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BlackBox
signal-limits-service
service
RateLimiter.java
RateLimitConfig.java
RateLimitMetrics.java
delivery
MessageDeliveryLoop.java
NoopDeliveryLoop.java
RedisMessageDelivery.java
challenges
ChallengeManager.java
ChallengeOption.java
RateLimitMetrics.java
RateLimiter.java
ChallengeOption.java
package service.limits;
import java.time.Instant;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
public class RateLimitMetrics {
allowedCount, blockedCount, resetCount
markReset(),getResetCount()
private final AtomicLong allowed = new AtomicLong();
private final AtomicLong blocked = new AtomicLong();
private final AtomicReference lastAllowed = new AtomicReference<>();
private final AtomicReference lastBlocked = new AtomicReference<>();
private final ConcurrentLinkedQueue samples = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService cleaner Executors.newSingleThreadScheduledExecutor();
public RateLimitMetrics() {
cleaner.scheduleAtFixedRate(this::trim, 1, 1, TimeUnit.SECONDS);
}
public void markAllowed() {
allowed.incrementAndGet();
lastAllowed.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public void markBlocked() {
blocked.incrementAndGet();
lastBlocked.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public long getAllowed() {
return allowed.get();
}
public long getBlocked() {
return blocked.get();
}
// inserted: approximate requests per second over last 60 seconds
public double requestsPerSecond() {
long now = System.currentTimeMillis();
long count = samples.stream().filter(t -> now - t <= 60_000).count();
return count / 60.0;
}
private void trim() {
long now = System.currentTimeMillis();
while (true) {
Long head = samples.peek();
if (head == null) break;
if (now - head > 60_000) samples.poll();
else break;
}
}
@Override
public String toString() {
return "allowed=" + allowed.get()
+ ", blocked=" + blocked.get()
+ ", rps=" + requestsPerSecond();
}
}
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-
+
+
+
+
+
+
Task
Improve the rate limiting implementation.
Thought for 2 minutes.
I'll proceed with implementing all the improvements in a logical order starting with Enhanced Metrics and Observability, then Performance Optimizations, and finally Advanced Rate Limiting Features. Let me create a TODO.md file to track our progress:
TODO.md
BlackBox.AI is thinking...
Now let's start with Phase 1: Enhanced Metrics and
Observability. First, I’ll create a comprehensive metrics class for rate limiting:
RateLimitMetrics.java
Cancel
Type a message (@ to add files)...
Auto
BLACKBOX AI
signal-server
service
RateLimiter.java
RateLimitConfig.java
RateLimitMetrics.java
delivery
MessageDeliveryLoop.java
NoopDeliveryLoop.java
RedisMessageDelivery.java
challenges
ChallengeManager.java
ChallengeOption.java
RateLimitMetrics.java
RateLimiter.java
ChallengeOption.java
package service.limits;
import java.time.Instant;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
public class RateLimitMetrics {
allowedCount, blockedCount, resetCount
markReset(), getResetCount()
private final AtomicLong allowed = new AtomicLong();
private final AtomicLong blocked = new AtomicLong();
private final AtomicReference lastAllowed = new AtomicReference<>();
private final AtomicReference lastBlocked = new AtomicReference<>();
private final ConcurrentLinkedQueue samples = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService cleaner Executors.newSingleThreadScheduledExecutor();
public RateLimitMetrics() {
cleaner.scheduleAtFixedRate(this::trim, 1, 1, TimeUnit.SECONDS);
}
public void markAllowed() {
allowed.incrementAndGet();
lastAllowed.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public void markBlocked() {
blocked.incrementAndGet();
lastBlocked.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public long getAllowed() {
return allowed.get();
}
public long getBlocked() {
return blocked.get();
}
// inserted: approximate requests per second over last 60 seconds
public double requestsPerSecond() {
long now = System.currentTimeMillis();
long count = samples.stream().filter(t -> now - t <= 60_000).count();
return count / 60.0;
}
private void trim() {
long now = System.currentTimeMillis();
while (true) {
Long head = samples.peek();
if (head == null) break;
if (now - head > 60_000) samples.poll();
else break;
}
}
@Override
public String toString() {
return "allowed=" + allowed.get()
+ ", blocked=" + blocked.get()
+ ", rps=" + requestsPerSecond();
}
}
-
-
+
+
+
+
+
+
Task
Improve the rate limiting implementation.
Thought for 2 minutes.
I'll proceed with implementing all the improvements in a logical order starting with Enhanced Metrics and Observability, then Performance Optimizations, and finally Advanced Rate Limiting Features. Let me create a TODO.md file to track our progress:
TODO.md
BLACKBOX AI thinking...
Now let's start with Phase 1: Enhanced Metrics and
Observability. First, I’ll create a comprehensive metrics class for rate limiting:
RateLimitMetrics.java
Cancel
Type a message (@ to add files)...
Auto
BlackBox
signal-limits-service
service
RateLimiter.java
RateLimitConfig.java
RateLimitMetrics.java
delivery
MessageDeliveryLoop.java
NoopDeliveryLoop.java
RedisMessageDelivery.java
challenges
ChallengeManager.java
ChallengeOption.java
RateLimitMetrics.java
RateLimiter.java
ChallengeOption.java
package service.limits;
import java.time.Instant;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
public class RateLimitMetrics {
allowedCount, blockedCount, resetCount
markReset(),getResetCount()
private final AtomicLong allowed = new AtomicLong();
private final AtomicLong blocked = new AtomicLong();
private final AtomicReference lastAllowed = new AtomicReference<>();
private final AtomicReference lastBlocked = new AtomicReference<>();
private final ConcurrentLinkedQueue samples = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService cleaner Executors.newSingleThreadScheduledExecutor();
public RateLimitMetrics() {
cleaner.scheduleAtFixedRate(this::trim, 1, 1, TimeUnit.SECONDS);
}
public void markAllowed() {
allowed.incrementAndGet();
lastAllowed.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public void markBlocked() {
blocked.incrementAndGet();
lastBlocked.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public long getAllowed() {
return allowed.get();
}
public long getBlocked() {
return blocked.get();
}
// inserted: approximate requests per second over last 60 seconds
public double requestsPerSecond() {
long now = System.currentTimeMillis();
long count = samples.stream().filter(t -> now - t <= 60_000).count();
return count / 60.0;
}
private void trim() {
long now = System.currentTimeMillis();
while (true) {
Long head = samples.peek();
if (head == null) break;
if (now - head > 60_000) samples.poll();
else break;
}
}
@Override
public String toString() {
return "allowed=" + allowed.get()
+ ", blocked=" + blocked.get()
+ ", rps=" + requestsPerSecond();
}
}
-
-
+
+
+
+
+
+
Task
Improve the rate limiting implementation.
Thought for 2 minutes.
I'll proceed with implementing all the improvements in a logical order starting with Enhanced Metrics and Observability, then Performance Optimizations, and finally Advanced Rate Limiting Features. Let me create a TODO.md file to track our progress:
TODO.md
BlackBox.AI is thinking...
Now let's start with Phase 1: Enhanced Metrics and
Observability. First, I’ll create a comprehensive metrics class for rate limiting:
RateLimitMetrics.java
Cancel
Type a message (@ to add files)...
Auto
BLACKBOX AI
signal-server
service
RateLimiter.java
RateLimitConfig.java
RateLimitMetrics.java
delivery
MessageDeliveryLoop.java
NoopDeliveryLoop.java
RedisMessageDelivery.java
challenges
ChallengeManager.java
ChallengeOption.java
RateLimitMetrics.java
RateLimiter.java
ChallengeOption.java
package service.limits;
import java.time.Instant;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
public class RateLimitMetrics {
allowedCount, blockedCount, resetCount
markReset(), getResetCount()
private final AtomicLong allowed = new AtomicLong();
private final AtomicLong blocked = new AtomicLong();
private final AtomicReference lastAllowed = new AtomicReference<>();
private final AtomicReference lastBlocked = new AtomicReference<>();
private final ConcurrentLinkedQueue samples = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService cleaner Executors.newSingleThreadScheduledExecutor();
public RateLimitMetrics() {
cleaner.scheduleAtFixedRate(this::trim, 1, 1, TimeUnit.SECONDS);
}
public void markAllowed() {
allowed.incrementAndGet();
lastAllowed.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public void markBlocked() {
blocked.incrementAndGet();
lastBlocked.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public long getAllowed() {
return allowed.get();
}
public long getBlocked() {
return blocked.get();
}
// inserted: approximate requests per second over last 60 seconds
public double requestsPerSecond() {
long now = System.currentTimeMillis();
long count = samples.stream().filter(t -> now - t <= 60_000).count();
return count / 60.0;
}
private void trim() {
long now = System.currentTimeMillis();
while (true) {
Long head = samples.peek();
if (head == null) break;
if (now - head > 60_000) samples.poll();
else break;
}
}
@Override
public String toString() {
return "allowed=" + allowed.get()
+ ", blocked=" + blocked.get()
+ ", rps=" + requestsPerSecond();
}
}
-
-
+
+
+
+
+
+
Task
Improve the rate limiting implementation.
Thought for 2 minutes.
I'll proceed with implementing all the improvements in a logical order starting with Enhanced Metrics and Observability, then Performance Optimizations, and finally Advanced Rate Limiting Features. Let me create a TODO.md file to track our progress:
TODO.md
BLACKBOX AI thinking...
Now let's start with Phase 1: Enhanced Metrics and
Observability. First, I’ll create a comprehensive metrics class for rate limiting:
RateLimitMetrics.java
Cancel
Type a message (@ to add files)...
Auto
BlackBox
signal-limits-service
service
RateLimiter.java
RateLimitConfig.java
RateLimitMetrics.java
delivery
MessageDeliveryLoop.java
NoopDeliveryLoop.java
RedisMessageDelivery.java
challenges
ChallengeManager.java
ChallengeOption.java
RateLimitMetrics.java
RateLimiter.java
ChallengeOption.java
package service.limits;
import java.time.Instant;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
public class RateLimitMetrics {
allowedCount, blockedCount, resetCount
markReset(),getResetCount()
private final AtomicLong allowed = new AtomicLong();
private final AtomicLong blocked = new AtomicLong();
private final AtomicReference lastAllowed = new AtomicReference<>();
private final AtomicReference lastBlocked = new AtomicReference<>();
private final ConcurrentLinkedQueue samples = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService cleaner Executors.newSingleThreadScheduledExecutor();
public RateLimitMetrics() {
cleaner.scheduleAtFixedRate(this::trim, 1, 1, TimeUnit.SECONDS);
}
public void markAllowed() {
allowed.incrementAndGet();
lastAllowed.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public void markBlocked() {
blocked.incrementAndGet();
lastBlocked.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public long getAllowed() {
return allowed.get();
}
public long getBlocked() {
return blocked.get();
}
// inserted: approximate requests per second over last 60 seconds
public double requestsPerSecond() {
long now = System.currentTimeMillis();
long count = samples.stream().filter(t -> now - t <= 60_000).count();
return count / 60.0;
}
private void trim() {
long now = System.currentTimeMillis();
while (true) {
Long head = samples.peek();
if (head == null) break;
if (now - head > 60_000) samples.poll();
else break;
}
}
@Override
public String toString() {
return "allowed=" + allowed.get()
+ ", blocked=" + blocked.get()
+ ", rps=" + requestsPerSecond();
}
}
-
-
+
+
+
+
+
+
Task
Improve the rate limiting implementation.
Thought for 2 minutes.
I'll proceed with implementing all the improvements in a logical order starting with Enhanced Metrics and Observability, then Performance Optimizations, and finally Advanced Rate Limiting Features. Let me create a TODO.md file to track our progress:
TODO.md
BlackBox.AI is thinking...
Now let's start with Phase 1: Enhanced Metrics and
Observability. First, I’ll create a comprehensive metrics class for rate limiting:
RateLimitMetrics.java
Cancel
Type a message (@ to add files)...
Auto
BLACKBOX AI
signal-server
service
RateLimiter.java
RateLimitConfig.java
RateLimitMetrics.java
delivery
MessageDeliveryLoop.java
NoopDeliveryLoop.java
RedisMessageDelivery.java
challenges
ChallengeManager.java
ChallengeOption.java
RateLimitMetrics.java
RateLimiter.java
ChallengeOption.java
package service.limits;
import java.time.Instant;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
public class RateLimitMetrics {
allowedCount, blockedCount, resetCount
markReset(), getResetCount()
private final AtomicLong allowed = new AtomicLong();
private final AtomicLong blocked = new AtomicLong();
private final AtomicReference lastAllowed = new AtomicReference<>();
private final AtomicReference lastBlocked = new AtomicReference<>();
private final ConcurrentLinkedQueue samples = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService cleaner Executors.newSingleThreadScheduledExecutor();
public RateLimitMetrics() {
cleaner.scheduleAtFixedRate(this::trim, 1, 1, TimeUnit.SECONDS);
}
public void markAllowed() {
allowed.incrementAndGet();
lastAllowed.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public void markBlocked() {
blocked.incrementAndGet();
lastBlocked.set(Instant.now());
samples.add(System.currentTimeMillis());
}
public long getAllowed() {
return allowed.get();
}
public long getBlocked() {
return blocked.get();
}
// inserted: approximate requests per second over last 60 seconds
public double requestsPerSecond() {
long now = System.currentTimeMillis();
long count = samples.stream().filter(t -> now - t <= 60_000).count();
return count / 60.0;
}
private void trim() {
long now = System.currentTimeMillis();
while (true) {
Long head = samples.peek();
if (head == null) break;
if (now - head > 60_000) samples.poll();
else break;
}
}
@Override
public String toString() {
return "allowed=" + allowed.get()
+ ", blocked=" + blocked.get()
+ ", rps=" + requestsPerSecond();
}
}
-
-
+
+
+
+
+
+
Task
Improve the rate limiting implementation.
Thought for 2 minutes.
I'll proceed with implementing all the improvements in a logical order starting with Enhanced Metrics and Observability, then Performance Optimizations, and finally Advanced Rate Limiting Features. Let me create a TODO.md file to track our progress:
TODO.md
BLACKBOX AI thinking...
Now let's start with Phase 1: Enhanced Metrics and
Observability. First, I’ll create a comprehensive metrics class for rate limiting:
RateLimitMetrics.java
Cancel
Type a message (@ to add files)...
Auto
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Can I try BLACKBOX AI Enterprise before committing?
We offer generous credits for teams at large enterprises to experiment with the BLACKBOX Agent before committing.
Can BLACKBOX AI be deployed on-premise?
What integrations are available?
Do you offer volume discounts for large teams?
What support response times can we expect?
How does BLACKBOX AI ensure code privacy?
Can I try BLACKBOX AI Enterprise before committing?
We offer generous credits for teams at large enterprises to experiment with the BLACKBOX Agent before committing.
Can BLACKBOX AI be deployed on-premise?
What integrations are available?
Do you offer volume discounts for large teams?
What support response times can we expect?
How does BLACKBOX AI ensure code privacy?
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Start with BLACKBOX
Join top Fortune 500 companies using
BlackBox AI Enterprise.
Money-back guarantee
Enterprise-grade security
Response within 4 hours
Start with BLACKBOX
Join top Fortune 500 companies using
BlackBox AI Enterprise.
Money-back guarantee
Enterprise-grade security
Response within 4 hours
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©2025 BlackBox. 535 Mission Street, San Francisco, CA, USA
©2025 BlackBox. 535 Mission Street, San Francisco, CA, USA