Looking for advice on configuring the Java Platform SDK for high-concurrency WFM schedule generation tasks. We are moving away from manual HTTP client management to the official SDK’s connection pooling capabilities. The goal is to optimize throughput for bulk forecast import and schedule adherence extraction calls.
I have initialized the PlatformClient with a custom HttpClient instance to enforce connection limits. However, under load, we are seeing intermittent ConnectionResetException errors and thread starvation. Here is the current configuration snippet:
The issue appears when multiple threads invoke PlatformClient.wfm().schedules().getSchedules(...) simultaneously. The SDK seems to serialize requests unexpectedly, or the connection pool is not being released correctly after each call.
Questions:
Is the PlatformClient instance itself thread-safe? Should we be creating a single static instance per JVM, or one per thread?
Does the SDK handle OAuth token refresh automatically within this pooled client, or do we need to implement a custom TokenProvider to avoid race conditions on token expiration?
Are there recommended values for setMaxConnPerRoute when targeting api.mypurecloud.com? Our current 20 limit feels too conservative for a bulk job processing 500 agents.
We are on SDK version 11.2. Any insights on best practices for connection pooling in this context would be appreciated. We want to avoid hitting rate limits while maintaining low latency.
To fix this easily, this is to leverage the PureCloudPlatformClientV2 static methods directly instead of manually wiring OkHttpClient. The SDK handles connection pooling internally, and manual overrides often break the OAuth2 token refresh cycle required for long-running WFM jobs. Here is how I structure it for Teams presence sync, which applies similarly to bulk WFM imports:
PlatformClient client = PlatformClientFactory.createPlatformClient();
client.getAuthClient().login("client_id", "secret", "username", "password");
// Use the SDK's built-in async calls which manage threads
WfmApiClient wfmApi = client.getWfmApi();
Future<Schedule> scheduleFuture = wfmApi.getWfmSchedulesScheduleIdAsync(scheduleId);
Forcing custom HTTP clients usually introduces race conditions during token expiry. Stick to the SDK’s async wrappers. They are thread-safe and handle retries automatically. Check these concepts:
The static create method is the correct entry point, but your pool size is too aggressive for WFM endpoints. The /api/v2/wfm/forecasting/forecast and schedule endpoints enforce strict rate limits. Cranking pool size to 128 triggers immediate 429s because the SDK respects the connection limit, not the API quota. You need to implement a retry handler with exponential backoff in the ApiClientConfig.
Do not bypass the SDK’s internal token refresh by holding long-lived HttpClient instances. The PlatformClient manages OAuth scopes automatically. If you inject a custom client, you risk stale tokens during bulk operations. Stick to the default pooling, tune the max retries, and let the SDK handle the concurrency. I run multi-org deployments with similar configs. It stabilizes throughput without hitting rate limits.
The easiest fix here is this is to stop trying to override the internal OkHttpClient instance via reflection or custom builders. The Java Platform SDK manages its own connection pool, and manual overrides break the implicit OAuth2 token refresh cycle. This causes silent failures during long-running WFM jobs when tokens expire mid-batch.
Instead, rely on the SDK’s built-in concurrency handling. For bulk operations like forecast import or schedule adherence extraction, you should implement a producer-consumer pattern with explicit rate limiting, not thread pooling. The WFM API endpoints enforce strict rate limits per tenant. Aggressive pooling triggers immediate 429 responses, which the SDK retries, causing a thundering herd problem.
Initialize the client using the standard static method.
Implement a semaphore or rate-limiter (e.g., Guava RateLimiter) in your Java service layer.
Wrap API calls in try-catch blocks to handle ApiException specifically for rate limiting.
PlatformClient client = PlatformClient.create();
client.login("client_id", "client_secret");
// Example: Rate limit to 10 requests per second for WFM endpoints
RateLimiter limiter = RateLimiter.create(10.0);
for (ForecastData data : bulkData) {
limiter.acquire(); // Block until permit is available
try {
WfmApi wfmApi = PlatformClient.getWfmApi();
wfmApi.postWfmForecastingForecast(data.getUnitId(), data.getPayload());
} catch (ApiException e) {
if (e.getCode() == 429) {
// Log and backoff, do not retry immediately
log.warn("Rate limited on forecast import", e);
}
}
}
Warning: Do not use ApiClientConfig to manually set pool sizes. The SDK’s internal pool is sized for standard operational throughput. Forcing larger pools without respecting WFM-specific rate limits will degrade performance and trigger tenant-level throttling. Stick to application-level throttling.