH access to the loadtest cluster.</p>
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<p>You can use either IntelliJ or the gradle command line to start the tests.</p>
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<p>To use gradle: <code class="docutils literal"><span class="pre">./gradlew</span> <span class="pre">tools:loadtest:run</span> <span class="pre">-Ploadtest-config=PATH_TO_LOADTEST_CONF</span></code></p>
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<p>To use IntelliJ simply run Main.kt with the config path supplied as an argument.</p>
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</div>
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<div class="section" id="configuration-of-individual-load-tests">
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<h2>Configuration of individual load tests<a class="headerlink" href="#configuration-of-individual-load-tests" title="Permalink to this headline">¶</a></h2>
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<p>The load testing configurations are not set-in-stone and are meant to be played with to see how the nodes react.</p>
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<p>There are a couple of top-level knobs to tweak test behaviour:</p>
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<div class="highlight-kotlin"><div class="highlight"><pre><span></span> <span class="cm">/**</span>
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<span class="cm"> * @param parallelism Number of concurrent threads to use to run commands. Note that the actual parallelism may be</span>
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<span class="cm"> * further limited by the batches that [generate] returns.</span>
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<span class="cm"> * @param generateCount Number of total commands to generate. Note that the actual number of generated commands may</span>
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<span class="cm"> * exceed this, it is used just for cutoff.</span>
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<span class="cm"> * @param clearDatabaseBeforeRun Indicates whether the node databases should be cleared before running the test. May</span>
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<span class="cm"> * significantly slow down testing as this requires bringing the nodes down and up again.</span>
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<span class="cm"> * @param gatherFrequency Indicates after how many commands we should gather the remote states.</span>
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<span class="cm"> * @param disruptionPatterns A list of disruption-lists. The test will be run for each such list, and the test will</span>
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<span class="cm"> * be interleaved with the specified disruptions.</span>
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<span class="cm"> */</span>
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<span class="k">data</span> <span class="k">class</span> <span class="nc">RunParameters</span><span class="p">(</span>
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<span class="k">val</span> <span class="py">parallelism</span><span class="p">:</span> <span class="n">Int</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">generateCount</span><span class="p">:</span> <span class="n">Int</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">clearDatabaseBeforeRun</span><span class="p">:</span> <span class="n">Boolean</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">gatherFrequency</span><span class="p">:</span> <span class="n">Int</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">disruptionPatterns</span><span class="p">:</span> <span class="n">List</span><span class="p"><</span><span class="n">List</span><span class="p"><</span><span class="n">DisruptionSpec</span><span class="p">>></span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<p>The one thing of note is <code class="docutils literal"><span class="pre">disruptionPatterns</span></code>, which may be used to specify ways of disrupting the normal running of the load tests.</p>
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<div class="highlight-kotlin"><div class="highlight"><pre><span></span><span class="k">data</span> <span class="k">class</span> <span class="nc">Disruption</span><span class="p">(</span>
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<span class="k">val</span> <span class="py">name</span><span class="p">:</span> <span class="n">String</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">disrupt</span><span class="p">:</span> <span class="p">(</span><span class="n">NodeHandle</span><span class="p">,</span> <span class="n">SplittableRandom</span><span class="p">)</span> <span class="p">-></span> <span class="n">Unit</span>
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<span class="p">)</span>
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<span class="k">data</span> <span class="k">class</span> <span class="nc">DisruptionSpec</span><span class="p">(</span>
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<span class="k">val</span> <span class="py">nodeFilter</span><span class="p">:</span> <span class="p">(</span><span class="n">NodeHandle</span><span class="p">)</span> <span class="p">-></span> <span class="n">Boolean</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">disruption</span><span class="p">:</span> <span class="n">Disruption</span><span class="p">,</span>
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<span class="k">val</span> <span class="py">noDisruptionWindowMs</span><span class="p">:</span> <span class="n">LongRange</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<p>Disruptions run concurrently in loops on randomly chosen nodes filtered by <code class="docutils literal"><span class="pre">nodeFilter</span></code> at somewhat random intervals.</p>
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<p>As an example take <code class="docutils literal"><span class="pre">strainCpu</span></code> which overutilises the processor:</p>
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<div class="highlight-kotlin"><div class="highlight"><pre><span></span><span class="k">fun</span> <span class="nf">strainCpu</span><span class="p">(</span><span class="n">parallelism</span><span class="p">:</span> <span class="n">Int</span><span class="p">,</span> <span class="n">durationSeconds</span><span class="p">:</span> <span class="n">Int</span><span class="p">)</span> <span class="p">=</span> <span class="n">Disruption</span><span class="p">(</span><span class="s">"Put strain on cpu"</span><span class="p">)</span> <span class="p">{</span> <span class="n">node</span><span class="p">,</span> <span class="n">random</span> <span class="p">-></span>
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<span class="k">val</span> <span class="py">shell</span> <span class="p">=</span> <span class="s">"for c in {1..$parallelism} ; do openssl enc -aes-128-cbc -in /dev/urandom -pass pass: -e > /dev/null & done && JOBS=\$(jobs -p) && (sleep $durationSeconds && kill \$JOBS) & wait"</span>
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<span class="n">node</span><span class="p">.</span><span class="n">connection</span><span class="p">.</span><span class="n">runShellCommandGetOutput</span><span class="p">(</span><span class="n">shell</span><span class="p">).</span><span class="n">getResultOrThrow</span><span class="p">()</span>
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<span class="p">}</span>
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</pre></div>
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</div>
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<p>We can use this by specifying a <code class="docutils literal"><span class="pre">DisruptionSpec</span></code> in the load test’s <code class="docutils literal"><span class="pre">RunParameters</span></code>:</p>
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<div class="highlight-kotlin"><div class="highlight"><pre><span></span> <span class="n">DisruptionSpec</span><span class="p">(</span>
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<span class="n">disruption</span> <span class="p">=</span> <span class="n">strainCpu</span><span class="p">(</span><span class="n">parallelism</span> <span class="p">=</span> <span class="m">4</span><span class="p">,</span> <span class="n">durationSeconds</span> <span class="p">=</span> <span class="m">10</span><span class="p">),</span>
|
|
<span class="n">nodeFilter</span> <span class="p">=</span> <span class="p">{</span> <span class="k">true</span> <span class="p">},</span>
|
|
<span class="n">noDisruptionWindowMs</span> <span class="p">=</span> <span class="m">5000L</span><span class="p">..</span><span class="m">10000L</span>
|
|
<span class="p">)</span>
|
|
</pre></div>
|
|
</div>
|
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<p>This means every 5-10 seconds at least one randomly chosen nodes’ cores will be spinning 100% for 10 seconds.</p>
|
|
</div>
|
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<div class="section" id="how-to-write-a-load-test">
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<h2>How to write a load test<a class="headerlink" href="#how-to-write-a-load-test" title="Permalink to this headline">¶</a></h2>
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<p>A load test is basically defined by a random datastructure generator that specifies a unit of work a node should perform, a function that performs this work, and a function that predicts what state the node should end up in by doing so:</p>
|
|
<div class="highlight-kotlin"><div class="highlight"><pre><span></span><span class="k">data</span> <span class="k">class</span> <span class="nc">LoadTest</span><span class="p"><</span><span class="n">T</span><span class="p">,</span> <span class="n">S</span><span class="p">>(</span>
|
|
<span class="k">val</span> <span class="py">testName</span><span class="p">:</span> <span class="n">String</span><span class="p">,</span>
|
|
<span class="k">val</span> <span class="py">generate</span><span class="p">:</span> <span class="n">Nodes</span><span class="p">.(</span><span class="n">S</span><span class="p">,</span> <span class="n">Int</span><span class="p">)</span> <span class="p">-></span> <span class="n">Generator</span><span class="p"><</span><span class="n">List</span><span class="p"><</span><span class="n">T</span><span class="p">>>,</span>
|
|
<span class="k">val</span> <span class="py">interpret</span><span class="p">:</span> <span class="p">(</span><span class="n">S</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span> <span class="p">-></span> <span class="n">S</span><span class="p">,</span>
|
|
<span class="k">val</span> <span class="py">execute</span><span class="p">:</span> <span class="n">Nodes</span><span class="p">.(</span><span class="n">T</span><span class="p">)</span> <span class="p">-></span> <span class="n">Unit</span><span class="p">,</span>
|
|
<span class="k">val</span> <span class="py">gatherRemoteState</span><span class="p">:</span> <span class="n">Nodes</span><span class="p">.(</span><span class="n">S</span><span class="p">?)</span> <span class="p">-></span> <span class="n">S</span><span class="p">,</span>
|
|
<span class="k">val</span> <span class="py">isConsistent</span><span class="p">:</span> <span class="p">(</span><span class="n">S</span><span class="p">)</span> <span class="p">-></span> <span class="n">Boolean</span> <span class="p">=</span> <span class="p">{</span> <span class="k">true</span> <span class="p">}</span>
|
|
<span class="p">)</span> <span class="p">{</span>
|
|
</pre></div>
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</div>
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<p><code class="docutils literal"><span class="pre">LoadTest</span></code> is parameterised over <code class="docutils literal"><span class="pre">T</span></code>, the unit of work, and <code class="docutils literal"><span class="pre">S</span></code>, the state type that aims to track remote node states. As an example let’s look at the Self Issue test. This test simply creates Cash Issues from nodes to themselves, and then checks the vault to see if the numbers add up:</p>
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|
<div class="highlight-kotlin"><div class="highlight"><pre><span></span><span class="k">data</span> <span class="k">class</span> <span class="nc">SelfIssueCommand</span><span class="p">(</span>
|
|
<span class="k">val</span> <span class="py">command</span><span class="p">:</span> <span class="n">CashFlowCommand</span><span class="p">.</span><span class="n">IssueCash</span><span class="p">,</span>
|
|
<span class="k">val</span> <span class="py">node</span><span class="p">:</span> <span class="n">NodeHandle</span>
|
|
<span class="p">)</span>
|
|
|
|
<span class="k">data</span> <span class="k">class</span> <span class="nc">SelfIssueState</span><span class="p">(</span>
|
|
<span class="k">val</span> <span class="py">vaultsSelfIssued</span><span class="p">:</span> <span class="n">Map</span><span class="p"><</span><span class="n">AbstractParty</span><span class="p">,</span> <span class="n">Long</span><span class="p">></span>
|
|
<span class="p">)</span> <span class="p">{</span>
|
|
<span class="k">fun</span> <span class="nf">copyVaults</span><span class="p">():</span> <span class="n">HashMap</span><span class="p"><</span><span class="n">AbstractParty</span><span class="p">,</span> <span class="n">Long</span><span class="p">></span> <span class="p">{</span>
|
|
<span class="k">return</span> <span class="n">HashMap</span><span class="p">(</span><span class="n">vaultsSelfIssued</span><span class="p">)</span>
|
|
<span class="p">}</span>
|
|
<span class="p">}</span>
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|
|
|
<span class="k">val</span> <span class="py">selfIssueTest</span> <span class="p">=</span> <span class="n">LoadTest</span><span class="p"><</span><span class="n">SelfIssueCommand</span><span class="p">,</span> <span class="n">SelfIssueState</span><span class="p">>(</span>
|
|
</pre></div>
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</div>
|
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<p>The unit of work <code class="docutils literal"><span class="pre">SelfIssueCommand</span></code> simply holds an Issue and a handle to a node where the issue should be submitted. The <code class="docutils literal"><span class="pre">generate</span></code> method should provide a generator for these.</p>
|
|
<p>The state <code class="docutils literal"><span class="pre">SelfIssueState</span></code> then holds a map from node identities to a Long that describes the sum quantity of the generated issues (we fixed the currency to be USD).</p>
|
|
<p>The invariant we want to hold then simply is: The sum of submitted Issues should be the sum of the quantities in the vaults.</p>
|
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<p>The <code class="docutils literal"><span class="pre">interpret</span></code> function should take a <code class="docutils literal"><span class="pre">SelfIssueCommand</span></code> and update <code class="docutils literal"><span class="pre">SelfIssueState</span></code> to reflect the change we’re expecting in the remote nodes. In our case this will simply be adding the issued amount to the corresponding node’s Long.</p>
|
|
<p>The <code class="docutils literal"><span class="pre">execute</span></code> function should perform the action on the cluster. In our case it will simply take the node handle and submit an RPC request for the Issue.</p>
|
|
<p>The <code class="docutils literal"><span class="pre">gatherRemoteState</span></code> function should check the actual remote nodes’ states and see whether they conflict with our local predictions (and should throw if they do). This function deserves its own paragraph.</p>
|
|
<div class="highlight-kotlin"><div class="highlight"><pre><span></span> <span class="k">val</span> <span class="py">gatherRemoteState</span><span class="p">:</span> <span class="n">Nodes</span><span class="p">.(</span><span class="n">S</span><span class="p">?)</span> <span class="p">-></span> <span class="n">S</span><span class="p">,</span>
|
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</pre></div>
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</div>
|
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<p><code class="docutils literal"><span class="pre">gatherRemoteState</span></code> gets as input handles to all the nodes, and the current predicted state, or null if this is the initial gathering.</p>
|
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<p>The reason it gets the previous state boils down to allowing non-deterministic predictions about the nodes’ remote states. Say some piece of work triggers an asynchronous notification of a node. We need to account both for the case when the node hasn’t received the notification and for the case when it has. In these cases <code class="docutils literal"><span class="pre">S</span></code> should somehow represent a collection of possible states, and <code class="docutils literal"><span class="pre">gatherRemoteState</span></code> should “collapse” the collection based on the observations it makes. Of course we don’t need this for the simple case of the Self Issue test.</p>
|
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<p>The last parameter <code class="docutils literal"><span class="pre">isConsistent</span></code> is used to poll for eventual consistency at the end of a load test. This is not needed for self-issuance.</p>
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