{
        "subprojects": [
          "configuration-problems-base",
          "daemon-server-worker",
          "docs-asciidoctor-extensions-base",
          "java-api-extractor",
          "javadoc",
          "logging-api",
          "model-reflect",
          "plugin-use",
          "request-handler-worker",
          "resources",
          "security"
        ],
        "parallelizationMethod": {

#### Docker Compose { #docker-compose }

You could be deploying to a **single server** (not a cluster) with **Docker Compose**, so you wouldn't have an easy way to manage replication of containers (with Docker Compose) while preserving the shared network and **load balancing**.

Then you could want to have **a single container** with a **process manager** starting **several worker processes** inside.

---

		workers = workers[:len(workers)-1]
		worker <- expiryOp(nil)
		es.stats.workers.Add(-1)
	}
	// Atomically replace workers.
	es.workers.Store(&workers)
}

// Worker handles 4 types of expiration tasks.
// 1. Expiry of objects, includes regular and transitioned objects
// 2. Expiry of noncurrent versions due to NewerNoncurrentVersions

    if (jenkinsUrl?.host?.endsWith('elastic.co') || jenkinsUrl?.host?.endsWith('elastic.dev')) {
      publishAlways()
      buildScan.server = 'https://gradle-enterprise.elastic.co'
    }

    // Link to Jenkins worker logs and system metrics
    if (nodeName) {
      link 'System logs', "https://infra-stats.elastic.co/app/infra#/logs?" +
        "&logFilter=(expression:'host.name:${nodeName}',kind:kuery)"
      buildFinished {

  // Replace worker1 using a new worker, and update the contexts.
  // Read the variable using `ctx_1`. This read should succeed.
  //
  // 1. Create a variable on `remote_device`, using `ctx_0`.
  TFE_TensorHandle* handle_0 =
      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var");

  // 2. Wait for `var` to be created and initialized on the worker.
  TF_Status* status = TF_NewStatus();

namespace {

TEST(CAPI_EXPERIMENTAL, GetServerDefTest) {
  const std::string expected_text_proto(R"(cluster {
  job {
    name: "worker"
    tasks {
      key: 0
      value: "tpuserver:0"
    }
    tasks {
      key: 1
      value: "localhost:1"
    }
  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();

- **CrawlerContext** (`CrawlerContext.java`): Execution context - `sessionId`, `status`, `accessCount`, `numOfThread`, `maxDepth`, `maxAccessCount`
- **CrawlerThread** (`CrawlerThread.java`): Worker thread - Poll URL → Validate → Execute → Process → Queue children

### HTTP Client Architecture

```
SwitchableHttpClient (extends FaultTolerantClient)
    ├── Hc5HttpClient (default) - Apache HttpComponents 5.x

// EagerService to maintain consistency between client and worker. The
// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during
// the process lifetime although this should cause the worker to be
// reinitialized (e.g. cleared caches) as well.

        }

        def stylesheetFile = new File(getTemporaryDir(), "dslHtml.xsl")
        def xslthlConfigFile = getStylesheetHighlightFile().get().asFile.toURI()

        // TODO: Implement this with the worker API
        workerLeaseService.runAsIsolatedTask({
            source.visit { FileVisitDetails fvd ->
                if (fvd.isDirectory()) {
                    return
                }

      int64_t init_timeout_in_ms, int retries,
      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created
  // for remote workers. Currently this only works for resetting context.
  // TODO(b/289445025): Consider removing this when we find a proper fix.
  virtual absl::Status InitializeLocalOnlyContext(const ServerDef& server_def,