流程數(shù)據(jù)的定義在flowable中是比較復(fù)雜的, 涉及到多張數(shù)據(jù)庫表關(guān)聯(lián)關(guān)系，這些在一個(gè)流程引擎中也是最為核心的數(shù)據(jù)，并且需要進(jìn)行頻繁的讀取，所以為了實(shí)現(xiàn)讀取的高效性，flowable將流程定義的數(shù)據(jù)放在內(nèi)存中，需要時(shí)直接從內(nèi)存中獲取。本文就簡單看一看flowable是怎么實(shí)現(xiàn)緩存的。

1. 用戶定義的入口

先回顧一下，如果我們要在Spring環(huán)境中使用flowable必須采用類似方式定義一個(gè)bean

<bean id="processEngineConfiguration"
      class="org.flowable.spring.SpringProcessEngineConfiguration">

SpringProcessEngineConfiguration繼承于ProcessEngineConfigurationImpl，在ProcessEngineConfigurationImpl源碼中可以看到:

    protected int processDefinitionCacheLimit = -1; // By default, no limit
    protected DeploymentCache<ProcessDefinitionCacheEntry> processDefinitionCache;

    protected int processDefinitionInfoCacheLimit = -1; // By default, no limit
    protected ProcessDefinitionInfoCache processDefinitionInfoCache;

    protected int knowledgeBaseCacheLimit = -1;
    protected DeploymentCache<Object> knowledgeBaseCache;

    protected int appResourceCacheLimit = -1;
    protected DeploymentCache<Object> appResourceCache;

因此我們可以在bean定義中可以設(shè)置processDefinitionCacheLimit等屬性的值即可控制緩存的容量，如果沒有進(jìn)行設(shè)置將不會(huì)對其限制，為以防止OOM異常，建議可以設(shè)置一個(gè)，當(dāng)超出容量時(shí)flowable引擎將會(huì)通過LRU算法進(jìn)行移除。

2. 緩存的初始化

在flowable流程引擎在Spring環(huán)境中的啟動(dòng)源碼分析這篇文章我們已經(jīng)知道了flowble流程的初始化過程，那么對緩存的初始化肯定也能在相關(guān)內(nèi)里找到，在ProcessEngineConfigurationImpl的init方法中有如下幾行代碼：

        initProcessDefinitionCache();
        initProcessDefinitionInfoCache();
        initAppResourceCache();
        initKnowledgeBaseCache();

以initProcessDefinitionCache為例看一下方法實(shí)現(xiàn)，不用多說:

    public void initProcessDefinitionCache() {
        if (processDefinitionCache == null) {
            if (processDefinitionCacheLimit <= 0) {
                processDefinitionCache = new DefaultDeploymentCache<ProcessDefinitionCacheEntry>();
            } else {
                processDefinitionCache = new DefaultDeploymentCache<ProcessDefinitionCacheEntry>(processDefinitionCacheLimit);
            }
        }
    }

進(jìn)入DefaultDeploymentCache的構(gòu)造方法，當(dāng)沒有設(shè)置緩存大小時(shí)通過無參構(gòu)造方法創(chuàng)建的是一個(gè)同步的Map, 重點(diǎn)可以看一下下面的有參構(gòu)造函數(shù)實(shí)現(xiàn):

/** Cache with no limit */
public DefaultDeploymentCache() {
    this.cache = Collections.synchronizedMap(new HashMap<String, T>());
}

/**
 * Cache which has a hard limit: no more elements will be cached than the limit.
 */
public DefaultDeploymentCache(final int limit) {
    this.cache = Collections.synchronizedMap(new LinkedHashMap<String, T>(limit + 1, 0.75f, true) { // +1 is needed, because the entry is inserted first, before it is removed
        // 0.75 is the default (see javadocs)
        // true will keep the 'access-order', which is needed to have a real LRU cache
        private static final long serialVersionUID = 1L;

        protected boolean removeEldestEntry(Map.Entry<String, T> eldest) {
            boolean removeEldest = size() > limit;
            if (removeEldest && logger.isTraceEnabled()) {
                logger.trace("Cache limit is reached, {} will be evicted", eldest.getKey());
            }
            return removeEldest;
        }

    });
}

有參構(gòu)造方法核心是基于LinkedHashMap并且重寫了removeEldestEntry方法，當(dāng)超出容量時(shí)會(huì)返回true, 查看LinkedHashMap可以知道當(dāng)調(diào)用put或putAll返回前會(huì)根據(jù)該方法返回的值決定是否移除最老的一個(gè)元素，從而實(shí)現(xiàn)了LRU緩存算法。

3. 緩存的寫入與更新

在流程部署時(shí)肯定會(huì)涉及到相關(guān)數(shù)據(jù)的更新，通過RepositoryServiceImpl.deploy->DeployCmd.executeDeploy查看有如下代碼:

        // Actually deploy
        commandContext.getProcessEngineConfiguration().getDeploymentManager().deploy(deployment, deploymentSettings);

查看DeploymentManager.deploy->BpmnDeployer.deploy代碼:

        cachingAndArtifactsManager.updateCachingAndArtifacts(parsedDeployment);

然后想看CachingAndArtifactsManager.updateCachingAndArtifacts方法源碼即具體更新緩存的實(shí)現(xiàn):

    /**
     * Ensures that the process definition is cached in the appropriate places, including the deployment's collection of deployed artifacts and the deployment manager's cache, as well as caching any
     * ProcessDefinitionInfos.
     */
    public void updateCachingAndArtifacts(ParsedDeployment parsedDeployment) {
        CommandContext commandContext = Context.getCommandContext();
        final ProcessEngineConfigurationImpl processEngineConfiguration = Context.getProcessEngineConfiguration();
        DeploymentCache<ProcessDefinitionCacheEntry> processDefinitionCache = processEngineConfiguration.getDeploymentManager().getProcessDefinitionCache();
        DeploymentEntity deployment = parsedDeployment.getDeployment();

        for (ProcessDefinitionEntity processDefinition : parsedDeployment.getAllProcessDefinitions()) {
            BpmnModel bpmnModel = parsedDeployment.getBpmnModelForProcessDefinition(processDefinition);
            Process process = parsedDeployment.getProcessModelForProcessDefinition(processDefinition);
            ProcessDefinitionCacheEntry cacheEntry = new ProcessDefinitionCacheEntry(processDefinition, bpmnModel, process);
            processDefinitionCache.add(processDefinition.getId(), cacheEntry);
            addDefinitionInfoToCache(processDefinition, processEngineConfiguration, commandContext);

            // Add to deployment for further usage
            deployment.addDeployedArtifact(processDefinition);
        }
    }

4. 緩存的讀取

一個(gè)典型的讀取場景就是在啟動(dòng)流程的時(shí)候，所以查看RuntimeServiceImpl.startProcessInstanceByKey->StartProcessInstanceCmd.execute方法源碼:

// Find the process definition
ProcessDefinition processDefinition = null;
if (processDefinitionId != null) {

    processDefinition = deploymentCache.findDeployedProcessDefinitionById(processDefinitionId);
    if (processDefinition == null) {
        throw new FlowableObjectNotFoundException("No process definition found for id = '" + processDefinitionId + "'", ProcessDefinition.class);
    }

}

接下來進(jìn)入DeploymentManager.findDeployedProcessDefinitionById可以看到, 首先會(huì)從緩存中查找，如果沒有則從數(shù)據(jù)庫中加載:

public ProcessDefinition findDeployedProcessDefinitionById(String processDefinitionId) {
    if (processDefinitionId == null) {
        throw new FlowableIllegalArgumentException("Invalid process definition id : null");
    }

    // first try the cache
    ProcessDefinitionCacheEntry cacheEntry = processDefinitionCache.get(processDefinitionId);
    ProcessDefinition processDefinition = cacheEntry != null ? cacheEntry.getProcessDefinition() : null;

    if (processDefinition == null) {
        processDefinition = processDefinitionEntityManager.findById(processDefinitionId);
        if (processDefinition == null) {
            throw new FlowableObjectNotFoundException("no deployed process definition found with id '" + processDefinitionId + "'", ProcessDefinition.class);
        }
        processDefinition = resolveProcessDefinition(processDefinition).getProcessDefinition();
    }
    return processDefinition;
}

然后看一下resolveProcessDefinition方法, 當(dāng)緩存中沒有數(shù)據(jù)時(shí)會(huì)調(diào)用deploy方法來重新加載緩存。

/**
 * Resolving the process definition will fetch the BPMN 2.0, parse it and store the {@link BpmnModel} in memory.
 */
public ProcessDefinitionCacheEntry resolveProcessDefinition(ProcessDefinition processDefinition) {
    String processDefinitionId = processDefinition.getId();
    String deploymentId = processDefinition.getDeploymentId();

    ProcessDefinitionCacheEntry cachedProcessDefinition = processDefinitionCache.get(processDefinitionId);

    if (cachedProcessDefinition == null) {
        if (Flowable5Util.isFlowable5ProcessDefinition(processDefinition, processEngineConfiguration)) {
            return Flowable5Util.getFlowable5CompatibilityHandler().resolveProcessDefinition(processDefinition);
        }

        DeploymentEntity deployment = deploymentEntityManager.findById(deploymentId);
        deployment.setNew(false);
        deploy(deployment, null);
        cachedProcessDefinition = processDefinitionCache.get(processDefinitionId);

        if (cachedProcessDefinition == null) {
            throw new FlowableException("deployment '" + deploymentId + "' didn't put process definition '" + processDefinitionId + "' in the cache");
        }
    }
    return cachedProcessDefinition;
}

總結(jié)一下：flowable緩存的實(shí)現(xiàn)核心即基于LinkedHashMap并通過重寫其removeEldestEntry方法實(shí)現(xiàn)LRU緩存移除算法。以流程定義緩存為例可以知道，每次部署時(shí)會(huì)將流程定義的數(shù)據(jù)加入緩存，每次流程啟動(dòng)時(shí)都會(huì)嘗試去緩存中獲取數(shù)據(jù)，如果緩存中有就直接返回，如果沒有就從數(shù)據(jù)庫中加載并放入緩存以供下次使用。